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+ ~OpusDecoderWrapper(); + + bool Decode(std::vector&& opus, std::vector& pcm); + void ResetState(); + + inline int sample_rate() const { + return sample_rate_; + } + + inline int duration_ms() const { + return duration_ms_; + } + +private: + std::mutex mutex_; + struct OpusDecoder* audio_dec_ = nullptr; + int frame_size_; + int sample_rate_; + int duration_ms_; +}; + +#endif // _OPUS_DECODER_WRAPPER_H_ diff --git a/components/78__esp-opus-encoder/include/opus_encoder.h b/components/78__esp-opus-encoder/include/opus_encoder.h new file mode 100644 index 0000000..2ed3457 --- /dev/null +++ b/components/78__esp-opus-encoder/include/opus_encoder.h @@ -0,0 +1,43 @@ +#ifndef _OPUS_ENCODER_WRAPPER_H_ +#define _OPUS_ENCODER_WRAPPER_H_ + +#include +#include +#include +#include +#include + +#include "opus.h" + +#define MAX_OPUS_PACKET_SIZE 1000 + + +class OpusEncoderWrapper { +public: + OpusEncoderWrapper(int sample_rate, int channels, int duration_ms = 60); + ~OpusEncoderWrapper(); + + inline int sample_rate() const { + return sample_rate_; + } + + inline int duration_ms() const { + return duration_ms_; + } + + void SetDtx(bool enable); + void SetComplexity(int complexity); + void Encode(std::vector&& pcm, std::function&& opus)> handler); + bool IsBufferEmpty() const { return in_buffer_.empty(); } + void ResetState(); + +private: + std::mutex mutex_; + struct OpusEncoder* audio_enc_ = nullptr; + int sample_rate_; + int duration_ms_; + int frame_size_; + std::vector in_buffer_; +}; + +#endif // _OPUS_ENCODER_H_ diff --git a/components/78__esp-opus-encoder/include/opus_resampler.h b/components/78__esp-opus-encoder/include/opus_resampler.h new file mode 100644 index 0000000..0e4a5d8 --- /dev/null +++ b/components/78__esp-opus-encoder/include/opus_resampler.h @@ -0,0 +1,28 @@ +#ifndef OPUS_RESAMPLER_H +#define OPUS_RESAMPLER_H + +#include +#include "opus.h" +#include "resampler_structs.h" + +class OpusResampler { +public: + OpusResampler(); + ~OpusResampler(); + + void Configure(int input_sample_rate, int output_sample_rate); + void Process(const int16_t *input, int input_samples, int16_t *output); + int GetOutputSamples(int input_samples) const; + + int input_sample_rate() const { return input_sample_rate_; } + int output_sample_rate() const { return output_sample_rate_; } + +private: + silk_resampler_state_struct resampler_state_; + int input_sample_rate_; + int output_sample_rate_; +}; + +#endif + + diff --git a/components/78__esp-opus-encoder/include/resampler_structs.h b/components/78__esp-opus-encoder/include/resampler_structs.h new file mode 100644 index 0000000..9e9457d --- /dev/null +++ b/components/78__esp-opus-encoder/include/resampler_structs.h @@ -0,0 +1,60 @@ +/*********************************************************************** +Copyright (c) 2006-2011, Skype Limited. All rights reserved. +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +- Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. +- Neither the name of Internet Society, IETF or IETF Trust, nor the +names of specific contributors, may be used to endorse or promote +products derived from this software without specific prior written +permission. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. +***********************************************************************/ + +#ifndef SILK_RESAMPLER_STRUCTS_H +#define SILK_RESAMPLER_STRUCTS_H + +#ifdef __cplusplus +extern "C" { +#endif + +#define SILK_RESAMPLER_MAX_FIR_ORDER 36 +#define SILK_RESAMPLER_MAX_IIR_ORDER 6 + +typedef struct _silk_resampler_state_struct{ + opus_int32 sIIR[ SILK_RESAMPLER_MAX_IIR_ORDER ]; /* this must be the first element of this struct */ + union{ + opus_int32 i32[ SILK_RESAMPLER_MAX_FIR_ORDER ]; + opus_int16 i16[ SILK_RESAMPLER_MAX_FIR_ORDER ]; + } sFIR; + opus_int16 delayBuf[ 48 ]; + opus_int resampler_function; + opus_int batchSize; + opus_int32 invRatio_Q16; + opus_int FIR_Order; + opus_int FIR_Fracs; + opus_int Fs_in_kHz; + opus_int Fs_out_kHz; + opus_int inputDelay; + const opus_int16 *Coefs; +} silk_resampler_state_struct; + +#ifdef __cplusplus +} +#endif +#endif /* SILK_RESAMPLER_STRUCTS_H */ + diff --git a/components/78__esp-opus-encoder/opus_decoder.cc b/components/78__esp-opus-encoder/opus_decoder.cc new file mode 100644 index 0000000..94a6ac3 --- /dev/null +++ b/components/78__esp-opus-encoder/opus_decoder.cc @@ -0,0 +1,48 @@ +#include "opus_decoder.h" +#include + +#define TAG "OpusDecoderWrapper" + +OpusDecoderWrapper::OpusDecoderWrapper(int sample_rate, int channels, int duration_ms) + : sample_rate_(sample_rate), duration_ms_(duration_ms) { + int error; + audio_dec_ = opus_decoder_create(sample_rate, channels, &error); + if (audio_dec_ == nullptr) { + ESP_LOGE(TAG, "Failed to create audio decoder, error code: %d", error); + return; + } + + frame_size_ = sample_rate / 1000 * channels * duration_ms; +} + +OpusDecoderWrapper::~OpusDecoderWrapper() { + std::lock_guard lock(mutex_); + if (audio_dec_ != nullptr) { + opus_decoder_destroy(audio_dec_); + } +} + +bool OpusDecoderWrapper::Decode(std::vector&& opus, std::vector& pcm) { + std::lock_guard lock(mutex_); + if (audio_dec_ == nullptr) { + ESP_LOGE(TAG, "Audio decoder is not configured"); + return false; + } + + pcm.resize(frame_size_); + auto ret = opus_decode(audio_dec_, opus.data(), opus.size(), pcm.data(), pcm.size(), 0); + if (ret < 0) { + ESP_LOGE(TAG, "Failed to decode audio, error code: %d", ret); + return false; + } + + return true; +} + +void OpusDecoderWrapper::ResetState() { + std::lock_guard lock(mutex_); + if (audio_dec_ != nullptr) { + opus_decoder_ctl(audio_dec_, OPUS_RESET_STATE); + } +} + diff --git a/components/78__esp-opus-encoder/opus_encoder.cc b/components/78__esp-opus-encoder/opus_encoder.cc new file mode 100644 index 0000000..7c80e78 --- /dev/null +++ b/components/78__esp-opus-encoder/opus_encoder.cc @@ -0,0 +1,81 @@ +#include "opus_encoder.h" +#include + +#define TAG "OpusEncoderWrapper" + +OpusEncoderWrapper::OpusEncoderWrapper(int sample_rate, int channels, int duration_ms) + : sample_rate_(sample_rate), duration_ms_(duration_ms) { + int error; + audio_enc_ = opus_encoder_create(sample_rate, channels, OPUS_APPLICATION_VOIP, &error); + if (audio_enc_ == nullptr) { + ESP_LOGE(TAG, "Failed to create audio encoder, error code: %d", error); + return; + } + + // Default DTX enabled + SetDtx(true); + // Complexity 5 almost uses up all CPU of ESP32C3 + SetComplexity(5); + + frame_size_ = sample_rate / 1000 * channels * duration_ms; +} + +OpusEncoderWrapper::~OpusEncoderWrapper() { + std::lock_guard lock(mutex_); + if (audio_enc_ != nullptr) { + opus_encoder_destroy(audio_enc_); + } +} + +void OpusEncoderWrapper::Encode(std::vector&& pcm, std::function&& opus)> handler) { + std::lock_guard lock(mutex_); + if (audio_enc_ == nullptr) { + ESP_LOGE(TAG, "Audio encoder is not configured"); + return; + } + + if (in_buffer_.empty()) { + in_buffer_ = std::move(pcm); + } else { + /* ISSUE: https://github.com/78/esp-opus-encoder/issues/1 */ + in_buffer_.reserve(in_buffer_.size() + pcm.size()); + in_buffer_.insert(in_buffer_.end(), pcm.begin(), pcm.end()); + } + + while (in_buffer_.size() >= frame_size_) { + uint8_t opus[MAX_OPUS_PACKET_SIZE]; + auto ret = opus_encode(audio_enc_, in_buffer_.data(), frame_size_, opus, MAX_OPUS_PACKET_SIZE); + if (ret < 0) { + ESP_LOGE(TAG, "Failed to encode audio, error code: %ld", ret); + return; + } + + if (handler != nullptr) { + handler(std::vector(opus, opus + ret)); + } + + in_buffer_.erase(in_buffer_.begin(), in_buffer_.begin() + frame_size_); + } +} + +void OpusEncoderWrapper::ResetState() { + std::lock_guard lock(mutex_); + if (audio_enc_ != nullptr) { + opus_encoder_ctl(audio_enc_, OPUS_RESET_STATE); + in_buffer_.clear(); + } +} + +void OpusEncoderWrapper::SetDtx(bool enable) { + std::lock_guard lock(mutex_); + if (audio_enc_ != nullptr) { + opus_encoder_ctl(audio_enc_, OPUS_SET_DTX(enable ? 1 : 0)); + } +} + +void OpusEncoderWrapper::SetComplexity(int complexity) { + std::lock_guard lock(mutex_); + if (audio_enc_ != nullptr) { + opus_encoder_ctl(audio_enc_, OPUS_SET_COMPLEXITY(complexity)); + } +} diff --git a/components/78__esp-opus-encoder/opus_resampler.cc b/components/78__esp-opus-encoder/opus_resampler.cc new file mode 100644 index 0000000..e7aaf2a --- /dev/null +++ b/components/78__esp-opus-encoder/opus_resampler.cc @@ -0,0 +1,34 @@ +#include "opus_resampler.h" +#include "silk_resampler.h" +#include "esp_log.h" + +#define TAG "OpusResampler" + +OpusResampler::OpusResampler() { +} + +OpusResampler::~OpusResampler() { +} + +void OpusResampler::Configure(int input_sample_rate, int output_sample_rate) { + int encode = input_sample_rate > output_sample_rate ? 1 : 0; + auto ret = silk_resampler_init(&resampler_state_, input_sample_rate, output_sample_rate, encode); + if (ret != 0) { + ESP_LOGE(TAG, "Failed to initialize resampler"); + return; + } + input_sample_rate_ = input_sample_rate; + output_sample_rate_ = output_sample_rate; + ESP_LOGI(TAG, "Resampler configured with input sample rate %d and output sample rate %d", input_sample_rate_, output_sample_rate_); +} + +void OpusResampler::Process(const int16_t *input, int input_samples, int16_t *output) { + auto ret = silk_resampler(&resampler_state_, output, input, input_samples); + if (ret != 0) { + ESP_LOGE(TAG, "Failed to process resampler"); + } +} + +int OpusResampler::GetOutputSamples(int input_samples) const { + return input_samples * output_sample_rate_ / input_sample_rate_; +} diff --git a/components/78__esp-opus-encoder/silk_resampler.h b/components/78__esp-opus-encoder/silk_resampler.h new file mode 100644 index 0000000..d45405f --- /dev/null +++ b/components/78__esp-opus-encoder/silk_resampler.h @@ -0,0 +1,26 @@ +#ifndef _SILK_RESAMPLER_H_ +#define _SILK_RESAMPLER_H_ + +#include "opus.h" +#include "resampler_structs.h" +/*! + * Initialize/reset the resampler state for a given pair of input/output sampling rates +*/ +extern "C" opus_int silk_resampler_init( + silk_resampler_state_struct *S, /* I/O Resampler state */ + opus_int32 Fs_Hz_in, /* I Input sampling rate (Hz) */ + opus_int32 Fs_Hz_out, /* I Output sampling rate (Hz) */ + opus_int forEnc /* I If 1: encoder; if 0: decoder */ +); + +/*! + * Resampler: convert from one sampling rate to another + */ +extern "C" opus_int silk_resampler( + silk_resampler_state_struct *S, /* I/O Resampler state */ + opus_int16 out[], /* O Output signal */ + const opus_int16 in[], /* I Input signal */ + opus_int32 inLen /* I Number of input samples */ +); + +#endif // _SILK_RESAMPLER_H_ diff --git a/docs/AtomMatrix-echo-base.jpg b/docs/AtomMatrix-echo-base.jpg new file mode 100644 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接口的 WebSocket 协议实例（`WebsocketProtocol`） + - 进入主循环等待事件（音频输入、音频输出、调度任务等）。 + +2. **建立 WebSocket 连接** + - 当设备需要开始语音会话时（例如用户唤醒、手动按键触发等），调用 `OpenAudioChannel()`： + - 根据编译配置获取 WebSocket URL（`CONFIG_WEBSOCKET_URL`） + - 设置若干请求头（`Authorization`, `Protocol-Version`, `Device-Id`, `Client-Id`） + - 调用 `Connect()` 与服务器建立 WebSocket 连接 + +3. **发送客户端 “hello” 消息** + - 连接成功后，设备会发送一条 JSON 消息，示例结构如下： + ```json + { + "type": "hello", + "version": 1, + "transport": "websocket", + "audio_params": { + "format": "opus", + "sample_rate": 16000, + "channels": 1, + "frame_duration": 60 + } + } + ``` + - 其中 `"frame_duration"` 的值对应 `OPUS_FRAME_DURATION_MS`（例如 60ms）。 + +4. **服务器回复 “hello”** + - 设备等待服务器返回一条包含 `"type": "hello"` 的 JSON 消息，并检查 `"transport": "websocket"` 是否匹配。 + - 如果匹配，则认为服务器已就绪，标记音频通道打开成功。 + - 如果在超时时间（默认 10 秒）内未收到正确回复，认为连接失败并触发网络错误回调。 + +5. **后续消息交互** + - 设备端和服务器端之间可发送两种主要类型的数据： + 1. **二进制音频数据**（Opus 编码） + 2. **文本 JSON 消息**（用于传输聊天状态、TTS/STT 事件、IoT 命令等） + + - 在代码里，接收回调主要分为： + - `OnData(...)`: + - 当 `binary` 为 `true` 时，认为是音频帧；设备会将其当作 Opus 数据进行解码。 + - 当 `binary` 为 `false` 时，认为是 JSON 文本，需要在设备端用 cJSON 进行解析并做相应业务逻辑处理（见下文消息结构）。 + + - 当服务器或网络出现断连，回调 `OnDisconnected()` 被触发： + - 设备会调用 `on_audio_channel_closed_()`，并最终回到空闲状态。 + +6. **关闭 WebSocket 连接** + - 设备在需要结束语音会话时，会调用 `CloseAudioChannel()` 主动断开连接，并回到空闲状态。 + - 或者如果服务器端主动断开，也会引发同样的回调流程。 + +--- + +## 2. 通用请求头 + +在建立 WebSocket 连接时，代码示例中设置了以下请求头： + +- `Authorization`: 用于存放访问令牌，形如 `"Bearer "` +- `Protocol-Version`: 固定示例中为 `"1"` +- `Device-Id`: 设备物理网卡 MAC 地址 +- `Client-Id`: 设备 UUID（可在应用中唯一标识设备） + +这些头会随着 WebSocket 握手一起发送到服务器，服务器可根据需求进行校验、认证等。 + +--- + +## 3. JSON 消息结构 + +WebSocket 文本帧以 JSON 方式传输，以下为常见的 `"type"` 字段及其对应业务逻辑。若消息里包含未列出的字段，可能为可选或特定实现细节。 + +### 3.1 客户端→服务器 + +1. **Hello** + - 连接成功后，由客户端发送，告知服务器基本参数。 + - 例： + ```json + { + "type": "hello", + "version": 1, + "transport": "websocket", + "audio_params": { + "format": "opus", + "sample_rate": 16000, + "channels": 1, + "frame_duration": 60 + } + } + ``` + +2. **Listen** + - 表示客户端开始或停止录音监听。 + - 常见字段： + - `"session_id"`：会话标识 + - `"type": "listen"` + - `"state"`：`"start"`, `"stop"`, `"detect"`（唤醒检测已触发） + - `"mode"`：`"auto"`, `"manual"` 或 `"realtime"`，表示识别模式。 + - 例：开始监听 + ```json + { + "session_id": "xxx", + "type": "listen", + "state": "start", + "mode": "manual" + } + ``` + +3. **Abort** + - 终止当前说话（TTS 播放）或语音通道。 + - 例： + ```json + { + "session_id": "xxx", + "type": "abort", + "reason": "wake_word_detected" + } + ``` + - `reason` 值可为 `"wake_word_detected"` 或其他。 + +4. **Wake Word Detected** + - 用于客户端向服务器告知检测到唤醒词。 + - 例： + ```json + { + "session_id": "xxx", + "type": "listen", + "state": "detect", + "text": "你好小明" + } + ``` + +5. **IoT** + - 发送当前设备的物联网相关信息： + - **Descriptors**（描述设备功能、属性等） + - **States**（设备状态的实时更新） + - 例： + ```json + { + "session_id": "xxx", + "type": "iot", + "descriptors": { ... } + } + ``` + 或 + ```json + { + "session_id": "xxx", + "type": "iot", + "states": { ... } + } + ``` + +--- + +### 3.2 服务器→客户端 + +1. **Hello** + - 服务器端返回的握手确认消息。 + - 必须包含 `"type": "hello"` 和 `"transport": "websocket"`。 + - 可能会带有 `audio_params`，表示服务器期望的音频参数，或与客户端对齐的配置。 + - 成功接收后客户端会设置事件标志，表示 WebSocket 通道就绪。 + +2. **STT** + - `{"type": "stt", "text": "..."}` + - 表示服务器端识别到了用户语音。（例如语音转文本结果） + - 设备可能将此文本显示到屏幕上，后续再进入回答等流程。 + +3. **LLM** + - `{"type": "llm", "emotion": "happy", "text": "😀"}` + - 服务器指示设备调整表情动画 / UI 表达。 + +4. **TTS** + - `{"type": "tts", "state": "start"}`：服务器准备下发 TTS 音频，客户端进入 “speaking” 播放状态。 + - `{"type": "tts", "state": "stop"}`：表示本次 TTS 结束。 + - `{"type": "tts", "state": "sentence_start", "text": "..."}` + - 让设备在界面上显示当前要播放或朗读的文本片段（例如用于显示给用户）。 + +5. **IoT** + - `{"type": "iot", "commands": [ ... ]}` + - 服务器向设备发送物联网的动作指令，设备解析并执行（如打开灯、设置温度等）。 + +6. **音频数据：二进制帧** + - 当服务器发送音频二进制帧（Opus 编码）时，客户端解码并播放。 + - 若客户端正在处于 “listening” （录音）状态，收到的音频帧会被忽略或清空以防冲突。 + +--- + +## 4. 音频编解码 + +1. **客户端发送录音数据** + - 音频输入经过可能的回声消除、降噪或音量增益后，通过 Opus 编码打包为二进制帧发送给服务器。 + - 如果客户端每次编码生成的二进制帧大小为 N 字节，则会通过 WebSocket 的 **binary** 消息发送这块数据。 + +2. **客户端播放收到的音频** + - 收到服务器的二进制帧时，同样认定是 Opus 数据。 + - 设备端会进行解码，然后交由音频输出接口播放。 + - 如果服务器的音频采样率与设备不一致，会在解码后再进行重采样。 + +--- + +## 5. 常见状态流转 + +以下简述设备端关键状态流转，与 WebSocket 消息对应： + +1. **Idle** → **Connecting** + - 用户触发或唤醒后，设备调用 `OpenAudioChannel()` → 建立 WebSocket 连接 → 发送 `"type":"hello"`。 + +2. **Connecting** → **Listening** + - 成功建立连接后，若继续执行 `SendStartListening(...)`，则进入录音状态。此时设备会持续编码麦克风数据并发送到服务器。 + +3. **Listening** → **Speaking** + - 收到服务器 TTS Start 消息 (`{"type":"tts","state":"start"}`) → 停止录音并播放接收到的音频。 + +4. **Speaking** → **Idle** + - 服务器 TTS Stop (`{"type":"tts","state":"stop"}`) → 音频播放结束。若未继续进入自动监听，则返回 Idle；如果配置了自动循环，则再度进入 Listening。 + +5. **Listening** / **Speaking** → **Idle**（遇到异常或主动中断） + - 调用 `SendAbortSpeaking(...)` 或 `CloseAudioChannel()` → 中断会话 → 关闭 WebSocket → 状态回到 Idle。 + +--- + +## 6. 错误处理 + +1. **连接失败** + - 如果 `Connect(url)` 返回失败或在等待服务器 “hello” 消息时超时，触发 `on_network_error_()` 回调。设备会提示“无法连接到服务”或类似错误信息。 + +2. **服务器断开** + - 如果 WebSocket 异常断开，回调 `OnDisconnected()`： + - 设备回调 `on_audio_channel_closed_()` + - 切换到 Idle 或其他重试逻辑。 + +--- + +## 7. 其它注意事项 + +1. **鉴权** + - 设备通过设置 `Authorization: Bearer ` 提供鉴权，服务器端需验证是否有效。 + - 如果令牌过期或无效，服务器可拒绝握手或在后续断开。 + +2. **会话控制** + - 代码中部分消息包含 `session_id`，用于区分独立的对话或操作。服务端可根据需要对不同会话做分离处理，WebSocket 协议为空。 + +3. **音频负载** + - 代码里默认使用 Opus 格式，并设置 `sample_rate = 16000`，单声道。帧时长由 `OPUS_FRAME_DURATION_MS` 控制，一般为 60ms。可根据带宽或性能做适当调整。 + +4. **IoT 指令** + - `"type":"iot"` 的消息用户端代码对接 `thing_manager` 执行具体命令，因设备定制而不同。服务器端需确保下发格式与客户端保持一致。 + +5. **错误或异常 JSON** + - 当 JSON 中缺少必要字段，例如 `{"type": ...}`，客户端会记录错误日志（`ESP_LOGE(TAG, "Missing message type, data: %s", data);`），不会执行任何业务。 + +--- + +## 8. 消息示例 + +下面给出一个典型的双向消息示例（流程简化示意）： + +1. **客户端 → 服务器**（握手） + ```json + { + "type": "hello", + "version": 1, + "transport": "websocket", + "audio_params": { + "format": "opus", + "sample_rate": 16000, + "channels": 1, + "frame_duration": 60 + } + } + ``` + +2. **服务器 → 客户端**（握手应答） + ```json + { + "type": "hello", + "transport": "websocket", + "audio_params": { + "sample_rate": 16000 + } + } + ``` + +3. **客户端 → 服务器**（开始监听） + ```json + { + "session_id": "", + "type": "listen", + "state": "start", + "mode": "auto" + } + ``` + 同时客户端开始发送二进制帧（Opus 数据）。 + +4. **服务器 → 客户端**（ASR 结果） + ```json + { + "type": "stt", + "text": "用户说的话" + } + ``` + +5. **服务器 → 客户端**（TTS开始） + ```json + { + "type": "tts", + "state": "start" + } + ``` + 接着服务器发送二进制音频帧给客户端播放。 + +6. **服务器 → 客户端**（TTS结束） + ```json + { + "type": "tts", + "state": "stop" + } + ``` + 客户端停止播放音频，若无更多指令，则回到空闲状态。 + +--- + +## 9. 总结 + +本协议通过在 WebSocket 上层传输 JSON 文本与二进制音频帧，完成功能包括音频流上传、TTS 音频播放、语音识别与状态管理、IoT 指令下发等。其核心特征： + +- **握手阶段**：发送 `"type":"hello"`，等待服务器返回。 +- **音频通道**：采用 Opus 编码的二进制帧双向传输语音流。 +- **JSON 消息**：使用 `"type"` 为核心字段标识不同业务逻辑，包括 TTS、STT、IoT、WakeWord 等。 +- **扩展性**：可根据实际需求在 JSON 消息中添加字段，或在 headers 里进行额外鉴权。 + +服务器与客户端需提前约定各类消息的字段含义、时序逻辑以及错误处理规则，方能保证通信顺畅。上述信息可作为基础文档，便于后续对接、开发或扩展。 diff --git a/docs/wiring.jpg b/docs/wiring.jpg new file mode 100644 index 0000000..764c170 Binary files /dev/null and b/docs/wiring.jpg differ diff --git a/docs/wiring2.jpg b/docs/wiring2.jpg new file mode 100644 index 0000000..f3a67ae Binary files /dev/null and b/docs/wiring2.jpg differ diff --git a/docs/xmini-c3.jpg b/docs/xmini-c3.jpg new file mode 100644 index 0000000..f1ed8c2 Binary files /dev/null and b/docs/xmini-c3.jpg differ diff --git a/esp-spot/.gitignore b/esp-spot/.gitignore new file mode 100644 index 0000000..d045aeb --- /dev/null +++ b/esp-spot/.gitignore @@ -0,0 +1,16 @@ +# Example project files +build_esp*_*/ +sdkconfig.old +sdkconfig +.DS_Store + +# ESP-IDF default build directory name +build + +# lock files for examples and components +dependencies.lock + +# managed_components for examples +managed_components + +.vscode \ No newline at end of file diff --git a/esp-spot/368777eb08bb78ecf68e5fb12ee0bc1.png b/esp-spot/368777eb08bb78ecf68e5fb12ee0bc1.png new file mode 100644 index 0000000..97bcad0 Binary files /dev/null and b/esp-spot/368777eb08bb78ecf68e5fb12ee0bc1.png differ diff --git a/esp-spot/3D_Print/spot-v1.1-外壳.zip b/esp-spot/3D_Print/spot-v1.1-外壳.zip new file mode 100644 index 0000000..58f2c21 Binary files /dev/null and b/esp-spot/3D_Print/spot-v1.1-外壳.zip differ diff --git a/esp-spot/3D_Print/spot-v1.2_外壳.zip b/esp-spot/3D_Print/spot-v1.2_外壳.zip new file mode 100644 index 0000000..7f0c389 Binary files /dev/null and b/esp-spot/3D_Print/spot-v1.2_外壳.zip differ diff --git a/esp-spot/LICENSE b/esp-spot/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/esp-spot/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/esp-spot/README.md b/esp-spot/README.md new file mode 100644 index 0000000..3000b1e --- /dev/null +++ b/esp-spot/README.md @@ -0,0 +1,42 @@ +# ESP-Spot：AI 语音交互核心模块 + +
+ +
+ +## 项目简介 + +ESP-Spot 是一款基于 ESP32-S3 / ESP32-C5 的 **AI 动作语音交互核心模块**，专注于**语音交互、AI感知与智能控制**，适用于智能玩具、语音助手、家居控制等物联网应用场景。它不仅可以通过离线语音实现唤醒、AI对话（默认使用 xiaozhi 平台）等功能，而且通过ESP32-S3 自带的**触摸/接近感应**外设实现玩偶触摸感知，同时设备内置加速度传感器， 可以识别玩偶姿态与动作，从而实现更丰富的交互。 + +
+ +
+ +
+ +
+ +## 视频展示 + +[用触摸交互升级大模型 AI 玩具【ESP-SPOT】](https://www.bilibili.com/video/BV1ekRAYVEZ1/) +- 本视频对应的例程为：[llm_touch_toy](./example/adf/llm_touch_toy) + +## 软件资源 + +目前已开放部分代码例程，请参考 [example 文件夹](example)，后续会持续升级更新 + +## 硬件设计 + +硬件已开源在立创平台：[ESP-Spot](https://oshwhub.com/esp-college/esp-spot) + +## 3D 结构设计 + +- 3D 打印文件已[开放附件](3D_Print)，欢迎下载！ + +- **主体结构** + + ESP-Spot 的主体结构炸视图如下： + +
+ +
\ No newline at end of file diff --git a/esp-spot/_static/esp-spot-3d.png b/esp-spot/_static/esp-spot-3d.png new file mode 100644 index 0000000..ac6d6b6 Binary files /dev/null and b/esp-spot/_static/esp-spot-3d.png differ diff --git a/esp-spot/_static/spot-3d-1.jpg b/esp-spot/_static/spot-3d-1.jpg new file mode 100644 index 0000000..53edca7 Binary files /dev/null and b/esp-spot/_static/spot-3d-1.jpg differ diff --git a/esp-spot/_static/spot-3d-2.jpg b/esp-spot/_static/spot-3d-2.jpg new file mode 100644 index 0000000..9e3b77d Binary files /dev/null and b/esp-spot/_static/spot-3d-2.jpg differ diff --git a/esp-spot/_static/spot-3d-3.jpg b/esp-spot/_static/spot-3d-3.jpg new file mode 100644 index 0000000..3dd66a5 Binary files /dev/null and b/esp-spot/_static/spot-3d-3.jpg differ diff --git a/esp-spot/_static/spot-board.png b/esp-spot/_static/spot-board.png new file mode 100644 index 0000000..786e6f1 Binary files /dev/null and b/esp-spot/_static/spot-board.png differ diff --git a/esp-spot/_static/spot-cover.jpg b/esp-spot/_static/spot-cover.jpg new file mode 100644 index 0000000..e3633cf Binary files /dev/null and b/esp-spot/_static/spot-cover.jpg differ diff --git a/esp-spot/_static/spot-pin.png b/esp-spot/_static/spot-pin.png new file mode 100644 index 0000000..b0ce3a7 Binary files /dev/null and b/esp-spot/_static/spot-pin.png differ diff --git a/esp-spot/b5a2b9017aabd47c7ea867d0d5e43e8.png b/esp-spot/b5a2b9017aabd47c7ea867d0d5e43e8.png new file mode 100644 index 0000000..3180e77 Binary files /dev/null and b/esp-spot/b5a2b9017aabd47c7ea867d0d5e43e8.png differ diff --git a/esp-spot/example/README.md b/esp-spot/example/README.md new file mode 100644 index 0000000..9d32168 --- /dev/null +++ b/esp-spot/example/README.md @@ -0,0 +1,15 @@ +# ESP-Spot 示例 + +此目录包含一系列 ESP-Spot 示例项目。这些示例旨在演示模块的部分功能 + +# 示例列表 +- [s3_factory_bin](./s3_factory_bin): 编译好的小智语音交流固件，可直接烧录到开发板 +- [adf](./adf)：基于 ESP-ADF 的音频例程 + - [components](./adf/components)：内置 ESP-Spot 在 ADF 中的硬件初始化组件，替换 ADF 中对应的原组件 + - **[llm_touch_toy](./adf/llm_touch_toy)：将 AI 大模型与 ESP32-S3 触摸传感器结合，打造可以进行动作交互的 AI 玩具** + - [touch_play_mp3](./adf/touch_play_mp3)：通过触摸播放 MP3 音频和点亮灯环的基础例程 + - [coze_websocket](./adf/coze_websocket)：通过扣子 WebSocket 服务进行大模型语音交互 + - [volc_rtc_spot](./adf/volc_rtc_spot)：通过火山引擎 RTC 服务进行大模型语音交互 +- [imu_led](./imu_led)：加速度传感器基础例程 +- [simple_touch](./simple_touch) +- [xiaozhi](./xiaozhi)：基于小智 AI 的项目 diff --git a/esp-spot/example/adf/components/audio_board/CMakeLists.txt b/esp-spot/example/adf/components/audio_board/CMakeLists.txt new file mode 100644 index 0000000..3f3ff90 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/CMakeLists.txt @@ -0,0 +1,155 @@ +set(COMPONENT_ADD_INCLUDEDIRS ./include) + +# Edit following two lines to set component requirements (see docs) +set(COMPONENT_REQUIRES ) +set(COMPONENT_PRIV_REQUIRES esp_peripherals audio_sal audio_hal esp_dispatcher display_service) + + +if (CONFIG_ESP_LYRAT_V4_2_BOARD) +message(STATUS "Current board name is " CONFIG_ESP_LYRAT_V4_2_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./lyrat_v4_2) +set(COMPONENT_SRCS +./lyrat_v4_2/board.c +./lyrat_v4_2/board_pins_config.c +) +endif() + +if (CONFIG_ESP_LYRAT_V4_3_BOARD) +message(STATUS "Current board name is " CONFIG_ESP_LYRAT_V4_3_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./lyrat_v4_3) +set(COMPONENT_SRCS +./lyrat_v4_3/board.c +./lyrat_v4_3/board_pins_config.c +) +endif() + +if (CONFIG_ESP_LYRAT_MINI_V1_1_BOARD) +message(STATUS "Current board name is " CONFIG_ESP_LYRAT_MINI_V1_1_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./lyrat_mini_v1_1) +set(COMPONENT_SRCS +./lyrat_mini_v1_1/board.c +./lyrat_mini_v1_1/board_pins_config.c +) +endif() + + +if (CONFIG_ESP_LYRATD_MSC_V2_1_BOARD) +message(STATUS "Current board name is " CONFIG_ESP_LYRATD_MSC_V2_1_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./lyratd_msc_v2_1) +set(COMPONENT_SRCS +./lyratd_msc_v2_1/board.c +./lyratd_msc_v2_1/board_pins_config.c +) +endif() + +if (CONFIG_ESP_LYRATD_MSC_V2_2_BOARD) +message(STATUS "Current board name is " CONFIG_ESP_LYRATD_MSC_V2_2_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./lyratd_msc_v2_2) +set(COMPONENT_SRCS +./lyratd_msc_v2_2/board.c +./lyratd_msc_v2_2/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_KORVO_DU1906_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_KORVO_DU1906_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_korvo_du1906) +set(COMPONENT_SRCS +./esp32_korvo_du1906/board.c +./esp32_korvo_du1906/board_pins_config.c +./esp32_korvo_du1906/du1906_bar_pattern.c +) +endif() + +if (CONFIG_ESP32_S2_KALUGA_1_V1_2_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S2_KALUGA_1_V1_2_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s2_kaluga_1_v1_2) +set(COMPONENT_SRCS +./esp32_s2_kaluga_1_v1_2/board.c +./esp32_s2_kaluga_1_v1_2/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_KORVO2_V3_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_KORVO2_V3_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_korvo2_v3) +set(COMPONENT_SRCS +./esp32_s3_korvo2_v3/board.c +./esp32_s3_korvo2_v3/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_KORVO2L_V1_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_KORVO2L_V1_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_korvo2l_v1) +set(COMPONENT_SRCS +./esp32_s3_korvo2l_v1/board.c +./esp32_s3_korvo2l_v1/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_SPOT_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_SPOT_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_spot) +set(COMPONENT_SRCS +./esp32_s3_spot/board.c +./esp32_s3_spot/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_BOX_LITE_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_BOX_LITE_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_box_lite) +set(COMPONENT_SRCS +./esp32_s3_box_lite/board.c +./esp32_s3_box_lite/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_BOX_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_BOX_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_box) +set(COMPONENT_SRCS +./esp32_s3_box/board.c +./esp32_s3_box/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_S3_BOX_3_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_S3_BOX_3_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_s3_box_3) +set(COMPONENT_SRCS +./esp32_s3_box_3/board.c +./esp32_s3_box_3/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_C3_LYRA_V2_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_C3_LYRA_V2_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_c3_lyra) +set(COMPONENT_SRCS +./esp32_c3_lyra/board.c +./esp32_c3_lyra/board_pins_config.c +./esp32_c3_lyra/C3_lyra_sys_pattern.c +) +endif() + +if (CONFIG_ESP32_C6_DEVKIT_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_C6_DEVKIT_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_c6_devkit) +set(COMPONENT_SRCS +./esp32_c6_devkit/board.c +./esp32_c6_devkit/board_pins_config.c +) +endif() + +if (CONFIG_ESP32_P4_FUNCTION_EV_BOARD) +message(STATUS "Current board name is " CONFIG_ESP32_P4_FUNCTION_EV_BOARD) +list(APPEND COMPONENT_ADD_INCLUDEDIRS ./esp32_p4_function_ev_board) +set(COMPONENT_SRCS +./esp32_p4_function_ev_board/board.c +./esp32_p4_function_ev_board/board_pins_config.c +) +endif() + +register_component() diff --git a/esp-spot/example/adf/components/audio_board/Kconfig.projbuild b/esp-spot/example/adf/components/audio_board/Kconfig.projbuild new file mode 100644 index 0000000..6a1f0ea --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/Kconfig.projbuild @@ -0,0 +1,71 @@ +menu "Audio HAL" + +choice AUDIO_BOARD + prompt "Audio board" + default ESP_LYRAT_V4_3_BOARD + help + Select an audio board to use with the ESP-ADF +config AUDIO_BOARD_CUSTOM + bool "Custom audio board" +config ESP_LYRAT_V4_3_BOARD + bool "ESP32-Lyrat V4.3" +config ESP_LYRAT_V4_2_BOARD + bool "ESP32-Lyrat V4.2" +config ESP_LYRATD_MSC_V2_1_BOARD + bool "ESP32-LyraTD-MSC V2.1" +config ESP_LYRATD_MSC_V2_2_BOARD + bool "ESP32-LyraTD-MSC V2.2" +config ESP_LYRAT_MINI_V1_1_BOARD + bool "ESP32-Lyrat-Mini V1.1" +config ESP32_KORVO_DU1906_BOARD + bool "ESP32_KORVO_DU1906" +config ESP32_S2_KALUGA_1_V1_2_BOARD + bool "ESP32-S2-Kaluga-1 v1.2" +config ESP32_S3_KORVO2_V3_BOARD + bool "ESP32-S3-Korvo-2 v3" +config ESP32_S3_KORVO2L_V1_BOARD + bool "ESP32-S3-Korvo-2L v1" +config ESP32_S3_BOX_LITE_BOARD + bool "ESP32-S3-BOX-Lite" +config ESP32_S3_BOX_BOARD + bool "ESP32-S3-BOX" +config ESP32_S3_BOX_3_BOARD + bool "ESP32-S3-BOX-3" +config ESP32_C3_LYRA_V2_BOARD + bool "ESP32-C3-Lyra-v2.0" +config ESP32_C6_DEVKIT_BOARD + bool "ESP32-C6-DEVKIT" +config ESP32_P4_FUNCTION_EV_BOARD + bool "ESP32-P4-FUNCTION-EV-BOARD" +config ESP32_S3_SPOT_BOARD + bool "ESP32-S3-Spot" + +endchoice + +choice ESP32_KORVO_DU1906_DAC + prompt "ESP32 KORVO DU1906 Board DAC chip" + depends on ESP32_KORVO_DU1906_BOARD + default ESP32_KORVO_DU1906_DAC_TAS5805M + help + Select DAC chip to use on ESP32_KORVO_DU1906 board + +config ESP32_KORVO_DU1906_DAC_TAS5805M + bool "ESP32_KORVO_DU1906_DAC_TAS5805M" +config ESP32_KORVO_DU1906_DAC_ES7148 + bool "ESP32_KORVO_DU1906_DAC_ES7148" + +endchoice + +choice ESP32_KORVO_DU1906_ADC + prompt "ESP32 KORVO DU1906 Board ADC chip" + depends on ESP32_KORVO_DU1906_BOARD + default ESP32_KORVO_DU1906_ADC_ES7243 + help + Select ADC chip to use on ESP32_KORVO_DU1906 board + +config ESP32_KORVO_DU1906_ADC_ES7243 + bool "ESP32_KORVO_DU1906_ADC_ES7243" +endchoice + +endmenu + diff --git a/esp-spot/example/adf/components/audio_board/component.mk b/esp-spot/example/adf/components/audio_board/component.mk new file mode 100644 index 0000000..61cf50f --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/component.mk @@ -0,0 +1,53 @@ +# +# "main" pseudo-component makefile. +# +# (Uses default behaviour of compiling all source files in directory, adding 'include' to include path.) + +COMPONENT_ADD_INCLUDEDIRS := ./include + +ifdef CONFIG_ESP_LYRAT_V4_3_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./lyrat_v4_3 +COMPONENT_SRCDIRS += ./lyrat_v4_3 +endif + +ifdef CONFIG_ESP_LYRAT_V4_2_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./lyrat_v4_2 +COMPONENT_SRCDIRS += ./lyrat_v4_2 +endif + +ifdef CONFIG_ESP_LYRATD_MSC_V2_1_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./lyratd_msc_v2_1 +COMPONENT_SRCDIRS += ./lyratd_msc_v2_1 +COMPONENT_ADD_LDFLAGS += -L$(COMPONENT_PATH)/../audio_hal/driver/zl38063/firmware -lfirmware +endif + +ifdef CONFIG_ESP_LYRATD_MSC_V2_2_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./lyratd_msc_v2_2 +COMPONENT_SRCDIRS += ./lyratd_msc_v2_2 +COMPONENT_ADD_LDFLAGS += -L$(COMPONENT_PATH)/../audio_hal/driver/zl38063/firmware -lfirmware +endif + +ifdef CONFIG_ESP_LYRAT_MINI_V1_1_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./lyrat_mini_v1_1 +COMPONENT_SRCDIRS += ./lyrat_mini_v1_1 +endif + +ifdef CONFIG_ESP32_KORVO_DU1906_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./esp32_korvo_du1906 +COMPONENT_SRCDIRS += ./esp32_korvo_du1906 +endif + +ifdef CONFIG_ESP32_S2_KALUGA_1_V1_2_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./esp32_s2_kaluga_1_v1_2 +COMPONENT_SRCDIRS += ./esp32_s2_kaluga_1_v1_2 +endif + +ifdef CONFIG_ESP32_S3_KORVO2_V3_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./esp32_s3_korvo2_v3 +COMPONENT_SRCDIRS += ./esp32_s3_korvo2_v3 +endif + +ifdef CONFIG_ESP32_C3_LYRA_V2_BOARD +COMPONENT_ADD_INCLUDEDIRS += ./esp32_c3_lyra +COMPONENT_SRCDIRS += ./esp32_c3_lyra +endif \ No newline at end of file diff --git a/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/C3_lyra_sys_pattern.c b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/C3_lyra_sys_pattern.c new file mode 100644 index 0000000..4b3f825 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/C3_lyra_sys_pattern.c @@ -0,0 +1,311 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "display_service.h" +#include "periph_ws2812.h" +#include "board_def.h" +#include "driver/gpio.h" +#include "string.h" +#include "esp_log.h" + +const struct periph_ws2812_ctrl_cfg ws2812_display_pattern[DISPLAY_PATTERN_MAX][WS2812_LED_BAR_NUMBERS] = { + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 400, + } // 0 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 0xFFFFFFFF, + .time_off_ms = 1500, + .time_on_ms = 1500, + } // 1 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 0xFFFFFFFF, + .time_off_ms = 200, + .time_on_ms = 800, + } // 2 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 3 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_RED, + .loop = 1, + .time_off_ms = 1000, + .time_on_ms = 4000, + } // 4 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 5, + .time_off_ms = 200, + .time_on_ms = 1000, + } // 5 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 100, + .time_off_ms = 200, + .time_on_ms = 800, + } // 6 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + } // 7 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + } // 8 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 9 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + } // 10 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 100, + .time_off_ms = 2000, + .time_on_ms = 2000, + } // 11 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + } // 12 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLUE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 13 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 14 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_YELLOW, + .loop = 2000, + .time_off_ms = 200, + .time_on_ms = 600, + } // 15 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 16 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_GREEN, + .loop = 1, + .time_off_ms = 0, + .time_on_ms = 1000, + } // 17 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_ORANGE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 18 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 19 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_WHITE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 20 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 21 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 800, + } // 22 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1500, + .time_on_ms = 1500, + } // 23 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1000, + .time_on_ms = 2000, + } // 24 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 25 + } + , + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 0xFFFFFFFF, + .time_off_ms = 2000, + .time_on_ms = 2000, + } // 26 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 0xFFFFFFFF, + .time_off_ms = 500, + .time_on_ms = 500, + } // 27, + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 28, + } +}; + +int8_t get_ws2812_gpio_pin(void) +{ + return WS2812_LED_GPIO_PIN; +} + +int get_ws2812_num(void) +{ + return WS2812_LED_BAR_NUMBERS; +} + +void ws2812_pattern_copy(struct periph_ws2812_ctrl_cfg *p) +{ + ESP_LOGD("ws2812_pattern_copy", "has been called, %s %d", __FILE__, __LINE__); + memcpy(p, ws2812_display_pattern, sizeof(ws2812_display_pattern)); +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.c b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.c new file mode 100644 index 0000000..2fa09ba --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.c @@ -0,0 +1,123 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_adc_button.h" +#include "led_bar_ws2812.h" +#include "display_service.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = NULL; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + gpio_config_t io_conf = {0}; + bool enable = true; + io_conf.mode = GPIO_MODE_OUTPUT; + io_conf.pin_bit_mask = BIT64(get_pa_enable_gpio()); + io_conf.pull_down_en = 0; + io_conf.pull_up_en = 0; + gpio_config(&io_conf); + if (enable) { + gpio_set_level(get_pa_enable_gpio(), 1); + } else { + gpio_set_level(get_pa_enable_gpio(), 0); + } + return NULL; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + esp_err_t ret = ESP_FAIL; + return ret; +} + +display_service_handle_t audio_board_led_init(void) +{ + led_bar_ws2812_handle_t led = led_bar_ws2812_init(get_ws2812_gpio_pin(), get_ws2812_num()); + AUDIO_NULL_CHECK(TAG, led, return NULL); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_bar_ws2812_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.adc_ch = ADC1_CHANNEL_2; + adc_btn_tag.total_steps = 6; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 2700}; + adc_btn_tag.adc_level_step = (int *)(&btn_array); + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_board->audio_hal = NULL; + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.h b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.h new file mode 100644 index 0000000..2eb336a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board.h @@ -0,0 +1,126 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return ESP_OK success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +/** + * @brief Get the ws2812 gpio pin + * + * @return GPIO pin + */ +int8_t get_ws2812_gpio_pin(void); + +/** + * @brief Get the number of ws2812 + * + * @return number of ws2812 + */ +int get_ws2812_num(void); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_def.h new file mode 100644 index 0000000..8ed3af9 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_def.h @@ -0,0 +1,134 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define BOARD_PA_GAIN (10) /* Power amplifier gain defined by board (dB) */ + + +/** + * @brief ADC Function Definition + */ +#define MIC_ADC_GPIO GPIO_NUM_0 +#define PA_ENABLE_GPIO GPIO_NUM_1 +#define BUTTON_ADC_GPIO GPIO_NUM_2 + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (0) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK -1 +#define ESP_SD_PIN_CMD -1 +#define ESP_SD_PIN_D0 -1 +#define ESP_SD_PIN_D3 -1 + + +/** + * @brief PDM TX Function Definition + */ +#define PDM_TX_GPIO GPIO_NUM_3 + + +/** + * @brief LED Function Definition + */ +#define LED_PWM_BLUE_GPIO GPIO_NUM_4 +#define LED_PWM_RED_GPIO GPIO_NUM_5 +#define LED_PWM_GREEN_GPIO GPIO_NUM_6 +#define WS2812_LED_GPIO_PIN GPIO_NUM_10 +#define WS2812_LED_BAR_NUMBERS 1 +#define LED_STRIP_CTRL_GPIO GPIO_NUM_7 + + +/** + * @brief IR Function Definition + */ +#define ESP_IR_TX_GPIO GPIO_NUM_18 +#define ESP_IR_RX_GPIO GPIO_NUM_19 + + +/** + * @brief I2C Function Definition + */ +#define I2C_CLK_GPIO GPIO_NUM_8 +#define I2C_DATA_GPIO GPIO_NUM_9 + + +/** + * @brief Button Function Definition + */ +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_COLOR_ID 4 +#define BUTTON_MODE_ID 5 + +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_COLOR, \ + .act_id = BUTTON_COLOR_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_pins_config.c new file mode 100644 index 0000000..c07e207 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c3_lyra/board_pins_config.c @@ -0,0 +1,141 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_C3_Lyra"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == 0) { + i2c_config->sda_io_num = GPIO_NUM_9; + i2c_config->scl_io_num = GPIO_NUM_8; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = PDM_TX_GPIO; + i2s_config->data_in_num = -1; + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id +int8_t get_input_rec_id(void) +{ + return -1; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins +int8_t get_ws2812_led_gpio(void) +{ + return WS2812_LED_GPIO_PIN; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} \ No newline at end of file diff --git a/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.c b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.c new file mode 100644 index 0000000..91f02cd --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.c @@ -0,0 +1,109 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_adc_button.h" +#include "led_bar_ws2812.h" +#include "display_service.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = NULL; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + + board_handle->audio_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + return board_handle; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + esp_err_t ret = ESP_FAIL; + return ret; +} + +display_service_handle_t audio_board_led_init(void) +{ + led_bar_ws2812_handle_t led = led_bar_ws2812_init(get_ws2812_gpio_pin(), get_ws2812_num()); + AUDIO_NULL_CHECK(TAG, led, return NULL); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_bar_ws2812_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + esp_err_t ret = ESP_OK; + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.adc_ch = ADC1_CHANNEL_2; + adc_btn_tag.total_steps = 6; + int btn_array[7] = {380, 820, 1100, 1650, 1980, 2410, 2700}; + adc_btn_tag.adc_level_step = (int *)(&btn_array); + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + ret = esp_periph_start(set, adc_btn_handle); + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_board->audio_hal = NULL; + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.h b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.h new file mode 100644 index 0000000..2eb336a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board.h @@ -0,0 +1,126 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return ESP_OK success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +/** + * @brief Get the ws2812 gpio pin + * + * @return GPIO pin + */ +int8_t get_ws2812_gpio_pin(void); + +/** + * @brief Get the number of ws2812 + * + * @return number of ws2812 + */ +int get_ws2812_num(void); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_def.h new file mode 100644 index 0000000..d576555 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_def.h @@ -0,0 +1,147 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define BOARD_PA_GAIN (10) /* Power amplifier gain defined by board (dB) */ + + +/** + * @brief ADC Function Definition + */ +#define MIC_ADC_GPIO -1 +#define PA_ENABLE_GPIO GPIO_NUM_7 +#define BUTTON_ADC_GPIO GPIO_NUM_2 + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (0) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK -1 +#define ESP_SD_PIN_CMD -1 +#define ESP_SD_PIN_D0 -1 +#define ESP_SD_PIN_D3 -1 + + +/** + * @brief PDM TX Function Definition + */ +#define PDM_TX_GPIO GPIO_NUM_3 + + +/** + * @brief LED Function Definition + */ +#define WS2812_LED_GPIO_PIN GPIO_NUM_8 +#define WS2812_LED_BAR_NUMBERS 1 + + +/** + * @brief IR Function Definition + */ +#define ESP_IR_TX_GPIO GPIO_NUM_18 +#define ESP_IR_RX_GPIO GPIO_NUM_19 + + +/** + * @brief I2C Function Definition + */ +#define I2C_CLK_GPIO GPIO_NUM_18 +#define I2C_DATA_GPIO GPIO_NUM_19 + + +/** + * @brief Button Function Definition + */ +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "MR" + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_pins_config.c new file mode 100644 index 0000000..d3b1e65 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_c6_devkit/board_pins_config.c @@ -0,0 +1,140 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" + +static const char *TAG = "ESP32_C6_DevKit"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == 0) { + i2c_config->sda_io_num = GPIO_NUM_19; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + i2s_config->mck_io_num = GPIO_NUM_20; + i2s_config->bck_io_num = GPIO_NUM_22; + i2s_config->ws_io_num = GPIO_NUM_21; + i2s_config->data_out_num = GPIO_NUM_23; + i2s_config->data_in_num = GPIO_NUM_15; + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id +int8_t get_input_rec_id(void) +{ + return -1; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins +int8_t get_ws2812_led_gpio(void) +{ + return WS2812_LED_GPIO_PIN; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} \ No newline at end of file diff --git a/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.c b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.c new file mode 100644 index 0000000..b3de4fd --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.c @@ -0,0 +1,163 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +#include "periph_sdcard.h" +#include "led_indicator.h" +#include "periph_adc_button.h" +#include "led_bar_ws2812.h" +#include "display_service.h" +#include "es7243.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle; + + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_dac_init(); + es7243_adc_set_addr(0x24); + board_handle->adc_line_in_hal = audio_board_adc_init(); + es7243_adc_set_addr(0x26); + board_handle->adc_ref_pa_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_dac_init(void) +{ + audio_hal_handle_t dac_hal = NULL; + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); +#ifdef CONFIG_ESP32_KORVO_DU1906_DAC_TAS5805M + dac_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_TAS5805M_DEFAULT_HANDLE); +#elif CONFIG_ESP32_KORVO_DU1906_DAC_ES7148 + dac_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7148_DEFAULT_HANDLE); +#endif + + AUDIO_NULL_CHECK(TAG, dac_hal, return NULL); + return dac_hal; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_handle_t adc_hal = NULL; +#ifdef CONFIG_ESP32_KORVO_DU1906_ADC_ES7243 + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7243_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); +#endif + return adc_hal; +} + +display_service_handle_t audio_board_led_init(void) +{ + led_bar_ws2812_handle_t led = led_bar_ws2812_init(get_ws2812_gpio_pin(), get_ws2812_num()); + AUDIO_NULL_CHECK(TAG, led, return NULL); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_bar_ws2812_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_btn_cfg.task_cfg.ext_stack = true; + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.adc_ch = ADC1_CHANNEL_0; // GPIO36 + adc_btn_tag.total_steps = 4; + int btn_array[5] = {200, 900, 1500, 2100, 2930}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), // GPIO_NUM_34 + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.h b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.h new file mode 100644 index 0000000..4ec40e8 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board.h @@ -0,0 +1,139 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +extern audio_hal_func_t AUDIO_CODEC_TAS5805M_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7148_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7243_DEFAULT_HANDLE; + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< pa hardware abstract layer handle */ + audio_hal_handle_t adc_line_in_hal; /*!< adc hardware abstract layer handle */ + audio_hal_handle_t adc_ref_pa_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize DAC chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_dac_init(void); + +/** + * @brief Initialize ADC chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return ESP_OK, success + * others, fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +/** + * @brief Get the ws2812 gpio pin + * + * @return GPIO pin + */ +int8_t get_ws2812_gpio_pin(void); + +/** + * @brief Get the number of ws2812 + * + * @return number of ws2812 + */ +int get_ws2812_num(void); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_def.h new file mode 100644 index 0000000..2fc56d9 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_def.h @@ -0,0 +1,120 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO GPIO_NUM_39 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D3 -1 + + +/** + * @brief LED Function Definition + */ +#define FUNC_SYS_LEN_EN (1) +#define WS2812_LED_GPIO_PIN 3 +#define WS2812_LED_BAR_NUMBERS 2 + + +/** + * @brief Battery Detect Function Definition + */ +#define FUNC_BATTERY_DET_EN (1) +#define BATTERY_DETECT_GPIO GPIO_NUM_37 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define ES7243_MCLK_GPIO GPIO_NUM_0 +#define PA_ENABLE_GPIO GPIO_NUM_12 +#define BOARD_PA_GAIN (10) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define ADC_DETECT_GPIO GPIO_NUM_36 +#define INPUT_KEY_NUM 4 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_MUTE_ID 2 +#define BUTTON_SET_ID 3 +#define BUTTON_REC_ID (-1) +#define BUTTON_MODE_ID (-1) +#define GREEN_LED_GPIO (-1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MUTE, \ + .act_id = BUTTON_MUTE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_pins_config.c new file mode 100644 index 0000000..b4502a3 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/board_pins_config.c @@ -0,0 +1,149 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "string.h" +#include "driver/gpio.h" +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/io_mux_reg.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_Korvo_DU1906"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->mck_io_num = GPIO_NUM_0; + i2s_config->bck_io_num = GPIO_NUM_4; + i2s_config->ws_io_num = GPIO_NUM_13; + i2s_config->data_out_num = GPIO_NUM_16; + i2s_config->data_in_num = GPIO_NUM_39; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +// Using "mute" button instead of "play" button as the audio control button, +// since `ESP32-Korvo-DU1906` board has not "play" button +int8_t get_input_play_id(void) +{ + return BUTTON_MUTE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +int8_t get_es7243_mclk_gpio(void) +{ + return ES7243_MCLK_GPIO; +} + +// led pins +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/du1906_bar_pattern.c b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/du1906_bar_pattern.c new file mode 100644 index 0000000..85de880 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_korvo_du1906/du1906_bar_pattern.c @@ -0,0 +1,513 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "display_service.h" +#include "periph_ws2812.h" +#include "board_def.h" +#include "string.h" +#include "esp_log.h" + +const struct periph_ws2812_ctrl_cfg ws2812_display_pattern[DISPLAY_PATTERN_MAX][WS2812_LED_BAR_NUMBERS] = { + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 400, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 400, + } // 0 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 0xFFFFFFFF, + .time_off_ms = 1500, + .time_on_ms = 1500, + }, + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 0xFFFFFFFF, + .time_off_ms = 1500, + .time_on_ms = 1500, + } // 1 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 0xFFFFFFFF, + .time_off_ms = 200, + .time_on_ms = 800, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 0xFFFFFFFF, + .time_off_ms = 200, + .time_on_ms = 800, + } // 2 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK , + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 3 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_RED, + .loop = 1, + .time_off_ms = 1000, + .time_on_ms = 4000, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_RED, + .loop = 1, + .time_off_ms = 1000, + .time_on_ms = 4000, + } // 4 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 5, + .time_off_ms = 200, + .time_on_ms = 1000, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_WHITE, + .loop = 5, + .time_off_ms = 200, + .time_on_ms = 1000, + } // 5 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 100, + .time_off_ms = 200, + .time_on_ms = 800, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 100, + .time_off_ms = 200, + .time_on_ms = 800, + } // 6 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + } // 7 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 4, + .time_off_ms = 100, + .time_on_ms = 100, + } // 8 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 9 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + } // 10 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 100, + .time_off_ms = 2000, + .time_on_ms = 2000, + }, + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 100, + .time_off_ms = 2000, + .time_on_ms = 2000, + } // 11 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 2, + .time_off_ms = 200, + .time_on_ms = 400, + } // 12 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLUE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLUE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 13 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 14 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_YELLOW, + .loop = 2000, + .time_off_ms = 200, + .time_on_ms = 600, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_YELLOW, + .loop = 2000, + .time_off_ms = 200, + .time_on_ms = 600, + } // 15 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 16 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_GREEN, + .loop = 1, + .time_off_ms = 0, + .time_on_ms = 1000, + }, + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_GREEN, + .loop = 1, + .time_off_ms = 0, + .time_on_ms = 1000, + } // 17 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_ORANGE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_ORANGE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 18 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 19 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_WHITE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_WHITE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 20 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 21 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 800, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 1000, + .time_off_ms = 200, + .time_on_ms = 800, + } // 22 + }, + { + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1500, + .time_on_ms = 1500, + }, + { + .mode = PERIPH_WS2812_FADE, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1500, + .time_on_ms = 1500, + } // 23 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1000, + .time_on_ms = 2000, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_PURPLE, + .loop = 1000, + .time_off_ms = 1000, + .time_on_ms = 2000, + } // 24 + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_PURPLE, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 25 + } + , + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 0xFFFFFFFF, + .time_off_ms = 2000, + .time_on_ms = 2000, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_RED, + .loop = 0xFFFFFFFF, + .time_off_ms = 2000, + .time_on_ms = 2000, + } // 26 + }, + { + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 0xFFFFFFFF, + .time_off_ms = 500, + .time_on_ms = 500, + }, + { + .mode = PERIPH_WS2812_BLINK, + .color = LED2812_COLOR_BLUE, + .loop = 0xFFFFFFFF, + .time_off_ms = 500, + .time_on_ms = 500, + } // 27, + }, + { + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + }, + { + .mode = PERIPH_WS2812_ONE, + .color = LED2812_COLOR_BLACK, + .loop = 0, + .time_off_ms = 0, + .time_on_ms = 0, + } // 28, + } +}; + +int8_t get_ws2812_gpio_pin(void) +{ + return WS2812_LED_GPIO_PIN; +} + +int get_ws2812_num(void) +{ + return WS2812_LED_BAR_NUMBERS; +} + +void ws2812_pattern_copy(struct periph_ws2812_ctrl_cfg *p) +{ + ESP_LOGD("ws2812_pattern_copy", "has been called, %s %d", __FILE__, __LINE__); + memcpy(p, ws2812_display_pattern, sizeof(ws2812_display_pattern)); +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.c b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.c new file mode 100644 index 0000000..73c678f --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.c @@ -0,0 +1,99 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2024 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t)audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time--) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + ESP_LOGE(TAG, "esp32_p4_function_ev_board not support key"); + return ESP_FAIL; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.h b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.h new file mode 100644 index 0000000..c72b840 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board.h @@ -0,0 +1,114 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2024 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return + * - NULL If initialization failed + * - Others The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return + * - NULL If initialization failed + * - Others The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize sdcard peripheral + * + * @param[in] set The handle of esp_periph_set_handle_t + * @param[in] mode SDCard mode + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Initialize key peripheral + * + * @param[in] set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Query audio_board_handle + * + * @return + * - NULL If board not initialized + * - Others The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param[in] audio_board The handle of audio board + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* _AUDIO_BOARD_H_ */ diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_def.h new file mode 100644 index 0000000..9fe590d --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_def.h @@ -0,0 +1,112 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2024 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief SDCARD Function Definition + */ + +#define SD_PWR_CTRL_LDO_INTERNAL_IO (4) + +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX (5) +#define SDCARD_INTR_GPIO (-1) +#define SDCARD_PWR_CTRL (-1) + +// In order to coexist with ESP host, slot0 is required and matrix will not be used, +// so all default configurations are set to 0 +#define ESP_SD_PIN_CLK (0) +#define ESP_SD_PIN_CMD (0) +#define ESP_SD_PIN_D0 (0) +#define ESP_SD_PIN_D1 (0) +#define ESP_SD_PIN_D2 (0) +#define ESP_SD_PIN_D3 (0) +#define ESP_SD_PIN_D4 (0) +#define ESP_SD_PIN_D5 (0) +#define ESP_SD_PIN_D6 (0) +#define ESP_SD_PIN_D7 (0) +#define ESP_SD_PIN_CD (-1) +#define ESP_SD_PIN_WP (-1) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define ES8311_MCLK_SOURCE (1) /* 0 From MCLK of esp32 1 From BCLK */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_53 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "MR" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (0) +#define INPUT_KEY_NUM (0) +#define BUTTON_VOLUP_ID (-1) +#define BUTTON_VOLDOWN_ID (-1) +#define BUTTON_SET_ID (-1) +#define BUTTON_PLAY_ID (0) +#define BUTTON_MODE_ID (-1) +#define BUTTON_REC_ID (1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + } \ +} + +#endif /* _AUDIO_BOARD_DEFINITION_H_ */ + diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_pins_config.c new file mode 100644 index 0000000..7cc71ec --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_board/board_pins_config.c @@ -0,0 +1,166 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2024 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include "esp_log.h" +#include "driver/gpio.h" +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_P4_FUNCTION_EV_BOARD"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_7; + i2c_config->scl_io_num = GPIO_NUM_8; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_12; + i2s_config->ws_io_num = GPIO_NUM_10; + i2s_config->data_out_num = GPIO_NUM_9; + i2s_config->data_in_num = GPIO_NUM_11; + i2s_config->mck_io_num = GPIO_NUM_13; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return -1; +} + +int8_t get_input_mode_id(void) +{ + return -1; +} + +int8_t get_input_set_id(void) +{ + return -1; +} + +int8_t get_input_play_id(void) +{ + return -1; +} + +int8_t get_input_volup_id(void) +{ + return -1; +} + +int8_t get_input_voldown_id(void) +{ + return -1; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return -1; +} + +int8_t get_blue_led_gpio(void) +{ + return -1; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.c b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.c new file mode 100644 index 0000000..744fc14 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.c @@ -0,0 +1,139 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" + +#include "sd_pwr_ctrl_by_on_chip_ldo.h" +#include "sd_pwr_ctrl_interface.h" +#include "esp_ldo_regulator.h" + +#define LDO_CHANNEL_ID (4) +#define LDO_CFG_VOL_MV (3300) + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; +static esp_ldo_channel_handle_t ldo_audio_board = NULL; + +static void _enable_audio_board_power(void) +{ + // Turn on the power for audio board, so it can go from "No Power" state to "Shutdown" state + esp_ldo_channel_config_t ldo_audio_board_config = { + .chan_id = LDO_CHANNEL_ID, + .voltage_mv = LDO_CFG_VOL_MV, + }; + esp_ldo_acquire_channel(&ldo_audio_board_config, &ldo_audio_board); + ESP_LOGI(TAG, "Audio board powered on"); +} + +static void _disable_audio_board_power(void) +{ + if (ldo_audio_board) { + esp_ldo_release_channel(ldo_audio_board); + ldo_audio_board = NULL; + } +} + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t)audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + _enable_audio_board_power(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + audio_hal_handle_t adc_hal = NULL; + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7210_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time--) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + ESP_LOGE(TAG, "esp32_p4_function_ev_board not support key"); + return ESP_FAIL; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + _disable_audio_board_power(); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.h b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.h new file mode 100644 index 0000000..a197e26 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board.h @@ -0,0 +1,122 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif /* __cplusplus */ + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return + * - NULL If initialization failed + * - Others The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return + * - NULL If initialization failed + * - Others The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize sdcard peripheral + * + * @param[in] set The handle of esp_periph_set_handle_t + * @param[in] mode SDCard mode + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Initialize key peripheral + * + * @param[in] set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Query audio_board_handle + * + * @return + * - NULL If board not initialized + * - Others The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param[in] audio_board The handle of audio board + * + * @return + * - ESP_OK On success + * - Others On failure + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif /* __cplusplus */ + +#endif /* _AUDIO_BOARD_H_ */ diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_def.h new file mode 100644 index 0000000..fbe8b9a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_def.h @@ -0,0 +1,113 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief SDCARD Function Definition + */ + +#define SD_PWR_CTRL_LDO_INTERNAL_IO (4) + +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX (5) +#define SDCARD_INTR_GPIO (-1) +#define SDCARD_PWR_CTRL (-1) + +// In order to coexist with ESP host, slot0 is required and matrix will not be used, +// so all default configurations are set to 0 +#define ESP_SD_PIN_CLK (0) +#define ESP_SD_PIN_CMD (0) +#define ESP_SD_PIN_D0 (0) +#define ESP_SD_PIN_D1 (0) +#define ESP_SD_PIN_D2 (0) +#define ESP_SD_PIN_D3 (0) +#define ESP_SD_PIN_D4 (0) +#define ESP_SD_PIN_D5 (0) +#define ESP_SD_PIN_D6 (0) +#define ESP_SD_PIN_D7 (0) +#define ESP_SD_PIN_CD (-1) +#define ESP_SD_PIN_WP (-1) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define ES8311_MCLK_SOURCE (1) /* 0 From MCLK of esp32 1 From BCLK */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_53 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "RMNM" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7210_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (0) +#define INPUT_KEY_NUM (0) +#define BUTTON_VOLUP_ID (-1) +#define BUTTON_VOLDOWN_ID (-1) +#define BUTTON_SET_ID (-1) +#define BUTTON_PLAY_ID (0) +#define BUTTON_MODE_ID (-1) +#define BUTTON_REC_ID (1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + } \ +} + +#endif /* _AUDIO_BOARD_DEFINITION_H_ */ + diff --git a/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_pins_config.c new file mode 100644 index 0000000..39ffc00 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_p4_function_ev_sub_board/board_pins_config.c @@ -0,0 +1,166 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include "esp_log.h" +#include "driver/gpio.h" +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_P4_FUNCTION_EV_BOARD"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_21; + i2c_config->scl_io_num = GPIO_NUM_20; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { // 7210 + i2s_config->bck_io_num = GPIO_NUM_47; + i2s_config->ws_io_num = GPIO_NUM_48; + i2s_config->data_out_num = GPIO_NUM_45; + i2s_config->data_in_num = GPIO_NUM_27; + i2s_config->mck_io_num = GPIO_NUM_46; + } else if (port == 1) { // 8311 + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return -1; +} + +int8_t get_input_mode_id(void) +{ + return -1; +} + +int8_t get_input_set_id(void) +{ + return -1; +} + +int8_t get_input_play_id(void) +{ + return -1; +} + +int8_t get_input_volup_id(void) +{ + return -1; +} + +int8_t get_input_voldown_id(void) +{ + return -1; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return -1; +} + +int8_t get_blue_led_gpio(void) +{ + return -1; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.c b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.c new file mode 100644 index 0000000..822574f --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.c @@ -0,0 +1,156 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_adc_button.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = NULL; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + esp_err_t ret = ESP_FAIL; + return ret; +} + +display_service_handle_t audio_board_led_init(void) +{ + // TODO + return NULL; +} +#if (ESP_IDF_VERSION >= ESP_IDF_VERSION_VAL(4, 4, 0)) +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + gpio_config_t bk_gpio_config = { + .mode = GPIO_MODE_OUTPUT, + .pin_bit_mask = 1ULL << LCD_CTRL_GPIO + }; + // Initialize the GPIO of backlight + ESP_ERROR_CHECK(gpio_config(&bk_gpio_config)); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = 16 * LCD_H_RES * 2 + 8 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = LCD_CS_GPIO, + .pclk_hz = 60 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = LCD_RST_GPIO, + .color_space = ESP_LCD_COLOR_SPACE_BGR, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = NULL, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} +#endif + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.adc_ch = ADC1_CHANNEL_5; + adc_btn_tag.total_steps = 6; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 2610}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_board->audio_hal = NULL; + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.h b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.h new file mode 100644 index 0000000..1c4c473 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board.h @@ -0,0 +1,123 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return ESP_OK success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_def.h new file mode 100644 index 0000000..fe2a7c0 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_def.h @@ -0,0 +1,147 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +#include "driver/touch_pad.h" + +/** + * @brief LED Function Definition + */ +#define FUNC_SYS_LEN_EN (1) +#define WS2812_LED_GPIO 45 + + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO GPIO_NUM_6 +#define LCD_RST_GPIO GPIO_NUM_16 +#define LCD_CS_GPIO GPIO_NUM_11 +// LCD SPI Pins +#define LCD_DC_GPIO GPIO_NUM_13 +#define LCD_CLK_GPIO GPIO_NUM_15 +#define LCD_MOSI_GPIO GPIO_NUM_9 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (true) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (0) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK -1 +#define ESP_SD_PIN_CMD -1 +#define ESP_SD_PIN_D0 -1 +#define ESP_SD_PIN_D3 -1 + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define PA_ENABLE_GPIO (10) +#define ES8311_MCLK_SOURCE (1) /* 0 From MCLK, 1 From BCLK */ +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (0) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "MR" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define BUTTON_ADC 6 +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 +#define GREEN_LED_GPIO (-1) +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_pins_config.c new file mode 100644 index 0000000..fd9d41b --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s2_kaluga_1_v1_2/board_pins_config.c @@ -0,0 +1,152 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2020 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" +#include "esp_idf_version.h" +#if ESP_IDF_VERSION >= ESP_IDF_VERSION_VAL(5, 0, 0) +#include "rom/gpio.h" +#include "soc/gpio_sig_map.h" +#endif +static const char *TAG = "KALUGA_V1_2"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0) { + i2c_config->sda_io_num = GPIO_NUM_8; + i2c_config->scl_io_num = GPIO_NUM_7; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + + i2s_config->bck_io_num = GPIO_NUM_18; + i2s_config->ws_io_num = GPIO_NUM_17; + i2s_config->data_out_num = GPIO_NUM_12; + i2s_config->data_in_num = GPIO_NUM_34; + i2s_config->mck_io_num = GPIO_NUM_35; + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins +int8_t get_ws2812_led_gpio(void) +{ + return WS2812_LED_GPIO; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.c new file mode 100644 index 0000000..0825e3c --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.c @@ -0,0 +1,196 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "esp_lcd_panel_ops.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + audio_hal_handle_t adc_hal = NULL; + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7210_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + vTaskDelay(20 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + gpio_config_t bk_gpio_config = { + .mode = GPIO_MODE_OUTPUT, + /*!< Prevent left shift negtive value warning */ + .pin_bit_mask = LCD_CTRL_GPIO > 0 ? 1ULL << LCD_CTRL_GPIO : 0ULL, + }; + gpio_config(&bk_gpio_config); + gpio_set_level(LCD_CTRL_GPIO, true); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = LCD_CS_GPIO, + .pclk_hz = 10 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = LCD_RST_GPIO, + .color_space = LCD_COLOR_SPACE, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = NULL, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 3; + adc_btn_tag.adc_ch = ADC1_CHANNEL_0; + int btn_array[4] = {190, 1000, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.h new file mode 100644 index 0000000..d86684e --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board.h @@ -0,0 +1,131 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_def.h new file mode 100644 index 0000000..f9df7cb --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_def.h @@ -0,0 +1,141 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO GPIO_NUM_45 +#define LCD_RST_GPIO GPIO_NUM_48 +#define LCD_DC_GPIO GPIO_NUM_4 +#define LCD_CS_GPIO GPIO_NUM_5 +#define LCD_CLK_GPIO GPIO_NUM_7 +#define LCD_MOSI_GPIO GPIO_NUM_6 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) +#define LCD_COLOR_SPACE ESP_LCD_COLOR_SPACE_BGR + + +/** + * @brief SDCARD Function Definition + * PMOD2 for one line sdcard + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 +#define ESP_SD_PIN_CLK GPIO_NUM_13 +#define ESP_SD_PIN_CMD GPIO_NUM_11 +#define ESP_SD_PIN_D0 GPIO_NUM_14 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)32) /* 32bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (0) /* Power amplifier gain defined by board (dB) */ +#define PA_ENABLE_GPIO GPIO_NUM_46 +#define HEADPHONE_DETECT -1 +#define ES7210_MIC_SELECT (ES7210_INPUT_MIC1 | ES7210_INPUT_MIC2 | ES7210_INPUT_MIC3) + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "RMNM" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7210_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (0) +#define INPUT_KEY_NUM 3 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_pins_config.c new file mode 100644 index 0000000..249b5fe --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box/board_pins_config.c @@ -0,0 +1,172 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_BOX"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_8; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_17; + i2s_config->ws_io_num = GPIO_NUM_47; + i2s_config->data_out_num = GPIO_NUM_15; + i2s_config->data_in_num = GPIO_NUM_16; + i2s_config->mck_io_num = GPIO_NUM_2; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +int8_t get_es7243_mclk_gpio(void) +{ + return GPIO_NUM_2; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return -1; +} + +int8_t get_blue_led_gpio(void) +{ + return -1; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.c new file mode 100644 index 0000000..03503b0 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.c @@ -0,0 +1,199 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2023 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "esp_lcd_panel_ops.h" + +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" +#include "periph_button.h" +#include "tca9554.h" +#include "board.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + audio_hal_handle_t adc_hal = NULL; + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7210_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +static esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + if (LCD_CTRL_GPIO >=0) { + gpio_config_t bk_gpio_config = { + .mode = GPIO_MODE_OUTPUT, + .pin_bit_mask = 1ULL << LCD_CTRL_GPIO + }; + gpio_config(&bk_gpio_config); + gpio_set_level(LCD_CTRL_GPIO, true); + } + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = LCD_CS_GPIO, + .pclk_hz = 10 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = LCD_RST_GPIO, + .flags.reset_active_high = 1, + .color_space = LCD_COLOR_SPACE, + .bits_per_pixel = 16 + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = NULL, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_button_cfg_t btn_cfg = { + .gpio_mask = (1ULL << get_input_rec_id()) | (1ULL << get_input_play_id()), //REC BTN & PLAY BTN + }; + esp_periph_handle_t button_handle = periph_button_init(&btn_cfg); + AUDIO_NULL_CHECK(TAG, button_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, button_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE && mode != SD_MODE_4_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line and 4-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + + // Enable SDCard power + if (get_sdcard_power_ctrl_gpio() >= 0) { + gpio_config_t gpio_cfg = { + .mode = GPIO_MODE_OUTPUT, + .pin_bit_mask = 1ULL << get_sdcard_power_ctrl_gpio() + }; + gpio_config(&gpio_cfg); + gpio_set_level(get_sdcard_power_ctrl_gpio(), 0); + } + + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + AUDIO_NULL_CHECK(TAG, audio_board, return ESP_FAIL); + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.h new file mode 100644 index 0000000..edf9ce5 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board.h @@ -0,0 +1,131 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2023 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_def.h new file mode 100644 index 0000000..2bad8d6 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_def.h @@ -0,0 +1,131 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2023 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO GPIO_NUM_47 +#define LCD_RST_GPIO GPIO_NUM_48 +#define LCD_DC_GPIO GPIO_NUM_4 +#define LCD_CS_GPIO GPIO_NUM_5 +#define LCD_CLK_GPIO GPIO_NUM_7 +#define LCD_MOSI_GPIO GPIO_NUM_6 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES (320) +#define LCD_V_RES (240) +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) +#define LCD_COLOR_SPACE ESP_LCD_COLOR_SPACE_BGR + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX (5) +#define SDCARD_INTR_GPIO (-1) +#define SDCARD_PWR_CTRL GPIO_NUM_43 +#define ESP_SD_PIN_CLK GPIO_NUM_11 +#define ESP_SD_PIN_CMD GPIO_NUM_14 +#define ESP_SD_PIN_D0 GPIO_NUM_9 +#define ESP_SD_PIN_D1 GPIO_NUM_13 +#define ESP_SD_PIN_D2 GPIO_NUM_42 +#define ESP_SD_PIN_D3 GPIO_NUM_12 +#define ESP_SD_PIN_D4 (-1) +#define ESP_SD_PIN_D5 (-1) +#define ESP_SD_PIN_D6 (-1) +#define ESP_SD_PIN_D7 (-1) +#define ESP_SD_PIN_CD (-1) +#define ESP_SD_PIN_WP (-1) + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)32) /* 32bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (0) /* Power amplifier gain defined by board (dB) */ +#define PA_ENABLE_GPIO GPIO_NUM_46 +#define HEADPHONE_DETECT (-1) +#define ES7210_MIC_SELECT (ES7210_INPUT_MIC1 | ES7210_INPUT_MIC2 | ES7210_INPUT_MIC3) + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "RMNM" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7210_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM (2) +#define BUTTON_VOLUP_ID (-1) +#define BUTTON_VOLDOWN_ID (-1) +#define BUTTON_SET_ID (-1) +#define BUTTON_PLAY_ID (0) +#define BUTTON_MODE_ID (-1) +#define BUTTON_REC_ID (1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + } \ +} +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_pins_config.c new file mode 100644 index 0000000..00da382 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_3/board_pins_config.c @@ -0,0 +1,161 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2023 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include "soc/soc_caps.h" +#include "esp_log.h" +#include "driver/gpio.h" + +#include "audio_error.h" +#include "audio_mem.h" +#include "board.h" + +static const char *TAG = "ESP32_S3_BOX_3"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_8; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_17; + i2s_config->ws_io_num = GPIO_NUM_45; + i2s_config->data_out_num = GPIO_NUM_15; + i2s_config->data_in_num = GPIO_NUM_16; + i2s_config->mck_io_num = GPIO_NUM_2; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "I2S PORT %d is not supported, please use I2S PORT 0", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return -1; +} + +int8_t get_blue_led_gpio(void) +{ + return -1; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.c new file mode 100644 index 0000000..69ff7d6 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.c @@ -0,0 +1,196 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "esp_lcd_panel_ops.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + audio_hal_handle_t adc_hal = NULL; + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7243E_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8156_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + vTaskDelay(200 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + gpio_config_t bk_gpio_config = { + .mode = GPIO_MODE_OUTPUT, + /*!< Prevent left shift negtive value warning */ + .pin_bit_mask = LCD_CTRL_GPIO > 0 ? 1ULL << LCD_CTRL_GPIO : 0ULL, + }; + gpio_config(&bk_gpio_config); + gpio_set_level(LCD_CTRL_GPIO, false); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = 16 * LCD_H_RES * 2 + 8 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = LCD_CS_GPIO, + .pclk_hz = 10 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = LCD_RST_GPIO, + .color_space = LCD_COLOR_SPACE, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = NULL, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 3; + adc_btn_tag.adc_ch = ADC1_CHANNEL_0; + int btn_array[4] = {190, 1000, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.h new file mode 100644 index 0000000..d86684e --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board.h @@ -0,0 +1,131 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_def.h new file mode 100644 index 0000000..a8f344a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_def.h @@ -0,0 +1,131 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO GPIO_NUM_45 +#define LCD_RST_GPIO GPIO_NUM_48 +#define LCD_DC_GPIO GPIO_NUM_4 +#define LCD_CS_GPIO GPIO_NUM_5 +#define LCD_MOSI_GPIO GPIO_NUM_6 +#define LCD_CLK_GPIO GPIO_NUM_7 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (true) +#define LCD_MIRROR_X (false) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (true) +#define LCD_COLOR_SPACE ESP_LCD_COLOR_SPACE_RGB + + +/** + * @brief SDCARD Function Definition + * PMOD2 for one line sdcard + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 +#define ESP_SD_PIN_CLK GPIO_NUM_13 +#define ESP_SD_PIN_CMD GPIO_NUM_11 +#define ESP_SD_PIN_D0 GPIO_NUM_14 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define HEADPHONE_DETECT -1 +#define PA_ENABLE_GPIO GPIO_NUM_46 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)I2S_BITS_PER_SAMPLE_32BIT) +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (0) /* Power amplifier gain defined by board (dB) */ + +extern audio_hal_func_t AUDIO_CODEC_ES8156_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7243E_DEFAULT_HANDLE; + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define INPUT_KEY_NUM 3 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_pins_config.c new file mode 100644 index 0000000..d0fd207 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_box_lite/board_pins_config.c @@ -0,0 +1,172 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_BOX_LITE"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_8; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_17; + i2s_config->ws_io_num = GPIO_NUM_47; + i2s_config->data_out_num = GPIO_NUM_15; + i2s_config->data_in_num = GPIO_NUM_16; + i2s_config->mck_io_num = GPIO_NUM_2; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +int8_t get_es7243_mclk_gpio(void) +{ + return GPIO_NUM_2; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return -1; +} + +int8_t get_blue_led_gpio(void) +{ + return -1; +} + +int8_t get_es8311_mclk_src(void) +{ + return -1; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.c new file mode 100644 index 0000000..45b5422 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.c @@ -0,0 +1,215 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + audio_hal_handle_t adc_hal = NULL; + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7210_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_LOW); + vTaskDelay(100 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_HIGH); + vTaskDelay(200 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +display_service_handle_t audio_board_led_init(void) +{ + return NULL; +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + esp_tca9554_config_t pca_cfg = { + .i2c_scl = GPIO_NUM_18, + .i2c_sda = GPIO_NUM_17, + .interrupt_output = -1, + }; + tca9554_init(&pca_cfg); + // Set LCD_BL_CTRL output + tca9554_set_io_config(LCD_CTRL_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_RST output + tca9554_set_io_config(LCD_RST_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_CS pin output + tca9554_set_io_config(LCD_CS_GPIO, TCA9554_IO_OUTPUT); + + tca9554_set_output_state(LCD_CTRL_GPIO, TCA9554_IO_HIGH); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = -1, + .pclk_hz = 60 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = -1, + .color_space = ESP_LCD_COLOR_SPACE_BGR, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = _lcd_rest, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 6; + adc_btn_tag.adc_ch = ADC1_CHANNEL_4; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + if (audio_mem_spiram_stack_is_enabled()) { + adc_btn_cfg.task_cfg.ext_stack = true; + } + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.h new file mode 100644 index 0000000..707c8cb --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board.h @@ -0,0 +1,138 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_def.h new file mode 100644 index 0000000..c7d3300 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_def.h @@ -0,0 +1,188 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LED Function Definition + */ +#define GREEN_LED_GPIO -1 +#define BLUE_LED_GPIO BIT(7) // TCA9554_GPIO_NUM_7 +#define RED_LED_GPIO BIT(6) // TCA9554_GPIO_NUM_6 + + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO BIT(1) // TCA9554_GPIO_NUM_1 +#define LCD_RST_GPIO BIT(2) // TCA9554_GPIO_NUM_2 +#define LCD_CS_GPIO BIT(3) // TCA9554_GPIO_NUM_3 +// LCD SPI Pins +#define LCD_DC_GPIO GPIO_NUM_2 +#define LCD_CLK_GPIO GPIO_NUM_1 +#define LCD_MOSI_GPIO GPIO_NUM_0 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK GPIO_NUM_15 +#define ESP_SD_PIN_CMD GPIO_NUM_7 +#define ESP_SD_PIN_D0 GPIO_NUM_4 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief Camera Function Definition + */ +#define FUNC_CAMERA_EN (1) +#define CAM_PIN_PWDN -1 +#define CAM_PIN_RESET -1 +#define CAM_PIN_XCLK GPIO_NUM_40 +#define CAM_PIN_SIOD GPIO_NUM_17 +#define CAM_PIN_SIOC GPIO_NUM_18 + +#define CAM_PIN_D7 GPIO_NUM_39 +#define CAM_PIN_D6 GPIO_NUM_41 +#define CAM_PIN_D5 GPIO_NUM_42 +#define CAM_PIN_D4 GPIO_NUM_12 +#define CAM_PIN_D3 GPIO_NUM_3 +#define CAM_PIN_D2 GPIO_NUM_14 +#define CAM_PIN_D1 GPIO_NUM_47 +#define CAM_PIN_D0 GPIO_NUM_13 +#define CAM_PIN_VSYNC GPIO_NUM_21 +#define CAM_PIN_HREF GPIO_NUM_38 +#define CAM_PIN_PCLK GPIO_NUM_11 + + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)32) /* 32bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_48 +#define ES8311_MCLK_SOURCE (0) /* 0 From MCLK of esp32 1 From BCLK */ +#define ES7210_MIC_SELECT (ES7210_INPUT_MIC1 | ES7210_INPUT_MIC2 | ES7210_INPUT_MIC3) + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "RMNM" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7210_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MUTE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_pins_config.c new file mode 100644 index 0000000..be01c35 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2_v3/board_pins_config.c @@ -0,0 +1,167 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_KORVO_2"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_17; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_9; + i2s_config->ws_io_num = GPIO_NUM_45; + i2s_config->data_out_num = GPIO_NUM_8; + i2s_config->data_in_num = GPIO_NUM_10; + i2s_config->mck_io_num = GPIO_NUM_16; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} + +int8_t get_blue_led_gpio(void) +{ + return BLUE_LED_GPIO; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.c new file mode 100644 index 0000000..a720d7d --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.c @@ -0,0 +1,219 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t audio_board_usb_cam_init(void) +{ + gpio_config_t usb_camera_pins_cfg = { + .intr_type = GPIO_INTR_DISABLE, + .mode = GPIO_MODE_OUTPUT, + .pin_bit_mask = (BIT64(USB_CAM_PIN_PWDN)), + .pull_up_en = 0, + .pull_down_en = 0, + }; + gpio_config(&usb_camera_pins_cfg); + gpio_set_level(USB_CAM_PIN_PWDN, 0); + + return ESP_OK; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +display_service_handle_t audio_board_led_init(void) +{ + return NULL; +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + esp_tca9554_config_t pca_cfg = { + .i2c_scl = GPIO_NUM_18, + .i2c_sda = GPIO_NUM_17, + .interrupt_output = -1, + }; + tca9554_init(&pca_cfg); + // Set LCD_BL_CTRL output + tca9554_set_io_config(LCD_CTRL_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_RST output + tca9554_set_io_config(LCD_RST_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_CS pin output + tca9554_set_io_config(LCD_CS_GPIO, TCA9554_IO_OUTPUT); + + tca9554_set_output_state(LCD_CTRL_GPIO, TCA9554_IO_HIGH); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = -1, + .pclk_hz = 60 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = -1, + .color_space = ESP_LCD_COLOR_SPACE_BGR, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = _lcd_rest, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + + esp_periph_start(set, periph_lcd); + vTaskDelay(200 / portTICK_PERIOD_MS); + + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 6; + adc_btn_tag.adc_ch = ADC1_CHANNEL_4; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); + +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.h new file mode 100644 index 0000000..edab0e5 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board.h @@ -0,0 +1,139 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize USB Camera peripheral + * @note Just power on the USB camera + * + * @return + * - ESP_OK, success + */ +esp_err_t audio_board_usb_cam_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_def.h new file mode 100644 index 0000000..d12d93a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_def.h @@ -0,0 +1,192 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LED Function Definition + */ +#define GREEN_LED_GPIO -1 +#define BLUE_LED_GPIO BIT(7) // TCA9554_GPIO_NUM_7 +#define RED_LED_GPIO BIT(6) // TCA9554_GPIO_NUM_6 + + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO BIT(1) // TCA9554_GPIO_NUM_1 +#define LCD_RST_GPIO BIT(2) // TCA9554_GPIO_NUM_2 +#define LCD_CS_GPIO BIT(3) // TCA9554_GPIO_NUM_3 +// LCD SPI Pins +#define LCD_DC_GPIO GPIO_NUM_2 +#define LCD_CLK_GPIO GPIO_NUM_1 +#define LCD_MOSI_GPIO GPIO_NUM_0 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK GPIO_NUM_6 +#define ESP_SD_PIN_CMD GPIO_NUM_7 +#define ESP_SD_PIN_D0 GPIO_NUM_4 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief Camera Function Definition + */ +#define FUNC_CAMERA_EN (1) +#define CAM_PIN_PWDN -1 +#define CAM_PIN_RESET -1 +#define CAM_PIN_XCLK GPIO_NUM_40 +#define CAM_PIN_SIOD GPIO_NUM_17 +#define CAM_PIN_SIOC GPIO_NUM_18 + +#define CAM_PIN_D7 GPIO_NUM_39 +#define CAM_PIN_D6 GPIO_NUM_41 +#define CAM_PIN_D5 GPIO_NUM_42 +#define CAM_PIN_D4 GPIO_NUM_12 +#define CAM_PIN_D3 GPIO_NUM_3 +#define CAM_PIN_D2 GPIO_NUM_14 +#define CAM_PIN_D1 GPIO_NUM_47 +#define CAM_PIN_D0 GPIO_NUM_13 +#define CAM_PIN_VSYNC GPIO_NUM_21 +#define CAM_PIN_HREF GPIO_NUM_38 +#define CAM_PIN_PCLK GPIO_NUM_11 + +#define FUNC_USB_CAMERA_EN (1) +#define USB_CAM_PIN_PWDN GPIO_NUM_48 +#define USB_CAM_PIN_DP GPIO_NUM_19 +#define USB_CAM_PIN_DM GPIO_NUM_20 + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define ES8311_MCLK_SOURCE (1) /* 0 From MCLK of esp32 1 From BCLK */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_46 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "MR" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MUTE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_pins_config.c new file mode 100644 index 0000000..454b742 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_korvo2l_v1/board_pins_config.c @@ -0,0 +1,167 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include "esp_log.h" +#include "driver/gpio.h" +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_KORVO_2L"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_17; + i2c_config->scl_io_num = GPIO_NUM_18; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_9; + i2s_config->ws_io_num = GPIO_NUM_45; + i2s_config->data_out_num = GPIO_NUM_8; + i2s_config->data_in_num = GPIO_NUM_10; + i2s_config->mck_io_num = -1; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} + +int8_t get_blue_led_gpio(void) +{ + return BLUE_LED_GPIO; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.c new file mode 100644 index 0000000..c99e585 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.c @@ -0,0 +1,204 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_LOW); + vTaskDelay(100 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_HIGH); + vTaskDelay(200 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +display_service_handle_t audio_board_led_init(void) +{ + return NULL; +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + esp_tca9554_config_t pca_cfg = { + .i2c_scl = GPIO_NUM_18, + .i2c_sda = GPIO_NUM_17, + .interrupt_output = -1, + }; + tca9554_init(&pca_cfg); + // Set LCD_BL_CTRL output + tca9554_set_io_config(LCD_CTRL_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_RST output + tca9554_set_io_config(LCD_RST_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_CS pin output + tca9554_set_io_config(LCD_CS_GPIO, TCA9554_IO_OUTPUT); + + tca9554_set_output_state(LCD_CTRL_GPIO, TCA9554_IO_HIGH); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = -1, + .pclk_hz = 60 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = -1, + .color_space = ESP_LCD_COLOR_SPACE_BGR, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = _lcd_rest, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + esp_periph_start(set, periph_lcd); + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 6; + adc_btn_tag.adc_ch = ADC1_CHANNEL_4; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + if (audio_mem_spiram_stack_is_enabled()) { + adc_btn_cfg.task_cfg.ext_stack = true; + } + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.h new file mode 100644 index 0000000..707c8cb --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board.h @@ -0,0 +1,138 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_def.h new file mode 100644 index 0000000..7ab184f --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_def.h @@ -0,0 +1,172 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO -1 // need change +#define LCD_RST_GPIO -1 // need change +#define LCD_CS_GPIO -1 // need change +// LCD SPI Pins +#define LCD_DC_GPIO -1 // need change +#define LCD_CLK_GPIO -1 // need change +#define LCD_MOSI_GPIO -1 // need change +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) + +/** + * @brief SDCARD Function Definition + * PMOD2 for one line sdcard + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 +#define ESP_SD_PIN_CLK -1 +#define ESP_SD_PIN_CMD -1 +#define ESP_SD_PIN_D0 -1 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief Camera Function Definition + */ +#define FUNC_CAMERA_EN (1) +#define CAM_PIN_PWDN -1 +#define CAM_PIN_RESET -1 // need change +#define CAM_PIN_XCLK -1 // need change +#define CAM_PIN_SIOD -1 // need change +#define CAM_PIN_SIOC -1 // need change + +#define CAM_PIN_D7 -1 // need change +#define CAM_PIN_D6 -1 // need change +#define CAM_PIN_D5 -1 // need change +#define CAM_PIN_D4 -1 // need change +#define CAM_PIN_D3 -1 // need change +#define CAM_PIN_D2 -1 // need change +#define CAM_PIN_D1 -1 // need change +#define CAM_PIN_D0 -1 // need change +#define CAM_PIN_VSYNC -1 // need change +#define CAM_PIN_HREF -1 // need change +#define CAM_PIN_PCLK -1 // need change + + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (16000) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_46 +#define ES8311_MCLK_SOURCE (0) /* 0 From MCLK of esp32 1 From BCLK */ + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID -1 +#define BUTTON_VOLDOWN_ID -1 +#define BUTTON_SET_ID -1 +#define BUTTON_PLAY_ID -1 +#define BUTTON_MODE_ID -1 +#define BUTTON_REC_ID -1 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MUTE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_pins_config.c new file mode 100644 index 0000000..a69841a --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_sparkbot/board_pins_config.c @@ -0,0 +1,155 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2021 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_KORVO_2"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_4; + i2c_config->scl_io_num = GPIO_NUM_5; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_39; + i2s_config->ws_io_num = GPIO_NUM_41; + i2s_config->data_out_num = GPIO_NUM_42; + i2s_config->data_in_num = GPIO_NUM_40; + i2s_config->mck_io_num = GPIO_NUM_45; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.c b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.c new file mode 100644 index 0000000..c3d0071 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.c @@ -0,0 +1,230 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "tca9554.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + // Pullup to fix the leakage issue of the ES8311 in standby mode. + gpio_config_t io_conf = { + .pin_bit_mask = 1ULL << GPIO_NUM_6, + .mode = GPIO_MODE_OUTPUT, + .pull_up_en = 0, + .pull_down_en = 0, + .intr_type = GPIO_INTR_DISABLE + }; + gpio_config(&io_conf); + gpio_set_level(GPIO_NUM_6, 1); + + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +esp_err_t audio_board_usb_cam_init(void) +{ + gpio_config_t usb_camera_pins_cfg = { + .intr_type = GPIO_INTR_DISABLE, + .mode = GPIO_MODE_OUTPUT, + .pin_bit_mask = (BIT64(USB_CAM_PIN_PWDN)), + .pull_up_en = 0, + .pull_down_en = 0, + }; + gpio_config(&usb_camera_pins_cfg); + gpio_set_level(USB_CAM_PIN_PWDN, 0); + + return ESP_OK; +} + +esp_err_t _lcd_rest(esp_periph_handle_t self, void *ctx) +{ + // Reset the LCD + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_RST_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + return ESP_OK; +} + +esp_err_t _get_lcd_io_bus (void *bus, esp_lcd_panel_io_spi_config_t *io_config, + esp_lcd_panel_io_handle_t *out_panel_io) +{ + return esp_lcd_new_panel_io_spi((esp_lcd_spi_bus_handle_t)bus, io_config, out_panel_io); +} + +display_service_handle_t audio_board_led_init(void) +{ + return NULL; +} + +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb) +{ + esp_tca9554_config_t pca_cfg = { + .i2c_scl = GPIO_NUM_18, + .i2c_sda = GPIO_NUM_17, + .interrupt_output = -1, + }; + tca9554_init(&pca_cfg); + // Set LCD_BL_CTRL output + tca9554_set_io_config(LCD_CTRL_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_RST output + tca9554_set_io_config(LCD_RST_GPIO, TCA9554_IO_OUTPUT); + // Set LCD_CS pin output + tca9554_set_io_config(LCD_CS_GPIO, TCA9554_IO_OUTPUT); + + tca9554_set_output_state(LCD_CTRL_GPIO, TCA9554_IO_HIGH); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_HIGH); + vTaskDelay(10 / portTICK_PERIOD_MS); + tca9554_set_output_state(LCD_CS_GPIO, TCA9554_IO_LOW); + vTaskDelay(10 / portTICK_PERIOD_MS); + + spi_bus_config_t buscfg = { + .sclk_io_num = LCD_CLK_GPIO, + .mosi_io_num = LCD_MOSI_GPIO, + .miso_io_num = -1, + .quadwp_io_num = -1, + .quadhd_io_num = -1, + .max_transfer_sz = LCD_V_RES * LCD_H_RES * 2 + }; + ESP_ERROR_CHECK(spi_bus_initialize(SPI2_HOST, &buscfg, SPI_DMA_CH_AUTO)); + + esp_lcd_panel_io_spi_config_t io_config = { + .dc_gpio_num = LCD_DC_GPIO, + .cs_gpio_num = -1, + .pclk_hz = 60 * 1000 * 1000, + .lcd_cmd_bits = 8, + .lcd_param_bits = 8, + .spi_mode = 0, + .trans_queue_depth = 10, + .on_color_trans_done = cb, + .user_ctx = NULL, + }; + esp_lcd_panel_dev_config_t panel_config = { + .reset_gpio_num = -1, + .color_space = ESP_LCD_COLOR_SPACE_BGR, + .bits_per_pixel = 16, + }; + periph_lcd_cfg_t cfg = { + .io_bus = (void *)SPI2_HOST, + .new_panel_io = _get_lcd_io_bus, + .lcd_io_cfg = &io_config, + .new_lcd_panel = esp_lcd_new_panel_st7789, + .lcd_dev_cfg = &panel_config, + .rest_cb = _lcd_rest, + .rest_cb_ctx = NULL, + .lcd_swap_xy = LCD_SWAP_XY, + .lcd_mirror_x = LCD_MIRROR_X, + .lcd_mirror_y = LCD_MIRROR_Y, + .lcd_color_invert = LCD_COLOR_INV, + }; + esp_periph_handle_t periph_lcd = periph_lcd_init(&cfg); + AUDIO_NULL_CHECK(TAG, periph_lcd, return NULL); + + esp_periph_start(set, periph_lcd); + vTaskDelay(200 / portTICK_PERIOD_MS); + + return (void *)periph_lcd_get_panel_handle(periph_lcd); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 6; + adc_btn_tag.adc_ch = ADC1_CHANNEL_4; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 3000}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); + +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.h b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.h new file mode 100644 index 0000000..edab0e5 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board.h @@ -0,0 +1,139 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" +#include "periph_lcd.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize USB Camera peripheral + * @note Just power on the USB camera + * + * @return + * - ESP_OK, success + */ +esp_err_t audio_board_usb_cam_init(void); + +/** + * @brief Initialize lcd peripheral + * + * @param set The handle of esp_periph_set_handle_t + * @param cb The `on_color_trans_done` callback in `esp_lcd_panel_io_spi_config_t` + * + * @return The `esp_lcd_panel_handle_t` handle + */ +void *audio_board_lcd_init(esp_periph_set_handle_t set, void *cb); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_def.h b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_def.h new file mode 100644 index 0000000..8b000cf --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_def.h @@ -0,0 +1,192 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "MR" + +/** + * @brief LED Function Definition + */ +#define GREEN_LED_GPIO -1 +#define BLUE_LED_GPIO BIT(7) // TCA9554_GPIO_NUM_7 +#define RED_LED_GPIO BIT(6) // TCA9554_GPIO_NUM_6 + + +/** + * @brief LCD SCREEN Function Definition + */ +#define FUNC_LCD_SCREEN_EN (1) +#define LCD_CTRL_GPIO BIT(1) // TCA9554_GPIO_NUM_1 +#define LCD_RST_GPIO BIT(2) // TCA9554_GPIO_NUM_2 +#define LCD_CS_GPIO BIT(3) // TCA9554_GPIO_NUM_3 +// LCD SPI Pins +#define LCD_DC_GPIO GPIO_NUM_2 +#define LCD_CLK_GPIO GPIO_NUM_1 +#define LCD_MOSI_GPIO GPIO_NUM_0 +// The LCD pixel number in horizontal and vertical +#define LCD_H_RES 320 +#define LCD_V_RES 240 +#define LCD_SWAP_XY (false) +#define LCD_MIRROR_X (true) +#define LCD_MIRROR_Y (true) +#define LCD_COLOR_INV (false) + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO -1 +#define SDCARD_PWR_CTRL -1 + +#define ESP_SD_PIN_CLK GPIO_NUM_6 +#define ESP_SD_PIN_CMD GPIO_NUM_7 +#define ESP_SD_PIN_D0 GPIO_NUM_4 +#define ESP_SD_PIN_D1 -1 +#define ESP_SD_PIN_D2 -1 +#define ESP_SD_PIN_D3 -1 +#define ESP_SD_PIN_D4 -1 +#define ESP_SD_PIN_D5 -1 +#define ESP_SD_PIN_D6 -1 +#define ESP_SD_PIN_D7 -1 +#define ESP_SD_PIN_CD -1 +#define ESP_SD_PIN_WP -1 + + +/** + * @brief Camera Function Definition + */ +#define FUNC_CAMERA_EN (1) +#define CAM_PIN_PWDN -1 +#define CAM_PIN_RESET -1 +#define CAM_PIN_XCLK GPIO_NUM_40 +#define CAM_PIN_SIOD GPIO_NUM_17 +#define CAM_PIN_SIOC GPIO_NUM_18 + +#define CAM_PIN_D7 GPIO_NUM_39 +#define CAM_PIN_D6 GPIO_NUM_41 +#define CAM_PIN_D5 GPIO_NUM_42 +#define CAM_PIN_D4 GPIO_NUM_12 +#define CAM_PIN_D3 GPIO_NUM_3 +#define CAM_PIN_D2 GPIO_NUM_14 +#define CAM_PIN_D1 GPIO_NUM_47 +#define CAM_PIN_D0 GPIO_NUM_13 +#define CAM_PIN_VSYNC GPIO_NUM_21 +#define CAM_PIN_HREF GPIO_NUM_38 +#define CAM_PIN_PCLK GPIO_NUM_11 + +#define FUNC_USB_CAMERA_EN (1) +#define USB_CAM_PIN_PWDN GPIO_NUM_48 +#define USB_CAM_PIN_DP GPIO_NUM_19 +#define USB_CAM_PIN_DM GPIO_NUM_20 + +/** + * @brief LCD TOUCH PANEL Function Definition + */ +#define FUNC_LCD_TOUCH_EN (1) +#define TOUCH_PANEL_SWAP_XY (0) +#define TOUCH_PANEL_INVERSE_X (1) +#define TOUCH_PANEL_INVERSE_Y (0) + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define ES8311_MCLK_SOURCE (1) /* 0 From MCLK of esp32 1 From BCLK */ +#define HEADPHONE_DETECT (-1) +#define PA_ENABLE_GPIO GPIO_NUM_40 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (6) /* Power amplifier gain defined by board (dB) */ + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_VOLUP_ID 0 +#define BUTTON_VOLDOWN_ID 1 +#define BUTTON_SET_ID 2 +#define BUTTON_PLAY_ID 3 +#define BUTTON_MODE_ID 4 +#define BUTTON_REC_ID 5 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MUTE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_pins_config.c b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_pins_config.c new file mode 100644 index 0000000..4e9d784 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/esp32_s3_spot/board_pins_config.c @@ -0,0 +1,167 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2022 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include "esp_log.h" +#include "driver/gpio.h" +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/soc_caps.h" + +static const char *TAG = "ESP32_S3_KORVO_2L"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_2; + i2c_config->scl_io_num = GPIO_NUM_1; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->bck_io_num = GPIO_NUM_16; + i2s_config->ws_io_num = GPIO_NUM_17; + i2s_config->data_out_num = GPIO_NUM_18; + i2s_config->data_in_num = GPIO_NUM_15; + i2s_config->mck_io_num = -1; + } else if (port == 1) { + i2s_config->bck_io_num = -1; + i2s_config->ws_io_num = -1; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = -1; + i2s_config->mck_io_num = -1; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} + +int8_t get_blue_led_gpio(void) +{ + return BLUE_LED_GPIO; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} diff --git a/esp-spot/example/adf/components/audio_board/include/board_pins_config.h b/esp-spot/example/adf/components/audio_board/include/board_pins_config.h new file mode 100644 index 0000000..6289392 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/include/board_pins_config.h @@ -0,0 +1,232 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _BOARD_PINS_CONFIG_H_ +#define _BOARD_PINS_CONFIG_H_ + +#include "driver/i2c.h" +#include "driver/spi_common.h" +#include "driver/spi_master.h" +#include "driver/spi_slave.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Board i2s pin definition + */ +typedef struct { + int mck_io_num; /*!< MCK pin, output */ + int bck_io_num; /*!< BCK pin, input in slave role, output in master role */ + int ws_io_num; /*!< WS pin, input in slave role, output in master role */ + int data_out_num; /*!< DATA pin, output */ + int data_in_num; /*!< DATA pin, input */ +} board_i2s_pin_t; + +/** + * @brief Get i2c pins configuration + * + * @param port i2c port number to get configuration + * @param i2c_config i2c configuration parameters + * + * @return + * - ESP_OK + * - ESP_FAIL + */ +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config); + +/** + * @brief Get i2s pins configuration + * + * @param port i2s port number to get configuration + * @param i2s_config i2s configuration parameters + * + * @return + * - ESP_OK + * - ESP_FAIL + */ +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config); + +/** + * @brief Get spi pins configuration + * + * @param spi_config spi bus configuration parameters + * @param spi_device_interface_config spi device configuration parameters + * + * @return + * - ESP_OK + * - ESP_FAIL + */ +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config); + +/** + * @brief Get the gpio number for sdcard interrupt + * + * @return -1 non-existent + * Others sdcard interrupt gpio number + */ +int8_t get_sdcard_intr_gpio(void); + +/** + * @brief Get sdcard maximum number of open files + * + * @return -1 error + * Others max num + */ +int8_t get_sdcard_open_file_num_max(void); + +/** + * @brief Get the gpio number for sdcard power control + * + * @return -1 error + * Others SDCard power GPIO + */ +int8_t get_sdcard_power_ctrl_gpio(void); + +/** + * @brief Get the gpio number for auxin detection + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_auxin_detect_gpio(void); + +/** + * @brief Get the gpio number for headphone detection + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_headphone_detect_gpio(void); + +/** + * @brief Get the gpio number for PA enable + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_pa_enable_gpio(void); + +/** + * @brief Get the gpio number for adc detection + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_adc_detect_gpio(void); + +/** + * @brief Get the mclk gpio number of es7243 + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_es7243_mclk_gpio(void); + +/** + * @brief Get the record-button id for adc-button + * + * @return -1 non-existent + * Others button id + */ +int8_t get_input_rec_id(void); + +/** + * @brief Get the number for mode-button + * + * @return -1 non-existent + * Others number + */ +int8_t get_input_mode_id(void); + +/** + * @brief Get number for set function + * + * @return -1 non-existent + * Others number + */ +int8_t get_input_set_id(void); + +/** + * @brief Get number for play function + * + * @return -1 non-existent + * Others number + */ +int8_t get_input_play_id(void); + +/** + * @brief number for volume up function + * + * @return -1 non-existent + * Others number + */ +int8_t get_input_volup_id(void); + +/** + * @brief Get number for volume down function + * + * @return -1 non-existent + * Others number + */ +int8_t get_input_voldown_id(void); + +/** + * @brief Get green led gpio number + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_reset_codec_gpio(void); + +/** + * @brief Get DSP reset gpio number + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_reset_board_gpio(void); + +/** + * @brief Get DSP reset gpio number + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_green_led_gpio(void); + +/** + * @brief Get green led gpio number + * + * @return -1 non-existent + * Others gpio number + */ +int8_t get_blue_led_gpio(void); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.c b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.c new file mode 100644 index 0000000..4f5ee65 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.c @@ -0,0 +1,222 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +#include "periph_sdcard.h" +#include "led_indicator.h" +#include "periph_adc_button.h" +#include "i2c_bus.h" + +#define ES7243E_ADDR 0x20 +#define ES7243_ADDR 0x26 + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +static int audio_board_probe_adc(uint8_t addr) +{ + static i2c_bus_handle_t i2c_handle = NULL; + int res = 0; + + if (i2c_handle == NULL) { + i2c_config_t es_i2c_cfg = { + .mode = I2C_MODE_MASTER, + .sda_pullup_en = GPIO_PULLUP_ENABLE, + .scl_pullup_en = GPIO_PULLUP_ENABLE, + .master.clk_speed = 100000, + }; + res = get_i2c_pins(I2C_NUM_0, &es_i2c_cfg); + if (res != 0) { + return res; + } + i2c_handle = i2c_bus_create(I2C_NUM_0, &es_i2c_cfg); + } + + if (i2c_handle) { + return i2c_bus_probe_addr(i2c_handle, addr); + } + return ESP_FAIL; +} + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + board_handle->adc_hal = audio_board_adc_init(); + return board_handle; +} + +audio_hal_handle_t audio_board_adc_init(void) +{ + audio_hal_handle_t adc_hal = NULL; + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_codec_cfg.codec_mode = AUDIO_HAL_CODEC_MODE_ENCODE; + + // The new version of the esp32_lyrat_mini development board has replaced the ADC from ES7243 to ES7243E. + // Check the current ADC in use and initialize it. + if (audio_board_probe_adc(ES7243E_ADDR) == ESP_OK) { + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7243E_DEFAULT_HANDLE); + } else if (audio_board_probe_adc(ES7243_ADDR) == ESP_OK) { + adc_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES7243_DEFAULT_HANDLE); + } + + AUDIO_NULL_CHECK(TAG, adc_hal, return NULL); + return adc_hal; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8311_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +display_service_handle_t audio_board_led_init(void) +{ + led_indicator_handle_t led = led_indicator_init((gpio_num_t)get_green_led_gpio()); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_indicator_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +display_service_handle_t audio_board_blue_led_init(void) +{ + led_indicator_handle_t led = led_indicator_init((gpio_num_t)get_blue_led_gpio()); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_indicator_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + esp_err_t ret = ESP_OK; + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_tag.total_steps = 6; + int btn_array[7] = {190, 600, 1000, 1375, 1775, 2195, 3100}; + adc_btn_tag.adc_level_step = btn_array; + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + if (audio_mem_spiram_stack_is_enabled()) { + adc_btn_cfg.task_cfg.ext_stack = true; + } + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + ret = esp_periph_start(set, adc_btn_handle); + return ret; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode != SD_MODE_1_LINE) { + ESP_LOGE(TAG, "Current board only support 1-line SD mode!"); + return ESP_FAIL; + } + gpio_config_t sdcard_pwr_pin_cfg = { + .pin_bit_mask = 1UL << SDCARD_PWR_CTRL, + .mode = GPIO_MODE_OUTPUT, + .pull_up_en = GPIO_PULLUP_DISABLE, + .pull_down_en = GPIO_PULLDOWN_DISABLE, + .intr_type = GPIO_INTR_DISABLE, + }; + + gpio_config(&sdcard_pwr_pin_cfg); + gpio_set_level(SDCARD_PWR_CTRL, 0); + + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), // GPIO_NUM_34 + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret |= audio_hal_deinit(audio_board->audio_hal); + ret |= audio_hal_deinit(audio_board->adc_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.h b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.h new file mode 100644 index 0000000..a7317f3 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board.h @@ -0,0 +1,127 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ + audio_hal_handle_t adc_hal; /*!< adc hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize adc + * + * @return The adc hal handle + */ +audio_hal_handle_t audio_board_adc_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_blue_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_def.h b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_def.h new file mode 100644 index 0000000..60d58e7 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_def.h @@ -0,0 +1,129 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX (5) +#define SDCARD_INTR_GPIO GPIO_NUM_34 +#define SDCARD_PWR_CTRL GPIO_NUM_13 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D3 (-1) + +/** + * @brief LED Function Definition + */ +#define FUNC_SYS_LEN_EN (1) +#define BLUE_LED_GPIO GPIO_NUM_27 +#define GREEN_LED_GPIO GPIO_NUM_22 + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define ES7243_MCLK_GPIO GPIO_NUM_0 +#define HEADPHONE_DETECT GPIO_NUM_19 +#define PA_ENABLE_GPIO GPIO_NUM_21 +#define ES8311_MCLK_SOURCE 1 /* 0 From MCLK of esp32 1 From BCLK */ +#define CODEC_ADC_I2S_PORT ((i2s_port_t)1) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (16000) +#define RECORD_HARDWARE_AEC (true) +#define BOARD_PA_GAIN (20) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "RMNM" + +extern audio_hal_func_t AUDIO_CODEC_ES8311_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7243E_DEFAULT_HANDLE; +extern audio_hal_func_t AUDIO_CODEC_ES7243_DEFAULT_HANDLE; + +#define AUDIO_CODEC_DEFAULT_CONFIG() { \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define ADC_DETECT_GPIO GPIO_NUM_39 +#define INPUT_KEY_NUM (6) +#define BUTTON_VOLUP_ID (0) +#define BUTTON_VOLDOWN_ID (1) +#define BUTTON_SET_ID (2) +#define BUTTON_PLAY_ID (3) +#define BUTTON_MODE_ID (4) +#define BUTTON_REC_ID (5) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif /* _AUDIO_BOARD_DEFINITION_H_ */ diff --git a/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_pins_config.c b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_pins_config.c new file mode 100644 index 0000000..4e8e795 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_mini_v1_1/board_pins_config.c @@ -0,0 +1,173 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/io_mux_reg.h" +#include "soc/soc_caps.h" + +static const char *TAG = "LYRAT_MINI_V1_1"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0) { + i2s_config->mck_io_num = -1; + i2s_config->bck_io_num = GPIO_NUM_5; + i2s_config->ws_io_num = GPIO_NUM_25; + i2s_config->data_out_num = GPIO_NUM_26; + i2s_config->data_in_num = GPIO_NUM_35; + } else if (port == 1) { + i2s_config->mck_io_num = GPIO_NUM_0; + i2s_config->bck_io_num = GPIO_NUM_32; + i2s_config->ws_io_num = GPIO_NUM_33; + i2s_config->data_out_num = -1; + i2s_config->data_in_num = GPIO_NUM_36; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +int8_t get_sdcard_power_ctrl_gpio(void) +{ + return SDCARD_PWR_CTRL; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button id + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} + +int8_t get_blue_led_gpio(void) +{ + return BLUE_LED_GPIO; +} + +int8_t get_es8311_mclk_src(void) +{ + return ES8311_MCLK_SOURCE; +} + +int8_t get_es7243_mclk_gpio(void) +{ + return ES7243_MCLK_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.c b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.c new file mode 100644 index 0000000..8f428e3 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.c @@ -0,0 +1,66 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + + board_handle->audio_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8388_DEFAULT_HANDLE); + + return board_handle; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + esp_err_t ret = ESP_FAIL; + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.h b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.h new file mode 100644 index 0000000..935addf --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board.h @@ -0,0 +1,112 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_def.h b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_def.h new file mode 100644 index 0000000..ed32e6d --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_def.h @@ -0,0 +1,127 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +#include "driver/touch_pad.h" + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO GPIO_NUM_34 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D3 -1 + + +/** + * @brief LED Function Definition + */ +#define FUNC_SYS_LEN_EN (1) +#define GREEN_LED_GPIO GPIO_NUM_22 +#define RED_LED_GPIO GPIO_NUM_19 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define AUXIN_DETECT_GPIO GPIO_NUM_12 +#define PA_ENABLE_GPIO GPIO_NUM_21 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (10) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +extern audio_hal_func_t AUDIO_CODEC_ES8388_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_REC_ID GPIO_NUM_36 +#define BUTTON_MODE_ID GPIO_NUM_39 +#define BUTTON_SET_ID TOUCH_PAD_NUM9 +#define BUTTON_PLAY_ID TOUCH_PAD_NUM8 +#define BUTTON_VOLUP_ID TOUCH_PAD_NUM7 +#define BUTTON_VOLDOWN_ID TOUCH_PAD_NUM4 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_pins_config.c b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_pins_config.c new file mode 100644 index 0000000..674dbd3 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_2/board_pins_config.c @@ -0,0 +1,153 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/io_mux_reg.h" +#include "soc/soc_caps.h" + +static const char *TAG = "LYRAT_V4_2"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0 || port == 1) { + i2s_config->mck_io_num = GPIO_NUM_0; + i2s_config->bck_io_num = GPIO_NUM_5; + i2s_config->ws_io_num = GPIO_NUM_25; + i2s_config->data_out_num = GPIO_NUM_26; + i2s_config->data_in_num = GPIO_NUM_35; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +// input-output pins + +int8_t get_auxin_detect_gpio(void) +{ + return AUXIN_DETECT_GPIO; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// button pins + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +// touch pins + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_red_led_gpio(void) +{ + return RED_LED_GPIO; +} + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.c b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.c new file mode 100644 index 0000000..92d7d6c --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.c @@ -0,0 +1,145 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +#include "periph_sdcard.h" +#include "led_indicator.h" +#include "periph_touch.h" +#include "periph_button.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ES8388_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +display_service_handle_t audio_board_led_init(void) +{ + led_indicator_handle_t led = led_indicator_init((gpio_num_t)get_green_led_gpio()); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_indicator_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_button_cfg_t btn_cfg = { + .gpio_mask = (1ULL << get_input_rec_id()) | (1ULL << get_input_mode_id()), //REC BTN & MODE BTN + }; + esp_periph_handle_t button_handle = periph_button_init(&btn_cfg); + AUDIO_NULL_CHECK(TAG, button_handle, return ESP_ERR_ADF_MEMORY_LACK); + esp_err_t ret = ESP_OK; + ret = esp_periph_start(set, button_handle); + if (ret != ESP_OK) { + return ret; + } + periph_touch_cfg_t touch_cfg = { + .touch_mask = TOUCH_PAD_SEL4 | TOUCH_PAD_SEL7 | TOUCH_PAD_SEL8 | TOUCH_PAD_SEL9, + .tap_threshold_percent = 70, + }; + esp_periph_handle_t touch_periph = periph_touch_init(&touch_cfg); + AUDIO_NULL_CHECK(TAG, touch_periph, return ESP_ERR_ADF_MEMORY_LACK); + ret = esp_periph_start(set, touch_periph); + return ret; +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode >= SD_MODE_4_LINE) { + ESP_LOGE(TAG, "Please select the correct sd mode!, current mode is %d", mode); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), // GPIO_NUM_34 + .mode = mode, + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.h b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.h new file mode 100644 index 0000000..935addf --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board.h @@ -0,0 +1,112 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_def.h b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_def.h new file mode 100644 index 0000000..8bfa7c5 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_def.h @@ -0,0 +1,129 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +#include "driver/touch_pad.h" + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO GPIO_NUM_34 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D1 GPIO_NUM_4 +#define ESP_SD_PIN_D2 GPIO_NUM_12 +#define ESP_SD_PIN_D3 GPIO_NUM_13 + + +/** + * @brief LED Function Definition + */ +#define FUNC_SYS_LEN_EN (1) +#define GREEN_LED_GPIO GPIO_NUM_22 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define AUXIN_DETECT_GPIO GPIO_NUM_12 +#define HEADPHONE_DETECT GPIO_NUM_19 +#define PA_ENABLE_GPIO GPIO_NUM_21 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (48000) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (10) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +extern audio_hal_func_t AUDIO_CODEC_ES8388_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define BUTTON_REC_ID GPIO_NUM_36 +#define BUTTON_MODE_ID GPIO_NUM_39 +#define BUTTON_SET_ID TOUCH_PAD_NUM9 +#define BUTTON_PLAY_ID TOUCH_PAD_NUM8 +#define BUTTON_VOLUP_ID TOUCH_PAD_NUM7 +#define BUTTON_VOLDOWN_ID TOUCH_PAD_NUM4 + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_BUTTON, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_TOUCH, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_pins_config.c b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_pins_config.c new file mode 100644 index 0000000..558d273 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyrat_v4_3/board_pins_config.c @@ -0,0 +1,153 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "soc/io_mux_reg.h" +#include "soc/soc_caps.h" + +static const char *TAG = "LYRAT_V4_3"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0 || port == 1) { + i2s_config->mck_io_num = GPIO_NUM_0; + i2s_config->bck_io_num = GPIO_NUM_5; + i2s_config->ws_io_num = GPIO_NUM_25; + i2s_config->data_out_num = GPIO_NUM_26; + i2s_config->data_in_num = GPIO_NUM_35; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_device_interface_config, return ESP_FAIL); + + spi_config->mosi_io_num = -1; + spi_config->miso_io_num = -1; + spi_config->sclk_io_num = -1; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = -1; + + ESP_LOGW(TAG, "SPI interface is not supported"); + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +// input-output pins + +int8_t get_auxin_detect_gpio(void) +{ + return AUXIN_DETECT_GPIO; +} + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_DETECT; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// button pins + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +// touch pins + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// led pins + +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.c b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.c new file mode 100644 index 0000000..40f1f4c --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.c @@ -0,0 +1,134 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "led_bar_is31x.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ZL38063_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +display_service_handle_t audio_board_led_init(void) +{ + esp_periph_handle_t led = led_bar_is31x_init(); + AUDIO_NULL_CHECK(TAG, led, return NULL); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_bar_is31x_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode >= SD_MODE_8_LINE) { + ESP_LOGE(TAG, "Please select the correct sd mode!, current mode is %d", mode); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), // GPIO_NUM_34 + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.h b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.h new file mode 100644 index 0000000..935addf --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board.h @@ -0,0 +1,112 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_def.h b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_def.h new file mode 100644 index 0000000..feae857 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_def.h @@ -0,0 +1,123 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO GPIO_NUM_34 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D1 GPIO_NUM_4 +#define ESP_SD_PIN_D2 GPIO_NUM_12 +#define ESP_SD_PIN_D3 GPIO_NUM_13 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define PA_ENABLE_GPIO GPIO_NUM_22 +#define CODEC_RESET_GPIO GPIO_NUM_19 +#define DSP_RESET_GPIO GPIO_NUM_21 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (44100) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (20) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +extern audio_hal_func_t AUDIO_CODEC_ZL38063_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define ADC_DETECT_GPIO GPIO_NUM_39 +#define INPUT_KEY_NUM 6 +#define BUTTON_SET_ID 0 +#define BUTTON_PLAY_ID 1 +#define BUTTON_REC_ID 2 +#define BUTTON_MODE_ID 3 +#define BUTTON_VOLDOWN_ID 4 +#define BUTTON_VOLUP_ID 5 +#define GREEN_LED_GPIO (-1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_pins_config.c b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_pins_config.c new file mode 100644 index 0000000..e825967 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_1/board_pins_config.c @@ -0,0 +1,152 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" + +static const char *TAG = "LYRATD_MSC_V2_1"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0 || port == 1) { + i2s_config->bck_io_num = GPIO_NUM_5; + i2s_config->ws_io_num = GPIO_NUM_25; + i2s_config->data_out_num = GPIO_NUM_26; + i2s_config->data_in_num = GPIO_NUM_35; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + spi_config->mosi_io_num = GPIO_NUM_33; + spi_config->miso_io_num = GPIO_NUM_27; + spi_config->sclk_io_num = GPIO_NUM_32; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = GPIO_NUM_0; + + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +// input-output pins + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + + +// adc button + +int8_t get_adc_detect_gpio(void) +{ + return ADC_DETECT_GPIO; +} + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// board related +int8_t get_reset_codec_gpio(void) +{ + return CODEC_RESET_GPIO; +} + +int8_t get_reset_board_gpio(void) +{ + return DSP_RESET_GPIO; +} + +// led pins +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.c b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.c new file mode 100644 index 0000000..40f1f4c --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.c @@ -0,0 +1,134 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "board.h" +#include "audio_mem.h" + +#include "periph_sdcard.h" +#include "periph_adc_button.h" +#include "led_bar_is31x.h" + +static const char *TAG = "AUDIO_BOARD"; + +static audio_board_handle_t board_handle = 0; + +audio_board_handle_t audio_board_init(void) +{ + if (board_handle) { + ESP_LOGW(TAG, "The board has already been initialized!"); + return board_handle; + } + board_handle = (audio_board_handle_t) audio_calloc(1, sizeof(struct audio_board_handle)); + AUDIO_MEM_CHECK(TAG, board_handle, return NULL); + board_handle->audio_hal = audio_board_codec_init(); + + return board_handle; +} + +audio_hal_handle_t audio_board_codec_init(void) +{ + audio_hal_codec_config_t audio_codec_cfg = AUDIO_CODEC_DEFAULT_CONFIG(); + audio_hal_handle_t codec_hal = audio_hal_init(&audio_codec_cfg, &AUDIO_CODEC_ZL38063_DEFAULT_HANDLE); + AUDIO_NULL_CHECK(TAG, codec_hal, return NULL); + return codec_hal; +} + +display_service_handle_t audio_board_led_init(void) +{ + esp_periph_handle_t led = led_bar_is31x_init(); + AUDIO_NULL_CHECK(TAG, led, return NULL); + display_service_config_t display = { + .based_cfg = { + .task_stack = 0, + .task_prio = 0, + .task_core = 0, + .task_func = NULL, + .service_start = NULL, + .service_stop = NULL, + .service_destroy = NULL, + .service_ioctl = led_bar_is31x_pattern, + .service_name = "DISPLAY_serv", + .user_data = NULL, + }, + .instance = led, + }; + + return display_service_create(&display); +} + +esp_err_t audio_board_key_init(esp_periph_set_handle_t set) +{ + periph_adc_button_cfg_t adc_btn_cfg = PERIPH_ADC_BUTTON_DEFAULT_CONFIG(); + adc_arr_t adc_btn_tag = ADC_DEFAULT_ARR(); + adc_btn_cfg.arr = &adc_btn_tag; + adc_btn_cfg.arr_size = 1; + esp_periph_handle_t adc_btn_handle = periph_adc_button_init(&adc_btn_cfg); + AUDIO_NULL_CHECK(TAG, adc_btn_handle, return ESP_ERR_ADF_MEMORY_LACK); + return esp_periph_start(set, adc_btn_handle); +} + +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode) +{ + if (mode >= SD_MODE_8_LINE) { + ESP_LOGE(TAG, "Please select the correct sd mode!, current mode is %d", mode); + return ESP_FAIL; + } + periph_sdcard_cfg_t sdcard_cfg = { + .root = "/sdcard", + .card_detect_pin = get_sdcard_intr_gpio(), // GPIO_NUM_34 + .mode = mode + }; + esp_periph_handle_t sdcard_handle = periph_sdcard_init(&sdcard_cfg); + esp_err_t ret = esp_periph_start(set, sdcard_handle); + int retry_time = 5; + bool mount_flag = false; + while (retry_time --) { + if (periph_sdcard_is_mounted(sdcard_handle)) { + mount_flag = true; + break; + } else { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + } + if (mount_flag == false) { + ESP_LOGE(TAG, "Sdcard mount failed"); + return ESP_FAIL; + } + return ret; +} + +audio_board_handle_t audio_board_get_handle(void) +{ + return board_handle; +} + +esp_err_t audio_board_deinit(audio_board_handle_t audio_board) +{ + esp_err_t ret = ESP_OK; + ret = audio_hal_deinit(audio_board->audio_hal); + audio_free(audio_board); + board_handle = NULL; + return ret; +} diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.h b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.h new file mode 100644 index 0000000..935addf --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board.h @@ -0,0 +1,112 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_H_ +#define _AUDIO_BOARD_H_ + +#include "audio_hal.h" +#include "board_def.h" +#include "board_pins_config.h" +#include "esp_peripherals.h" +#include "display_service.h" +#include "periph_sdcard.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Audio board handle + */ +struct audio_board_handle { + audio_hal_handle_t audio_hal; /*!< audio hardware abstract layer handle */ +}; + +typedef struct audio_board_handle *audio_board_handle_t; + +/** + * @brief Initialize audio board + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_init(void); + +/** + * @brief Initialize codec chip + * + * @return The audio hal handle + */ +audio_hal_handle_t audio_board_codec_init(void); + +/** + * @brief Initialize led peripheral and display service + * + * @return The audio display service handle + */ +display_service_handle_t audio_board_led_init(void); + +/** + * @brief Initialize key peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_key_init(esp_periph_set_handle_t set); + +/** + * @brief Initialize sdcard peripheral + * + * @param set The handle of esp_periph_set_handle_t + * + * @return + * - ESP_OK, success + * - Others, fail + */ +esp_err_t audio_board_sdcard_init(esp_periph_set_handle_t set, periph_sdcard_mode_t mode); + +/** + * @brief Query audio_board_handle + * + * @return The audio board handle + */ +audio_board_handle_t audio_board_get_handle(void); + +/** + * @brief Uninitialize the audio board + * + * @param audio_board The handle of audio board + * + * @return 0 success, + * others fail + */ +esp_err_t audio_board_deinit(audio_board_handle_t audio_board); + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_def.h b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_def.h new file mode 100644 index 0000000..5335e63 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_def.h @@ -0,0 +1,125 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#ifndef _AUDIO_BOARD_DEFINITION_H_ +#define _AUDIO_BOARD_DEFINITION_H_ + + +/** + * @brief SDCARD Function Definition + */ +#define FUNC_SDCARD_EN (1) +#define SDCARD_OPEN_FILE_NUM_MAX 5 +#define SDCARD_INTR_GPIO GPIO_NUM_34 + +#define ESP_SD_PIN_CLK GPIO_NUM_14 +#define ESP_SD_PIN_CMD GPIO_NUM_15 +#define ESP_SD_PIN_D0 GPIO_NUM_2 +#define ESP_SD_PIN_D1 GPIO_NUM_4 +#define ESP_SD_PIN_D2 GPIO_NUM_12 +#define ESP_SD_PIN_D3 GPIO_NUM_13 + + +/** + * @brief Audio Codec Chip Function Definition + */ +#define FUNC_AUDIO_CODEC_EN (1) +#define HEADPHONE_GPIO GPIO_NUM_36 +#define PA_ENABLE_GPIO GPIO_NUM_22 +#define CODEC_RESET_GPIO GPIO_NUM_19 +#define DSP_RESET_GPIO GPIO_NUM_21 +#define CODEC_ADC_I2S_PORT ((i2s_port_t)0) +#define CODEC_ADC_BITS_PER_SAMPLE ((i2s_data_bit_width_t)16) /* 16bit */ +#define CODEC_ADC_SAMPLE_RATE (44100) +#define RECORD_HARDWARE_AEC (false) +#define BOARD_PA_GAIN (20) /* Power amplifier gain defined by board (dB) */ + +/** + * @brief ADC input data format + */ +#define AUDIO_ADC_INPUT_CH_FORMAT "N" + +extern audio_hal_func_t AUDIO_CODEC_ZL38063_DEFAULT_HANDLE; +#define AUDIO_CODEC_DEFAULT_CONFIG(){ \ + .adc_input = AUDIO_HAL_ADC_INPUT_LINE1, \ + .dac_output = AUDIO_HAL_DAC_OUTPUT_ALL, \ + .codec_mode = AUDIO_HAL_CODEC_MODE_BOTH, \ + .i2s_iface = { \ + .mode = AUDIO_HAL_MODE_SLAVE, \ + .fmt = AUDIO_HAL_I2S_NORMAL, \ + .samples = AUDIO_HAL_48K_SAMPLES, \ + .bits = AUDIO_HAL_BIT_LENGTH_16BITS, \ + }, \ +}; + + +/** + * @brief Button Function Definition + */ +#define FUNC_BUTTON_EN (1) +#define INPUT_KEY_NUM 6 +#define ADC_DETECT_GPIO GPIO_NUM_39 +#define BUTTON_SET_ID 0 +#define BUTTON_PLAY_ID 1 +#define BUTTON_REC_ID 2 +#define BUTTON_MODE_ID 3 +#define BUTTON_VOLDOWN_ID 4 +#define BUTTON_VOLUP_ID 5 +#define GREEN_LED_GPIO (-1) + +#define INPUT_KEY_DEFAULT_INFO() { \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_REC, \ + .act_id = BUTTON_REC_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_MODE, \ + .act_id = BUTTON_MODE_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_SET, \ + .act_id = BUTTON_SET_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_PLAY, \ + .act_id = BUTTON_PLAY_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLUP, \ + .act_id = BUTTON_VOLUP_ID, \ + }, \ + { \ + .type = PERIPH_ID_ADC_BTN, \ + .user_id = INPUT_KEY_USER_ID_VOLDOWN, \ + .act_id = BUTTON_VOLDOWN_ID, \ + } \ +} + + +#endif diff --git a/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_pins_config.c b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_pins_config.c new file mode 100644 index 0000000..79f8602 --- /dev/null +++ b/esp-spot/example/adf/components/audio_board/lyratd_msc_v2_2/board_pins_config.c @@ -0,0 +1,156 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include "esp_log.h" +#include "driver/gpio.h" +#include +#include "board.h" +#include "audio_error.h" +#include "audio_mem.h" + +static const char *TAG = "LYRATD_MSC_V2_2"; + +esp_err_t get_i2c_pins(i2c_port_t port, i2c_config_t *i2c_config) +{ + AUDIO_NULL_CHECK(TAG, i2c_config, return ESP_FAIL); + if (port == I2C_NUM_0 || port == I2C_NUM_1) { + i2c_config->sda_io_num = GPIO_NUM_18; + i2c_config->scl_io_num = GPIO_NUM_23; + } else { + i2c_config->sda_io_num = -1; + i2c_config->scl_io_num = -1; + ESP_LOGE(TAG, "i2c port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_i2s_pins(int port, board_i2s_pin_t *i2s_config) +{ + AUDIO_NULL_CHECK(TAG, i2s_config, return ESP_FAIL); + if (port == 0 || port == 1) { + i2s_config->bck_io_num = GPIO_NUM_5; + i2s_config->ws_io_num = GPIO_NUM_25; + i2s_config->data_out_num = GPIO_NUM_26; + i2s_config->data_in_num = GPIO_NUM_35; + } else { + memset(i2s_config, -1, sizeof(board_i2s_pin_t)); + ESP_LOGE(TAG, "i2s port %d is not supported", port); + return ESP_FAIL; + } + return ESP_OK; +} + +esp_err_t get_spi_pins(spi_bus_config_t *spi_config, spi_device_interface_config_t *spi_device_interface_config) +{ + AUDIO_NULL_CHECK(TAG, spi_config, return ESP_FAIL); + spi_config->mosi_io_num = GPIO_NUM_33; + spi_config->miso_io_num = GPIO_NUM_27; + spi_config->sclk_io_num = GPIO_NUM_32; + spi_config->quadwp_io_num = -1; + spi_config->quadhd_io_num = -1; + + spi_device_interface_config->spics_io_num = GPIO_NUM_0; + + return ESP_OK; +} + +// sdcard + +int8_t get_sdcard_intr_gpio(void) +{ + return SDCARD_INTR_GPIO; +} + +int8_t get_sdcard_open_file_num_max(void) +{ + return SDCARD_OPEN_FILE_NUM_MAX; +} + +// input-output pins + +int8_t get_headphone_detect_gpio(void) +{ + return HEADPHONE_GPIO; +} + +int8_t get_pa_enable_gpio(void) +{ + return PA_ENABLE_GPIO; +} + +// adc button + +int8_t get_adc_detect_gpio(void) +{ + return ADC_DETECT_GPIO; +} + +int8_t get_input_rec_id(void) +{ + return BUTTON_REC_ID; +} + +int8_t get_input_mode_id(void) +{ + return BUTTON_MODE_ID; +} + +int8_t get_input_play_id(void) +{ + return BUTTON_PLAY_ID; +} + +int8_t get_input_set_id(void) +{ + return BUTTON_SET_ID; +} + +int8_t get_input_volup_id(void) +{ + return BUTTON_VOLUP_ID; +} + +int8_t get_input_voldown_id(void) +{ + return BUTTON_VOLDOWN_ID; +} + +// board related + +int8_t get_reset_codec_gpio(void) +{ + return CODEC_RESET_GPIO; +} + +int8_t get_reset_board_gpio(void) +{ + return DSP_RESET_GPIO; +} + +// led pins +int8_t get_green_led_gpio(void) +{ + return GREEN_LED_GPIO; +} diff --git a/esp-spot/example/adf/components/tone_stream.c b/esp-spot/example/adf/components/tone_stream.c new file mode 100644 index 0000000..643fb17 --- /dev/null +++ b/esp-spot/example/adf/components/tone_stream.c @@ -0,0 +1,200 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2019 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include +#include + +#include "esp_log.h" + +#include "audio_element.h" +#include "audio_error.h" +#include "audio_mem.h" +#include "tone_partition.h" +#include "tone_stream.h" +#include + +static const char *TAG = "TONE_STREAM"; + +/** + * @brief Parameters of each tone + */ +typedef struct tone_stream { + audio_stream_type_t type; /*!< File operation type */ + bool is_open; /*!< Tone stream status */ + bool use_delegate; /*!< Tone read with delegate*/ + tone_partition_handle_t tone_handle; /*!< Tone partition's operation handle*/ + tone_file_info_t cur_file; /*!< Address to read tone file */ + const char *partition_label; /*!< Label of tone stored in flash */ +} tone_stream_t; + +static esp_err_t _tone_open(audio_element_handle_t self) +{ + tone_stream_t *stream = (tone_stream_t *)audio_element_getdata(self); + if (stream->is_open) { + ESP_LOGE(TAG, "already opened"); + return ESP_FAIL; + } + stream->tone_handle = tone_partition_init(stream->partition_label, stream->use_delegate); + if (stream->tone_handle == NULL) { + return ESP_FAIL; + } + + char *flash_url = audio_element_get_uri(self); + + flash_url += strlen("flash://tone/"); + char *temp = strchr(flash_url, '_'); + char find_num[3] = { 0 }; + int file_index = 0; + if (temp != NULL) { + strncpy(find_num, flash_url, temp - flash_url); + file_index = strtoul(find_num, 0, 10); + ESP_LOGD(TAG, "Wanted read flash tone index is %d", file_index); + } else { + ESP_LOGE(TAG, "Tone file name is not correct!"); + return ESP_FAIL; + } + + tone_partition_get_file_info(stream->tone_handle, file_index, &stream->cur_file); + ESP_LOGI(TAG, "Tone offset:%08"PRIX32", Tone length:%"PRIu32", pos:%d\n", stream->cur_file.song_adr, stream->cur_file.song_len, file_index); + if (stream->cur_file.song_len <= 0) { + ESP_LOGE(TAG, "Mayebe the flash tone is empty, please ensure the flash's contex"); + return ESP_FAIL; + } + + audio_element_info_t info = { 0 }; + info.total_bytes = stream->cur_file.song_len; + audio_element_setdata(self, stream); + audio_element_set_total_bytes(self, info.total_bytes); + + stream->is_open = true; + return ESP_OK; +} + +static int _tone_read(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + audio_element_info_t info = { 0 }; + tone_stream_t *stream = NULL; + + stream = (tone_stream_t *)audio_element_getdata(self); + audio_element_getinfo(self, &info); + + if (info.byte_pos + len > info.total_bytes) { + len = info.total_bytes - info.byte_pos; + } + if (ESP_OK != tone_partition_file_read(stream->tone_handle, &stream->cur_file, info.byte_pos, buffer, len)) { + ESP_LOGE(TAG, "get tone data error, line:%d", __LINE__); + return ESP_FAIL; + } + if (len <= 0) { + ESP_LOGW(TAG, "No more data,ret:%d ,info.byte_pos:%llu", len, info.byte_pos); + return ESP_OK; + } + audio_element_update_byte_pos(self, len); + + return len; +} + +static int _tone_process(audio_element_handle_t self, char *in_buffer, int in_len) +{ + int r_size = audio_element_input(self, in_buffer, in_len); + int w_size = 0; + if (r_size > 0) { + w_size = audio_element_output(self, in_buffer, r_size); + } else { + w_size = r_size; + } + return w_size; +} + +static esp_err_t _tone_close(audio_element_handle_t self) +{ + tone_stream_t *stream = (tone_stream_t *)audio_element_getdata(self); + if (stream->is_open) { + stream->is_open = false; + } + tone_partition_deinit(stream->tone_handle); + stream->tone_handle = NULL; + if (AEL_STATE_PAUSED != audio_element_get_state(self)) { + audio_element_set_byte_pos(self, 0); + } + return ESP_OK; +} + +static esp_err_t _tone_destroy(audio_element_handle_t self) +{ + tone_stream_t *stream = (tone_stream_t *)audio_element_getdata(self); + audio_free(stream); + return ESP_OK; +} + +audio_element_handle_t tone_stream_init(tone_stream_cfg_t *config) +{ + audio_element_handle_t el; + tone_stream_t *stream = audio_calloc(1, sizeof(tone_stream_t)); + + AUDIO_MEM_CHECK(TAG, stream, return NULL); + + audio_element_cfg_t cfg = DEFAULT_AUDIO_ELEMENT_CONFIG(); + cfg.open = _tone_open; + cfg.close = _tone_close; + cfg.process = _tone_process; + cfg.destroy = _tone_destroy; + cfg.task_stack = config->task_stack; + cfg.task_prio = config->task_prio; + cfg.task_core = config->task_core; + cfg.out_rb_size = config->out_rb_size; + cfg.buffer_len = config->buf_sz; + cfg.stack_in_ext = config->extern_stack; + if (cfg.stack_in_ext == true && config->use_delegate == false) { + ESP_LOGE(TAG, "Tone stream must read flash with delegate when stack is allocate in external ram"); + goto _tone_init_exit; + } + if (cfg.buffer_len == 0) { + cfg.buffer_len = TONE_STREAM_BUF_SIZE; + } + cfg.tag = "flash"; + stream->type = config->type; + stream->use_delegate = config->use_delegate; + + if (config->label == NULL) { + ESP_LOGE(TAG, "Please set your tone label"); + return NULL; + } else { + stream->partition_label = config->label; + } + if (config->type == AUDIO_STREAM_READER) { + cfg.read = _tone_read; + } else if (config->type == AUDIO_STREAM_WRITER) { + ESP_LOGE(TAG, "No writer for tone stream"); + goto _tone_init_exit; + } + el = audio_element_init(&cfg); + + AUDIO_MEM_CHECK(TAG, el, goto _tone_init_exit); + audio_element_setdata(el, stream); + return el; +_tone_init_exit: + audio_free(stream); + return NULL; +} diff --git a/esp-spot/example/adf/coze_websocket/CMakeLists.txt b/esp-spot/example/adf/coze_websocket/CMakeLists.txt new file mode 100644 index 0000000..4a5478d --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/CMakeLists.txt @@ -0,0 +1,8 @@ +# The following lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +include($ENV{ADF_PATH}/CMakeLists.txt) +include($ENV{IDF_PATH}/tools/cmake/project.cmake) + +project(coze_websocket) \ No newline at end of file diff --git a/esp-spot/example/adf/coze_websocket/README_CN.md b/esp-spot/example/adf/coze_websocket/README_CN.md new file mode 100644 index 0000000..5ce141d --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/README_CN.md @@ -0,0 +1,209 @@ +# 火山 Websocket 双向流式对话 + +## 例程简介 + +本例程实现了扣子智能语音对话 Websocket OpenAPI， 主要是通过按键的方式实现与智能体的对讲。 + +## 示例创建 + +### IDF 默认分支 + +本例程支持 IDF release/v5.4 及以后的分支。 + +### 预备知识 + +首先需要在[Coze文档中](https://bytedance.larkoffice.com/docx/Da6qd87pQodvNrxdFYrcnzMxnsh)申请 `Access token` 和 `BOT ID`账号 +更多的 Websocket 文档可以参考 [双向流式对话事件](https://www.coze.cn/open/docs/developer_guides/streaming_chat_event) + +### 配置 + +1. 将获取到的 `Access token` 和 `BOT ID` 信息填入 `Menuconfig->Example Configuration` 中。 +2. 將 wifi 信息填入 `Menuconfig->>Example Configuration` 中。 + +### 编译和下载 + +编译本例程前需要先确保已配置 ESP-IDF 的环境，如果已配置可跳到下一项配置，如果未配置需要先在 ESP-IDF 根目录运行下面脚本设置编译环境，有关配置和使用 ESP-IDF 完整步骤，请参阅 [《ESP-IDF 编程指南》](https://docs.espressif.com/projects/esp-idf/zh_CN/latest/esp32s3/index.html) + +``` +./install.sh +. ./export.sh +``` + + +- 选择编译芯片，以 esp32s3 为例： + +``` +idf.py set-target esp32s3 +``` + +- 编译例子程序 + +``` +idf.py build +``` + +- 烧录程序并运行 monitor 工具来查看串口输出 (替换 PORT 为端口名称)： + +``` +idf.py -p PORT flash monitor +``` + +- 退出调试界面使用 ``Ctrl-]`` + +## 如何使用例程 + +### 功能和用法 + +- 例程开始运行后， 当出现以下log就说明了与服务端建立了连接， 就可以对话了: +```c +I (931) main: Initialize board peripherals +W (932) i2c_bus_v2: I2C master handle is NULL, will create new one +I (941) DRV8311: ES8311 in Slave mode +I (958) ES7210: ES7210 in Slave mode +I (967) ES7210: Enable ES7210_INPUT_MIC1 +I (970) ES7210: Enable ES7210_INPUT_MIC2 +I (972) ES7210: Enable ES7210_INPUT_MIC3 +W (976) ES7210: Enable TDM mode. ES7210_SDP_INTERFACE2_REG12: 2 +I (980) ES7210: config fmt 60 +I (982) AUDIO_HAL: Codec mode is 3, Ctrl:1 +I (990) pp: pp rom version: e7ae62f +I (990) net80211: net80211 rom version: e7ae62f +I (990) AUDIO_THREAD: The esp_periph task allocate stack on internal memory +I (991) wifi:wifi driver task: 3fcee994, prio:23, stack:6656, core=0 +I (1001) wifi:wifi firmware version: 21fc8af6de +I (1003) wifi:wifi certification version: v7.0 +I (1007) wifi:config NVS flash: enabled +I (1011) wifi:config nano formatting: disabled +I (1015) wifi:Init data frame dynamic rx buffer num: 32 +I (1020) wifi:Init static rx mgmt buffer num: 5 +I (1024) wifi:Init management short buffer num: 32 +I (1029) wifi:Init dynamic tx buffer num: 32 +I (1033) wifi:Init static tx FG buffer num: 2 +I (1037) wifi:Init static rx buffer size: 1600 +I (1041) wifi:Init static rx buffer num: 16 +I (1045) wifi:Init dynamic rx buffer num: 32 +I (1049) wifi_init: rx ba win: 16 +I (1052) wifi_init: accept mbox: 32 +I (1055) wifi_init: tcpip mbox: 32 +I (1058) wifi_init: udp mbox: 6 +I (1061) wifi_init: tcp mbox: 32 +I (1064) wifi_init: tcp tx win: 65535 +I (1067) wifi_init: tcp rx win: 32768 +I (1071) wifi_init: tcp mss: 1440 +I (1074) wifi_init: WiFi IRAM OP enabled +I (1078) wifi_init: WiFi RX IRAM OP enabled +W (1082) wifi:Password length matches WPA2 standards, authmode threshold changes from OPEN to WPA2 +I (1090) wifi:Set ps type: 1, coexist: 0 + +I (1094) phy_init: phy_version 701,f4f1da3a,Mar 3 2025,15:50:10 +I (1155) wifi:mode : sta (74:4d:bd:9d:b6:30) +I (1155) wifi:enable tsf +W (1155) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:43 +I (2446) wifi:new:<11,0>, old:<1,0>, ap:<255,255>, sta:<11,0>, prof:1, snd_ch_cfg:0x0 +I (2446) wifi:state: init -> auth (0xb0) +W (2447) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:43 +I (2462) wifi:state: auth -> assoc (0x0) +I (2468) wifi:state: assoc -> run (0x10) +I (2485) wifi:connected with xtworks, aid = 88, channel 11, BW20, bssid = ec:56:23:e9:7e:f0 +I (2486) wifi:security: WPA2-PSK, phy: bgn, rssi: -40 +I (2488) wifi:pm start, type: 1 + +I (2490) wifi:dp: 1, bi: 102400, li: 3, scale listen interval from 307200 us to 307200 us +I (2498) wifi:set rx beacon pti, rx_bcn_pti: 0, bcn_timeout: 25000, mt_pti: 0, mt_time: 10000 +W (2508) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:4 +I (2532) wifi:AP's beacon interval = 102400 us, DTIM period = 1 +I (3571) esp_netif_handlers: sta ip: 192.168.3.7, mask: 255.255.255.0, gw: 192.168.3.1 +I (3571) PERIPH_WIFI: Got ip:192.168.3.7 +I (3571) coze_chat: wss_url: ws://ws.coze.cn/v1/chat?bot_id=74800678******** +I (3579) AUDIO_THREAD: The coze_data_pull_task task allocate stack on external memory +I (3586) AUDIO_THREAD: The coze_data_push_task task allocate stack on external memory +I (3594) coze_chat: WEBSOCKET_EVENT_BEGIN +I (3598) websocket_client: Started +I (3600) coze_chat: Wait for websocket connected +I (4104) coze_chat: Wait for websocket connected +I (4192) coze_chat: WEBSOCKET_EVENT_CONNECTED +I (5215) wifi:idx:0 (ifx:0, ec:56:23:e9:7e:f0), tid:6, ssn:2665, winSize:64 +I (5265) coze_chat: Request conversation_id : 7491159********* +I (5266) coze_chat: WS connected +I (5266) coze_chat: WS updata chat +I (5268) coze_chat: Update chat: { + "id": "15deeac7-bc4c-731a-c81e-2585128c3557", + "event_type": "chat.update", + "data": { + "chat_config": { + "auto_save_history": true, + "conversation_id": "7491159*********", + "user_id": "userid_123", + "meta_data": { + }, + "custom_variables": { + }, + "extra_params": { + }, + "parameters": { + "custom_var_1": "测试" + } + }, + "input_audio": { + "format": "pcm", + "codec": "pcm", + "sample_rate": 16000, + "channel": 1, + "bit_depth": 16 + }, + "turn_detection": { + "type": "server_vad", + "prefix_padding_ms": 600, + "silence_duration_ms": 500 + }, + "output_audio": { + "codec": "opus", + "opus_config": { + "bitrate": 16000, + "frame_size_ms": 60, + "limit_config": { + "period": 1, + "max_frame_num": 18 + } + }, + "speech_rate": 20, + "voice_id": "7426720361733144585" + }, + "event_subscriptions": ["conversation.audio.delta", "conversation.chat.completed", "input_audio_buffer.speech_started", "input_audio_buffer.speech_stopped", "chat.created", "error"] + } +} +I (5365) MODEL_LOADER: The storage free size is 23104 KB +I (5367) MODEL_LOADER: The partition size is 5168 KB +I (5372) MODEL_LOADER: Successfully load srmodels +I (5376) ALGORITHM_STREAM: Load: wn9_hilexin +I (5381) AUDIO_PIPELINE: link el->rb, el:0x3c17e144, tag:algo_stream, rb:0x3c17e3d0 +I (5389) AUDIO_PIPELINE: link el->rb, el:0x3c185108, tag:raw_stream, rb:0x3c185538 +I (5395) AUDIO_PIPELINE: link el->rb, el:0x3c185250, tag:raw_opus, rb:0x3c187580 +I (5402) AUDIO_THREAD: The algo_stream task allocate stack on external memory +I (5409) AUDIO_ELEMENT: [algo_stream-0x3c17e144] Element task created +I (5415) AUDIO_ELEMENT: [raw_read-0x3c17e274] Element task created +I (5421) AUDIO_PIPELINE: Func:audio_pipeline_run, Line:359, MEM Total:8447912 Bytes, Inter:201343 Bytes, Dram:201343 Bytes, Dram largest free:102400Bytes + +I (5435) AUDIO_ELEMENT: [algo_stream] AEL_MSG_CMD_RESUME,state:1 +I (5440) AUDIO_PIPELINE: Pipeline started +I (5440) AFE_CONFIG: Set WakeNet Model: wn9_hilexin +I (5444) AUDIO_ELEMENT: [raw_stream-0x3c185108] Element task created +W (5450) AFE_CONFIG: For single microphone channel, SE is deactivated. +I (5456) AUDIO_THREAD: The raw_opus task allocate stack on external memory +I (5485) AFE: AFE Version: (1MIC_V250121) +I (5485) AFE: Input PCM Config: total 2 channels(1 microphone, 1 playback), sample rate:16000 +I (5488) AFE: AFE Pipeline: [input] -> |AEC(VOIP_LOW_COST)| -> |NS(WebRTC)| -> [output] +I (5497) AUDIO_THREAD: The algo_fetch task allocate stack on external memory +I (5522) AUDIO_ELEMENT: [raw_opus-0x3c185250] Element task created +I (5522) AUDIO_THREAD: The filter task allocate stack on external memory +I (5523) AUDIO_ELEMENT: [filter-0x3c1853c8] Element task created +I (5529) AUDIO_PIPELINE: Func:audio_pipeline_run, Line:359, MEM Total:8219944 Bytes, Inter:172823 Bytes, Dram:172823 Bytes, Dram largest free:98304Bytes + +I (5542) AUDIO_ELEMENT: [raw_opus] AEL_MSG_CMD_RESUME,state:1 +I (5557) AUDIO_ELEMENT: [filter] AEL_MSG_CMD_RESUME,state:1 +I (5557) AUDIO_PIPELINE: Pipeline started +I (5558) AUDIO_THREAD: The audio_data_read_task task allocate stack on external memory +I (5566) main: Func:app_main, Line:161, MEM Total:8187216 Bytes, Inter:171507 Bytes, Dram:171507 Bytes, Dram largest free:98304Bytes + +I (5582) main_task: Returned from app_main() +``` diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/CMakeLists.txt b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/CMakeLists.txt new file mode 100644 index 0000000..87e1522 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/CMakeLists.txt @@ -0,0 +1,8 @@ +idf_component_register( + INCLUDE_DIRS "include" +) + +add_prebuilt_library(LIB_PATH "${CMAKE_CURRENT_SOURCE_DIR}/libesp_coze_lib.a" + REQUIRES esp_common json mbedtls espressif__esp_websocket_client audio_sal esp_http_client log) + +target_link_libraries(${COMPONENT_LIB} INTERFACE LIB_PATH) \ No newline at end of file diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/component.mk b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/component.mk new file mode 100644 index 0000000..5c85c36 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/component.mk @@ -0,0 +1,3 @@ +COMPONENT_ADD_INCLUDEDIRS := . +COMPONENT_SRCDIRS := . + \ No newline at end of file diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/idf_component.yml b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/idf_component.yml new file mode 100644 index 0000000..ec40d68 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/idf_component.yml @@ -0,0 +1,3 @@ +dependencies: + espressif/zlib: "^1.3.0" + espressif/esp_websocket_client: ^1.2.3 \ No newline at end of file diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/coze_chat.h b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/coze_chat.h new file mode 100644 index 0000000..7ea56fe --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/coze_chat.h @@ -0,0 +1,187 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#pragma once + +#include "esp_err.h" + +/** + * @brief Chat operation modes for Coze interaction. + */ +typedef enum { + COZE_CHAT_SPEECH_INTERRUPT_MODE = 0, /*!< This mode allows direct voice-interruptible conversation. */ + COZE_CHAT_NORMAL_MODE, /*!< This mode is typically used for button-triggered conversations */ +} coze_chat_mode_t; + +typedef enum { + COZE_CHAT_EVENT_CHAT_CREATE = 0, + COZE_CHAT_EVENT_CHAT_UPDATE, + COZE_CHAT_EVENT_CHAT_COMPLETED, + COZE_CHAT_EVENT_CHAT_SPEECH_STARTED, + COZE_CHAT_EVENT_CHAT_SPEECH_STOPED, + COZE_CHAT_EVENT_CHAT_ERROR, + COZE_CHAT_EVENT_INPUT_AUDIO_BUFFER_COMPLETED, +} coze_chat_event_t; + +typedef enum { + COZE_CHAT_AUDIO_TYPE_PCM = 0, + COZE_CHAT_AUDIO_TYPE_OPUS, + COZE_CHAT_AUDIO_TYPE_G711, +} coze_chat_audio_type_t; + +typedef void *coze_chat_handle_t; +typedef void (*coze_chat_audio_callback_t)(char *data, int len, void *ctx); +typedef void (*coze_chat_event_callback_t)(coze_chat_event_t event, void *ctx); + +#define COZE_CHAT_DEFAULT_CONFIG() { \ + .bot_id = NULL, \ + .access_token = NULL, \ + .user_id = "userid_123", \ + .voice_id = "7426720361733144585", \ + .mode = COZE_CHAT_SPEECH_INTERRUPT_MODE, \ + .audio_type = COZE_CHAT_AUDIO_TYPE_PCM, \ + .audio_callback_ctx = NULL, \ + .event_callback_ctx = NULL, \ + .audio_callback = NULL, \ + .event_callback = NULL, \ +}; + +/** + * @brief Configuration structure for initializing a Coze chat session. + */ +typedef struct { + char *bot_id; /*!< Bot ID assigned by Coze platform */ + char *access_token; /*!< Access token for authentication */ + char *event_id; /*!< Unique event ID for the current session or interaction */ + char *user_id; /*!< User ID representing the client/user. see: https://www.coze.cn/open/docs/developer_guides/streaming_chat_event?from=search */ + char *voice_id; /*!< Voice ID specifying the system voice to use. see: https://www.coze.cn/open/docs/guides/sys_voice */ + coze_chat_mode_t mode; /*!< Chat mode: interruptible or normal */ + void *audio_callback_ctx; /*!< Context pointer passed to the audio callback */ + void *event_callback_ctx; /*!< Context pointer passed to the event callback */ + coze_chat_audio_type_t audio_type; /*!< Type of audio input/output to use */ + coze_chat_audio_callback_t audio_callback; /*!< Callback function for handling audio data */ + coze_chat_event_callback_t event_callback; /*!< Callback function for handling Coze events */ +} coze_chat_config_t; + +/** + * @brief Initialize the chat module. + * + * @param[in] config Pointer to the chat configuration structure. + * @return + */ +coze_chat_handle_t coze_chat_init(coze_chat_config_t *config); + +/** + * @brief Deinitialize the chat module. + * + * @param[in] chat Handle to the chat instance. + * +* @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_deinit(coze_chat_handle_t chat); + +/** + * @brief Start the chat session. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_start(coze_chat_handle_t chat); + +/** + * @brief Stop the chat session. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_stop(coze_chat_handle_t chat); + +/** + * @brief Send audio data to the chat session. + * + * @param[in] chat Handle to the chat instance. + * @param[in] data Pointer to the audio data buffer. + * @param[in] len Length of the audio data in bytes. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_data(coze_chat_handle_t chat, char *data, int len); + +/** + * @brief Clear the audio data buffer. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_audio_data_clearup(coze_chat_handle_t chat); + +/** + * @brief Indicate that the audio data transmission is complete. + * @note: This function should be called after all audio data has been sent. + * This is only used when triggered by a button, and after the recording is completed and the button is released, use this API + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_complete(coze_chat_handle_t chat); + +/** + * @brief Cancel the audio transmission. + * @note: Interrupt the current conversation. When it is necessary to stop the distribution of the current server, this API can be used + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_cancel(coze_chat_handle_t chat); + +/** + * @brief Update the chat session status. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_update_chat(coze_chat_handle_t chat); diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/http_client_request.h b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/http_client_request.h new file mode 100644 index 0000000..f08317b --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/include/http_client_request.h @@ -0,0 +1,38 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#pragma once + +typedef struct { + char *body; + int body_len; +} http_response_t; + +typedef struct { + const char *key; + const char *value; +} http_req_header_t; + +int http_client_post(const char *url, http_req_header_t *header , char *body, http_response_t *response); diff --git a/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/libesp_coze_lib.a b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/libesp_coze_lib.a new file mode 100644 index 0000000..c2eb62f Binary files /dev/null and b/esp-spot/example/adf/coze_websocket/components/esp_coze_lib/libesp_coze_lib.a differ diff --git a/esp-spot/example/adf/coze_websocket/main/CMakeLists.txt b/esp-spot/example/adf/coze_websocket/main/CMakeLists.txt new file mode 100644 index 0000000..0e52c90 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/CMakeLists.txt @@ -0,0 +1,4 @@ +idf_component_register(SRCS "main.c" "audio_processor.c" + INCLUDE_DIRS "" + PRIV_REQUIRES spi_flash nvs_flash esp_netif audio_sal esp_peripherals esp_common json audio_stream console esp_coze_lib + ) diff --git a/esp-spot/example/adf/coze_websocket/main/Kconfig.projbuild b/esp-spot/example/adf/coze_websocket/main/Kconfig.projbuild new file mode 100644 index 0000000..4421659 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/Kconfig.projbuild @@ -0,0 +1,22 @@ +menu "Example Configuration" + + config ESP_WIFI_SSID + string "WiFi SSID" + default "myssid" + help + SSID (network name) for the example to connect to. + + config ESP_WIFI_PASSWORD + string "WiFi Password" + default "mypassword" + help + WiFi password (WPA or WPA2) for the example to use. + + config ACCESS_TOKEN + string "ACCESS_TOKEN" + default "my acsess token" + + config BOT_ID + string "BOT_ID" + default "my bot id" +endmenu diff --git a/esp-spot/example/adf/coze_websocket/main/audio_processor.c b/esp-spot/example/adf/coze_websocket/main/audio_processor.c new file mode 100644 index 0000000..b4b3c94 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/audio_processor.c @@ -0,0 +1,230 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#include + +#include "esp_log.h" + +#include "audio_pipeline.h" +#include "raw_stream.h" +#include "filter_resample.h" +#include "algorithm_stream.h" +#include "raw_stream.h" +#include "i2s_stream.h" +#include "raw_opus_decoder.h" +#include "audio_mem.h" +#include "board.h" + +#include "audio_processor.h" + +static char *TAG = "audio_processor"; + +struct audio_recorder_s { + audio_element_handle_t i2s_reader; + audio_element_handle_t raw_stream; + audio_element_handle_t algo_stream; + audio_pipeline_handle_t pipeline; +}; + +struct audio_player_s { + audio_element_handle_t i2s_writer; + audio_element_handle_t raw_stream; + audio_element_handle_t filter; + audio_element_handle_t opus_decoder_stream; + audio_pipeline_handle_t pipeline; +}; + +static int algo_read_data_callback(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + audio_element_handle_t i2s_reader = (audio_element_handle_t)context; + return audio_element_input(i2s_reader, buffer, len); +} + +audio_recorder_handle_t recorder_pipeline_open() +{ + struct audio_recorder_s *recorder = audio_calloc(1, sizeof(struct audio_recorder_s)); + if (recorder == NULL) { + ESP_LOGE(TAG, "No mem for recorder"); + return NULL; + } + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + recorder->pipeline = audio_pipeline_init(&pipeline_cfg); + assert(recorder->pipeline); + +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_32BIT, AUDIO_STREAM_READER); + i2s_stream_set_channel_type(&i2s_cfg, I2S_CHANNEL_TYPE_ONLY_LEFT); +#else + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_16BIT, AUDIO_STREAM_READER); +#endif + i2s_cfg.task_stack = -1; + recorder->i2s_reader = i2s_stream_init(&i2s_cfg); + assert(recorder->i2s_reader); + + algorithm_stream_cfg_t algo_config = ALGORITHM_STREAM_CFG_DEFAULT(); + algo_config.sample_rate = 16000; + algo_config.out_rb_size = 26 * 1024; + algo_config.task_core = 1; +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + algo_config.input_format = "RM"; + #else + algo_config.input_format = "MR"; +#endif + recorder->algo_stream = algo_stream_init(&algo_config); + assert(recorder->algo_stream); + audio_element_set_music_info(recorder->algo_stream, 16000, 1, 16); + audio_element_set_read_cb(recorder->algo_stream, algo_read_data_callback, (void *)recorder->i2s_reader); + audio_element_set_input_timeout(recorder->algo_stream, portMAX_DELAY); + + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + recorder->raw_stream = raw_stream_init(&raw_cfg); + assert(recorder->raw_stream); + + audio_pipeline_register(recorder->pipeline, recorder->algo_stream, "algo_stream"); + audio_pipeline_register(recorder->pipeline, recorder->raw_stream, "raw_read"); + + const char *link_tag2[2] = {"algo_stream", "raw_read"}; + audio_pipeline_link(recorder->pipeline, &link_tag2[0], 2); + + return (audio_recorder_handle_t)recorder; +} + +esp_err_t recorder_pipeline_run(audio_recorder_handle_t recorder) +{ + return audio_pipeline_run(recorder->pipeline); +} + +esp_err_t recorder_pipeline_read(audio_recorder_handle_t recorder, char *buffer, int len) +{ + return raw_stream_read(recorder->raw_stream, buffer, len); +} + +esp_err_t recorder_pipeline_stop(audio_recorder_handle_t recorder) +{ + audio_pipeline_stop(recorder->pipeline); + audio_pipeline_wait_for_stop(recorder->pipeline); + audio_pipeline_reset_elements(recorder->pipeline); + audio_pipeline_reset_ringbuffer(recorder->pipeline); + audio_pipeline_reset_items_state(recorder->pipeline); + return ESP_OK; +} + +esp_err_t recorder_pipeline_close(audio_recorder_handle_t recorder) +{ + audio_pipeline_terminate(recorder->pipeline); + audio_pipeline_deinit(recorder->pipeline); + return ESP_OK; +} + +static int opus_audio_data_callback(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + audio_element_handle_t i2s_writer = (audio_element_handle_t)context; + return audio_element_output(i2s_writer, buffer, len); +} + +audio_player_handle_t player_pipeline_open() +{ + struct audio_player_s *player = audio_calloc(1, sizeof(struct audio_player_s)); + if (player == NULL) { + ESP_LOGE(TAG, "No mem for player"); + return NULL; + } + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + player->pipeline = audio_pipeline_init(&pipeline_cfg); + assert(player->pipeline); +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_32BIT, AUDIO_STREAM_WRITER); + i2s_stream_set_channel_type(&i2s_cfg, I2S_CHANNEL_TYPE_ONLY_LEFT); + i2s_cfg.need_expand = true; +#else + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_16BIT, AUDIO_STREAM_WRITER); +#endif + i2s_cfg.task_stack = -1; + player->i2s_writer = i2s_stream_init(&i2s_cfg); + assert(player->i2s_writer); + + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + player->raw_stream = raw_stream_init(&raw_cfg); + assert(player->raw_stream); + + raw_opus_dec_cfg_t opus_dec_cfg = RAW_OPUS_DEC_CONFIG_DEFAULT(); + opus_dec_cfg.enable_frame_length_prefix = true; + opus_dec_cfg.sample_rate = 16000; + opus_dec_cfg.channels = 1; + opus_dec_cfg.task_core = 1; + player->opus_decoder_stream = raw_opus_decoder_init(&opus_dec_cfg); + assert(player->opus_decoder_stream); + + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + filter_cfg.dest_ch = 1; +#else + filter_cfg.dest_ch = 2; +#endif + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 2; + player->filter = rsp_filter_init(&filter_cfg); + assert(player->filter); + audio_element_set_write_cb(player->filter, opus_audio_data_callback, (void *)player->i2s_writer); + + audio_pipeline_register(player->pipeline, player->raw_stream, "raw_stream"); + audio_pipeline_register(player->pipeline, player->opus_decoder_stream, "raw_opus"); + audio_pipeline_register(player->pipeline, player->filter, "filter"); + + const char *link_tag[3] = {"raw_stream", "raw_opus", "filter"}; + audio_pipeline_link(player->pipeline, &link_tag[0], 3); + return (audio_player_handle_t)player; +} + +esp_err_t player_pipeline_run(audio_player_handle_t player) +{ + return audio_pipeline_run(player->pipeline); +} + +esp_err_t player_pipeline_stop(audio_player_handle_t player) +{ + audio_pipeline_stop(player->pipeline); + audio_pipeline_wait_for_stop(player->pipeline); + audio_pipeline_reset_elements(player->pipeline); + audio_pipeline_reset_ringbuffer(player->pipeline); + audio_pipeline_reset_items_state(player->pipeline); + return ESP_OK; +} + +esp_err_t player_pipeline_write(audio_player_handle_t player, char *buffer, int len) +{ + return raw_stream_write(player->raw_stream, buffer, len); +} + +esp_err_t player_pipeline_close(audio_player_handle_t player) +{ + audio_pipeline_terminate(player->pipeline); + audio_pipeline_deinit(player->pipeline); + return ESP_OK; +} diff --git a/esp-spot/example/adf/coze_websocket/main/audio_processor.h b/esp-spot/example/adf/coze_websocket/main/audio_processor.h new file mode 100644 index 0000000..57ffbaa --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/audio_processor.h @@ -0,0 +1,42 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + + #pragma once + +typedef struct audio_player_s *audio_player_handle_t; +typedef struct audio_recorder_s *audio_recorder_handle_t; + +audio_recorder_handle_t recorder_pipeline_open(); +esp_err_t recorder_pipeline_run(audio_recorder_handle_t recorder); +esp_err_t recorder_pipeline_read(audio_recorder_handle_t recorder, char *buffer, int len); +esp_err_t recorder_pipeline_stop(audio_recorder_handle_t recorder); +esp_err_t recorder_pipeline_close(audio_recorder_handle_t recorder); + + +audio_player_handle_t player_pipeline_open(); +esp_err_t player_pipeline_run(audio_player_handle_t player); +esp_err_t player_pipeline_stop(audio_player_handle_t player); +esp_err_t player_pipeline_write(audio_player_handle_t player, char *buffer, int len); +esp_err_t player_pipeline_close(audio_player_handle_t player); diff --git a/esp-spot/example/adf/coze_websocket/main/idf_component.yml b/esp-spot/example/adf/coze_websocket/main/idf_component.yml new file mode 100644 index 0000000..368bc3f --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/idf_component.yml @@ -0,0 +1,27 @@ +## IDF Component Manager Manifest File +dependencies: + ## Required IDF version + idf: + version: '>=4.1.0' + # # Put list of dependencies here + # # For components maintained by Espressif: + # component: "~1.0.0" + # # For 3rd party components: + # username/component: ">=1.0.0,<2.0.0" + # username2/component2: + # version: "~1.0.0" + # # For transient dependencies `public` flag can be set. + # # `public` flag doesn't have an effect dependencies of the `main` component. + # # All dependencies of `main` are public by default. + # public: true + espressif/esp_websocket_client: ^1.2.3 + espressif/esp_hosted: + version: "~1.1" + rules: + - if: "target in [esp32p4]" + espressif/esp_wifi_remote: + matches: + - if: "idf_version <=5.4.0 && target in [esp32p4]" + version: "~0.4" + - if: "idf_version >5.4.0 && target in [esp32p4]" + version: "~0.6" diff --git a/esp-spot/example/adf/coze_websocket/main/main.c b/esp-spot/example/adf/coze_websocket/main/main.c new file mode 100644 index 0000000..8846613 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/main/main.c @@ -0,0 +1,142 @@ +/* http client request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#include +#include + +#include "freertos/idf_additions.h" +#include "freertos/task.h" + +#include "esp_log.h" +#include "nvs_flash.h" +#include "esp_wifi.h" + +#include "audio_sys.h" +#include "audio_thread.h" +#include "esp_peripherals.h" +#include "periph_wifi.h" +#include "periph_sdcard.h" +#include "audio_mem.h" +#include "board.h" +#include "es8311.h" +#include "es7210.h" + +#include "audio_processor.h" +#include "coze_chat.h" + +static char *TAG = "main"; +struct coze_ws_s { + coze_chat_handle_t chat; + audio_recorder_handle_t recorder; + audio_player_handle_t player; + char *recorder_buffer; +#define DEFAULT_RAW_OPUS_BUFFER_SIZE (1024) + char *opus_raw_buffer; + int opus_raw_buffer_len; + enum { + PLAYBACK_STATE_IDLE, + PLAYBACK_STATE_PLAYING, + } player_state; +}; + +static struct coze_ws_s s_coze_ws; + +static void audio_event_callback(coze_chat_event_t event, void *ctx) +{ + if (event == COZE_CHAT_EVENT_CHAT_SPEECH_STARTED) { + ESP_LOGI(TAG, "chat start"); + s_coze_ws.player_state = PLAYBACK_STATE_IDLE; + } else if (event == COZE_CHAT_EVENT_CHAT_SPEECH_STOPED) { + ESP_LOGI(TAG, "chat stop"); + s_coze_ws.player_state = PLAYBACK_STATE_PLAYING; + } +} + +static void audio_data_callback(char *data, int len, void *ctx) +{ +#define frame_length_prefix (2) + if (len > s_coze_ws.opus_raw_buffer_len) { + s_coze_ws.opus_raw_buffer_len = len + frame_length_prefix; + s_coze_ws.opus_raw_buffer = audio_realloc(s_coze_ws.opus_raw_buffer, s_coze_ws.opus_raw_buffer_len); + } + ESP_LOGD(TAG, "data: %p, len: %d", data, len); + s_coze_ws.opus_raw_buffer[0] = (len >> 8) & 0xFF; + s_coze_ws.opus_raw_buffer[1] = len & 0xFF; + memcpy(s_coze_ws.opus_raw_buffer + frame_length_prefix, data, len); + len += frame_length_prefix; + + player_pipeline_write(s_coze_ws.player, s_coze_ws.opus_raw_buffer, len); +} + +static void audio_if_open() +{ + s_coze_ws.recorder = recorder_pipeline_open(); + s_coze_ws.player = player_pipeline_open(); + recorder_pipeline_run(s_coze_ws.recorder); + player_pipeline_run(s_coze_ws.player); +} + +static void audio_data_read_task(void *pv) +{ +#define recorder_buffer_size (640) + s_coze_ws.recorder_buffer = malloc(recorder_buffer_size); + + while (1) { + int r_len = recorder_pipeline_read(s_coze_ws.recorder, s_coze_ws.recorder_buffer, recorder_buffer_size); + ESP_LOGD(TAG, "read len: %d", r_len); + if (r_len > 0) { + coze_chat_send_audio_data(s_coze_ws.chat, s_coze_ws.recorder_buffer, r_len); + } + } + vTaskDelete(NULL); +} + +void app_main(void) +{ + AUDIO_MEM_SHOW(TAG); + ESP_ERROR_CHECK(nvs_flash_init()); + ESP_ERROR_CHECK(esp_netif_init()); + + ESP_LOGI(TAG, "Initialize board peripherals"); + esp_periph_config_t periph_cfg = DEFAULT_ESP_PERIPH_SET_CONFIG(); + esp_periph_set_handle_t set = esp_periph_set_init(&periph_cfg); + + audio_board_handle_t board_handle = audio_board_init(); + audio_hal_ctrl_codec(board_handle->audio_hal, AUDIO_HAL_CODEC_MODE_BOTH, AUDIO_HAL_CTRL_START); +#if CONFIG_ESP32_S3_SPOT_BOARD + audio_hal_set_volume(board_handle->audio_hal, 90); + es8311_set_mic_gain(ES8311_MIC_GAIN_0DB); +#endif + + s_coze_ws.opus_raw_buffer_len = DEFAULT_RAW_OPUS_BUFFER_SIZE; + s_coze_ws.opus_raw_buffer = audio_malloc(s_coze_ws.opus_raw_buffer_len); + // Initialize SD Card peripheral + // audio_board_sdcard_init(set, SD_MODE_1_LINE); + periph_wifi_cfg_t wifi_cfg = { + .wifi_config.sta.ssid = CONFIG_ESP_WIFI_SSID, + .wifi_config.sta.password = CONFIG_ESP_WIFI_PASSWORD, + }; + esp_periph_handle_t wifi_handle = periph_wifi_init(&wifi_cfg); + esp_periph_start(set, wifi_handle); + periph_wifi_wait_for_connected(wifi_handle, portMAX_DELAY); + + coze_chat_config_t chat_config = COZE_CHAT_DEFAULT_CONFIG(); + chat_config.bot_id = CONFIG_BOT_ID; + chat_config.access_token = CONFIG_ACCESS_TOKEN; + chat_config.audio_callback = audio_data_callback; + chat_config.event_callback = audio_event_callback; + + s_coze_ws.chat = coze_chat_init(&chat_config); + coze_chat_start(s_coze_ws.chat); + + audio_if_open(); + audio_thread_create(NULL, "audio_data_read_task", audio_data_read_task, (void *)NULL, 1024 * 4, 12, true, 1); + + AUDIO_MEM_SHOW(TAG); +} diff --git a/esp-spot/example/adf/coze_websocket/partitions.csv b/esp-spot/example/adf/coze_websocket/partitions.csv new file mode 100644 index 0000000..651ee07 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/partitions.csv @@ -0,0 +1,6 @@ +# Name, Type, SubType, Offset, Size, Flags +# Note: if you have increased the bootloader size, make sure to update the offsets to avoid overlap +nvs, data, nvs, , 0x6000, +phy_init, data, phy, , 0x1000, +factory, app, factory, , 2M, +model, data, spiffs, , 5168K, diff --git a/esp-spot/example/adf/coze_websocket/sdkconfig.defaults b/esp-spot/example/adf/coze_websocket/sdkconfig.defaults new file mode 100644 index 0000000..65cd3f7 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/sdkconfig.defaults @@ -0,0 +1,38 @@ +# +# Chip and Board +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESP32_S3_SPOT_BOARD=y + +# +# Serial flasher config +# +CONFIG_ESPTOOLPY_FLASHSIZE_8MB=y +# end of Serial flasher config + +# +# Partition Table +# +# CONFIG_PARTITION_TABLE_SINGLE_APP is not set +# CONFIG_PARTITION_TABLE_SINGLE_APP_LARGE is not set +# CONFIG_PARTITION_TABLE_TWO_OTA is not set +CONFIG_PARTITION_TABLE_CUSTOM=y +CONFIG_PARTITION_TABLE_CUSTOM_FILENAME="partitions.csv" +CONFIG_PARTITION_TABLE_FILENAME="partitions.csv" +CONFIG_PARTITION_TABLE_MD5=y +# end of Partition Table + +# +# ESP-TLS +# +CONFIG_ESP_TLS_INSECURE=y +CONFIG_ESP_TLS_SKIP_SERVER_CERT_VERIFY=y + +# +# FREERTOS +# +CONFIG_FREERTOS_ENABLE_BACKWARD_COMPATIBILITY=y +CONFIG_FREERTOS_TIMER_TASK_STACK_DEPTH=4096 +CONFIG_FREERTOS_GENERATE_RUN_TIME_STATS=y +CONFIG_FREERTOS_VTASKLIST_INCLUDE_COREID=y +CONFIG_FREERTOS_HZ=1000 diff --git a/esp-spot/example/adf/coze_websocket/sdkconfig.defaults.esp32s3 b/esp-spot/example/adf/coze_websocket/sdkconfig.defaults.esp32s3 new file mode 100644 index 0000000..07ccb32 --- /dev/null +++ b/esp-spot/example/adf/coze_websocket/sdkconfig.defaults.esp32s3 @@ -0,0 +1,112 @@ +CONFIG_IDF_CMAKE=y +CONFIG_IDF_TARGET_ARCH_XTENSA=y +CONFIG_IDF_TARGET="esp32s3" +CONFIG_IDF_TARGET_ESP32S3=y +CONFIG_IDF_FIRMWARE_CHIP_ID=0x0009 + + +# +# Audio HAL +# +CONFIG_ESP32_S3_KORVO2_V3_BOARD=y +# end of Audio HAL + +# +# Audio Recorder +# +CONFIG_AFE_MIC_NUM=2 +# end of Audio Recorder + +# +# ESP Speech Recognition +# +CONFIG_MODEL_IN_FLASH=y +CONFIG_USE_AFE=y +CONFIG_AFE_INTERFACE_V1=y +CONFIG_USE_WAKENET=y +CONFIG_SR_WN_WN9_HILEXIN=y +CONFIG_USE_MULTINET=y +CONFIG_SR_MN_CN_MULTINET6_QUANT=y +CONFIG_SR_MN_EN_NONE=y +# end of ESP Speech Recognition + +# +# Component config +# + +# +# Driver configurations +# + +# +# ESP32S3-Specific +# +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_80 is not set +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_160 is not set +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_240=y +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_MHZ=240 + +# +# Cache config +# +# CONFIG_ESP32S3_INSTRUCTION_CACHE_16KB is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_32KB=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_SIZE=0x8000 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_4WAYS is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_8WAYS=y +CONFIG_ESP32S3_ICACHE_ASSOCIATED_WAYS=8 +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_32B=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_SIZE=32 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_WRAP is not set +# CONFIG_ESP32S3_DATA_CACHE_16KB is not set +# CONFIG_ESP32S3_DATA_CACHE_32KB is not set +CONFIG_ESP32S3_DATA_CACHE_64KB=y +CONFIG_ESP32S3_DATA_CACHE_SIZE=0x10000 +# CONFIG_ESP32S3_DATA_CACHE_4WAYS is not set +CONFIG_ESP32S3_DATA_CACHE_8WAYS=y +CONFIG_ESP32S3_DCACHE_ASSOCIATED_WAYS=8 +# CONFIG_ESP32S3_DATA_CACHE_LINE_32B is not set +CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y +CONFIG_ESP32S3_DATA_CACHE_LINE_SIZE=64 +# CONFIG_ESP32S3_DATA_CACHE_WRAP is not set +# end of Cache config + +CONFIG_ESP32S3_SPIRAM_SUPPORT=y + +# +# SPI RAM config +# +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_TYPE_AUTO=y +CONFIG_SPIRAM_SPEED_80M=y +CONFIG_SPIRAM=y +CONFIG_SPIRAM_BOOT_INIT=y +CONFIG_SPIRAM_USE_MALLOC=y +CONFIG_SPIRAM_MEMTEST=y +CONFIG_SPIRAM_MALLOC_ALWAYSINTERNAL=4096 +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_SPIRAM_MALLOC_RESERVE_INTERNAL=32768 +# end of SPI RAM config +# end of ESP32S3-Specific + +# +# Wi-Fi +# +CONFIG_ESP_WIFI_ENABLED=y +CONFIG_ESP_WIFI_STATIC_RX_BUFFER_NUM=16 +CONFIG_ESP_WIFI_DYNAMIC_RX_BUFFER_NUM=32 +CONFIG_ESP_WIFI_DYNAMIC_TX_BUFFER=y +CONFIG_ESP_WIFI_TX_BUFFER_TYPE=1 +CONFIG_ESP_WIFI_DYNAMIC_TX_BUFFER_NUM=32 +CONFIG_ESP_WIFI_STATIC_RX_MGMT_BUFFER=y +CONFIG_ESP_WIFI_AMPDU_TX_ENABLED=y +CONFIG_ESP_WIFI_TX_BA_WIN=16 +CONFIG_ESP_WIFI_AMPDU_RX_ENABLED=y +CONFIG_ESP_WIFI_RX_BA_WIN=16 + +# +# LWIP +# +CONFIG_LWIP_TCP_SND_BUF_DEFAULT=65535 +CONFIG_LWIP_TCP_WND_DEFAULT=65535 +CONFIG_LWIP_TCP_RECVMBOX_SIZE=32 \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/CMakeLists.txt new file mode 100644 index 0000000..27656f1 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/CMakeLists.txt @@ -0,0 +1,8 @@ +# The following lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +include($ENV{ADF_PATH}/CMakeLists.txt) +include($ENV{IDF_PATH}/tools/cmake/project.cmake) + +project(llm_touch_toy) \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/README.md b/esp-spot/example/adf/llm_touch_toy/README.md new file mode 100644 index 0000000..d274900 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/README.md @@ -0,0 +1,199 @@ +# 卡皮巴拉触摸玩偶 + +## 例程简介 + +ESP-Spot 体积小巧，内置扬声器、麦克风和电池，可轻松嵌入毛绒玩具中，让智能玩具“活”起来！本次 Demo 视频重点展示了如何将扣子智能语音对话与 ESP32-S3 触摸传感器结合，打造一款真正具有“情感交互”的 AI 玩具。 + +
+ +
+ +# 视频展示 + +[用触摸交互升级大模型 AI 玩具【ESP-SPOT】](https://www.bilibili.com/video/BV1ekRAYVEZ1) + +视频包含组装过程、成品演示、以及原理讲解 + +# 硬件接线 + +ESP-Spot 上板也可作为一块带音频的 ESP32 开发板单独使用，VIN 可以允许 3.3->5V 供电，如果需要默认使能 3V3 电源域，请将 VBUS 上拉，VBAT 接 3.3V。 + +**注意：当 PREP_VCC_CTL 引脚（GPIO6） 拉高时 CODEC_3V3 使能，音频编解码芯片工作。 也就是说，只有当 GPIO6 拉高时，音频功能才会正常工作** + +引脚布局如下 + +
+ +
+ +ESP32-S3 模组自带触摸传感器功能，技术细节请参考：：[ESP32-S3 数据手册](https://www.espressif.com/sites/default/files/documentation/esp32-s3_datasheet_cn.pdf) + + +# 预备知识 + +## 获取扣子账号 + +- 本例程默认使用 Coze 对接大模型进行语音交互，首先需要在[Coze文档中](https://bytedance.larkoffice.com/docx/Da6qd87pQodvNrxdFYrcnzMxnsh)申请 `Access token` 和 `BOT ID` 账号 +- 更多的 Websocket 文档可以参考 [双向流式对话事件](https://www.coze.cn/open/docs/developer_guides/streaming_chat_event) + +- 若想更换大模型对接平台，请替换 Coze 组件 [components/esp_coze_lib](./components/esp_coze_lib) 后更新 [main.c](./main/main.c) 中对应的逻辑 + +## 更多触摸交互逻辑 + +- 目前的触摸交互逻辑是：通过触摸对应部位，播放预设的音频内容或调用对应的应用逻辑 +- 后续可升级为：通过触摸触发不同的事件，并发送至大模型进行处理，实现更智能、更加丰富的语音交互体验。详情请参考 Coze 文档：[手动提交对话内容](https://www.coze.cn/open/docs/developer_guides/streaming_chat_event#46f6a7d0) + +## 本地音频 + +- 本例程在 `/tools/audio_tone.bin` 和 `/components/audio_flash_tone/` 目录下已经帮助用户生成了例程所需的 bin 文件和音频文件在 flash 中地址的源代码文件。 + +- 如果用户需要生成自己的 `audio_tone.bin`，则需要执行 `mk_audio_bin.py` 脚本（位于 $ADF_PATH/tools/audio_tone/mk_audio_tone.py），并且指定相关文件的路径。 + +- 源 MP3 文件在 `tone_mp3_folder` 文件夹中，生成的 C 文件、H 文件以及二进制 bin 文件都存放在此目录下。 + + ``` + python3 $ADF_PATH/tools/audio_tone/mk_audio_tone.py -f ./ -r tone_mp3_folder + ``` + +- 请使用 *python3 $ADF_PATH/tools/audio_tone/mk_audio_tone.py --help* 查看更多脚本信息。 + +- 本例程默认的 `audio_tone.bin` 包含如下音频文件： + + ```c + "flash://tone/0_belly_1.mp3", + "flash://tone/1_belly_2.mp3", + "flash://tone/2_belly_3.mp3", + "flash://tone/3_belly_4.mp3", + "flash://tone/4_bread_1.mp3", + "flash://tone/5_bread_2.mp3", + "flash://tone/6_capybara_song_1.mp3", + "flash://tone/7_hat_1.mp3", + "flash://tone/8_neck_1.mp3", + "flash://tone/9_neck_2.mp3", + "flash://tone/10_reverse.mp3", + "flash://tone/11_screaming.mp3", + "flash://tone/12_shake.mp3", + "flash://tone/13_touch_nose.mp3", + "flash://tone/14_woohoo.mp3", + ``` +- 本地音频在 `partition_flash_tone.csv` 中地址配置如下，用户可以根据自己的项目 flash 分区灵活配置地址： + ``` + flashTone,data, 0x04, 0x720000 , 500K, + ``` + + +## 动作和姿态识别 + +- 硬件方面采用 BMI270 传感器，用于识别不同的动作和姿态，后续将提供优化后的 BMI270 固件，以实现更精确、稳定的动作识别 + +## IDF 默认分支 + +- 本例程支持 IDF release/v5.4 及以后的分支。 + +# 配置工程 + +## 开发板 + +- 默认使用搭载 ESP32-S3 模组的 ``ESP-SPOT`` 开发板，配置项为 `CONFIG_ESP32_S3_SPOT_BOARD=y` +- **硬件初始化代码位于本仓库中的 [example/adf/components/audio_board](../components/audio_board) ，编译前将其替换掉 ADF 路径中的 `$ADF_PATH/components/audio_board`** + +## 音频文件 +- **如果 MP3 音频总数量大于 `9`，则需要修改 `$ADF_PATH/components/audio_stream` 目录下的 `tone_stream.c` 文件，可以直接使用本仓库中的 [example/adf/components/tone_stream.c](../components/tone_stream.c) 替换 ADF 路径下的原文件** +- 本仓库的中的 [tone_stream.c](../components/tone_stream.c) 改了什么：将 `_tone_open(audio_element_handle_t self)` 函数中 `char find_num[2]` 数组的长度修改为 `char find_num[3]`。这样可以确保函数在解析双位数的音频文件名（例如 10.mp3、11.mp3）时不会发生字符串截断错误，正确识别全部音频文件 + + +## 触摸功能 + +- 本例程默认使用 GPIO 3、9、13、14 引脚作触摸，分别对应卡皮巴拉玩偶的不同部位，触发不同的语音交互功能： + - GPIO3（鼻子）：短按触发一种语音交互，长按触发另一种语音交互。 + - GPIO9（帽子）：第一次按下触发语音对话，第二次按下播放《卡皮巴拉之歌》并点亮灯环。 + - GPIO13（肚子）：每次按下依次切换到下一阶段的语音交互，共有五种不同交互。 + - GPIO14（脖子）：作为开启/关闭大模型语音交互的总开关。 + +- 如需更换触摸引脚，请在 `main.c` 中修改 `TOUCH_CHANNEL_1` 及相关宏定义。注意参考 [ESP32-S3 数据手册](https://www.espressif.com/sites/default/files/documentation/esp32-s3_datasheet_cn.pdf) + 选择支持触摸功能的 IO + +- 触摸灵敏度可通过以下宏定义进行调整，阈值越小，感应越灵敏： + - `LIGHT_TOUCH_THRESHOLD`：设置轻触的触发阈值。 + - `HEAVY_TOUCH_THRESHOLD`：设置重触的触发阈值。 + +- 短按和长按的判定时间可在 `touch_task` 中进行配置 + ``` + const button_config_t btn_cfg = { + .short_press_time = 300, // 触发短按时长（ms） + .long_press_time = 2000, // 触发长按时长（ms） + }; + ``` + +## LED 灯环 + +- 本例程使用 WS2812 灯环，相关配置如下： + - `CONFIG_LED_GPIO_INPUT`：控制灯环的引脚，默认为 `GPIO45`，可通过 `idf.py menuconfig` 进行配置 + - `CONFIG_LED_COUNT`：用于设置灯环的 LED 数量，默认为 16 个，同样可在 `idf.py menuconfig` 中进行配置 + +## 扣子大模型鉴权 + +- 将获取到的 `Access token` 和 `BOT ID` 信息填入 `Menuconfig->Example Configuration` 中。`Access token` 默认是以 `pat_` 开头的 + +## Wi-Fi 信息 +- 將 wifi 信息填入 `Menuconfig->>Example Configuration` 中 + + +## 编译和下载 + +请先编译版本并烧录到开发板上，然后运行 monitor 工具来查看串口输出（替换 PORT 为端口名称）： + +``` +idf.py -p PORT flash monitor +``` + +**此外，本例程还需烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv` 的 `flashTone` 分区，请使用如下命令:** + +``` +esptool.py --chip esp32s3 --before default_reset --after hard_reset write_flash -z --flash_mode dio --flash_freq 40m --flash_size detect 0x720000 ./tools/audio_tone.bin +``` + +有关配置和使用 ESP-IDF 生成项目的完整步骤，请参阅 [《ESP-IDF 编程指南》](https://docs.espressif.com/projects/esp-idf/zh_CN/release-v5.3/esp32/index.html)。 + + +## 如何使用 + +### 功能和用法 + +- 例程开始运行后，如果事前没有烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv ` 的 `flashTone` 分区，例程将会报错，请参考 [编译和下载](#编译和下载) 的说明进行烧录 + +## 故障排除 + +- 如果遇到下方错误，请把按照 [编译和下载](#编译和下载) 的说明烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv ` 的 `flashTone` 分区。 + ```c + E (481) TONE_PARTITION: Not flash tone partition + E (481) AUDIO_ELEMENT: [tone] AEL_STATUS_ERROR_OPEN,-1 + W (491) AUDIO_ELEMENT: [tone] audio_element_on_cmd_error,7 + E (501) TONE_PARTITION: /repo/adfs/bugfix/esp-adf-internal/components/tone_partition/tone_partition.c:204 (tone_partition_deinit): Got NULL Pointer + W (511) AUDIO_ELEMENT: IN-[mp3] AEL_IO_ABORT + E (511) MP3_DECODER: failed to read audio data (line 119) + W (521) AUDIO_ELEMENT: [mp3] AEL_IO_ABORT, -3 + W (531) AUDIO_ELEMENT: IN-[i2s] AEL_IO_ABORT + ``` + +- 如果遇到下方错误，通常是由于网络波动导致数据未能及时上传。此时可以尝试更换网络，或通过配置参数提升 ESP32-S3 的 Wi-Fi 性能。具体优化方法可参考乐鑫官方文档：[如何提高 Wi-Fi 性能](https://docs.espressif.com/projects/esp-idf/zh_CN/latest/esp32s3/api-guides/wifi.html#how-to-improve-wi-fi-performance)。当前上传使用的音频格式为 `PCM`，扣子即将支持 `Opus` 编码格式，可有效降低带宽占用 + ``` + E (144192) coze_chat: Audio data send to queue failed + E (144222) coze_chat: Audio data send to queue failed + E (144243) coze_chat: Audio data send to queue failed + E (144267) coze_chat: Audio data send to queue failed + E (144288) coze_chat: Audio data send to queue failed + E (144309) coze_chat: Audio data send to queue failed + E (144347) coze_chat: Audio data send to queue failed + E (144368) coze_chat: Audio data send to queue failed + E (144399) coze_chat: Audio data send to queue failed + ``` + + +## 技术支持 +请按照下面的链接获取技术支持： + +- 技术支持参见 [esp32.com](https://esp32.com/viewforum.php?f=20) 论坛 +- 故障和新功能需求，请创建 [GitHub issue](https://github.com/espressif/esp-adf/issues) + +我们会尽快回复。 diff --git a/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/CMakeLists.txt new file mode 100644 index 0000000..203bc03 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/CMakeLists.txt @@ -0,0 +1,4 @@ +set(COMPONENT_SRCS "audio_tone_uri.c") +set(COMPONENT_ADD_INCLUDEDIRS .) + +register_component() \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.c b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.c new file mode 100644 index 0000000..e99bfc1 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.c @@ -0,0 +1,24 @@ +/*This is tone file*/ + +const char* tone_uri[] = { + "flash://tone/0_belly_1.mp3", + "flash://tone/1_belly_2.mp3", + "flash://tone/2_belly_3.mp3", + "flash://tone/3_belly_4.mp3", + "flash://tone/4_bread_1.mp3", + "flash://tone/5_bread_2.mp3", + "flash://tone/6_capybara_song_1.mp3", + "flash://tone/7_hat_1.mp3", + "flash://tone/8_neck_1.mp3", + "flash://tone/9_neck_2.mp3", + "flash://tone/10_reverse.mp3", + "flash://tone/11_screaming.mp3", + "flash://tone/12_shake.mp3", + "flash://tone/13_touch_nose.mp3", + "flash://tone/14_woohoo.mp3", +}; + +int get_tone_uri_num() +{ + return sizeof(tone_uri) / sizeof(char *) - 1; +} diff --git a/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.h b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.h new file mode 100644 index 0000000..16c2e92 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/audio_tone_uri.h @@ -0,0 +1,27 @@ +#ifndef __AUDIO_TONEURI_H__ +#define __AUDIO_TONEURI_H__ + +extern const char* tone_uri[]; + +typedef enum { + TONE_TYPE_BELLY_1, + TONE_TYPE_BELLY_2, + TONE_TYPE_BELLY_3, + TONE_TYPE_BELLY_4, + TONE_TYPE_BREAD_1, + TONE_TYPE_BREAD_2, + TONE_TYPE_CAPYBARA_SONG_1, + TONE_TYPE_HAT_1, + TONE_TYPE_NECK_1, + TONE_TYPE_NECK_2, + TONE_TYPE_REVERSE, + TONE_TYPE_SCREAMING, + TONE_TYPE_SHAKE, + TONE_TYPE_TOUCH_NOSE, + TONE_TYPE_WOOHOO, + TONE_TYPE_MAX, +} tone_type_t; + +int get_tone_uri_num(); + +#endif diff --git a/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/component.mk b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/component.mk new file mode 100644 index 0000000..5c85c36 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/audio_flash_tone/component.mk @@ -0,0 +1,3 @@ +COMPONENT_ADD_INCLUDEDIRS := . +COMPONENT_SRCDIRS := . + \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/CMakeLists.txt new file mode 100644 index 0000000..87e1522 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/CMakeLists.txt @@ -0,0 +1,8 @@ +idf_component_register( + INCLUDE_DIRS "include" +) + +add_prebuilt_library(LIB_PATH "${CMAKE_CURRENT_SOURCE_DIR}/libesp_coze_lib.a" + REQUIRES esp_common json mbedtls espressif__esp_websocket_client audio_sal esp_http_client log) + +target_link_libraries(${COMPONENT_LIB} INTERFACE LIB_PATH) \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/component.mk b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/component.mk new file mode 100644 index 0000000..5c85c36 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/component.mk @@ -0,0 +1,3 @@ +COMPONENT_ADD_INCLUDEDIRS := . +COMPONENT_SRCDIRS := . + \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/idf_component.yml b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/idf_component.yml new file mode 100644 index 0000000..ec40d68 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/idf_component.yml @@ -0,0 +1,3 @@ +dependencies: + espressif/zlib: "^1.3.0" + espressif/esp_websocket_client: ^1.2.3 \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/coze_chat.h b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/coze_chat.h new file mode 100644 index 0000000..7ea56fe --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/coze_chat.h @@ -0,0 +1,187 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#pragma once + +#include "esp_err.h" + +/** + * @brief Chat operation modes for Coze interaction. + */ +typedef enum { + COZE_CHAT_SPEECH_INTERRUPT_MODE = 0, /*!< This mode allows direct voice-interruptible conversation. */ + COZE_CHAT_NORMAL_MODE, /*!< This mode is typically used for button-triggered conversations */ +} coze_chat_mode_t; + +typedef enum { + COZE_CHAT_EVENT_CHAT_CREATE = 0, + COZE_CHAT_EVENT_CHAT_UPDATE, + COZE_CHAT_EVENT_CHAT_COMPLETED, + COZE_CHAT_EVENT_CHAT_SPEECH_STARTED, + COZE_CHAT_EVENT_CHAT_SPEECH_STOPED, + COZE_CHAT_EVENT_CHAT_ERROR, + COZE_CHAT_EVENT_INPUT_AUDIO_BUFFER_COMPLETED, +} coze_chat_event_t; + +typedef enum { + COZE_CHAT_AUDIO_TYPE_PCM = 0, + COZE_CHAT_AUDIO_TYPE_OPUS, + COZE_CHAT_AUDIO_TYPE_G711, +} coze_chat_audio_type_t; + +typedef void *coze_chat_handle_t; +typedef void (*coze_chat_audio_callback_t)(char *data, int len, void *ctx); +typedef void (*coze_chat_event_callback_t)(coze_chat_event_t event, void *ctx); + +#define COZE_CHAT_DEFAULT_CONFIG() { \ + .bot_id = NULL, \ + .access_token = NULL, \ + .user_id = "userid_123", \ + .voice_id = "7426720361733144585", \ + .mode = COZE_CHAT_SPEECH_INTERRUPT_MODE, \ + .audio_type = COZE_CHAT_AUDIO_TYPE_PCM, \ + .audio_callback_ctx = NULL, \ + .event_callback_ctx = NULL, \ + .audio_callback = NULL, \ + .event_callback = NULL, \ +}; + +/** + * @brief Configuration structure for initializing a Coze chat session. + */ +typedef struct { + char *bot_id; /*!< Bot ID assigned by Coze platform */ + char *access_token; /*!< Access token for authentication */ + char *event_id; /*!< Unique event ID for the current session or interaction */ + char *user_id; /*!< User ID representing the client/user. see: https://www.coze.cn/open/docs/developer_guides/streaming_chat_event?from=search */ + char *voice_id; /*!< Voice ID specifying the system voice to use. see: https://www.coze.cn/open/docs/guides/sys_voice */ + coze_chat_mode_t mode; /*!< Chat mode: interruptible or normal */ + void *audio_callback_ctx; /*!< Context pointer passed to the audio callback */ + void *event_callback_ctx; /*!< Context pointer passed to the event callback */ + coze_chat_audio_type_t audio_type; /*!< Type of audio input/output to use */ + coze_chat_audio_callback_t audio_callback; /*!< Callback function for handling audio data */ + coze_chat_event_callback_t event_callback; /*!< Callback function for handling Coze events */ +} coze_chat_config_t; + +/** + * @brief Initialize the chat module. + * + * @param[in] config Pointer to the chat configuration structure. + * @return + */ +coze_chat_handle_t coze_chat_init(coze_chat_config_t *config); + +/** + * @brief Deinitialize the chat module. + * + * @param[in] chat Handle to the chat instance. + * +* @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_deinit(coze_chat_handle_t chat); + +/** + * @brief Start the chat session. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_start(coze_chat_handle_t chat); + +/** + * @brief Stop the chat session. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_stop(coze_chat_handle_t chat); + +/** + * @brief Send audio data to the chat session. + * + * @param[in] chat Handle to the chat instance. + * @param[in] data Pointer to the audio data buffer. + * @param[in] len Length of the audio data in bytes. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_data(coze_chat_handle_t chat, char *data, int len); + +/** + * @brief Clear the audio data buffer. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_audio_data_clearup(coze_chat_handle_t chat); + +/** + * @brief Indicate that the audio data transmission is complete. + * @note: This function should be called after all audio data has been sent. + * This is only used when triggered by a button, and after the recording is completed and the button is released, use this API + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_complete(coze_chat_handle_t chat); + +/** + * @brief Cancel the audio transmission. + * @note: Interrupt the current conversation. When it is necessary to stop the distribution of the current server, this API can be used + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_send_audio_cancel(coze_chat_handle_t chat); + +/** + * @brief Update the chat session status. + * + * @param[in] chat Handle to the chat instance. + * + * @return + * - ESP_OK On success + * - ESP_FAIL On failure + */ +esp_err_t coze_chat_update_chat(coze_chat_handle_t chat); diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/http_client_request.h b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/http_client_request.h new file mode 100644 index 0000000..f08317b --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/include/http_client_request.h @@ -0,0 +1,38 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#pragma once + +typedef struct { + char *body; + int body_len; +} http_response_t; + +typedef struct { + const char *key; + const char *value; +} http_req_header_t; + +int http_client_post(const char *url, http_req_header_t *header , char *body, http_response_t *response); diff --git a/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/libesp_coze_lib.a b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/libesp_coze_lib.a new file mode 100644 index 0000000..c2eb62f Binary files /dev/null and b/esp-spot/example/adf/llm_touch_toy/components/esp_coze_lib/libesp_coze_lib.a differ diff --git a/esp-spot/example/adf/llm_touch_toy/components/led_driver/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/led_driver/CMakeLists.txt new file mode 100644 index 0000000..88537a3 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/led_driver/CMakeLists.txt @@ -0,0 +1,5 @@ +set(requires "led_strip") + +idf_component_register(SRC_DIRS "." + INCLUDE_DIRS "include" + REQUIRES ${requires}) diff --git a/esp-spot/example/adf/llm_touch_toy/components/led_driver/Kconfig.projbuild b/esp-spot/example/adf/llm_touch_toy/components/led_driver/Kconfig.projbuild new file mode 100644 index 0000000..2ba0c7a --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/led_driver/Kconfig.projbuild @@ -0,0 +1,15 @@ +menu "LED Srip Configuration" + + config LED_GPIO_INPUT + int "LED INPUT GPIO PIN" + default 45 + help + Set the input pin for LED strip + + config LED_COUNT + int "Count of LED" + default 16 + help + Set the total number of LED + +endmenu diff --git a/esp-spot/example/adf/llm_touch_toy/components/led_driver/idf_component.yml b/esp-spot/example/adf/llm_touch_toy/components/led_driver/idf_component.yml new file mode 100644 index 0000000..ce9a05f --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/led_driver/idf_component.yml @@ -0,0 +1,3 @@ +dependencies: + espressif/led_strip: + version: '^2' \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/components/led_driver/include/led_driver.h b/esp-spot/example/adf/llm_touch_toy/components/led_driver/include/led_driver.h new file mode 100644 index 0000000..954f4f9 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/led_driver/include/led_driver.h @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: 2023-2024 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include "led_strip.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define LED_STRIP_BLINK_GPIO CONFIG_LED_GPIO_INPUT /* LED GPIO assignment */ +#define LED_STRIP_LED_NUMBERS CONFIG_LED_COUNT /* Numbers of the LED in the strip */ + +#define LED_DEFAULT_RED 150 /* LED default value */ +#define LED_DEFAULT_GREEN 150 +#define LED_DEFAULT_BLUE 150 +#define LED_DEFAULT_BREATH_DIRECTION 3 +#define LED_DEFAULT_BRIGHTNESS 0 +#define LED_DEFAULT_MODE 2 + +/** + * @brief Configure the LED strip and obtain a handle for LED control. + * + * @return A handle to the LED strip for subsequent LED control operations. + */ +led_strip_handle_t led_create(void); + +/** + * @brief Convert hue to RGB and update LED values. + * + * @param[in] hue The hue value. Range: 0 ~ 360 + */ +void led_set_hue(uint16_t hue); + +/** + * @brief Switch the LED animation on/off based on power status. + * + * @param[in] power A boolean value indicating whether to turn the LED animation on or off. + */ +void led_set_switch(bool power); + +/** + * @brief Set the LED mode based on weather text data. + * + * @param[in] weather_text The weather text data to determine the LED mode. + * @return ESP_OK if the mode was set successfully, otherwise an error code. + */ +esp_err_t led_set_mode(int mode_update); + +/** + * @brief Run LED animations based on weather data. + * + * @param[in] led_strip The LED strip handle. + */ +esp_err_t led_animations_start(led_strip_handle_t led_strip); + +#ifdef __cplusplus +} /* end of extern "C" */ +#endif diff --git a/esp-spot/example/adf/llm_touch_toy/components/led_driver/led_driver.c b/esp-spot/example/adf/llm_touch_toy/components/led_driver/led_driver.c new file mode 100644 index 0000000..369aada --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/led_driver/led_driver.c @@ -0,0 +1,276 @@ +/* + * SPDX-FileCopyrightText: 2023-2024 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include "esp_log.h" + +#include "led_driver.h" + +#define LED_STRIP_RMT_RES_HZ (10 * 1000 * 1000) /* 10MHz resolution, 1 tick = 0.1us */ + +/** + * @brief To set LED mode + */ +typedef enum { + LED_MODE_OFF = 1, /** LED off */ + LED_MODE_BREATH, /** LED breath mode */ + LED_MODE_FLOW, /** LED flow mode */ +} led_mode_t; + +/** + * @brief To control LED RGB value + */ +typedef struct { + uint8_t red; + uint8_t green; + uint8_t blue; + int8_t direction; + uint8_t brightness; + led_mode_t mode; +} led_data_t; + +/* configure LED default data */ +#define LED_CONFIG_DEFAULT() { \ + .red = LED_DEFAULT_RED, \ + .green = LED_DEFAULT_GREEN, \ + .blue = LED_DEFAULT_BLUE, \ + .direction = LED_DEFAULT_BREATH_DIRECTION, \ + .brightness = LED_DEFAULT_BRIGHTNESS, \ + .mode = LED_DEFAULT_MODE \ +} + +static const char *TAG = "led_driver"; +led_data_t g_led_data = LED_CONFIG_DEFAULT(); + +/* Configure the LED strip and obtain a handle */ +led_strip_handle_t led_create(void) +{ + /* LED strip general initialization, according to your led board design */ + led_strip_config_t strip_config = { + .strip_gpio_num = LED_STRIP_BLINK_GPIO, // The GPIO that connected to the LED strip's data line + .max_leds = LED_STRIP_LED_NUMBERS, // The number of LEDs in the strip, + .led_pixel_format = LED_PIXEL_FORMAT_GRB, // Pixel format of your LED strip + .led_model = LED_MODEL_WS2812, // LED strip model + .flags.invert_out = false, // whether to invert the output signal + }; + + /* LED strip backend configuration: RMT */ + led_strip_rmt_config_t rmt_config = { + .clk_src = RMT_CLK_SRC_DEFAULT, // Different clock source can lead to different power consumption + .resolution_hz = LED_STRIP_RMT_RES_HZ, // RMT counter clock frequency + .flags.with_dma = false, // DMA feature is available on ESP target like ESP32-S3 + }; + + /* LED Strip object handle */ + led_strip_handle_t led_strip; + esp_err_t err = led_strip_new_rmt_device(&strip_config, &rmt_config, &led_strip); + + if (err == ESP_OK) { + ESP_LOGI(TAG, "Created LED strip object with RMT backend"); + } else { + ESP_LOGE(TAG, "Failed to created LED strip object"); + return NULL; + } + + return led_strip; +} + +/* Convert hue to RGB */ +void led_set_hue(uint16_t hue) +{ + /* Make sure input between 0 ~ 360 */ + if (hue > 360) { + hue = 360; + } + + /* Calculate the RGB color corresponding to the hue */ + if (hue < 120) { + g_led_data.red = 255 - hue * 2; + g_led_data.green = hue * 2; + g_led_data.blue = 0; + } else if (hue < 240) { + g_led_data.red = 0; + g_led_data.green = 255 - (hue - 120) * 2; + g_led_data.blue = (hue - 120) * 2; + } else { + g_led_data.red = (hue - 240) * 2; + g_led_data.green = 0; + g_led_data.blue = 255 - (hue - 240) * 2; + } +} + +/* Perform a breathing animation on the LED strip */ +static void led_animation_breath(led_strip_handle_t led_strip) +{ + uint8_t color_red = g_led_data.red * g_led_data.brightness / 255; + uint8_t color_green = g_led_data.green * g_led_data.brightness / 255; + uint8_t color_blue = g_led_data.blue * g_led_data.brightness / 255; + + for (int i = 0; i < LED_STRIP_LED_NUMBERS; i++) { + led_strip_set_pixel(led_strip, i, color_red, color_green, color_blue); + } + /* Refresh the strip to send data */ + esp_err_t err = led_strip_refresh(led_strip); + + if (err != ESP_OK) { + ESP_LOGE(TAG, "Refresh LED Strip failed"); + } + + g_led_data.brightness += g_led_data.direction; + + if (g_led_data.brightness >= 255 || g_led_data.brightness == 0) { + g_led_data.direction = -g_led_data.direction; + } +} + +/* Perform a flowing animation on the LED strip */ +static void led_animation_flow(led_strip_handle_t led_strip) +{ + /* Length of LED flow animation */ + int led_len = 3; + + for (int i = 0; i < LED_STRIP_LED_NUMBERS; i++) { + led_strip_clear(led_strip); + + for (int j = 0; j < led_len; j++) { + int position = i - j; + if (position >= 0) { + uint8_t tail_r = g_led_data.red - (j * (g_led_data.red / led_len)); + uint8_t tail_g = g_led_data.green - (j * (g_led_data.green / led_len)); + uint8_t tail_b = g_led_data.blue - (j * (g_led_data.blue / led_len)); + led_strip_set_pixel(led_strip, position, tail_r, tail_g, tail_b); + } + } + + led_strip_set_pixel(led_strip, i, g_led_data.red, g_led_data.green, g_led_data.blue); + esp_err_t err = led_strip_refresh(led_strip); + + if (err != ESP_OK) { + ESP_LOGE(TAG, "Refresh LED Strip failed"); + } + + vTaskDelay(pdMS_TO_TICKS(100)); + } +} + +/* Set the LED strip for cloudy weather */ +static void led_animation_off(led_strip_handle_t led_strip) +{ + led_strip_clear(led_strip); + vTaskDelay(pdMS_TO_TICKS(10)); +} + +/* Set the LED strip for sunny weather */ +static void led_set_sunny() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 255; + g_led_data.green = 153; + g_led_data.blue = 18; +} + +/* Set the LED strip for rainy weather */ +static void led_set_rain() +{ + g_led_data.mode = LED_MODE_FLOW; + g_led_data.red = 255; + g_led_data.green = 18; + g_led_data.blue = 18; +} + +/* Set the LED strip for cloudy weather */ +static void led_set_cloudy() +{ + g_led_data.mode = LED_MODE_FLOW; + g_led_data.red = 100; + g_led_data.green = 100; + g_led_data.blue = 100; +} + +/* Set the LED strip for snow weather */ +static void led_set_snow() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 200; + g_led_data.green = 200; + g_led_data.blue = 200; +} + +/* Set the LED strip for unknown weather */ +static void led_set_unknown_weather() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 255; + g_led_data.green = 0; + g_led_data.blue = 0; +} + +/* Switch the LED animation on/off based on power status */ +void led_set_switch(bool power) +{ + if (power) { + g_led_data.mode = LED_MODE_BREATH; + } else { + g_led_data.mode = LED_MODE_OFF; + } +} + +/* Set LED mode based on weather text data */ +esp_err_t led_set_mode(int mode_update) +{ + switch (mode_update) { + case 0: + g_led_data.mode = LED_MODE_OFF; + break; + case 1: + led_set_sunny(); + break; + case 2: + led_set_rain(); + break; + case 3: + led_set_cloudy(); + break; + case 4: + led_set_snow(); + break; + default: + ESP_LOGW(TAG, "Unknown mode number: %d", mode_update); + led_set_unknown_weather(); + return ESP_FAIL; + } + + return ESP_OK; +} + +/* Run LED animations based on weather data */ +esp_err_t led_animations_start(led_strip_handle_t led_strip) +{ + if (led_strip == NULL) { + return ESP_FAIL; + } + + /* Check LED mode */ + switch (g_led_data.mode) { + case LED_MODE_BREATH: + led_animation_breath(led_strip); + break; + case LED_MODE_FLOW: + led_animation_flow(led_strip); + break; + case LED_MODE_OFF: + led_animation_off(led_strip); + break; + default: + ESP_LOGW(TAG, "Unknown MODE, switch to LED_OFF"); + g_led_data.mode = LED_MODE_OFF; + break; + } + + return ESP_OK; +} diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/CHANGELOG.md b/esp-spot/example/adf/llm_touch_toy/components/touch_button/CHANGELOG.md new file mode 100644 index 0000000..802cc43 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/CHANGELOG.md @@ -0,0 +1,5 @@ +# ChangeLog + +## v0.1.0 - 2025-03-18 + +* Add Initial version diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/touch_button/CMakeLists.txt new file mode 100644 index 0000000..be47ea4 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/CMakeLists.txt @@ -0,0 +1,5 @@ +idf_component_register(SRC_DIRS "." + INCLUDE_DIRS ".") + +include(package_manager) +cu_pkg_define_version(${CMAKE_CURRENT_LIST_DIR}) diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/README.md b/esp-spot/example/adf/llm_touch_toy/components/touch_button/README.md new file mode 100644 index 0000000..ed31df6 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/README.md @@ -0,0 +1,25 @@ +[![Component Registry](https://components.espressif.com/components/espressif/touch_button/badge.svg)](https://components.espressif.com/components/espressif/touch_button) + +# Touch Button + +Supports a series of operations on touch buttons, such as press, release, long press, and short press. + +**Note:** This component is for developers testing only. It is not intended for production use. + +ESP32/ESP32-S2/ESP32-S3 touch-related components are intended for testing or demo purposes only. Due to the poor anti-interference capability of the touch functionality, it may not pass EMS testing, and therefore, it is not recommended for mass production products. + +This component is currently applicable to ESP32, ESP32-S2, and ESP32-S3, and requires an IDF version greater than or equal to v5.3. + +## Dependencies + +- [touch_sensor_fsm](https://components.espressif.com/components/espressif/touch_sensor_fsm) +- [touch_sensor_lowlevel](https://components.espressif.com/components/espressif/touch_sensor_lowlevel) +- [touch_button_sensor](https://components.espressif.com/components/espressif/touch_button_sensor) + +## Add component to your project + +Please use the component manager command `add-dependency` to add the `touch_button` to your project's dependency, during the `CMake` step the component will be downloaded automatically + +``` +idf.py add-dependency "espressif/touch_button=*" +``` diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/idf_component.yml b/esp-spot/example/adf/llm_touch_toy/components/touch_button/idf_component.yml new file mode 100644 index 0000000..4339e2d --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/idf_component.yml @@ -0,0 +1,19 @@ +dependencies: + cmake_utilities: '*' + espressif/button: + public: true + version: ^4 + espressif/touch_button_sensor: + public: true + version: ^0.2.1 + idf: '>=4.4' +description: Provide a complete set of button detection methods based on `iot_button` + to drive the touch FSM. +issues: https://github.com/espressif/esp-iot-solution/issues +repository: https://github.com/espressif/esp-iot-solution.git +targets: +- esp32s2 +- esp32s3 +- esp32p4 +url: https://github.com/espressif/esp-iot-solution/tree/master/components/touch/ +version: 0.1.1 diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/license.txt b/esp-spot/example/adf/llm_touch_toy/components/touch_button/license.txt new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/license.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/CMakeLists.txt new file mode 100644 index 0000000..81e5d2d --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/CMakeLists.txt @@ -0,0 +1,9 @@ + +# The following lines of boilerplate have to be in your project's CMakeLists +# in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +set(EXTRA_COMPONENT_DIRS "$ENV{IDF_PATH}/tools/unit-test-app/components" + "../../touch_button") +include($ENV{IDF_PATH}/tools/cmake/project.cmake) +project(button_test) diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/CMakeLists.txt new file mode 100644 index 0000000..0b90b03 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/CMakeLists.txt @@ -0,0 +1,3 @@ +idf_component_register(SRC_DIRS "." + PRIV_INCLUDE_DIRS "." + WHOLE_ARCHIVE) diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/test_main.c b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/test_main.c new file mode 100644 index 0000000..7d9cdf6 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/test_main.c @@ -0,0 +1,41 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "unity.h" +#include "unity_test_runner.h" +#include "unity_test_utils_memory.h" +#include "esp_heap_caps.h" +#include "sdkconfig.h" + +#define LEAKS (400) + +void setUp(void) +{ + unity_utils_record_free_mem(); +} + +void tearDown(void) +{ + unity_utils_evaluate_leaks_direct(LEAKS); +} + +void app_main(void) +{ + + /* _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ + *|_ _| _ | | | / __ \| | | | | ___ \ | | |_ _|_ _| _ | \ | | + * | | | | | | | | | / \/| |_| | | |_/ / | | | | | | | | | | | \| | + * | | | | | | | | | | | _ | | ___ \ | | | | | | | | | | | . ` | + * | | \ \_/ / |_| | \__/\| | | | | |_/ / |_| | | | | | \ \_/ / |\ | + * \_/ \___/ \___/ \____/\_| |_/ \____/ \___/ \_/ \_/ \___/\_| \_/ + */ + printf(" _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ \n"); + printf("|_ _| _ | | | / __ \\| | | | | ___ \\ | | |_ _|_ _| _ | \\ | |\n"); + printf(" | | | | | | | | | / \\/| |_| | | |_/ / | | | | | | | | | | | \\| |\n"); + printf(" | | | | | | | | | | | _ | | ___ \\ | | | | | | | | | | | . ` |\n"); + printf(" | | \\ \\_/ / |_| | \\__/\\| | | | | |_/ / |_| | | | | | \\ \\_/ / |\\ |\n"); + printf(" \\_/ \\___/ \\___/ \\____/\\_| |_/ \\____/ \\___/ \\_/ \\_/ \\___/\\_| \\_/\n"); + unity_run_menu(); +} diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/touch_button_test.c b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/touch_button_test.c new file mode 100644 index 0000000..193c962 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/main/touch_button_test.c @@ -0,0 +1,234 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_log.h" +#include "unity.h" +#include "iot_button.h" +#include "touch_button.h" +#include "touch_sensor_lowlevel.h" + +static const char *TAG = "GPIO BUTTON TEST"; + +#define TOUCH_CHANNEL_1 1 +#define TOUCH_CHANNEL_2 2 + +static void button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "%s", iot_button_get_event_str(event)); + if (BUTTON_PRESS_REPEAT == event || BUTTON_PRESS_REPEAT_DONE == event) { + ESP_LOGI(TAG, "\tREPEAT[%d]", iot_button_get_repeat(arg)); + } + + if (BUTTON_PRESS_UP == event || BUTTON_LONG_PRESS_HOLD == event || BUTTON_LONG_PRESS_UP == event) { + ESP_LOGI(TAG, "\tTICKS[%"PRIu32"]", iot_button_get_ticks_time(arg)); + } + + if (BUTTON_MULTIPLE_CLICK == event) { + ESP_LOGI(TAG, "\tMULTIPLE[%d]", (int)data); + } +} + +TEST_CASE("touch button test", "[button][touch]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + uint8_t level = 0; + level = iot_button_get_key_level(btn); + ESP_LOGI(TAG, "button level is %d", level); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn); +} + +static void multiple_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%d]: %s", (int)data, iot_button_get_event_str(event)); +} + +TEST_CASE("touch multiple button test", "[button][touch][multiple]") +{ + touch_lowlevel_type_t *channel_type = calloc(2, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 2; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2}; + touch_lowlevel_config_t low_config = { + .channel_num = 2, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +static void light_and_heavy_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%s]: %s", (int)data == 1 ? "light" : "heavy", iot_button_get_event_str(event)); +} + +TEST_CASE("touch button light and heavy press test", "[button][touch][light and heavy]") +{ + touch_lowlevel_type_t *channel_type = calloc(1, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 1; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1}; + touch_lowlevel_config_t low_config = { + .channel_num = 1, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_1; + touch_cfg.channel_threshold = 0.2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +TEST_CASE("touch button test memory leak", "[button][touch][memory leak]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + iot_button_delete(btn); +} diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/sdkconfig.defaults b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/sdkconfig.defaults new file mode 100644 index 0000000..9b28759 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/test_apps/sdkconfig.defaults @@ -0,0 +1,13 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.0 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESP_DEFAULT_CPU_FREQ_MHZ_240=y +CONFIG_ESP_TASK_WDT_EN=n +CONFIG_FREERTOS_HZ=1000 +CONFIG_TOUCH_BUTTON_SENSOR_BASELINE_COEF_X1000=1 +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_P_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_N_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_RESET_N=10 diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.c b/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.c new file mode 100644 index 0000000..851b1a7 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.c @@ -0,0 +1,75 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_check.h" +#include "iot_button.h" +#include "touch_button_sensor.h" +#include "touch_button.h" + +static const char *TAG = "button_touch"; +typedef struct { + button_driver_t base; + int32_t touch_channel; + touch_button_handle_t touch_handle; /**< touch handle */ +} button_touch_obj; + +static uint8_t button_touch_get_key_level(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + touch_button_sensor_handle_events(touch_btn->touch_handle); + touch_state_t state; + touch_button_sensor_get_state(touch_btn->touch_handle, touch_btn->touch_channel, &state); + return state == TOUCH_STATE_ACTIVE ? BUTTON_ACTIVE : BUTTON_INACTIVE; +} + +static esp_err_t button_touch_delete(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + esp_err_t ret = touch_button_sensor_delete(touch_btn->touch_handle); + free(touch_btn); + return ret; +} + +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button) +{ + ESP_LOGI(TAG, "Touch Button Version: %d.%d.%d", TOUCH_BUTTON_VER_MAJOR, TOUCH_BUTTON_VER_MINOR, TOUCH_BUTTON_VER_PATCH); + button_touch_obj *touch_btn = NULL; + esp_err_t ret = ESP_OK; + ESP_GOTO_ON_FALSE(button_config && touch_config && ret_button, ESP_ERR_INVALID_ARG, err, TAG, "Invalid argument"); + + touch_btn = (button_touch_obj *)calloc(1, sizeof(button_touch_obj)); + ESP_GOTO_ON_FALSE(touch_btn, ESP_ERR_NO_MEM, err, TAG, "No memory for gpio button"); + + touch_btn->touch_channel = touch_config->touch_channel; + + uint32_t channel_list[1] = {touch_config->touch_channel}; + float button_threshold[1] = {touch_config->channel_threshold}; + + touch_button_config_t config = { + .channel_num = 1, + .channel_list = channel_list, + .channel_threshold = button_threshold, + .skip_lowlevel_init = touch_config->skip_lowlevel_init, + .debounce_times = 1, + }; + ret = touch_button_sensor_create(&config, &touch_btn->touch_handle, NULL, NULL); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create touch button sensor failed"); + ret = iot_button_create(button_config, &touch_btn->base, ret_button); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create button failed"); + + touch_btn->base.get_key_level = button_touch_get_key_level; + touch_btn->base.del = button_touch_delete; + + return ESP_OK; +err: + if (touch_btn) { + free(touch_btn); + } + return ret; +} diff --git a/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.h b/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.h new file mode 100644 index 0000000..e3ccc89 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/components/touch_button/touch_button.h @@ -0,0 +1,48 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ +#pragma once + +#include "esp_err.h" +#include "button_types.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Configuration structure for a touch button. + * + * This structure defines the configuration parameters required to initialize a touch button. + * It specifies the touch channel, activation threshold, and whether to skip low-level initialization. + */ +typedef struct { + int32_t touch_channel; /*!< The touch sensor channel assigned to the button */ + float channel_threshold; /*!< Activation threshold for the touch channel (range: 0.0 - 1.0) */ + bool skip_lowlevel_init; /*!< If true, skips low-level initialization (useful for pre-configured setups) */ +} button_touch_config_t; + +/** + * @brief Create a new touch button device. + * + * This function initializes a new touch button device based on the given configurations. + * It sets up the touch channel, configures the touch button sensor, and integrates it + * with the iot_button framework. + * + * @param[in] button_config Pointer to the general button configuration. + * @param[in] touch_config Pointer to the touch-specific button configuration. + * @param[out] ret_button Pointer to store the created button handle. + * + * @return + * - ESP_OK on success + * - ESP_ERR_INVALID_ARG if any argument is NULL + * - ESP_ERR_NO_MEM if memory allocation fails + * - ESP_FAIL if touch button sensor or button creation fails + */ +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button); + +#ifdef __cplusplus +} +#endif diff --git a/esp-spot/example/adf/llm_touch_toy/main/CMakeLists.txt b/esp-spot/example/adf/llm_touch_toy/main/CMakeLists.txt new file mode 100644 index 0000000..63f0dab --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/CMakeLists.txt @@ -0,0 +1,4 @@ +idf_component_register(SRCS "main.c" "audio_processor.c" + INCLUDE_DIRS "" + PRIV_REQUIRES spi_flash nvs_flash esp_netif audio_sal esp_peripherals esp_common json audio_stream console touch_button audio_flash_tone esp_coze_lib led_driver + ) diff --git a/esp-spot/example/adf/llm_touch_toy/main/Kconfig.projbuild b/esp-spot/example/adf/llm_touch_toy/main/Kconfig.projbuild new file mode 100644 index 0000000..4421659 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/Kconfig.projbuild @@ -0,0 +1,22 @@ +menu "Example Configuration" + + config ESP_WIFI_SSID + string "WiFi SSID" + default "myssid" + help + SSID (network name) for the example to connect to. + + config ESP_WIFI_PASSWORD + string "WiFi Password" + default "mypassword" + help + WiFi password (WPA or WPA2) for the example to use. + + config ACCESS_TOKEN + string "ACCESS_TOKEN" + default "my acsess token" + + config BOT_ID + string "BOT_ID" + default "my bot id" +endmenu diff --git a/esp-spot/example/adf/llm_touch_toy/main/audio_processor.c b/esp-spot/example/adf/llm_touch_toy/main/audio_processor.c new file mode 100644 index 0000000..e68abce --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/audio_processor.c @@ -0,0 +1,472 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#include +#include "esp_log.h" +#include "esp_check.h" +#include "audio_pipeline.h" +#include "raw_stream.h" +#include "filter_resample.h" +#include "algorithm_stream.h" +#include "raw_stream.h" +#include "tone_stream.h" +#include "i2s_stream.h" +#include "mp3_decoder.h" +#include "raw_opus_decoder.h" +#include "audio_mem.h" +#include "audio_thread.h" +#include "board.h" +#include "audio_processor.h" + +static char *TAG = "audio_processor"; + +#define audio_pipe_safe_free(x, fn) do { \ + if (x) { \ + fn(x); \ + x = NULL; \ + } \ +} while (0) + +struct audio_recorder_s { + audio_element_handle_t i2s_reader; + audio_element_handle_t raw_stream; + audio_element_handle_t algo_stream; + audio_pipeline_handle_t pipeline; +}; + +struct audio_player_s { + audio_element_handle_t i2s_writer; + audio_element_handle_t raw_stream; + audio_element_handle_t filter; + audio_element_handle_t opus_decoder_stream; + audio_pipeline_handle_t pipeline; + pipe_player_state_e player_state; +}; + +typedef struct { + audio_pipeline_handle_t pipeline; + audio_element_handle_t tone_stream_reader; + audio_element_handle_t i2s_stream_writer; + audio_element_handle_t mp3_decoder; + audio_element_handle_t filter; + pipe_player_state_e player_state; + bool running; + tone_play_callback_t tone_cb; +} audio_player_t; + +static audio_player_t *s_audio_player = NULL; +static struct audio_player_s *s_player_pipeline = NULL; + +static int algo_read_data_callback(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + audio_element_handle_t i2s_reader = (audio_element_handle_t)context; + return audio_element_input(i2s_reader, buffer, len); +} + +audio_recorder_handle_t recorder_pipeline_open() +{ + struct audio_recorder_s *recorder = audio_calloc(1, sizeof(struct audio_recorder_s)); + if (recorder == NULL) { + ESP_LOGE(TAG, "No mem for recorder"); + return NULL; + } + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + recorder->pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_MEM_CHECK(TAG, recorder->pipeline, goto _exit); + +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_32BIT, AUDIO_STREAM_READER); + i2s_stream_set_channel_type(&i2s_cfg, I2S_CHANNEL_TYPE_ONLY_LEFT); +#else + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_16BIT, AUDIO_STREAM_READER); +#endif + i2s_cfg.task_stack = -1; + recorder->i2s_reader = i2s_stream_init(&i2s_cfg); + AUDIO_MEM_CHECK(TAG, recorder->i2s_reader, goto _exit); + + algorithm_stream_cfg_t algo_config = ALGORITHM_STREAM_CFG_DEFAULT(); + algo_config.sample_rate = 16000; + algo_config.out_rb_size = 26 * 1024; + algo_config.task_core = 1; +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + algo_config.input_format = "RM"; +#else + algo_config.input_format = "MR"; +#endif + recorder->algo_stream = algo_stream_init(&algo_config); + AUDIO_MEM_CHECK(TAG, recorder->algo_stream, goto _exit); + audio_element_set_music_info(recorder->algo_stream, 16000, 1, 16); + audio_element_set_read_cb(recorder->algo_stream, algo_read_data_callback, (void *)recorder->i2s_reader); + audio_element_set_input_timeout(recorder->algo_stream, portMAX_DELAY); + + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + recorder->raw_stream = raw_stream_init(&raw_cfg); + AUDIO_MEM_CHECK(TAG, recorder->raw_stream, goto _exit); + + audio_pipeline_register(recorder->pipeline, recorder->algo_stream, "algo_stream"); + audio_pipeline_register(recorder->pipeline, recorder->raw_stream, "raw_read"); + + const char *link_tag[2] = {"algo_stream", "raw_read"}; + audio_pipeline_link(recorder->pipeline, &link_tag[0], sizeof(link_tag) / sizeof(char *)); + audio_pipeline_run(recorder->pipeline); + return (audio_recorder_handle_t)recorder; +_exit: + ESP_LOGE(TAG, "Failed to init recorder pipeline"); + if (recorder->i2s_reader) { + audio_element_deinit(recorder->i2s_reader); + } + if (recorder->raw_stream) { + audio_element_deinit(recorder->raw_stream); + } + if (recorder->algo_stream) { + audio_element_deinit(recorder->algo_stream); + } + if (recorder->pipeline) { + audio_pipeline_deinit(recorder->pipeline); + } + return NULL; +} + +esp_err_t recorder_pipeline_run(audio_recorder_handle_t recorder) +{ + if (recorder == NULL) { + return ESP_ERR_INVALID_ARG; + } + return audio_pipeline_run(recorder->pipeline); +} + +esp_err_t recorder_pipeline_read(audio_recorder_handle_t recorder, char *buffer, int len) +{ + if (recorder == NULL || buffer == NULL || len <= 0) { + return ESP_ERR_INVALID_ARG; + } + // ESP_LOGE(TAG, "recorder_pipeline_read"); + return raw_stream_read(recorder->raw_stream, buffer, len); +} + +esp_err_t recorder_pipeline_stop(audio_recorder_handle_t recorder) +{ + if (recorder == NULL) { + return ESP_ERR_INVALID_ARG; + } + audio_pipeline_stop(recorder->pipeline); + audio_pipeline_wait_for_stop(recorder->pipeline); + audio_pipeline_reset_elements(recorder->pipeline); + audio_pipeline_reset_ringbuffer(recorder->pipeline); + audio_pipeline_reset_items_state(recorder->pipeline); + return ESP_OK; +} + +esp_err_t recorder_pipeline_close(audio_recorder_handle_t recorder) +{ + if (recorder == NULL) { + return ESP_ERR_INVALID_ARG; + } + audio_pipeline_terminate(recorder->pipeline); + audio_pipeline_deinit(recorder->pipeline); + return ESP_OK; +} + +static int opus_audio_data_callback(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + audio_element_handle_t i2s_writer = (audio_element_handle_t)context; + return audio_element_output(i2s_writer, buffer, len); +} + +audio_player_handle_t player_pipeline_open() +{ + struct audio_player_s *player = audio_calloc(1, sizeof(struct audio_player_s)); + if (player == NULL) { + ESP_LOGE(TAG, "No mem for player"); + return NULL; + } + player->player_state = PIPE_STATE_IDLE; + s_player_pipeline = player; + + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + player->pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_MEM_CHECK(TAG, player->pipeline, goto _exit) +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_32BIT, AUDIO_STREAM_WRITER); + i2s_stream_set_channel_type(&i2s_cfg, I2S_CHANNEL_TYPE_ONLY_LEFT); + i2s_cfg.need_expand = true; +#else + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(0, 16000, I2S_DATA_BIT_WIDTH_16BIT, AUDIO_STREAM_WRITER); +#endif + i2s_cfg.task_stack = -1; + player->i2s_writer = i2s_stream_init(&i2s_cfg); + AUDIO_MEM_CHECK(TAG, player->i2s_writer, goto _exit); + + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + player->raw_stream = raw_stream_init(&raw_cfg); + AUDIO_MEM_CHECK(TAG, player->raw_stream, goto _exit) + + raw_opus_dec_cfg_t opus_dec_cfg = RAW_OPUS_DEC_CONFIG_DEFAULT(); + opus_dec_cfg.enable_frame_length_prefix = true; + opus_dec_cfg.sample_rate = 16000; + opus_dec_cfg.channels = 1; + opus_dec_cfg.task_core = 1; + player->opus_decoder_stream = raw_opus_decoder_init(&opus_dec_cfg); + AUDIO_MEM_CHECK(TAG, player->opus_decoder_stream, goto _exit); + + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + filter_cfg.dest_ch = 1; +#else + filter_cfg.dest_ch = 2; +#endif + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 2; + player->filter = rsp_filter_init(&filter_cfg); + AUDIO_MEM_CHECK(TAG, player->filter, goto _exit); + audio_element_set_write_cb(player->filter, opus_audio_data_callback, (void *)player->i2s_writer); + + audio_pipeline_register(player->pipeline, player->raw_stream, "raw_stream"); + audio_pipeline_register(player->pipeline, player->opus_decoder_stream, "raw_opus"); + audio_pipeline_register(player->pipeline, player->filter, "filter"); + + const char *link_tag[3] = {"raw_stream", "raw_opus", "filter"}; + audio_pipeline_link(player->pipeline, &link_tag[0], 3); + audio_pipeline_run(player->pipeline); + return (audio_player_handle_t)player; +_exit: + ESP_LOGE(TAG, "Failed to init player pipeline"); + if (player->i2s_writer) { + audio_element_deinit(player->i2s_writer); + } + if (player->raw_stream) { + audio_element_deinit(player->raw_stream); + } + if (player->opus_decoder_stream) { + audio_element_deinit(player->opus_decoder_stream); + } + if (player->filter) { + audio_element_deinit(player->filter); + } + if (player->pipeline) { + audio_pipeline_deinit(player->pipeline); + } + return NULL; + +} + +static esp_err_t _player_i2s_write_cb(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + return audio_element_output(s_player_pipeline->i2s_writer, buffer, len); +} + +static esp_err_t _player_write_nop_cb(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + return len; +} + +esp_err_t player_pipeline_run(void) +{ + ESP_RETURN_ON_FALSE(s_player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + if (s_player_pipeline->player_state == PIPE_STATE_RUNNING) { + ESP_LOGW(TAG, "player pipe is already running state"); + return ESP_OK; + } + + ESP_LOGI(TAG, "player pipe start running"); + audio_element_set_write_cb(s_player_pipeline->filter, _player_i2s_write_cb, NULL); + s_player_pipeline->player_state = PIPE_STATE_RUNNING; + return ESP_OK; +} + +esp_err_t player_pipeline_stop(void) +{ + ESP_RETURN_ON_FALSE(s_player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + if (s_player_pipeline->player_state == PIPE_STATE_IDLE) { + ESP_LOGW(TAG, "player pipe is idle state"); + return ESP_FAIL; + } + + ESP_LOGI(TAG, "player pipe stop running"); + audio_element_set_write_cb(s_player_pipeline->filter, _player_write_nop_cb, NULL); + s_player_pipeline->player_state = PIPE_STATE_IDLE; + return ESP_OK; +} + +esp_err_t player_pipeline_write(audio_player_handle_t player, char *buffer, int len) +{ + if (player == NULL || buffer == NULL || len <= 0) { + return ESP_ERR_INVALID_ARG; + } + return raw_stream_write(player->raw_stream, buffer, len); +} + +esp_err_t player_pipeline_close(audio_player_handle_t player) +{ + if (player == NULL) { + return ESP_ERR_INVALID_ARG; + } + audio_pipeline_terminate(player->pipeline); + audio_pipeline_deinit(player->pipeline); + return ESP_OK; +} + +esp_err_t audio_tone_play(tone_type_t tone_url) +{ + ESP_RETURN_ON_FALSE(s_audio_player != NULL, ESP_FAIL, TAG, "audio tone not initialized"); + if (s_audio_player->player_state == PIPE_STATE_RUNNING) { + return ESP_FAIL; + } + ESP_LOGI(TAG, "audio_tone_play %d", tone_url); + + audio_element_set_uri(s_audio_player->tone_stream_reader, tone_uri[tone_url]); + audio_pipeline_run(s_audio_player->pipeline); + s_audio_player->player_state = PIPE_STATE_RUNNING; + return ESP_OK; +} + +esp_err_t audio_tone_stop(void) +{ + ESP_RETURN_ON_FALSE(s_audio_player != NULL, ESP_FAIL, TAG, "audio tone not initialized"); + if (s_audio_player->player_state == PIPE_STATE_IDLE) { + return ESP_FAIL; + } + audio_pipeline_stop(s_audio_player->pipeline); + audio_pipeline_wait_for_stop(s_audio_player->pipeline); + audio_pipeline_terminate(s_audio_player->pipeline); + audio_pipeline_reset_ringbuffer(s_audio_player->pipeline); + audio_pipeline_reset_elements(s_audio_player->pipeline); + s_audio_player->player_state = PIPE_STATE_IDLE; + return ESP_OK; +} + +static void audio_player_state_task(void *arg) +{ + audio_event_iface_handle_t evt = (audio_event_iface_handle_t) arg; + + s_audio_player->running = true; + while (1) { + audio_event_iface_msg_t msg; + esp_err_t ret = audio_event_iface_listen(evt, &msg, portMAX_DELAY); + if (ret != ESP_OK) { + ESP_LOGE(TAG, "[ * ] Event interface error : %d", ret); + continue; + } + if (msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT + && msg.source == (void *) s_audio_player->mp3_decoder + && msg.cmd == AEL_MSG_CMD_REPORT_MUSIC_INFO) { + audio_element_info_t music_info = {0}; + audio_element_getinfo(s_audio_player->mp3_decoder, &music_info); + ESP_LOGI(TAG, "[ * ] Receive music info from wav decoder, sample_rates=%d, bits=%d, ch=%d", + music_info.sample_rates, music_info.bits, music_info.channels); + rsp_filter_set_src_info(s_audio_player->filter, music_info.sample_rates, music_info.channels); + continue; + } + /* Stop when the last pipeline element receives stop event */ + if ((msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT && msg.source == (void *) s_audio_player->filter + && msg.cmd == AEL_MSG_CMD_REPORT_STATUS) + && ((int)msg.data == AEL_STATUS_STATE_FINISHED)) { + ESP_LOGI(TAG, "[ * ] Stop event received"); + // To suppress unwanted transition noise + vTaskDelay(pdMS_TO_TICKS(20)); + // Restore coze audio play + player_pipeline_run(); + s_audio_player->tone_cb(AEL_STATUS_STATE_FINISHED); + } + } +} + +static esp_err_t _player_write_mp3_cb(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + return audio_element_output(s_player_pipeline->i2s_writer, buffer, len); +} + +esp_err_t audio_tone_init(tone_play_callback_t callback) +{ + s_audio_player = (audio_player_t *)audio_calloc(1, sizeof(audio_player_t)); + AUDIO_MEM_CHECK(TAG, s_audio_player, goto _exit_open); + + ESP_LOGI(TAG, "Create audio pipeline for audio player"); + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + s_audio_player->pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_MEM_CHECK(TAG, s_audio_player->pipeline, goto _exit_open); + + ESP_LOGI(TAG, "Create tone stream to read data from flash"); + tone_stream_cfg_t tone_cfg = TONE_STREAM_CFG_DEFAULT(); + tone_cfg.type = AUDIO_STREAM_READER; + s_audio_player->tone_stream_reader = tone_stream_init(&tone_cfg); + AUDIO_NULL_CHECK(TAG, s_audio_player->tone_stream_reader, goto _exit_open); + + ESP_LOGI(TAG, "Create mp3 decoder to decode mp3 file"); + mp3_decoder_cfg_t mp3_cfg = DEFAULT_MP3_DECODER_CONFIG(); + s_audio_player->mp3_decoder = mp3_decoder_init(&mp3_cfg); + AUDIO_NULL_CHECK(TAG, s_audio_player->mp3_decoder, goto _exit_open); + + ESP_LOGI(TAG, "Create filter for mp3"); + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; +#if CONFIG_ESP32_S3_KORVO2_V3_BOARD || CONFIG_ESP32_S3_BOX_BOARD + filter_cfg.dest_ch = 1; +#else + filter_cfg.dest_ch = 2; +#endif + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 2; + s_audio_player->filter = rsp_filter_init(&filter_cfg); + AUDIO_MEM_CHECK(TAG, s_audio_player->filter, goto _exit_open); + audio_element_set_write_cb(s_audio_player->filter, _player_write_mp3_cb, NULL); + + ESP_LOGI(TAG, "Register all elements to audio pipeline"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->tone_stream_reader, "tone"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->mp3_decoder, "mp3"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->filter, "filter"); + + ESP_LOGI(TAG, "Link it together [flash]-->tone_stream-->mp3_decoder-->filter-->[codec_chip]"); + const char *link_tag[3] = {"tone", "mp3", "filter"}; + audio_pipeline_link(s_audio_player->pipeline, &link_tag[0], 3); + + + esp_periph_config_t periph_cfg = DEFAULT_ESP_PERIPH_SET_CONFIG(); + esp_periph_set_handle_t set = esp_periph_set_init(&periph_cfg); + audio_event_iface_cfg_t evt_cfg = AUDIO_EVENT_IFACE_DEFAULT_CFG(); + audio_event_iface_handle_t evt = audio_event_iface_init(&evt_cfg); + audio_pipeline_set_listener(s_audio_player->pipeline, evt); + audio_event_iface_set_listener(esp_periph_set_get_event_iface(set), evt); + + s_audio_player->tone_cb = callback; + audio_thread_create(NULL, "audio_player_state_task", audio_player_state_task, (void *)evt, 5 * 1024, 15, true, 1); + + return ESP_OK; + +_exit_open: + audio_pipe_safe_free(s_audio_player->tone_stream_reader, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->mp3_decoder, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->filter, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->pipeline, audio_pipeline_deinit); + audio_pipe_safe_free(s_audio_player, audio_free); + return ESP_FAIL; +} \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/main/audio_processor.h b/esp-spot/example/adf/llm_touch_toy/main/audio_processor.h new file mode 100644 index 0000000..5d72784 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/audio_processor.h @@ -0,0 +1,197 @@ +/* + * Espressif Modified MIT License + * + * Copyright (c) 2025 Espressif Systems (Shanghai) Co., LTD + * + * Permission is hereby granted for use **exclusively** with Espressif Systems products. + * This includes the right to use, copy, modify, merge, publish, distribute, and sublicense + * the Software, subject to the following conditions: + * + * 1. This Software **must be used in conjunction with Espressif Systems products**. + * 2. The above copyright notice and this permission notice shall be included in all copies + * or substantial portions of the Software. + * 3. Redistribution of the Software in source or binary form **for use with non-Espressif products** + * is strictly prohibited. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, + * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR + * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE + * FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR + * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER + * DEALINGS IN THE SOFTWARE. + * + * SPDX-License-Identifier: MIT-ESPRESSIF + */ + +#pragma once + +#include +#include +#include +#include "audio_pipeline.h" + +#if __has_include("audio_tone_uri.h") + #include "audio_tone_uri.h" +#else + #error "please refer the README, and then make the tone file" +#endif + +typedef void (*tone_play_callback_t)(audio_element_status_t evt); + +/** + * @brief Enumeration of player pipeline states + */ +typedef enum { + PIPE_STATE_IDLE, /**< The pipeline is idle and not processing any audio */ + PIPE_STATE_RUNNING, /**< The pipeline is actively processing and playing audio */ +} pipe_player_state_e; + +/** + * @brief Handle type for the audio player. + */ +typedef struct audio_player_s *audio_player_handle_t; + +/** + * @brief Handle type for the audio recorder. + */ +typedef struct audio_recorder_s *audio_recorder_handle_t; + +/** + * @brief Open the recorder pipeline and initialize necessary resources. + * + * @return audio_recorder_handle_t Recorder handle, or NULL on failure. + */ +audio_recorder_handle_t recorder_pipeline_open(); + +/** + * @brief Start the recorder pipeline and begin capturing audio data. + * + * @param[in] recorder Recorder handle. + * + * * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t recorder_pipeline_run(audio_recorder_handle_t recorder); + +/** + * @brief Read audio data from the recorder pipeline. + * + * @param[in] recorder Recorder handle. + * @param[in] buffer Pointer to the buffer where audio data will be stored. + * @param[in] len Maximum number of bytes to read. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t recorder_pipeline_read(audio_recorder_handle_t recorder, char *buffer, int len); + +/** + * @brief Stop the recorder pipeline. + * + * @param[in] recorder Recorder handle. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t recorder_pipeline_stop(audio_recorder_handle_t recorder); + +/** + * @brief Close the recorder pipeline and release all resources. + * + * @param[in] recorder Recorder handle. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t recorder_pipeline_close(audio_recorder_handle_t recorder); + +/** + * @brief Open the player pipeline and initialize necessary resources. + * + * @return audio_player_handle_t Player handle, or NULL on failure. + */ +audio_player_handle_t player_pipeline_open(); + +/** + * @brief Start the player pipeline and begin playback. + * + * @param[in] player Player handle. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t player_pipeline_run(void); + +/** + * @brief Stop the player pipeline. + * + * @param[in] player Player handle. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t player_pipeline_stop(void); + +/** + * @brief Write audio data to the player pipeline for playback. + * + * @param[in] player Player handle. + * @param[in] buffer Pointer to the buffer containing audio data. + * @param[in] len Number of bytes to write. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t player_pipeline_write(audio_player_handle_t player, char *buffer, int len); + +/** + * @brief Close the player pipeline and release all resources. + * + * @param[in] player Player handle. + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG NULL pointer or invalid configuration + */ +esp_err_t player_pipeline_close(audio_player_handle_t player); + +/** + * @brief Initialize the tone playback system. + * + * @param[in] callback Callback function to be called when tone playback is triggered + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG Invalid callback or other configuration error + */ +esp_err_t audio_tone_init(tone_play_callback_t callback); + +/** + * @brief Stop the currently playing tone, if any. + * + * @return + * - ESP_OK On success or if no tone is playing + * - ESP_FAIL If stopping tone fails + */ +esp_err_t audio_tone_stop(void); + +/** + * @brief Play a tone from the specified URL + * + * @param[in] tone_url The tone resource to be played, check 'audio_tone_uri.h' + * + * @return + * - ESP_OK On success + * - ESP_ERR_INVALID_ARG Invalid tone type or URL + * - ESP_FAIL Other playback errors + */ +esp_err_t audio_tone_play(tone_type_t tone_url); + + diff --git a/esp-spot/example/adf/llm_touch_toy/main/idf_component.yml b/esp-spot/example/adf/llm_touch_toy/main/idf_component.yml new file mode 100644 index 0000000..7ee2cf7 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/idf_component.yml @@ -0,0 +1,15 @@ +## IDF Component Manager Manifest File +dependencies: + ## Required IDF version + # idf: ">=5.4" + espressif/esp_websocket_client: ^1.2.3 + # espressif/esp_hosted: + # version: "~1.1" + # rules: + # - if: "target in [esp32p4]" + # espressif/esp_wifi_remote: + # matches: + # - if: "idf_version <=5.4.0 && target in [esp32p4]" + # version: "~0.4" + # - if: "idf_version >5.4.0 && target in [esp32p4]" + # version: "~0.6" diff --git a/esp-spot/example/adf/llm_touch_toy/main/main.c b/esp-spot/example/adf/llm_touch_toy/main/main.c new file mode 100644 index 0000000..a6de53e --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/main/main.c @@ -0,0 +1,410 @@ +/* http client request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. + + Demo video: https://www.bilibili.com/video/BV1ekRAYVEZ1/ + Hardware: https://oshwhub.com/esp-college/esp-spot +*/ + +#include +#include + +#include "freertos/FreeRTOS.h" +#include "freertos/event_groups.h" +#include "freertos/idf_additions.h" +#include "freertos/task.h" + +#include "esp_log.h" +#include "nvs_flash.h" +#include "esp_wifi.h" + +#include "audio_sys.h" +#include "audio_thread.h" +#include "esp_peripherals.h" +#include "periph_wifi.h" +#include "periph_sdcard.h" +#include "audio_mem.h" +#include "board.h" +#include "es8311.h" +#include "es7210.h" + +#include "touch_button.h" +#include "iot_button.h" +#include "touch_sensor_lowlevel.h" + +#include "audio_processor.h" +#include "coze_chat.h" +#include "led_driver.h" + +#define DEFAULT_RAW_OPUS_BUFFER_SIZE (1024) + +static char *TAG = "main"; + +#define TOUCH_CHANNEL_1 (3) +#define TOUCH_CHANNEL_2 (9) +#define TOUCH_CHANNEL_3 (13) +#define TOUCH_CHANNEL_4 (14) + +#define LIGHT_TOUCH_THRESHOLD (0.15) +#define HEAVY_TOUCH_THRESHOLD (0.4) + +/* To controll the audio event */ +#define BIT_RECORDING_START (1 << 0) +static EventGroupHandle_t s_audio_event_group; + +struct coze_ws_s { + coze_chat_handle_t chat; + audio_recorder_handle_t recorder; + audio_player_handle_t player; + char *recorder_buffer; + char *opus_raw_buffer; + int opus_raw_buffer_len; + enum { + PLAYBACK_STATE_IDLE, + PLAYBACK_STATE_PLAYING, + } player_state; +}; + +static struct coze_ws_s s_coze_ws; +static audio_board_handle_t board_handle = NULL; + +static void audio_event_callback(coze_chat_event_t event, void *ctx) +{ + if (event == COZE_CHAT_EVENT_CHAT_SPEECH_STARTED) { + ESP_LOGI(TAG, "chat start"); + s_coze_ws.player_state = PLAYBACK_STATE_IDLE; + } else if (event == COZE_CHAT_EVENT_CHAT_SPEECH_STOPED) { + ESP_LOGI(TAG, "chat stop"); + s_coze_ws.player_state = PLAYBACK_STATE_PLAYING; + } +} + +static void audio_data_callback(char *data, int len, void *ctx) +{ +#define frame_length_prefix (2) + if (len > s_coze_ws.opus_raw_buffer_len) { + s_coze_ws.opus_raw_buffer_len = len + frame_length_prefix; + s_coze_ws.opus_raw_buffer = audio_realloc(s_coze_ws.opus_raw_buffer, s_coze_ws.opus_raw_buffer_len); + } + ESP_LOGD(TAG, "data: %p, len: %d", data, len); + s_coze_ws.opus_raw_buffer[0] = (len >> 8) & 0xFF; + s_coze_ws.opus_raw_buffer[1] = len & 0xFF; + memcpy(s_coze_ws.opus_raw_buffer + frame_length_prefix, data, len); + len += frame_length_prefix; + + player_pipeline_write(s_coze_ws.player, s_coze_ws.opus_raw_buffer, len); +} + +static void audio_if_open() +{ + s_coze_ws.recorder = recorder_pipeline_open(); + s_coze_ws.player = player_pipeline_open(); + recorder_pipeline_run(s_coze_ws.recorder); + player_pipeline_run(); +} + +/** + * @brief Temporarily mute the audio output to prevent pop or click noise + * during audio state transitions. + * + * This function briefly mutes the audio hardware for a few milliseconds + * and then unmutes it. It's typically used before starting or stopping + * playback to suppress unwanted transition noise. + */ +static void short_mute() +{ + audio_hal_set_mute(board_handle->audio_hal, true); + vTaskDelay(pdMS_TO_TICKS(30)); + audio_hal_set_mute(board_handle->audio_hal, false); +} + +static void audio_data_read_task(void *pv) +{ +#define recorder_buffer_size (640) + s_coze_ws.recorder_buffer = malloc(recorder_buffer_size); + + s_audio_event_group = xEventGroupCreate(); + xEventGroupSetBits(s_audio_event_group, BIT_RECORDING_START); + while (1) { + /* Stop reading audio if the WebSocket stopped */ + xEventGroupWaitBits(s_audio_event_group, + BIT_RECORDING_START, + pdFALSE, + pdTRUE, + portMAX_DELAY); + int r_len = recorder_pipeline_read(s_coze_ws.recorder, s_coze_ws.recorder_buffer, recorder_buffer_size); + if (r_len > 0) { + coze_chat_send_audio_data(s_coze_ws.chat, s_coze_ws.recorder_buffer, r_len); + } + } + vTaskDelete(NULL); +} + +static void audio_tone_player_event_cb(audio_element_status_t evt) +{ + if (evt == AEL_STATUS_STATE_FINISHED) { + // add more functions here + } +} + +static void touch_event_nose(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event nose: %s", iot_button_get_event_str(event)); + + // Stop coze or local audio + player_pipeline_stop(); + audio_tone_stop(); + + audio_tone_play(TONE_TYPE_TOUCH_NOSE); +} + +static void touch_event_nose_long(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event nose long: %s", iot_button_get_event_str(event)); + + // Stop coze or local audio + player_pipeline_stop(); + audio_tone_stop(); + + audio_tone_play(TONE_TYPE_SCREAMING); +} + +static void touch_event_hat(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event hat: %s", iot_button_get_event_str(event)); + + // Stop coze or local audio + player_pipeline_stop(); + audio_tone_stop(); + + static tone_type_t hat_state = TONE_TYPE_HAT_1; + audio_tone_play(hat_state); + + // Toggle between two tone states and update LED mode + if (hat_state == TONE_TYPE_CAPYBARA_SONG_1) { + // Turn on LED + led_set_mode(4); + hat_state = TONE_TYPE_HAT_1; + } else { + hat_state = TONE_TYPE_CAPYBARA_SONG_1; + } + +} + +static void touch_event_belly(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event hat: %s", iot_button_get_event_str(event)); + + // Stop coze or local audio + player_pipeline_stop(); + audio_tone_stop(); + + static int audio_belly_count_g = 0; + audio_tone_play(audio_belly_count_g); + + // Update the counter for next playback + if (audio_belly_count_g < TONE_TYPE_BELLY_4) { + audio_belly_count_g++; + } else if (audio_belly_count_g == TONE_TYPE_BELLY_4) { + audio_belly_count_g = TONE_TYPE_SCREAMING; + } else { + audio_belly_count_g = TONE_TYPE_BELLY_1; + } +} + +static void touch_event_neck(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event hat: %s", iot_button_get_event_str(event)); + + // Stop coze or local audio + player_pipeline_stop(); + audio_tone_stop(); + + static tone_type_t neck_state = TONE_TYPE_NECK_2; + + // Toggle between two tone states and update LED mode + if (neck_state == TONE_TYPE_NECK_2) { + // Turn on LED + xEventGroupClearBits(s_audio_event_group, BIT_RECORDING_START); + recorder_pipeline_stop(s_coze_ws.recorder); + coze_chat_stop(s_coze_ws.chat); + audio_tone_play(TONE_TYPE_NECK_2); + neck_state = TONE_TYPE_NECK_1; + } else { + audio_tone_play(TONE_TYPE_NECK_1); + neck_state = TONE_TYPE_NECK_2; + coze_chat_start(s_coze_ws.chat); + xEventGroupSetBits(s_audio_event_group, BIT_RECORDING_START); + recorder_pipeline_run(s_coze_ws.recorder); + } +} + +static void touch_task(void *arg) +{ + // Register all touch channel + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2, TOUCH_CHANNEL_3, TOUCH_CHANNEL_4}; + int total_channel_num = sizeof(touch_channel_list) / sizeof(touch_channel_list[0]); + + // calloc channel_type for every button from the list + touch_lowlevel_type_t *channel_type = calloc(total_channel_num, sizeof(touch_lowlevel_type_t)); + assert(channel_type); + for (int i = 0; i < total_channel_num; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + touch_lowlevel_config_t low_config = { + .channel_num = total_channel_num, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + assert(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = { + .short_press_time = 300, + .long_press_time = 2000, + }; + + /* ============================= Init touch IO3 ============================= */ + button_touch_config_t touch_cfg_1 = { + .touch_channel = touch_channel_list[0], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create button for nose */ + button_handle_t btn_nose = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_1, &btn_nose); + assert(ret == ESP_OK); + + /* ============================= Init touch IO9 ============================= */ + button_touch_config_t touch_cfg_2 = { + .touch_channel = touch_channel_list[1], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create button for hat */ + button_handle_t btn_hat = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_2, &btn_hat); + assert(ret == ESP_OK); + + /* ============================= Init touch IO13 ============================= */ + button_touch_config_t touch_cfg_3 = { + .touch_channel = touch_channel_list[2], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create light press button */ + button_handle_t btn_belly = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_3, &btn_belly); + assert(ret == ESP_OK); + + /* ============================= Init touch IO14 ============================= */ + button_touch_config_t touch_cfg_4 = { + .touch_channel = touch_channel_list[3], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create button for nose */ + button_handle_t btn_neck = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_4, &btn_neck); + assert(ret == ESP_OK); + + /* ========================== Register touch callback ========================== */ + // Register touch callback for nose + iot_button_register_cb(btn_nose, BUTTON_PRESS_DOWN, NULL, touch_event_nose, NULL); + iot_button_register_cb(btn_nose, BUTTON_LONG_PRESS_START, NULL, touch_event_nose_long, NULL); + + // Register touch callback for hat + iot_button_register_cb(btn_hat, BUTTON_PRESS_DOWN, NULL, touch_event_hat, NULL); + + // Register touch callback for belly + iot_button_register_cb(btn_belly, BUTTON_PRESS_DOWN, NULL, touch_event_belly, NULL); + + // Register touch callback for nose + iot_button_register_cb(btn_neck, BUTTON_PRESS_DOWN, NULL, touch_event_neck, NULL); + + touch_sensor_lowlevel_start(); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } +} + +static void led_task(void *arg) +{ + /* Configure the LED strip and obtain a handle */ + led_strip_handle_t led_strip = led_create(); + led_set_mode(1); + vTaskDelay(1000); + led_set_mode(0); + + while(1) { + /* Run the LED animation based on LED configuration and weather data */ + ESP_ERROR_CHECK(led_animations_start(led_strip)); + } + + vTaskDelete(NULL); +} + +void app_main(void) +{ + AUDIO_MEM_SHOW(TAG); + ESP_ERROR_CHECK(nvs_flash_init()); + ESP_ERROR_CHECK(esp_netif_init()); + + ESP_LOGI(TAG, "Initialize board peripherals"); + esp_periph_config_t periph_cfg = DEFAULT_ESP_PERIPH_SET_CONFIG(); + esp_periph_set_handle_t set = esp_periph_set_init(&periph_cfg); + + board_handle = audio_board_init(); + audio_hal_ctrl_codec(board_handle->audio_hal, AUDIO_HAL_CODEC_MODE_BOTH, AUDIO_HAL_CTRL_START); +#if CONFIG_ESP32_S3_SPOT_BOARD + audio_hal_set_volume(board_handle->audio_hal, 90); + es8311_set_mic_gain(ES8311_MIC_GAIN_0DB); +#endif + + s_coze_ws.opus_raw_buffer_len = DEFAULT_RAW_OPUS_BUFFER_SIZE; + s_coze_ws.opus_raw_buffer = audio_malloc(s_coze_ws.opus_raw_buffer_len); + // Initialize SD Card peripheral + // audio_board_sdcard_init(set, SD_MODE_1_LINE); + periph_wifi_cfg_t wifi_cfg = { + .wifi_config.sta.ssid = CONFIG_ESP_WIFI_SSID, + .wifi_config.sta.password = CONFIG_ESP_WIFI_PASSWORD, + }; + esp_periph_handle_t wifi_handle = periph_wifi_init(&wifi_cfg); + esp_periph_start(set, wifi_handle); + periph_wifi_wait_for_connected(wifi_handle, portMAX_DELAY); + + coze_chat_config_t chat_config = COZE_CHAT_DEFAULT_CONFIG(); + chat_config.bot_id = CONFIG_BOT_ID; + chat_config.access_token = CONFIG_ACCESS_TOKEN; + chat_config.audio_callback = audio_data_callback; + chat_config.event_callback = audio_event_callback; + + s_coze_ws.chat = coze_chat_init(&chat_config); + coze_chat_start(s_coze_ws.chat); + + audio_if_open(); + audio_thread_create(NULL, "audio_data_read_task", audio_data_read_task, (void *)NULL, 1024 * 4, 12, true, 1); + + xTaskCreate(touch_task, "touch_task", 1024 * 5, NULL, 5, NULL); + xTaskCreate(led_task, "led_task", 1024 * 3, NULL, 5, NULL); + + audio_tone_init(audio_tone_player_event_cb); + + AUDIO_MEM_SHOW(TAG); +} diff --git a/esp-spot/example/adf/llm_touch_toy/partitions.csv b/esp-spot/example/adf/llm_touch_toy/partitions.csv new file mode 100644 index 0000000..61d2050 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/partitions.csv @@ -0,0 +1,8 @@ +# Name, Type, SubType, Offset, Size, Flags +# Note: if you have increased the bootloader size, make sure to update the offsets to avoid overlap +nvs, data, nvs, , 0x6000, +phy_init, data, phy, , 0x1000, +factory, app, factory, , 2M, +model, data, spiffs, , 5168K, +flash_tone,data, 0xff, 0x720000 ,2M, + diff --git a/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults b/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults new file mode 100644 index 0000000..e9f5b98 --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults @@ -0,0 +1,39 @@ +# +# Serial flasher config +# +CONFIG_ESPTOOLPY_FLASHSIZE_8MB=y +# end of Serial flasher config + +# +# Partition Table +# +# CONFIG_PARTITION_TABLE_SINGLE_APP is not set +# CONFIG_PARTITION_TABLE_SINGLE_APP_LARGE is not set +# CONFIG_PARTITION_TABLE_TWO_OTA is not set +CONFIG_PARTITION_TABLE_CUSTOM=y +CONFIG_PARTITION_TABLE_CUSTOM_FILENAME="partitions.csv" +CONFIG_PARTITION_TABLE_FILENAME="partitions.csv" +CONFIG_PARTITION_TABLE_MD5=y +# end of Partition Table + +# +# ESP-TLS +# +CONFIG_ESP_TLS_INSECURE=y +CONFIG_ESP_TLS_SKIP_SERVER_CERT_VERIFY=y + +# +# FREERTOS +# +CONFIG_FREERTOS_ENABLE_BACKWARD_COMPATIBILITY=y +CONFIG_FREERTOS_TIMER_TASK_STACK_DEPTH=4096 +CONFIG_FREERTOS_GENERATE_RUN_TIME_STATS=y +CONFIG_FREERTOS_VTASKLIST_INCLUDE_COREID=y +CONFIG_FREERTOS_HZ=1000 + +# +# TOUCH +# +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_HYSTERESIS_P_X1000=200 \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults.esp32s3 b/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults.esp32s3 new file mode 100644 index 0000000..e308daa --- /dev/null +++ b/esp-spot/example/adf/llm_touch_toy/sdkconfig.defaults.esp32s3 @@ -0,0 +1,112 @@ +CONFIG_IDF_CMAKE=y +CONFIG_IDF_TARGET_ARCH_XTENSA=y +CONFIG_IDF_TARGET="esp32s3" +CONFIG_IDF_TARGET_ESP32S3=y +CONFIG_IDF_FIRMWARE_CHIP_ID=0x0009 + + +# +# Audio HAL +# +CONFIG_ESP32_S3_SPOT_BOARD=y +# end of Audio HAL + +# +# Audio Recorder +# +CONFIG_AFE_MIC_NUM=2 +# end of Audio Recorder + +# +# ESP Speech Recognition +# +CONFIG_MODEL_IN_FLASH=y +CONFIG_USE_AFE=y +CONFIG_AFE_INTERFACE_V1=y +CONFIG_USE_WAKENET=y +CONFIG_SR_WN_WN9_HILEXIN=y +CONFIG_USE_MULTINET=y +CONFIG_SR_MN_CN_MULTINET6_QUANT=y +CONFIG_SR_MN_EN_NONE=y +# end of ESP Speech Recognition + +# +# Component config +# + +# +# Driver configurations +# + +# +# ESP32S3-Specific +# +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_80 is not set +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_160 is not set +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_240=y +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_MHZ=240 + +# +# Cache config +# +# CONFIG_ESP32S3_INSTRUCTION_CACHE_16KB is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_32KB=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_SIZE=0x8000 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_4WAYS is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_8WAYS=y +CONFIG_ESP32S3_ICACHE_ASSOCIATED_WAYS=8 +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_32B=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_SIZE=32 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_WRAP is not set +# CONFIG_ESP32S3_DATA_CACHE_16KB is not set +# CONFIG_ESP32S3_DATA_CACHE_32KB is not set +CONFIG_ESP32S3_DATA_CACHE_64KB=y +CONFIG_ESP32S3_DATA_CACHE_SIZE=0x10000 +# CONFIG_ESP32S3_DATA_CACHE_4WAYS is not set +CONFIG_ESP32S3_DATA_CACHE_8WAYS=y +CONFIG_ESP32S3_DCACHE_ASSOCIATED_WAYS=8 +# CONFIG_ESP32S3_DATA_CACHE_LINE_32B is not set +CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y +CONFIG_ESP32S3_DATA_CACHE_LINE_SIZE=64 +# CONFIG_ESP32S3_DATA_CACHE_WRAP is not set +# end of Cache config + +CONFIG_ESP32S3_SPIRAM_SUPPORT=y + +# +# SPI RAM config +# +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_TYPE_AUTO=y +CONFIG_SPIRAM_SPEED_80M=y +CONFIG_SPIRAM=y +CONFIG_SPIRAM_BOOT_INIT=y +CONFIG_SPIRAM_USE_MALLOC=y +CONFIG_SPIRAM_MEMTEST=y +CONFIG_SPIRAM_MALLOC_ALWAYSINTERNAL=4096 +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_SPIRAM_MALLOC_RESERVE_INTERNAL=32768 +# end of SPI RAM config +# end of ESP32S3-Specific + +# +# Wi-Fi +# +CONFIG_ESP_WIFI_ENABLED=y +CONFIG_ESP_WIFI_STATIC_RX_BUFFER_NUM=16 +CONFIG_ESP_WIFI_DYNAMIC_RX_BUFFER_NUM=32 +CONFIG_ESP_WIFI_DYNAMIC_TX_BUFFER=y +CONFIG_ESP_WIFI_TX_BUFFER_TYPE=1 +CONFIG_ESP_WIFI_DYNAMIC_TX_BUFFER_NUM=32 +CONFIG_ESP_WIFI_STATIC_RX_MGMT_BUFFER=y +CONFIG_ESP_WIFI_AMPDU_TX_ENABLED=y +CONFIG_ESP_WIFI_TX_BA_WIN=16 +CONFIG_ESP_WIFI_AMPDU_RX_ENABLED=y +CONFIG_ESP_WIFI_RX_BA_WIN=16 + +# +# LWIP +# +CONFIG_LWIP_TCP_SND_BUF_DEFAULT=65535 +CONFIG_LWIP_TCP_WND_DEFAULT=65535 +CONFIG_LWIP_TCP_RECVMBOX_SIZE=32 \ No newline at end of file diff --git a/esp-spot/example/adf/llm_touch_toy/tools/audio_tone.bin b/esp-spot/example/adf/llm_touch_toy/tools/audio_tone.bin new file mode 100644 index 0000000..62d0997 Binary files /dev/null and b/esp-spot/example/adf/llm_touch_toy/tools/audio_tone.bin differ diff --git a/esp-spot/example/adf/touch_play_mp3/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/CMakeLists.txt new file mode 100644 index 0000000..5d2fea4 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/CMakeLists.txt @@ -0,0 +1,8 @@ +# The following lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +include($ENV{ADF_PATH}/CMakeLists.txt) +include($ENV{IDF_PATH}/tools/cmake/project.cmake) + +project(touch_play_mp3) \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/Makefile b/esp-spot/example/adf/touch_play_mp3/Makefile new file mode 100644 index 0000000..952d8ed --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/Makefile @@ -0,0 +1,2 @@ +PROJECT_NAME := play_tone_mp3 +include $(ADF_PATH)/project.mk diff --git a/esp-spot/example/adf/touch_play_mp3/README_CN.md b/esp-spot/example/adf/touch_play_mp3/README_CN.md new file mode 100644 index 0000000..da87e1e --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/README_CN.md @@ -0,0 +1,114 @@ +# 从 Flash 中播放 MP3 文件例程 + +## 例程简介 + +本例程源自 ADF 例程：[pipeline_flash_tone](https://github.com/espressif/esp-adf/tree/master/examples/player/pipeline_flash_tone)，需在 [ADF](https://github.com/espressif/esp-adf/tree/master) 环境下开发 + +通过触摸事件，触发音频管道 API 播放存储在 flash 中的 MP3 文件，同时驱动 WS2812 灯环。 + +## 预备知识 + +本例程在 `/tools/audio_tone.bin` 和 `/components/audio_flash_tone/` 目录下已经帮助用户生成了例程所需的 bin 文件和音频文件在 flash 中地址的源代码文件。 + +如果用户需要生成自己的 `audio_tone.bin`，则需要执行 `mk_audio_bin.py` 脚本（位于 $ADF_PATH/tools/audio_tone/mk_audio_tone.py），并且指定相关文件的路径。 + + 源 MP3 文件在 `tone_mp3_folder` 文件夹中，生成的 C 文件、H 文件以及二进制 bin 文件都存放在此目录下。 + +``` +python3 $ADF_PATH/tools/audio_tone/mk_audio_tone.py -f ./ -r tone_mp3_folder +``` + +请使用 *python3 $ADF_PATH/tools/audio_tone/mk_audio_tone.py --help* 查看更多脚本信息。 + +本例程默认的 `audio_tone.bin` 包含如下音频文件： + +```c + "flash://tone/0_belly_1.mp3", + "flash://tone/1_belly_2.mp3", + "flash://tone/2_belly_3.mp3", + "flash://tone/3_belly_4.mp3", + "flash://tone/4_bread_1.mp3", + "flash://tone/5_bread_2.mp3", + "flash://tone/6_capybara_song_1.mp3", + "flash://tone/7_hat_1.mp3", + "flash://tone/8_neck_1.mp3", + "flash://tone/9_neck_2.mp3", + "flash://tone/10_reverse.mp3", + "flash://tone/11_screaming.mp3", + "flash://tone/12_shake.mp3", + "flash://tone/13_touch_nose.mp3", + "flash://tone/14_woohoo.mp3", +``` + +## 环境配置 + +### IDF 默认分支 + +本例程支持 IDF release/v5.0 及以后的分支，例程默认使用 ADF 的內建分支 `$ADF_PATH/esp-idf`。 + +## 配置说明 + +1. 开发板 + - 默认使用搭载 ESP32-S3 模组的 ``ESP-SPOT`` 开发板。该模组集成触摸传感器功能，技术细节请参考：：[ESP32-S3 数据手册](https://www.espressif.com/sites/default/files/documentation/esp32-s3_datasheet_cn.pdf) + +2. LED 灯环 + - 控制灯环的 IO 默认为 `GPIO45`，可通过 `idf.py menuconfig` 修改 `CONFIG_LED_GPIO_INPUT` 控制灯环的 IO，配置 `CONFIG_LED_COUNT` 修改 LED 数量，默认为 16 个 + +3. 音频文件 + - **如果 MP3 音频总数量大于 `9`，则需要修改 `$ADF_PATH/components/audio_stream` 文件夹中的 `tone_stream.c` 文件** + - 将 `_tone_open(audio_element_handle_t self)` 函数中 `char find_num[2]` 数组的长度修改为 `char find_num[3]`。这样可以确保函数在解析双位数的音频文件名（例如 10.mp3、11.mp3）时不会发生字符串截断错误，正确识别全部音频文件 + +4. 音频储存地址 + - 此例程的 `flashTone` 在 `partition_flash_tone.csv` 中地址配置如下，用户可以根据自己的项目 flash 分区灵活配置地址 + + ``` + flashTone,data, 0x04, 0x110000 , 500K, + ``` + + +### 编译和下载 + +请先编译版本并烧录到开发板上，然后运行 monitor 工具来查看串口输出（替换 PORT 为端口名称）： + +``` +idf.py -p PORT flash monitor +``` + +**此外，本例程还需烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv` 的 `flashTone` 分区，请使用如下命令:** + +``` +esptool.py --chip esp32s3 --before default_reset --after hard_reset write_flash -z --flash_mode dio --flash_freq 40m --flash_size detect 0x110000 ./tools/audio_tone.bin +``` + +有关配置和使用 ESP-IDF 生成项目的完整步骤，请参阅 [《ESP-IDF 编程指南》](https://docs.espressif.com/projects/esp-idf/zh_CN/release-v5.3/esp32/index.html)。 + + +## 如何使用 + +### 功能和用法 + +- 例程开始运行后，如果事前没有烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv ` 的 `flashTone` 分区，例程将会报错，请参考 [编译和下载](#编译和下载) 的说明进行烧录 + +## 故障排除 + +```c +E (481) TONE_PARTITION: Not flash tone partition +E (481) AUDIO_ELEMENT: [tone] AEL_STATUS_ERROR_OPEN,-1 +W (491) AUDIO_ELEMENT: [tone] audio_element_on_cmd_error,7 +E (501) TONE_PARTITION: /repo/adfs/bugfix/esp-adf-internal/components/tone_partition/tone_partition.c:204 (tone_partition_deinit): Got NULL Pointer +W (511) AUDIO_ELEMENT: IN-[mp3] AEL_IO_ABORT +E (511) MP3_DECODER: failed to read audio data (line 119) +W (521) AUDIO_ELEMENT: [mp3] AEL_IO_ABORT, -3 +W (531) AUDIO_ELEMENT: IN-[i2s] AEL_IO_ABORT +``` + +如果遇到上述的错误，请把按照 [编译和下载](#编译和下载) 的说明烧录 `/tools/audio_tone.bin` 到 `partition_flash_tone.csv ` 的 `flashTone` 分区。 + + +## 技术支持 +请按照下面的链接获取技术支持： + +- 技术支持参见 [esp32.com](https://esp32.com/viewforum.php?f=20) 论坛 +- 故障和新功能需求，请创建 [GitHub issue](https://github.com/espressif/esp-adf/issues) + +我们会尽快回复。 diff --git a/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/CMakeLists.txt new file mode 100644 index 0000000..203bc03 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/CMakeLists.txt @@ -0,0 +1,4 @@ +set(COMPONENT_SRCS "audio_tone_uri.c") +set(COMPONENT_ADD_INCLUDEDIRS .) + +register_component() \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.c b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.c new file mode 100644 index 0000000..e99bfc1 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.c @@ -0,0 +1,24 @@ +/*This is tone file*/ + +const char* tone_uri[] = { + "flash://tone/0_belly_1.mp3", + "flash://tone/1_belly_2.mp3", + "flash://tone/2_belly_3.mp3", + "flash://tone/3_belly_4.mp3", + "flash://tone/4_bread_1.mp3", + "flash://tone/5_bread_2.mp3", + "flash://tone/6_capybara_song_1.mp3", + "flash://tone/7_hat_1.mp3", + "flash://tone/8_neck_1.mp3", + "flash://tone/9_neck_2.mp3", + "flash://tone/10_reverse.mp3", + "flash://tone/11_screaming.mp3", + "flash://tone/12_shake.mp3", + "flash://tone/13_touch_nose.mp3", + "flash://tone/14_woohoo.mp3", +}; + +int get_tone_uri_num() +{ + return sizeof(tone_uri) / sizeof(char *) - 1; +} diff --git a/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.h b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.h new file mode 100644 index 0000000..16c2e92 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/audio_tone_uri.h @@ -0,0 +1,27 @@ +#ifndef __AUDIO_TONEURI_H__ +#define __AUDIO_TONEURI_H__ + +extern const char* tone_uri[]; + +typedef enum { + TONE_TYPE_BELLY_1, + TONE_TYPE_BELLY_2, + TONE_TYPE_BELLY_3, + TONE_TYPE_BELLY_4, + TONE_TYPE_BREAD_1, + TONE_TYPE_BREAD_2, + TONE_TYPE_CAPYBARA_SONG_1, + TONE_TYPE_HAT_1, + TONE_TYPE_NECK_1, + TONE_TYPE_NECK_2, + TONE_TYPE_REVERSE, + TONE_TYPE_SCREAMING, + TONE_TYPE_SHAKE, + TONE_TYPE_TOUCH_NOSE, + TONE_TYPE_WOOHOO, + TONE_TYPE_MAX, +} tone_type_t; + +int get_tone_uri_num(); + +#endif diff --git a/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/component.mk b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/component.mk new file mode 100644 index 0000000..5c85c36 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/audio_flash_tone/component.mk @@ -0,0 +1,3 @@ +COMPONENT_ADD_INCLUDEDIRS := . +COMPONENT_SRCDIRS := . + \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/components/led_driver/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/components/led_driver/CMakeLists.txt new file mode 100644 index 0000000..88537a3 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/led_driver/CMakeLists.txt @@ -0,0 +1,5 @@ +set(requires "led_strip") + +idf_component_register(SRC_DIRS "." + INCLUDE_DIRS "include" + REQUIRES ${requires}) diff --git a/esp-spot/example/adf/touch_play_mp3/components/led_driver/Kconfig.projbuild b/esp-spot/example/adf/touch_play_mp3/components/led_driver/Kconfig.projbuild new file mode 100644 index 0000000..2ba0c7a --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/led_driver/Kconfig.projbuild @@ -0,0 +1,15 @@ +menu "LED Srip Configuration" + + config LED_GPIO_INPUT + int "LED INPUT GPIO PIN" + default 45 + help + Set the input pin for LED strip + + config LED_COUNT + int "Count of LED" + default 16 + help + Set the total number of LED + +endmenu diff --git a/esp-spot/example/adf/touch_play_mp3/components/led_driver/idf_component.yml b/esp-spot/example/adf/touch_play_mp3/components/led_driver/idf_component.yml new file mode 100644 index 0000000..ce9a05f --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/led_driver/idf_component.yml @@ -0,0 +1,3 @@ +dependencies: + espressif/led_strip: + version: '^2' \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/components/led_driver/include/led_driver.h b/esp-spot/example/adf/touch_play_mp3/components/led_driver/include/led_driver.h new file mode 100644 index 0000000..954f4f9 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/led_driver/include/led_driver.h @@ -0,0 +1,63 @@ +/* + * SPDX-FileCopyrightText: 2023-2024 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include "led_strip.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define LED_STRIP_BLINK_GPIO CONFIG_LED_GPIO_INPUT /* LED GPIO assignment */ +#define LED_STRIP_LED_NUMBERS CONFIG_LED_COUNT /* Numbers of the LED in the strip */ + +#define LED_DEFAULT_RED 150 /* LED default value */ +#define LED_DEFAULT_GREEN 150 +#define LED_DEFAULT_BLUE 150 +#define LED_DEFAULT_BREATH_DIRECTION 3 +#define LED_DEFAULT_BRIGHTNESS 0 +#define LED_DEFAULT_MODE 2 + +/** + * @brief Configure the LED strip and obtain a handle for LED control. + * + * @return A handle to the LED strip for subsequent LED control operations. + */ +led_strip_handle_t led_create(void); + +/** + * @brief Convert hue to RGB and update LED values. + * + * @param[in] hue The hue value. Range: 0 ~ 360 + */ +void led_set_hue(uint16_t hue); + +/** + * @brief Switch the LED animation on/off based on power status. + * + * @param[in] power A boolean value indicating whether to turn the LED animation on or off. + */ +void led_set_switch(bool power); + +/** + * @brief Set the LED mode based on weather text data. + * + * @param[in] weather_text The weather text data to determine the LED mode. + * @return ESP_OK if the mode was set successfully, otherwise an error code. + */ +esp_err_t led_set_mode(int mode_update); + +/** + * @brief Run LED animations based on weather data. + * + * @param[in] led_strip The LED strip handle. + */ +esp_err_t led_animations_start(led_strip_handle_t led_strip); + +#ifdef __cplusplus +} /* end of extern "C" */ +#endif diff --git a/esp-spot/example/adf/touch_play_mp3/components/led_driver/led_driver.c b/esp-spot/example/adf/touch_play_mp3/components/led_driver/led_driver.c new file mode 100644 index 0000000..369aada --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/led_driver/led_driver.c @@ -0,0 +1,276 @@ +/* + * SPDX-FileCopyrightText: 2023-2024 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include +#include "esp_log.h" + +#include "led_driver.h" + +#define LED_STRIP_RMT_RES_HZ (10 * 1000 * 1000) /* 10MHz resolution, 1 tick = 0.1us */ + +/** + * @brief To set LED mode + */ +typedef enum { + LED_MODE_OFF = 1, /** LED off */ + LED_MODE_BREATH, /** LED breath mode */ + LED_MODE_FLOW, /** LED flow mode */ +} led_mode_t; + +/** + * @brief To control LED RGB value + */ +typedef struct { + uint8_t red; + uint8_t green; + uint8_t blue; + int8_t direction; + uint8_t brightness; + led_mode_t mode; +} led_data_t; + +/* configure LED default data */ +#define LED_CONFIG_DEFAULT() { \ + .red = LED_DEFAULT_RED, \ + .green = LED_DEFAULT_GREEN, \ + .blue = LED_DEFAULT_BLUE, \ + .direction = LED_DEFAULT_BREATH_DIRECTION, \ + .brightness = LED_DEFAULT_BRIGHTNESS, \ + .mode = LED_DEFAULT_MODE \ +} + +static const char *TAG = "led_driver"; +led_data_t g_led_data = LED_CONFIG_DEFAULT(); + +/* Configure the LED strip and obtain a handle */ +led_strip_handle_t led_create(void) +{ + /* LED strip general initialization, according to your led board design */ + led_strip_config_t strip_config = { + .strip_gpio_num = LED_STRIP_BLINK_GPIO, // The GPIO that connected to the LED strip's data line + .max_leds = LED_STRIP_LED_NUMBERS, // The number of LEDs in the strip, + .led_pixel_format = LED_PIXEL_FORMAT_GRB, // Pixel format of your LED strip + .led_model = LED_MODEL_WS2812, // LED strip model + .flags.invert_out = false, // whether to invert the output signal + }; + + /* LED strip backend configuration: RMT */ + led_strip_rmt_config_t rmt_config = { + .clk_src = RMT_CLK_SRC_DEFAULT, // Different clock source can lead to different power consumption + .resolution_hz = LED_STRIP_RMT_RES_HZ, // RMT counter clock frequency + .flags.with_dma = false, // DMA feature is available on ESP target like ESP32-S3 + }; + + /* LED Strip object handle */ + led_strip_handle_t led_strip; + esp_err_t err = led_strip_new_rmt_device(&strip_config, &rmt_config, &led_strip); + + if (err == ESP_OK) { + ESP_LOGI(TAG, "Created LED strip object with RMT backend"); + } else { + ESP_LOGE(TAG, "Failed to created LED strip object"); + return NULL; + } + + return led_strip; +} + +/* Convert hue to RGB */ +void led_set_hue(uint16_t hue) +{ + /* Make sure input between 0 ~ 360 */ + if (hue > 360) { + hue = 360; + } + + /* Calculate the RGB color corresponding to the hue */ + if (hue < 120) { + g_led_data.red = 255 - hue * 2; + g_led_data.green = hue * 2; + g_led_data.blue = 0; + } else if (hue < 240) { + g_led_data.red = 0; + g_led_data.green = 255 - (hue - 120) * 2; + g_led_data.blue = (hue - 120) * 2; + } else { + g_led_data.red = (hue - 240) * 2; + g_led_data.green = 0; + g_led_data.blue = 255 - (hue - 240) * 2; + } +} + +/* Perform a breathing animation on the LED strip */ +static void led_animation_breath(led_strip_handle_t led_strip) +{ + uint8_t color_red = g_led_data.red * g_led_data.brightness / 255; + uint8_t color_green = g_led_data.green * g_led_data.brightness / 255; + uint8_t color_blue = g_led_data.blue * g_led_data.brightness / 255; + + for (int i = 0; i < LED_STRIP_LED_NUMBERS; i++) { + led_strip_set_pixel(led_strip, i, color_red, color_green, color_blue); + } + /* Refresh the strip to send data */ + esp_err_t err = led_strip_refresh(led_strip); + + if (err != ESP_OK) { + ESP_LOGE(TAG, "Refresh LED Strip failed"); + } + + g_led_data.brightness += g_led_data.direction; + + if (g_led_data.brightness >= 255 || g_led_data.brightness == 0) { + g_led_data.direction = -g_led_data.direction; + } +} + +/* Perform a flowing animation on the LED strip */ +static void led_animation_flow(led_strip_handle_t led_strip) +{ + /* Length of LED flow animation */ + int led_len = 3; + + for (int i = 0; i < LED_STRIP_LED_NUMBERS; i++) { + led_strip_clear(led_strip); + + for (int j = 0; j < led_len; j++) { + int position = i - j; + if (position >= 0) { + uint8_t tail_r = g_led_data.red - (j * (g_led_data.red / led_len)); + uint8_t tail_g = g_led_data.green - (j * (g_led_data.green / led_len)); + uint8_t tail_b = g_led_data.blue - (j * (g_led_data.blue / led_len)); + led_strip_set_pixel(led_strip, position, tail_r, tail_g, tail_b); + } + } + + led_strip_set_pixel(led_strip, i, g_led_data.red, g_led_data.green, g_led_data.blue); + esp_err_t err = led_strip_refresh(led_strip); + + if (err != ESP_OK) { + ESP_LOGE(TAG, "Refresh LED Strip failed"); + } + + vTaskDelay(pdMS_TO_TICKS(100)); + } +} + +/* Set the LED strip for cloudy weather */ +static void led_animation_off(led_strip_handle_t led_strip) +{ + led_strip_clear(led_strip); + vTaskDelay(pdMS_TO_TICKS(10)); +} + +/* Set the LED strip for sunny weather */ +static void led_set_sunny() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 255; + g_led_data.green = 153; + g_led_data.blue = 18; +} + +/* Set the LED strip for rainy weather */ +static void led_set_rain() +{ + g_led_data.mode = LED_MODE_FLOW; + g_led_data.red = 255; + g_led_data.green = 18; + g_led_data.blue = 18; +} + +/* Set the LED strip for cloudy weather */ +static void led_set_cloudy() +{ + g_led_data.mode = LED_MODE_FLOW; + g_led_data.red = 100; + g_led_data.green = 100; + g_led_data.blue = 100; +} + +/* Set the LED strip for snow weather */ +static void led_set_snow() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 200; + g_led_data.green = 200; + g_led_data.blue = 200; +} + +/* Set the LED strip for unknown weather */ +static void led_set_unknown_weather() +{ + g_led_data.mode = LED_MODE_BREATH; + g_led_data.red = 255; + g_led_data.green = 0; + g_led_data.blue = 0; +} + +/* Switch the LED animation on/off based on power status */ +void led_set_switch(bool power) +{ + if (power) { + g_led_data.mode = LED_MODE_BREATH; + } else { + g_led_data.mode = LED_MODE_OFF; + } +} + +/* Set LED mode based on weather text data */ +esp_err_t led_set_mode(int mode_update) +{ + switch (mode_update) { + case 0: + g_led_data.mode = LED_MODE_OFF; + break; + case 1: + led_set_sunny(); + break; + case 2: + led_set_rain(); + break; + case 3: + led_set_cloudy(); + break; + case 4: + led_set_snow(); + break; + default: + ESP_LOGW(TAG, "Unknown mode number: %d", mode_update); + led_set_unknown_weather(); + return ESP_FAIL; + } + + return ESP_OK; +} + +/* Run LED animations based on weather data */ +esp_err_t led_animations_start(led_strip_handle_t led_strip) +{ + if (led_strip == NULL) { + return ESP_FAIL; + } + + /* Check LED mode */ + switch (g_led_data.mode) { + case LED_MODE_BREATH: + led_animation_breath(led_strip); + break; + case LED_MODE_FLOW: + led_animation_flow(led_strip); + break; + case LED_MODE_OFF: + led_animation_off(led_strip); + break; + default: + ESP_LOGW(TAG, "Unknown MODE, switch to LED_OFF"); + g_led_data.mode = LED_MODE_OFF; + break; + } + + return ESP_OK; +} diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/CHANGELOG.md b/esp-spot/example/adf/touch_play_mp3/components/touch_button/CHANGELOG.md new file mode 100644 index 0000000..802cc43 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/CHANGELOG.md @@ -0,0 +1,5 @@ +# ChangeLog + +## v0.1.0 - 2025-03-18 + +* Add Initial version diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/components/touch_button/CMakeLists.txt new file mode 100644 index 0000000..be47ea4 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/CMakeLists.txt @@ -0,0 +1,5 @@ +idf_component_register(SRC_DIRS "." + INCLUDE_DIRS ".") + +include(package_manager) +cu_pkg_define_version(${CMAKE_CURRENT_LIST_DIR}) diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/README.md b/esp-spot/example/adf/touch_play_mp3/components/touch_button/README.md new file mode 100644 index 0000000..ed31df6 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/README.md @@ -0,0 +1,25 @@ +[![Component Registry](https://components.espressif.com/components/espressif/touch_button/badge.svg)](https://components.espressif.com/components/espressif/touch_button) + +# Touch Button + +Supports a series of operations on touch buttons, such as press, release, long press, and short press. + +**Note:** This component is for developers testing only. It is not intended for production use. + +ESP32/ESP32-S2/ESP32-S3 touch-related components are intended for testing or demo purposes only. Due to the poor anti-interference capability of the touch functionality, it may not pass EMS testing, and therefore, it is not recommended for mass production products. + +This component is currently applicable to ESP32, ESP32-S2, and ESP32-S3, and requires an IDF version greater than or equal to v5.3. + +## Dependencies + +- [touch_sensor_fsm](https://components.espressif.com/components/espressif/touch_sensor_fsm) +- [touch_sensor_lowlevel](https://components.espressif.com/components/espressif/touch_sensor_lowlevel) +- [touch_button_sensor](https://components.espressif.com/components/espressif/touch_button_sensor) + +## Add component to your project + +Please use the component manager command `add-dependency` to add the `touch_button` to your project's dependency, during the `CMake` step the component will be downloaded automatically + +``` +idf.py add-dependency "espressif/touch_button=*" +``` diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/idf_component.yml b/esp-spot/example/adf/touch_play_mp3/components/touch_button/idf_component.yml new file mode 100644 index 0000000..4339e2d --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/idf_component.yml @@ -0,0 +1,19 @@ +dependencies: + cmake_utilities: '*' + espressif/button: + public: true + version: ^4 + espressif/touch_button_sensor: + public: true + version: ^0.2.1 + idf: '>=4.4' +description: Provide a complete set of button detection methods based on `iot_button` + to drive the touch FSM. +issues: https://github.com/espressif/esp-iot-solution/issues +repository: https://github.com/espressif/esp-iot-solution.git +targets: +- esp32s2 +- esp32s3 +- esp32p4 +url: https://github.com/espressif/esp-iot-solution/tree/master/components/touch/ +version: 0.1.1 diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/license.txt b/esp-spot/example/adf/touch_play_mp3/components/touch_button/license.txt new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/license.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/CMakeLists.txt new file mode 100644 index 0000000..81e5d2d --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/CMakeLists.txt @@ -0,0 +1,9 @@ + +# The following lines of boilerplate have to be in your project's CMakeLists +# in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +set(EXTRA_COMPONENT_DIRS "$ENV{IDF_PATH}/tools/unit-test-app/components" + "../../touch_button") +include($ENV{IDF_PATH}/tools/cmake/project.cmake) +project(button_test) diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/CMakeLists.txt new file mode 100644 index 0000000..0b90b03 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/CMakeLists.txt @@ -0,0 +1,3 @@ +idf_component_register(SRC_DIRS "." + PRIV_INCLUDE_DIRS "." + WHOLE_ARCHIVE) diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/test_main.c b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/test_main.c new file mode 100644 index 0000000..7d9cdf6 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/test_main.c @@ -0,0 +1,41 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "unity.h" +#include "unity_test_runner.h" +#include "unity_test_utils_memory.h" +#include "esp_heap_caps.h" +#include "sdkconfig.h" + +#define LEAKS (400) + +void setUp(void) +{ + unity_utils_record_free_mem(); +} + +void tearDown(void) +{ + unity_utils_evaluate_leaks_direct(LEAKS); +} + +void app_main(void) +{ + + /* _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ + *|_ _| _ | | | / __ \| | | | | ___ \ | | |_ _|_ _| _ | \ | | + * | | | | | | | | | / \/| |_| | | |_/ / | | | | | | | | | | | \| | + * | | | | | | | | | | | _ | | ___ \ | | | | | | | | | | | . ` | + * | | \ \_/ / |_| | \__/\| | | | | |_/ / |_| | | | | | \ \_/ / |\ | + * \_/ \___/ \___/ \____/\_| |_/ \____/ \___/ \_/ \_/ \___/\_| \_/ + */ + printf(" _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ \n"); + printf("|_ _| _ | | | / __ \\| | | | | ___ \\ | | |_ _|_ _| _ | \\ | |\n"); + printf(" | | | | | | | | | / \\/| |_| | | |_/ / | | | | | | | | | | | \\| |\n"); + printf(" | | | | | | | | | | | _ | | ___ \\ | | | | | | | | | | | . ` |\n"); + printf(" | | \\ \\_/ / |_| | \\__/\\| | | | | |_/ / |_| | | | | | \\ \\_/ / |\\ |\n"); + printf(" \\_/ \\___/ \\___/ \\____/\\_| |_/ \\____/ \\___/ \\_/ \\_/ \\___/\\_| \\_/\n"); + unity_run_menu(); +} diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/touch_button_test.c b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/touch_button_test.c new file mode 100644 index 0000000..193c962 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/main/touch_button_test.c @@ -0,0 +1,234 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_log.h" +#include "unity.h" +#include "iot_button.h" +#include "touch_button.h" +#include "touch_sensor_lowlevel.h" + +static const char *TAG = "GPIO BUTTON TEST"; + +#define TOUCH_CHANNEL_1 1 +#define TOUCH_CHANNEL_2 2 + +static void button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "%s", iot_button_get_event_str(event)); + if (BUTTON_PRESS_REPEAT == event || BUTTON_PRESS_REPEAT_DONE == event) { + ESP_LOGI(TAG, "\tREPEAT[%d]", iot_button_get_repeat(arg)); + } + + if (BUTTON_PRESS_UP == event || BUTTON_LONG_PRESS_HOLD == event || BUTTON_LONG_PRESS_UP == event) { + ESP_LOGI(TAG, "\tTICKS[%"PRIu32"]", iot_button_get_ticks_time(arg)); + } + + if (BUTTON_MULTIPLE_CLICK == event) { + ESP_LOGI(TAG, "\tMULTIPLE[%d]", (int)data); + } +} + +TEST_CASE("touch button test", "[button][touch]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + uint8_t level = 0; + level = iot_button_get_key_level(btn); + ESP_LOGI(TAG, "button level is %d", level); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn); +} + +static void multiple_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%d]: %s", (int)data, iot_button_get_event_str(event)); +} + +TEST_CASE("touch multiple button test", "[button][touch][multiple]") +{ + touch_lowlevel_type_t *channel_type = calloc(2, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 2; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2}; + touch_lowlevel_config_t low_config = { + .channel_num = 2, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +static void light_and_heavy_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%s]: %s", (int)data == 1 ? "light" : "heavy", iot_button_get_event_str(event)); +} + +TEST_CASE("touch button light and heavy press test", "[button][touch][light and heavy]") +{ + touch_lowlevel_type_t *channel_type = calloc(1, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 1; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1}; + touch_lowlevel_config_t low_config = { + .channel_num = 1, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_1; + touch_cfg.channel_threshold = 0.2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +TEST_CASE("touch button test memory leak", "[button][touch][memory leak]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + iot_button_delete(btn); +} diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/sdkconfig.defaults b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/sdkconfig.defaults new file mode 100644 index 0000000..9b28759 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/test_apps/sdkconfig.defaults @@ -0,0 +1,13 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.0 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESP_DEFAULT_CPU_FREQ_MHZ_240=y +CONFIG_ESP_TASK_WDT_EN=n +CONFIG_FREERTOS_HZ=1000 +CONFIG_TOUCH_BUTTON_SENSOR_BASELINE_COEF_X1000=1 +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_P_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_N_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_RESET_N=10 diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.c b/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.c new file mode 100644 index 0000000..851b1a7 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.c @@ -0,0 +1,75 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_check.h" +#include "iot_button.h" +#include "touch_button_sensor.h" +#include "touch_button.h" + +static const char *TAG = "button_touch"; +typedef struct { + button_driver_t base; + int32_t touch_channel; + touch_button_handle_t touch_handle; /**< touch handle */ +} button_touch_obj; + +static uint8_t button_touch_get_key_level(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + touch_button_sensor_handle_events(touch_btn->touch_handle); + touch_state_t state; + touch_button_sensor_get_state(touch_btn->touch_handle, touch_btn->touch_channel, &state); + return state == TOUCH_STATE_ACTIVE ? BUTTON_ACTIVE : BUTTON_INACTIVE; +} + +static esp_err_t button_touch_delete(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + esp_err_t ret = touch_button_sensor_delete(touch_btn->touch_handle); + free(touch_btn); + return ret; +} + +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button) +{ + ESP_LOGI(TAG, "Touch Button Version: %d.%d.%d", TOUCH_BUTTON_VER_MAJOR, TOUCH_BUTTON_VER_MINOR, TOUCH_BUTTON_VER_PATCH); + button_touch_obj *touch_btn = NULL; + esp_err_t ret = ESP_OK; + ESP_GOTO_ON_FALSE(button_config && touch_config && ret_button, ESP_ERR_INVALID_ARG, err, TAG, "Invalid argument"); + + touch_btn = (button_touch_obj *)calloc(1, sizeof(button_touch_obj)); + ESP_GOTO_ON_FALSE(touch_btn, ESP_ERR_NO_MEM, err, TAG, "No memory for gpio button"); + + touch_btn->touch_channel = touch_config->touch_channel; + + uint32_t channel_list[1] = {touch_config->touch_channel}; + float button_threshold[1] = {touch_config->channel_threshold}; + + touch_button_config_t config = { + .channel_num = 1, + .channel_list = channel_list, + .channel_threshold = button_threshold, + .skip_lowlevel_init = touch_config->skip_lowlevel_init, + .debounce_times = 1, + }; + ret = touch_button_sensor_create(&config, &touch_btn->touch_handle, NULL, NULL); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create touch button sensor failed"); + ret = iot_button_create(button_config, &touch_btn->base, ret_button); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create button failed"); + + touch_btn->base.get_key_level = button_touch_get_key_level; + touch_btn->base.del = button_touch_delete; + + return ESP_OK; +err: + if (touch_btn) { + free(touch_btn); + } + return ret; +} diff --git a/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.h b/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.h new file mode 100644 index 0000000..e3ccc89 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/components/touch_button/touch_button.h @@ -0,0 +1,48 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ +#pragma once + +#include "esp_err.h" +#include "button_types.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Configuration structure for a touch button. + * + * This structure defines the configuration parameters required to initialize a touch button. + * It specifies the touch channel, activation threshold, and whether to skip low-level initialization. + */ +typedef struct { + int32_t touch_channel; /*!< The touch sensor channel assigned to the button */ + float channel_threshold; /*!< Activation threshold for the touch channel (range: 0.0 - 1.0) */ + bool skip_lowlevel_init; /*!< If true, skips low-level initialization (useful for pre-configured setups) */ +} button_touch_config_t; + +/** + * @brief Create a new touch button device. + * + * This function initializes a new touch button device based on the given configurations. + * It sets up the touch channel, configures the touch button sensor, and integrates it + * with the iot_button framework. + * + * @param[in] button_config Pointer to the general button configuration. + * @param[in] touch_config Pointer to the touch-specific button configuration. + * @param[out] ret_button Pointer to store the created button handle. + * + * @return + * - ESP_OK on success + * - ESP_ERR_INVALID_ARG if any argument is NULL + * - ESP_ERR_NO_MEM if memory allocation fails + * - ESP_FAIL if touch button sensor or button creation fails + */ +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button); + +#ifdef __cplusplus +} +#endif diff --git a/esp-spot/example/adf/touch_play_mp3/main/CMakeLists.txt b/esp-spot/example/adf/touch_play_mp3/main/CMakeLists.txt new file mode 100644 index 0000000..41822e6 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/main/CMakeLists.txt @@ -0,0 +1,6 @@ +set(COMPONENT_REQUIRES audio_flash_tone audio_stream audio_pipeline audio_hal esp_peripherals touch_button led_driver) + +set(COMPONENT_SRCS "touch_play_mp3.c") +set(COMPONENT_ADD_INCLUDEDIRS .) + +register_component() \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/main/component.mk b/esp-spot/example/adf/touch_play_mp3/main/component.mk new file mode 100644 index 0000000..44bd2b5 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/main/component.mk @@ -0,0 +1,3 @@ +# +# Main Makefile. This is basically the same as a component makefile. +# diff --git a/esp-spot/example/adf/touch_play_mp3/main/idf_component.yml b/esp-spot/example/adf/touch_play_mp3/main/idf_component.yml new file mode 100644 index 0000000..1797748 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/main/idf_component.yml @@ -0,0 +1,12 @@ +dependencies: + espressif/esp_hosted: + version: "~0.0.9" + rules: + - if: "target in [esp32p4]" + espressif/esp_wifi_remote: + version: "~0.3.0" + rules: + - if: "target in [esp32p4]" + # lijunru-hub/touch_button: + # version: "0.1.0" + # registry_url: https://components-staging.espressif.com \ No newline at end of file diff --git a/esp-spot/example/adf/touch_play_mp3/main/touch_play_mp3.c b/esp-spot/example/adf/touch_play_mp3/main/touch_play_mp3.c new file mode 100644 index 0000000..fc61015 --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/main/touch_play_mp3.c @@ -0,0 +1,349 @@ +/* Play MP3 file from flash + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. + + Demo video: https://www.bilibili.com/video/BV1ekRAYVEZ1/ +*/ + +#include +#include + +#include "esp_log.h" + +#include "audio_element.h" +#include "audio_pipeline.h" +#include "audio_event_iface.h" +#include "audio_error.h" +#include "tone_stream.h" +#include "i2s_stream.h" +#include "mp3_decoder.h" +#include "driver/gpio.h" + +#include "touch_button.h" +#include "iot_button.h" +#include "touch_sensor_lowlevel.h" + +#include "led_driver.h" + +#include "board.h" + +#if __has_include("audio_tone_uri.h") + #include "audio_tone_uri.h" +#else + #error "please refer the README, and then make the tone file" +#endif + +static const char *TAG = "main"; + +#define TOUCH_CHANNEL_1 (3) +#define TOUCH_CHANNEL_2 (9) +#define TOUCH_CHANNEL_3 (13) +#define TOUCH_CHANNEL_4 (14) + +#define LIGHT_TOUCH_THRESHOLD (0.15) +#define HEAVY_TOUCH_THRESHOLD (0.4) + +audio_pipeline_handle_t pipeline; +audio_element_handle_t tone_stream_reader, i2s_stream_writer, mp3_decoder; + +static void led_task(void *arg) +{ + /* Configure the LED strip and obtain a handle */ + led_strip_handle_t led_strip = led_create(); + led_set_mode(1); + vTaskDelay(1000); + led_set_mode(0); + + while(1) { + /* Run the LED animation based on LED configuration and weather data */ + ESP_ERROR_CHECK(led_animations_start(led_strip)); + } + + vTaskDelete(NULL); +} + +static void touch_event_nose(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event nose: %s", iot_button_get_event_str(event)); + + audio_pipeline_stop(pipeline); + audio_pipeline_wait_for_stop(pipeline); + audio_pipeline_reset_ringbuffer(pipeline); + audio_pipeline_reset_elements(pipeline); + audio_pipeline_reset_items_state(pipeline); + + audio_element_set_uri(tone_stream_reader, tone_uri[TONE_TYPE_TOUCH_NOSE]); + audio_pipeline_run(pipeline); +} + +static void touch_event_nose_long(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event nose long: %s", iot_button_get_event_str(event)); + + audio_pipeline_stop(pipeline); + audio_pipeline_wait_for_stop(pipeline); + audio_pipeline_reset_ringbuffer(pipeline); + audio_pipeline_reset_elements(pipeline); + audio_pipeline_reset_items_state(pipeline); + + audio_element_set_uri(tone_stream_reader, tone_uri[TONE_TYPE_SCREAMING]); + audio_pipeline_run(pipeline); +} + +static void touch_event_hat(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event hat: %s", iot_button_get_event_str(event)); + + static tone_type_t hat_state = TONE_TYPE_HAT_1; + audio_pipeline_stop(pipeline); + audio_pipeline_wait_for_stop(pipeline); + audio_pipeline_reset_ringbuffer(pipeline); + audio_pipeline_reset_elements(pipeline); + audio_pipeline_reset_items_state(pipeline); + + audio_element_set_uri(tone_stream_reader, tone_uri[hat_state]); + audio_pipeline_run(pipeline); + + // Toggle between two tone states and update LED mode + if (hat_state == TONE_TYPE_CAPYBARA_SONG_1) { + // Turn on LED + led_set_mode(4); + hat_state = TONE_TYPE_HAT_1; + } else { + hat_state = TONE_TYPE_CAPYBARA_SONG_1; + } + +} + +static void touch_event_belly(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch event hat: %s", iot_button_get_event_str(event)); + + static int audio_belly_count_g = 0; + + audio_pipeline_stop(pipeline); + audio_pipeline_wait_for_stop(pipeline); + audio_pipeline_reset_ringbuffer(pipeline); + audio_pipeline_reset_elements(pipeline); + audio_pipeline_reset_items_state(pipeline); + + audio_element_set_uri(tone_stream_reader, tone_uri[audio_belly_count_g]); + + audio_pipeline_run(pipeline); + + // Update the counter for next playback + if (audio_belly_count_g < 3) { + audio_belly_count_g++; + } else if (audio_belly_count_g == 3) { + audio_belly_count_g = TONE_TYPE_SCREAMING; + } else { + audio_belly_count_g = 0; + } +} + +static void touch_task(void *arg) +{ + // Register all touch channel + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2, TOUCH_CHANNEL_3, TOUCH_CHANNEL_4}; + int total_channel_num = sizeof(touch_channel_list) / sizeof(touch_channel_list[0]); + + // calloc channel_type for every button from the list + touch_lowlevel_type_t *channel_type = calloc(total_channel_num, sizeof(touch_lowlevel_type_t)); + assert(channel_type); + for (int i = 0; i < total_channel_num; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + touch_lowlevel_config_t low_config = { + .channel_num = total_channel_num, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + assert(ret == ESP_OK); + free(channel_type); + + /* ============================= Init touch IO3 ============================= */ + const button_config_t btn_cfg = { + .short_press_time = 300, + .long_press_time = 2000, + }; + button_touch_config_t touch_cfg_1 = { + .touch_channel = touch_channel_list[0], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create button for nose */ + button_handle_t btn_nose = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_1, &btn_nose); + assert(ret == ESP_OK); + + // Create button for nose heavy press (if neeeded) + // touch_cfg_1.channel_threshold = HEAVY_TOUCH_THRESHOLD; + // button_handle_t btn_heavy_1 = NULL; + // ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_1, &btn_heavy_1); + // assert(ret == ESP_OK); + + /* ============================= Init touch IO9 ============================= */ + button_touch_config_t touch_cfg_2 = { + .touch_channel = touch_channel_list[1], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create button for hat */ + button_handle_t btn_hat = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_2, &btn_hat); + assert(ret == ESP_OK); + + /* ============================= Init touch IO13 ============================= */ + button_touch_config_t touch_cfg_3 = { + .touch_channel = touch_channel_list[2], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + /* Create light press button */ + button_handle_t btn_belly = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_3, &btn_belly); + assert(ret == ESP_OK); + + /* ========================== Register touch callback ========================== */ + // Register touch callback for nose + iot_button_register_cb(btn_nose, BUTTON_PRESS_DOWN, NULL, touch_event_nose, NULL); + iot_button_register_cb(btn_nose, BUTTON_LONG_PRESS_START, NULL, touch_event_nose_long, NULL); + + // Register touch callback for hat + iot_button_register_cb(btn_hat, BUTTON_PRESS_DOWN, NULL, touch_event_hat, NULL); + + // Register touch callback for belly + iot_button_register_cb(btn_belly, BUTTON_PRESS_DOWN, NULL, touch_event_belly, NULL); + + touch_sensor_lowlevel_start(); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } +} + +void app_main(void) +{ + xTaskCreate(touch_task, "touch_task", 1024 * 5, NULL, 5, NULL); + xTaskCreate(led_task, "led_task", 1024 * 3, NULL, 5, NULL); + + esp_log_level_set("*", ESP_LOG_WARN); + esp_log_level_set(TAG, ESP_LOG_INFO); + + ESP_LOGI(TAG, "[ 1 ] Start codec chip"); + audio_board_handle_t board_handle = audio_board_init(); + audio_hal_ctrl_codec(board_handle->audio_hal, AUDIO_HAL_CODEC_MODE_DECODE, AUDIO_HAL_CTRL_START); + audio_hal_set_volume(board_handle->audio_hal, 80); + + ESP_LOGI(TAG, "[2.0] Create audio pipeline for playback"); + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_NULL_CHECK(TAG, pipeline, return); + + ESP_LOGI(TAG, "[2.1] Create tone stream to read data from flash"); + tone_stream_cfg_t tone_cfg = TONE_STREAM_CFG_DEFAULT(); + tone_cfg.type = AUDIO_STREAM_READER; + tone_stream_reader = tone_stream_init(&tone_cfg); + AUDIO_NULL_CHECK(TAG, tone_stream_reader, return); + + ESP_LOGI(TAG, "[2.2] Create i2s stream to write data to codec chip"); +#if defined CONFIG_ESP32_C3_LYRA_V2_BOARD + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_PDM_TX_CFG_DEFAULT(); +#else + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT(); +#endif + i2s_cfg.type = AUDIO_STREAM_WRITER; + i2s_stream_writer = i2s_stream_init(&i2s_cfg); + AUDIO_NULL_CHECK(TAG, i2s_stream_writer, return); + + ESP_LOGI(TAG, "[2.3] Create mp3 decoder to decode mp3 file"); + mp3_decoder_cfg_t mp3_cfg = DEFAULT_MP3_DECODER_CONFIG(); + mp3_decoder = mp3_decoder_init(&mp3_cfg); + AUDIO_NULL_CHECK(TAG, mp3_decoder, return); + + ESP_LOGI(TAG, "[2.4] Register all elements to audio pipeline"); + audio_pipeline_register(pipeline, tone_stream_reader, "tone"); + audio_pipeline_register(pipeline, mp3_decoder, "mp3"); + audio_pipeline_register(pipeline, i2s_stream_writer, "i2s"); + + ESP_LOGI(TAG, "[2.5] Link it together [flash]-->tone_stream-->mp3_decoder-->i2s_stream-->[codec_chip]"); + const char *link_tag[3] = {"tone", "mp3", "i2s"}; + audio_pipeline_link(pipeline, &link_tag[0], 3); + + ESP_LOGI(TAG, "[2.6] Set up uri (file as tone_stream, mp3 as mp3 decoder, and default output is i2s)"); + audio_element_set_uri(tone_stream_reader, tone_uri[TONE_TYPE_SCREAMING]); + + ESP_LOGI(TAG, "[ 3 ] Set up event listener"); + audio_event_iface_cfg_t evt_cfg = AUDIO_EVENT_IFACE_DEFAULT_CFG(); + audio_event_iface_handle_t evt = audio_event_iface_init(&evt_cfg); + + ESP_LOGI(TAG, "[3.1] Listening event from all elements of pipeline"); + audio_pipeline_set_listener(pipeline, evt); + + ESP_LOGI(TAG, "[ 4 ] Start audio_pipeline"); + audio_pipeline_run(pipeline); + + ESP_LOGI(TAG, "[ 4 ] Listen for all pipeline events"); + while (1) { + audio_event_iface_msg_t msg = { 0 }; + esp_err_t ret = audio_event_iface_listen(evt, &msg, portMAX_DELAY); + if (ret != ESP_OK) { + ESP_LOGE(TAG, "[ * ] Event interface error : %d", ret); + continue; + } + + if (msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT && msg.source == (void *) mp3_decoder + && msg.cmd == AEL_MSG_CMD_REPORT_MUSIC_INFO) { + audio_element_info_t music_info = {0}; + audio_element_getinfo(mp3_decoder, &music_info); + + ESP_LOGI(TAG, "[ * ] Receive music info from mp3 decoder, sample_rates=%d, bits=%d, ch=%d", + music_info.sample_rates, music_info.bits, music_info.channels); + + i2s_stream_set_clk(i2s_stream_writer, music_info.sample_rates, music_info.bits, music_info.channels); + continue; + } + + /* Stop when the last pipeline element (i2s_stream_writer in this case) receives stop event */ + if (msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT && msg.source == (void *) i2s_stream_writer + && msg.cmd == AEL_MSG_CMD_REPORT_STATUS + && (((int)msg.data == AEL_STATUS_STATE_FINISHED))) { + // Turn off LED + led_set_mode(0); + } + } + + ESP_LOGI(TAG, "[ 5 ] Stop audio_pipeline"); + audio_pipeline_stop(pipeline); + audio_pipeline_wait_for_stop(pipeline); + audio_pipeline_terminate(pipeline); + + audio_pipeline_unregister(pipeline, tone_stream_reader); + audio_pipeline_unregister(pipeline, i2s_stream_writer); + audio_pipeline_unregister(pipeline, mp3_decoder); + + /* Terminal the pipeline before removing the listener */ + audio_pipeline_remove_listener(pipeline); + + /* Make sure audio_pipeline_remove_listener & audio_event_iface_remove_listener are called before destroying event_iface */ + audio_event_iface_destroy(evt); + + /* Release all resources */ + audio_pipeline_deinit(pipeline); + audio_element_deinit(tone_stream_reader); + audio_element_deinit(i2s_stream_writer); + audio_element_deinit(mp3_decoder); +} diff --git a/esp-spot/example/adf/touch_play_mp3/partitions_flash_tone.csv b/esp-spot/example/adf/touch_play_mp3/partitions_flash_tone.csv new file mode 100644 index 0000000..b7e82fc --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/partitions_flash_tone.csv @@ -0,0 +1,6 @@ +# Name, Type, SubType, Offset, Size, Flags +# Note: if you change the phy_init or app partition offset, make sure to change the offset in Kconfig.projbuild +nvs, data, nvs, , 0x6000, +phy_init, data, phy, , 0x1000, +factory, app, factory, , 1M, +flash_tone,data, 0xff, 0x110000 ,5M, diff --git a/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults b/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults new file mode 100644 index 0000000..afc26bb --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults @@ -0,0 +1,16 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.1 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESPTOOLPY_FLASHSIZE_16MB=y +CONFIG_PARTITION_TABLE_CUSTOM=y +CONFIG_PARTITION_TABLE_CUSTOM_FILENAME="partitions_flash_tone.csv" +CONFIG_ESP32_S3_SPOT_BOARD=y +CONFIG_SPIRAM=y +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_ESP_COREDUMP_ENABLE_TO_UART=y +CONFIG_FREERTOS_ENABLE_BACKWARD_COMPATIBILITY=y +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_HYSTERESIS_P_X1000=200 diff --git a/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults.esp32s3 b/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults.esp32s3 new file mode 100644 index 0000000..0e524ba --- /dev/null +++ b/esp-spot/example/adf/touch_play_mp3/sdkconfig.defaults.esp32s3 @@ -0,0 +1,16 @@ +CONFIG_IDF_CMAKE=y +CONFIG_IDF_TARGET_ARCH_XTENSA=y +CONFIG_IDF_TARGET="esp32s3" +CONFIG_IDF_TARGET_ESP32S3=y +CONFIG_IDF_FIRMWARE_CHIP_ID=0x0009 + +# +# SPI RAM config +# +CONFIG_ESP32S3_SPIRAM_SUPPORT=y +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_BOOT_INIT=y +CONFIG_SPIRAM_ALLOW_STACK_EXTERNAL_MEMORY=y +CONFIG_SPIRAM_USE_MALLOC=y +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_SPIRAM_TYPE_AUTO=y diff --git a/esp-spot/example/adf/touch_play_mp3/tools/audio_tone.bin b/esp-spot/example/adf/touch_play_mp3/tools/audio_tone.bin new file mode 100644 index 0000000..62d0997 Binary files /dev/null and b/esp-spot/example/adf/touch_play_mp3/tools/audio_tone.bin differ diff --git a/esp-spot/example/adf/volc_rtc_spot/CMakeLists.txt b/esp-spot/example/adf/volc_rtc_spot/CMakeLists.txt new file mode 100644 index 0000000..18119e5 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/CMakeLists.txt @@ -0,0 +1,8 @@ +# The following lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +include($ENV{ADF_PATH}/CMakeLists.txt) +include($ENV{IDF_PATH}/tools/cmake/project.cmake) + +project(volc_rtc) \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/Makefile b/esp-spot/example/adf/volc_rtc_spot/Makefile new file mode 100644 index 0000000..a433a4c --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/Makefile @@ -0,0 +1,3 @@ +PROJECT_NAME := tcp_client_example +include $(ADF_PATH)/project.mk + diff --git a/esp-spot/example/adf/volc_rtc_spot/README_CN.md b/esp-spot/example/adf/volc_rtc_spot/README_CN.md new file mode 100644 index 0000000..0c4689f --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/README_CN.md @@ -0,0 +1,309 @@ +# VOLC RTC 语音交互例程 + +- [English Version](./README.md) +- 例程难度：![alt text](../../../docs/_static/level_complex.png "高级") + + +## 例程简介 + +本例程主要功能是连接豆包 volcano rtc 云端并进行语音交互，可以适用于智能音箱产品、智能玩具、语音控制设备等。此示例是一个综合性较强的例程，使用了 ADF 提供的高封装简易实用接口。建议用户构建项目时，优先使用 ADF 提供的高封装接口，可快速简便地构建项目。 + +## 环境配置 + +### 硬件要求 + +目前该 example 支持 `esp32s3` 和 `esp32` 相关的开发板。 +默认使用的是 `ESP32-S3-Korvo-2 v3` 开发板。 + +## 前期准备 + +1. 关于豆包简介 + +- 请参考 [火山·引擎 开放平台](https://www.volcengine.com/docs/6348/1315561) 开通火山引擎服务的正式版本，需要获取 `appid`, `userid`, `roomid` 和 `临时token`, 需要先使能 RTC_TOKEN_REQUEST, 将四个参数填到 `menuconfig-> Example Configuration` 对应的配置中， 然后在 [api-explorer](https://api.volcengine.com/api-explorer?action=StartVoiceChat&groupName=%E6%99%BA%E8%83%BD%E4%BD%93&serviceCode=rtc&version=2024-12-01) 中启动智能体，之后设备就可以连接到火山引擎进行语音交互了。 + +- 如果使用 [coze 方案](https://www.coze.cn/open/docs/developer_guides/coze_api_overview)，需要注册相关的账号，example 默认使能 coze 的测试账号，该测试账号每次的使用时间是 5 分钟。 + +2. 关于 wifi 配置 + - 在 menuconfig 中， 填写 SSID 和 PASSWORD， 然后编译下载到开发板中。 + +3. 关于编码格式 + - 在 menuconfig 中，可选择 `PCMA` , `OPUS` 和 `AAC` 编解码格式， 默认是 `OPUS` 格式。 + > `OPUS` 编码默认的是 32kbps。 + +4. 关于工作模式 + + 目前支持 普通模式 和 唤醒模式 + - **普通模式** 用户无需唤醒词，直接与设备进行语音交互。 +- **唤醒对话模式** 用户需要通过唤醒词唤醒设备，唤醒后用户可与设备进行语音交互。默认的唤醒词是 `Hi 乐鑫`, 可在 `menuconfig -> ESP Speech Recognition → use wakenet → Select wake words` 中选择唤醒词。 + +> 如果使用唤醒模式，建议使能 120M flash 和 120M PSRAM + - CONFIG_IDF_EXPERIMENTAL_FEATURES=y + - CONFIG_ESPTOOLPY_FLASHFREQ_120M=y + - CONFIG_SPIRAM_SPEED_120M=y + + +## 使用说明 + +### 配置 + +## 编译和下载 + +### IDF 默认分支 + +本例程支持 IDF release/v5.4 及以后的分支，例程默认使用 ADF 的內建分支 `$ADF_PATH/esp-idf`。 + +### 编译和下载 + +请先编译版本并烧录到开发板上，然后运行 monitor 工具来查看串口输出 (替换 PORT 为端口名称)： + +``` +idf.py -p PORT flash monitor +``` + +退出调试界面使用 ``Ctrl-]``。 + +有关配置和使用 ESP-IDF 生成项目的完整步骤，请参阅 [《ESP-IDF 编程指南》](https://docs.espressif.com/projects/esp-idf/zh_CN/release-v5.3/esp32/index.html)。 + +## 如何使用例程 + +### 功能和用法 + +- 例程运行时，会先尝试连接 Wi-Fi 网络，待成功获取 IP 地址后会进入 RTC 房间。log 中会有 `volc_rtc: join room success` 的打印，设备端播放服务器下发的"你好呀，很高兴认识你，哈哈哈"类的提示语，便可以与智能体进行对话。 +```c +I (25) boot: ESP-IDF v5.5-dev-972-gfa41fafd27-dirty 2nd stage bootloader +I (25) boot: compile time Jan 10 2025 10:39:14 +I (25) boot: Multicore bootloader +I (27) boot: chip revision: v0.2 +I (30) boot: efuse block revision: v1.3 +I (34) qio_mode: Enabling default flash chip QIO +I (38) boot.esp32s3: Boot SPI Speed : 80MHz +I (42) boot.esp32s3: SPI Mode : QIO +I (46) boot.esp32s3: SPI Flash Size : 16MB +I (49) boot: Enabling RNG early entropy source... +I (54) boot: Partition Table: +I (56) boot: ## Label Usage Type ST Offset Length +I (63) boot: 0 nvs WiFi data 01 02 00009000 00004000 +I (69) boot: 1 phy_init RF data 01 01 0000d000 00001000 +I (76) boot: 2 factory factory app 00 00 00010000 00300000 +I (82) boot: 3 model Unknown data 01 82 00310000 0040e000 +I (89) boot: 4 spiffs_data Unknown data 01 82 0071e000 00010000 +I (95) boot: End of partition table +I (99) esp_image: segment 0: paddr=00010020 vaddr=3c180020 size=5db38h (383800) map +I (163) esp_image: segment 1: paddr=0006db60 vaddr=3fca2600 size=024b8h ( 9400) load +I (165) esp_image: segment 2: paddr=00070020 vaddr=42000020 size=178fc4h (1544132) map +I (396) esp_image: segment 3: paddr=001e8fec vaddr=3fca4ab8 size=05db8h ( 23992) load +I (401) esp_image: segment 4: paddr=001eedac vaddr=40378000 size=1a520h (107808) load +I (421) esp_image: segment 5: paddr=002092d4 vaddr=600fe000 size=0001ch ( 28) load +I (433) boot: Loaded app from partition at offset 0x10000 +I (433) boot: Disabling RNG early entropy source... +W (443) flash HPM: HPM mode is optional feature that depends on flash model. Read Docs First! +W (443) flash HPM: HPM mode with DC adjustment is disabled. Some flash models may not be supported. Read Docs First! +W (452) flash HPM: High performance mode of this flash model hasn't been supported. +I (460) MSPI Timing: Flash timing tuning index: 2 +I (466) octal_psram: vendor id : 0x0d (AP) +I (471) octal_psram: dev id : 0x02 (generation 3) +I (476) octal_psram: density : 0x03 (64 Mbit) +I (482) octal_psram: good-die : 0x01 (Pass) +I (487) octal_psram: Latency : 0x01 (Fixed) +I (492) octal_psram: VCC : 0x01 (3V) +I (497) octal_psram: SRF : 0x01 (Fast Refresh) +I (503) octal_psram: BurstType : 0x01 (Hybrid Wrap) +I (509) octal_psram: BurstLen : 0x01 (32 Byte) +I (515) octal_psram: Readlatency : 0x02 (10 cycles@Fixed) +I (521) octal_psram: DriveStrength: 0x00 (1/1) +I (531) MSPI Timing: PSRAM timing tuning index: 2 +I (532) esp_psram: Found 8MB PSRAM device +I (536) esp_psram: Speed: 120MHz +I (557) mmu_psram: Read only data copied and mapped to SPIRAM +I (641) mmu_psram: Instructions copied and mapped to SPIRAM +I (641) cpu_start: Multicore app +I (821) esp_psram: SPI SRAM memory test OK +I (830) cpu_start: Pro cpu start user code +I (830) cpu_start: cpu freq: 240000000 Hz +I (830) app_init: Application information: +I (833) app_init: Project name: volc_rtc +I (838) app_init: App version: v2.7-48-g01596882-dirty +I (844) app_init: Compile time: Jan 9 2025 22:09:38 +I (850) app_init: ELF file SHA256: 75b7595ca... +I (856) app_init: ESP-IDF: v5.5-dev-972-gfa41fafd27-dirty +I (863) efuse_init: Min chip rev: v0.0 +I (867) efuse_init: Max chip rev: v0.99 +I (872) efuse_init: Chip rev: v0.2 +I (877) heap_init: Initializing. RAM available for dynamic allocation: +I (884) heap_init: At 3FCAEF20 len 0003A7F0 (233 KiB): RAM +I (890) heap_init: At 3FCE9710 len 00005724 (21 KiB): RAM +I (897) heap_init: At 600FE01C len 00001FCC (7 KiB): RTCRAM +I (903) esp_psram: Adding pool of 6258K of PSRAM memory to heap allocator +I (911) spi_flash: detected chip: gd +I (914) spi_flash: flash io: qio +W (919) ADC: legacy driver is deprecated, please migrate to `esp_adc/adc_oneshot.h` +I (927) sleep_gpio: Configure to isolate all GPIO pins in sleep state +I (934) sleep_gpio: Enable automatic switching of GPIO sleep configuration +I (942) main_task: Started on CPU0 +I (954) esp_psram: Reserving pool of 32K of internal memory for DMA/internal allocations +I (955) main_task: Calling app_main() +I (961) main: Initialize board peripherals +I (965) PERIPH_SPIFFS: Partition size: total: 52961, used: 0 +I (970) AUDIO_THREAD: The esp_periph task allocate stack on internal memory +W (978) i2c_bus_v2: I2C master handle is NULL, will create new one +I (989) DRV8311: ES8311 in Slave mode +I (999) gpio: GPIO[48]| InputEn: 0| OutputEn: 1| OpenDrain: 0| Pullup: 0| Pulldown: 0| Intr:0 +I (1006) ES7210: ES7210 in Slave mode +I (1015) ES7210: Enable ES7210_INPUT_MIC1 +I (1018) ES7210: Enable ES7210_INPUT_MIC2 +I (1020) ES7210: Enable ES7210_INPUT_MIC3 +W (1024) ES7210: Enable TDM mode. ES7210_SDP_INTERFACE2_REG12: 2 +I (1028) ES7210: config fmt 60 +I (1030) AUDIO_HAL: Codec mode is 3, Ctrl:1 +I (1040) pp: pp rom version: e7ae62f +I (1040) net80211: net80211 rom version: e7ae62f +I (1044) wifi:wifi driver task: 3fcbee58, prio:23, stack:6656, core=0 +I (1050) wifi:wifi firmware version: 34d97ea27 +I (1053) wifi:wifi certification version: v7.0 +I (1057) wifi:config NVS flash: enabled +I (1061) wifi:config nano formatting: disabled +I (1065) wifi:Init data frame dynamic rx buffer num: 32 +I (1070) wifi:Init static rx mgmt buffer num: 8 +I (1074) wifi:Init management short buffer num: 32 +I (1079) wifi:Init static tx buffer num: 16 +I (1083) wifi:Init tx cache buffer num: 32 +I (1086) wifi:Init static tx FG buffer num: 2 +I (1090) wifi:Init static rx buffer size: 1600 +I (1095) wifi:Init static rx buffer num: 16 +I (1098) wifi:Init dynamic rx buffer num: 32 +I (1103) wifi_init: rx ba win: 16 +I (1106) wifi_init: accept mbox: 6 +I (1110) wifi_init: tcpip mbox: 32 +I (1114) wifi_init: udp mbox: 32 +I (1118) wifi_init: tcp mbox: 6 +I (1122) wifi_init: tcp tx win: 65535 +I (1126) wifi_init: tcp rx win: 65535 +I (1131) wifi_init: tcp mss: 1440 +I (1135) wifi_init: WiFi/LWIP prefer SPIRAM +I (1140) wifi_init: WiFi IRAM OP enabled +I (1144) wifi_init: WiFi RX IRAM OP enabled +W (1149) wifi:Password length matches WPA2 standards, authmode threshold changes from OPEN to WPA2 +I (1158) wifi:Set ps type: 1, coexist: 0 + +I (1162) phy_init: phy_version 680,a6008b2,Jun 4 2024,16:41:10 +I (1227) wifi:mode : sta (74:4d:bd:9d:b6:30) +I (1227) wifi:enable tsf +W (1227) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:43 +I (2520) wifi:new:<10,0>, old:<1,0>, ap:<255,255>, sta:<10,0>, prof:1, snd_ch_cfg:0x0 +I (2521) wifi:state: init -> auth (0xb0) +W (2521) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:43 +I (2525) wifi:state: auth -> assoc (0x0) +I (2541) wifi:state: assoc -> run (0x10) +I (2761) wifi:connected with ESP-Audio, aid = 3, channel 10, BW20, bssid = fc:2f:ef:ab:db:70 +I (2762) wifi:security: WPA2-PSK, phy: bgn, rssi: -39 +I (2763) wifi:pm start, type: 1 + +I (2766) wifi:set rx beacon pti, rx_bcn_pti: 0, bcn_timeout: 25000, mt_pti: 0, mt_time: 10000 +W (2775) PERIPH_WIFI: WiFi Event cb, Unhandle event_base:WIFI_EVENT, event_id:4 +I (2792) wifi:AP's beacon interval = 204800 us, DTIM period = 1 +I (2792) wifi:idx:0 (ifx:0, fc:2f:ef:ab:db:70), tid:0, ssn:3, winSize:64 +I (3796) esp_netif_handlers: sta ip: 192.168.1.100, mask: 255.255.255.0, gw: 192.168.1.1 +I (3796) PERIPH_WIFI: Got ip:192.168.1.100 +I (3798) AUDIO_THREAD: The monitor_task task allocate stack on external memory +I (3807) audio pipeline: Create audio pipeline for audio player +I (3813) audio pipeline: Create audio player audio stream +I (3820) audio pipeline: Register all elements to playback pipeline +I (3826) audio pipeline: Link playback element together raw-->audio_decoder-->i2s_stream-->[codec_chip] +E (3836) gpio: gpio_install_isr_service(502): GPIO isr service already installed +E (3844) DISPATCHER: exe first list: 0x0 +I (3849) DISPATCHER: dispatcher_event_task is running... +I (3951) wifi:idx:1 (ifx:0, fc:2f:ef:ab:db:70), tid:1, ssn:0, winSize:64 +I (4807) AUDIO_SYS: | Task | Run Time | Per | Prio | HWM | State | CoreId | Stack +I (4808) AUDIO_SYS: | monitor_task | 675 | 0% | 23 | 4316 | Running | 0 | Extr +I (4817) AUDIO_SYS: | main | 254004 |12% | 1 | 1324 | Ready | 0 | Intr +I (4828) AUDIO_SYS: | IDLE1 | 994708 |49% | 0 | 700 | Ready | 1 | Intr +I (4838) AUDIO_SYS: | IDLE0 | 726577 |36% | 0 | 692 | Ready | 0 | Intr +I (4848) AUDIO_SYS: | tiT | 8424 | 0% | 18 | 1792 | Blocked | 7fffffff | Intr +I (4859) AUDIO_SYS: | esp_periph | 1842 | 0% | 5 | 1592 | Blocked | 0 | Intr +I (4869) AUDIO_SYS: | ipc1 | 0 | 0% | 24 | 540 | Suspended | 1 | Intr +I (4879) AUDIO_SYS: | ipc0 | 0 | 0% | 1 | 436 | Suspended | 0 | Intr +I (4890) AUDIO_SYS: | wifi | 7636 | 0% | 23 | 3452 | Blocked | 0 | Intr +I (4900) AUDIO_SYS: | esp_timer | 309 | 0% | 22 | 3092 | Suspended | 0 | Intr +I (4911) AUDIO_SYS: | sys_evt | 46 | 0% | 20 | 416 | Blocked | 0 | Intr +I (4921) AUDIO_SYS: | Tmr Svc | 0 | 0% | 1 | 3364 | Blocked | 7fffffff | Intr +I (4931) AUDIO_SYS: | audio_player_st | Created +I (4937) AUDIO_SYS: | esp_dispatcher | Created + +I (4942) main: Func:monitor_task, Line:29, MEM Total:6377072 Bytes, Inter:135047 Bytes, Dram:135047 Bytes, Dram largest free:94208Bytes + +1970-01-01 00:00:04.213 [E] VolcEngineRTCLite.c:105 ****************** HELLO BOOKA (671221aa298a540183df32d9)(1.56.001.58)(6059fcf26792a8820bc81f13662979d531e5504d) ******************** +1970-01-01 00:00:04.227 [E] Cache.c:270 operation returned status code: 0x00000009 +1970-01-01 00:00:04.242 [E] ThreadPool.c:92 coreid 1 set 1 stack_size 8192 priority 5 +I (5075) audio pipeline: Create audio pipeline for recording +I (5078) audio pipeline: Create player audio stream +I (5085) audio pipeline: Register all player elements to audio pipeline +I (5091) audio pipeline: Link all player elements to audio pipeline +I (5099) audio pipeline: Create audio pipeline for playback +I (5105) audio pipeline: Create playback audio stream +W (5110) I2S_STREAM_IDF5.x: I2S(2) already startup +I (5116) audio pipeline: Create opus decoder +I (5121) audio pipeline: Register all elements to playback pipeline +I (5128) audio pipeline: Link playback element together raw-->audio_decoder-->i2s_stream-->[codec_chip] +1970-01-01 00:00:05.039 [E] RoomImplX.c:167 operation returned status code: 0x52000057 +1970-01-01 00:00:05.459 [E] Cache.c:309 operation returned status code: 0x00000009 +1970-01-01 00:00:05.465 [E] RoomImplX.c:167 operation returned status code: 0x52000057 +1970-01-01 00:00:05.467 [E] LiteHttp.c:641 ID 340052878 E_LOGIC : NO need keepAlive +1970-01-01 00:00:05.475 [E] RoomImplX.c:167 operation returned status code: 0x52000057 +1970-01-01 00:00:05.583 [E] RoomImplX.c:167 operation returned status code: 0x52000057 +1970-01-01 00:00:06.042 [E] rx_net_delay_manager.c:1130 +I (6880) volc_rtc: join channel success ************8105400500486185 elapsed 239 ms now 239 ms + +I (6880) volc_rtc: join room success + +I (6880) volc_rtc: remote user joined ***** + +1970-01-01 00:00:06.063 [E] EngineImplX.c:103 callback pEngineImplX->eventHandler.on_user_joined used too many times 12 +I (6895) MODEL_LOADER: The storage free size is 24576 KB +I (6911) MODEL_LOADER: The partition size is 4152 KB +I (6917) MODEL_LOADER: Successfully load srmodels +I (6922) RECORDER_SR: The first wakenet model: wn9_hilexin + +I (6929) AFE_SR: afe interface for speech recognition +I (6935) AFE_SR: AFE version: SR_V220727 +I (6939) AFE_SR: Initial auido front-end, total channel: 2, mic num: 1, ref num: 1 +I (6951) AFE_SR: aec_init: 1, se_init: 0, vad_init: 0(min speech:64, min noise:256) +I (6956) AFE_SR: wakenet_init: 0 +I (7017) AFE_SR: afe mode: 0, (Jan 2 2025 19:06:11) +W (7017) RECORDER_SR: Multinet is not enabled in SDKCONFIG +I (7018) AUDIO_RECORDER: RECORDER_CMD_TRIGGER_START +I (7018) main_task: Returned from app_main() +1970-01-01 00:00:06.212 [E] rx_net_audio_jitterbuffer.c:1266 +1970-01-01 00:00:06.219 [E] rx_net_audio_jitterbuffer.c:1190 +1970-01-01 00:00:06.219 [E] rx_net_delay_manager.c:1130 +1970-01-01 00:00:06.219 [E] rx_net_delay_manager.c:1130 +1970-01-01 00:00:06.224 [E] Counter.c:90 AudioRecevied fps 0 +1970-01-01 00:00:07.007 [E] Counter.c:90 AudioRecevied fps 39 +1970-01-01 00:00:08.012 [E] Counter.c:90 AudioRecevied fps 49 +1970-01-01 00:00:09.163 [E] Counter.c:90 AudioRecevied fps 44 +1970-01-01 00:00:10.014 [E] Counter.c:90 AudioRecevied fps 56 +1970-01-01 00:00:11.016 [E] Counter.c:90 AudioRecevied fps 50 +1970-01-01 00:00:12.021 [E] Counter.c:90 AudioRecevied fps 46 +1970-01-01 00:00:13.001 [E] Counter.c:90 AudioRecevied fps 53 +``` + +## 故障排除 + +#### 加入房间后没有任何声音 +- 检查 token 有效性：token 是连接服务的关键凭证，其有效性直接关系到语音交互功能能否正常使用。您可通过 [web 端](https://www.volcengine.com/docs/6348/77374) 验证 token 的有效性。 +- 确认智能体是否开启：智能体未开启会致使无法正常进行语音交互与声音输出。 +- 查看常见集成问题：更多详细的排查方法与解决方案，可查看 [常见的集成问题](https://bytedance.larkoffice.com/docx/TEMCdrJ3VouilPxSpjbc6CyUnAh) + + +#### 出现自问自答的现象 + - 不同硬件所采用的麦克风、喇叭型号各异，且麦克风与喇叭之间的距离也不尽相同。这些因素可能致使采集到的数据出现增益过大或过小的情况，最终使得回声消除（AEC）效果大打折扣 。 + - 需要确认 `AEC` 是否生效, 在 `menuconfig` 中使能 `ENABLE_RECORDER_DEBUG`，然后查看录音数据。需要注意两点 + - 确定回采参考信号是否出现饱和 + - 定麦克风录音是否出现饱和 + +## 技术支持 +请按照下面的链接获取技术支持： + +- 技术支持参见 [esp32.com](https://esp32.com/viewforum.php?f=20) 论坛 +- 故障和新功能需求，请创建 [GitHub issue](https://github.com/espressif/esp-adf/issues) + +我们会尽快回复。 diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/CMakeLists.txt b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/CMakeLists.txt new file mode 100644 index 0000000..58cba2b --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/CMakeLists.txt @@ -0,0 +1,24 @@ +set(SRC_FILES "audio_processor.c") + +if (CONFIG_ESP32_S3_KORVO2_V3_BOARD OR CONFIG_ESP32_S3_BOX_3_BOARD) + list(APPEND SRC_FILES "audio_stream_dual_microphones.c") +elseif (CONFIG_ESP32_S3_KORVO2L_V1_BOARD) + list(APPEND SRC_FILES "audio_stream_single_microphone.c") +else () + list(APPEND SRC_FILES "audio_stream_single_microphone.c") + message(WARNING "Please add supported hardware and select the type of microphone, default use single microphone") +endif() + +set(COMPONENT_SRCS ${SRC_FILES}) +set(COMPONENT_ADD_INCLUDEDIRS include) +set(COMPONENT_REQUIRES audio_recorder audio_stream audio_pipeline audio_hal esp_peripherals) + +register_component() + +if (CONFIG_ESP32_S3_KORVO2_V3_BOARD OR CONFIG_ESP32_S3_BOX_3_BOARD) + add_definitions(-DENBALE_AUDIO_STREAM_DUAL_MIC) +elseif (CONFIG_ESP32_S3_KORVO2L_V1_BOARD) + add_definitions(-DENABLE_AUDIO_STREAM_SINGLE_MIC) +else () + add_definitions(-DENABLE_AUDIO_STREAM_SINGLE_MIC) +endif() \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_processor.c b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_processor.c new file mode 100644 index 0000000..93213a0 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_processor.c @@ -0,0 +1,531 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + +#include + +#include "esp_log.h" +#include "esp_timer.h" +#include "esp_check.h" +#include "sdkconfig.h" + +#include "audio_recorder.h" +#include "recorder_sr.h" +#include "recorder_encoder.h" +#include "audio_element.h" +#include "raw_stream.h" +#include "filter_resample.h" +#include "audio_mem.h" +#include "audio_thread.h" +#include "board.h" +#include "es7210.h" + +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#include "raw_opus_encoder.h" +#include "raw_opus_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) +#include "aac_encoder.h" +#include "aac_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) +#include "g711_encoder.h" +#include "g711_decoder.h" +#endif +#include "audio_stream.h" +#include "audio_processor.h" + +static const char *TAG = "AUDIO_PROCESSOR"; + +#if (defined CONFIG_ESP32_S3_KORVO2_V3_BOARD || defined CONFIG_ESP32_S3_BOX_3_BOARD) +#define USE_ES7210_AS_RECORD_DEVICE (1) +#define USE_ES8311_AS_RECORD_DEVICE (0) +#elif defined CONFIG_ESP32_S3_KORVO2L_V1_BOARD +#define USE_ES7210_AS_RECORD_DEVICE (0) +#define USE_ES8311_AS_RECORD_DEVICE (1) +#else +#warning "Please according your board to config" +#endif + +// aec debug +#if CONFIG_ENABLE_RECORDER_DEBUG +#include +#include +#include "esp_timer.h" + +static bool record_flag = true; +static FILE *sfp = NULL; +static int64_t start_tm = 0; +#define AEC_DEBUDG_FILE_NAME "/sdcard/rec.pcm" +#define AEC_RECORD_TIME (30) // s +#endif // ENABLE_AEC_DEBUG + +#define audio_pipe_safe_free(x, fn) do { \ + if (x) { \ + fn(x); \ + x = NULL; \ + } \ +} while (0) + +struct recorder_pipeline_t { + audio_pipeline_handle_t audio_pipeline; + audio_element_handle_t i2s_stream_reader; + audio_element_handle_t audio_encoder; + audio_element_handle_t raw_reader; + audio_rec_handle_t recorder_engine; + pipe_player_state_e record_state; +}; + +struct player_pipeline_t { + audio_pipeline_handle_t audio_pipeline; + audio_element_handle_t raw_writer; + audio_element_handle_t audio_decoder; + audio_element_handle_t i2s_stream_writer; + audio_element_handle_t player_rsp; + pipe_player_state_e player_state; +}; + +typedef struct { + audio_pipeline_handle_t pipeline; + audio_element_handle_t spiffs_stream; + audio_element_handle_t audio_decoder; // only support for wav; 16k, 16bit, double channel + audio_element_handle_t i2s_stream_writer; + pipe_player_state_e player_state; + bool running; + tone_play_callback_t tone_cb; +} audio_player_t; + +static audio_player_t *s_audio_player = NULL; +static struct recorder_pipeline_t *s_recorder_pipeline = NULL; +static struct player_pipeline_t *s_player_pipeline = NULL; + +#if CONFIG_ENABLE_RECORDER_DEBUG +void aec_debug_data_write(char *data, int len) +{ + if (record_flag) { + if (sfp == NULL) { + sfp = fopen(AEC_DEBUDG_FILE_NAME, "wb+"); + if (sfp == NULL) { + ESP_LOGI(TAG, "Cannot open file, reason: %s\n", strerror(errno)); + return; + } + } + fwrite(data, 1, len, sfp); + + if ((esp_timer_get_time() - start_tm) / 1000000 > AEC_RECORD_TIME) { + record_flag = false; + fclose(sfp); + sfp = NULL; + ESP_LOGI(TAG, "AEC debug data write done"); + } + } +} +#endif // ENABLE_AEC_DEBUG + +recorder_pipeline_handle_t recorder_pipeline_open(void) +{ + ESP_LOGI(TAG, "%s", __func__); + recorder_pipeline_handle_t pipeline = audio_calloc(1, sizeof(struct recorder_pipeline_t)); + pipeline->record_state = PIPE_STATE_IDLE; + s_recorder_pipeline = pipeline; + + ESP_LOGI(TAG, "Create audio pipeline for recording"); + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + pipeline->audio_pipeline = audio_pipeline_init(&pipeline_cfg); + mem_assert(pipeline->audio_pipeline); + + ESP_LOGI(TAG, "Create player audio stream"); + pipeline->i2s_stream_reader = create_record_i2s_stream(); + pipeline->raw_reader = create_record_raw_stream(); + + ESP_LOGI(TAG, " Register all player elements to audio pipeline"); + audio_pipeline_register(pipeline->audio_pipeline, pipeline->i2s_stream_reader, "record_i2s"); + audio_pipeline_register(pipeline->audio_pipeline, pipeline->raw_reader, "record_raw"); + + ESP_LOGI(TAG, " Link all player elements to audio pipeline"); + const char *link_tag[2] = {"record_i2s", "record_raw"}; + audio_pipeline_link(pipeline->audio_pipeline, &link_tag[0], 2); + return pipeline; +} + +esp_err_t recorder_pipeline_run(recorder_pipeline_handle_t pipeline) +{ + ESP_RETURN_ON_FALSE(pipeline != NULL, ESP_FAIL, TAG, "recorder pipeline not initialized"); + audio_pipeline_run(pipeline->audio_pipeline); + pipeline->record_state = PIPE_STATE_RUNNING; + return ESP_OK; +}; + +esp_err_t recorder_pipeline_stop(recorder_pipeline_handle_t pipeline) +{ + ESP_RETURN_ON_FALSE(pipeline != NULL, ESP_FAIL, TAG, "recorder pipeline not initialized"); + audio_pipeline_stop(pipeline->audio_pipeline); + audio_pipeline_wait_for_stop(pipeline->audio_pipeline); + pipeline->record_state = PIPE_STATE_IDLE; + return ESP_OK; +}; + +esp_err_t recorder_pipeline_close(recorder_pipeline_handle_t pipeline) +{ + ESP_RETURN_ON_FALSE(pipeline != NULL, ESP_FAIL, TAG, "recorder pipeline not initialized"); + audio_pipeline_terminate(pipeline->audio_pipeline); + audio_pipeline_unregister(pipeline->audio_pipeline, pipeline->i2s_stream_reader); + audio_pipeline_unregister(pipeline->audio_pipeline, pipeline->raw_reader); + + /* Release all resources */ + audio_pipe_safe_free(pipeline->audio_pipeline, audio_pipeline_deinit); + audio_pipe_safe_free(pipeline->raw_reader, audio_element_deinit); + audio_pipe_safe_free(pipeline->i2s_stream_reader, audio_element_deinit); + audio_pipe_safe_free(pipeline, audio_free); + return ESP_OK; +}; + +int recorder_pipeline_get_default_read_size(recorder_pipeline_handle_t pipeline) +{ + #if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + return 80; + #elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) + return 512; + #elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + return 160; + #endif + return -1; +}; + +audio_element_handle_t recorder_pipeline_get_raw_reader(recorder_pipeline_handle_t pipeline) +{ + return pipeline->raw_reader; +} + +audio_pipeline_handle_t recorder_pipeline_get_pipeline(recorder_pipeline_handle_t pipeline) +{ + return pipeline->audio_pipeline; +}; + +int recorder_pipeline_read(recorder_pipeline_handle_t pipeline,char *buffer, int buf_size) +{ + return raw_stream_read(pipeline->raw_reader, buffer,buf_size); +} + +static esp_err_t _player_i2s_write_cb(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + return audio_element_output(s_player_pipeline->i2s_stream_writer, buffer, len); +} + +static esp_err_t _player_write_nop_cb(audio_element_handle_t self, char *buffer, int len, TickType_t ticks_to_wait, void *context) +{ + return len; +} + +player_pipeline_handle_t player_pipeline_open(void) +{ + ESP_LOGI(TAG, "%s", __func__); + player_pipeline_handle_t player_pipeline = audio_calloc(1, sizeof(struct player_pipeline_t)); + AUDIO_MEM_CHECK(TAG, player_pipeline, goto _exit_open); + player_pipeline->player_state = PIPE_STATE_IDLE; + s_player_pipeline = player_pipeline; + + ESP_LOGI(TAG, "Create audio pipeline for playback"); + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + player_pipeline->audio_pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_MEM_CHECK(TAG, player_pipeline, goto _exit_open); + + ESP_LOGI(TAG, "Create playback audio stream"); + player_pipeline->raw_writer = create_player_raw_stream(); + AUDIO_MEM_CHECK(TAG, player_pipeline->raw_writer, goto _exit_open); + player_pipeline->audio_decoder = create_player_decoder_stream(); + AUDIO_MEM_CHECK(TAG, player_pipeline->audio_decoder, goto _exit_open); + player_pipeline->i2s_stream_writer = create_player_i2s_stream(false); + AUDIO_MEM_CHECK(TAG, player_pipeline->i2s_stream_writer, goto _exit_open); + + ESP_LOGI(TAG, "Register all elements to playback pipeline"); + audio_pipeline_register(player_pipeline->audio_pipeline, player_pipeline->raw_writer, "player_raw"); + audio_pipeline_register(player_pipeline->audio_pipeline, player_pipeline->audio_decoder, "player_dec"); + +#if (defined ENBALE_AUDIO_STREAM_DUAL_MIC) + ESP_LOGI(TAG, "ENBALE_AUDIO_STREAM_DUAL_MIC"); +#if CONFIG_AUDIO_SUPPORT_G711A_DECODER + player_pipeline->player_rsp = create_8k_ch1_to_16k_ch2_rsp_stream(); +#else + player_pipeline->player_rsp = create_ch1_to_ch2_rsp_stream(); +#endif +#elif (defined ENABLE_AUDIO_STREAM_SINGLE_MIC) + ESP_LOGI(TAG, "ENBALE_AUDIO_STREAM_DUAL_MIC"); +#if CONFIG_AUDIO_SUPPORT_G711A_DECODER + player_pipeline->player_rsp = create_8k_ch1_to_16k_ch2_rsp_stream(); +#else + player_pipeline->player_rsp = create_ch1_to_ch2_rsp_stream(); +#endif +#else + ESP_LOGE(TAG, "Invalid record device"); +#endif // USE_ES8311_AS_RECORD_DEVICE + AUDIO_MEM_CHECK(TAG, player_pipeline->player_rsp, goto _exit_open); + + ESP_LOGI(TAG, "Link playback element together raw-->audio_decoder-->rsp-->i2s_stream-->[codec_chip]"); + + audio_element_set_write_cb(player_pipeline->player_rsp, _player_write_nop_cb, NULL); + audio_pipeline_register(player_pipeline->audio_pipeline, player_pipeline->player_rsp, "player_rsp"); + const char *link_tag[3] = {"player_raw", "player_dec", "player_rsp"}; + audio_pipeline_link(player_pipeline->audio_pipeline, &link_tag[0], 3); + + audio_pipeline_run(player_pipeline->audio_pipeline); + return player_pipeline; + +_exit_open: + audio_pipe_safe_free(player_pipeline->player_rsp, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->raw_writer, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->audio_decoder, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->i2s_stream_writer, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->audio_pipeline, audio_pipeline_deinit); + audio_pipe_safe_free(player_pipeline, audio_free); + return NULL; +} + +esp_err_t player_pipeline_run(player_pipeline_handle_t player_pipeline) +{ + ESP_RETURN_ON_FALSE(player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + if (player_pipeline->player_state == PIPE_STATE_RUNNING) { + ESP_LOGW(TAG, "player pipe is already running state"); + return ESP_OK; + } + ESP_LOGI(TAG, "player pipe start running"); + audio_element_set_write_cb(player_pipeline->player_rsp, _player_i2s_write_cb, NULL); + player_pipeline->player_state = PIPE_STATE_RUNNING; + return ESP_OK; +}; + +esp_err_t player_pipeline_stop(player_pipeline_handle_t player_pipeline) +{ + ESP_RETURN_ON_FALSE(player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + if (player_pipeline->player_state == PIPE_STATE_IDLE) { + ESP_LOGW(TAG, "player pipe is idle state"); + return ESP_FAIL; + } + ESP_LOGI(TAG, "player pipe stop running"); + audio_element_set_write_cb(player_pipeline->player_rsp, _player_write_nop_cb, NULL); + player_pipeline->player_state = PIPE_STATE_IDLE; + return ESP_OK; +} + +esp_err_t player_pipeline_get_state(player_pipeline_handle_t player_pipeline, pipe_player_state_e *state) +{ + ESP_RETURN_ON_FALSE(player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + *state = player_pipeline->player_state; + return ESP_OK; +} + +esp_err_t player_pipeline_close(player_pipeline_handle_t player_pipeline) +{ + ESP_RETURN_ON_FALSE(player_pipeline != NULL, ESP_FAIL, TAG, "player pipeline not initialized"); + if (player_pipeline->player_state == PIPE_STATE_RUNNING) { + ESP_LOGW(TAG, "Please stop player pipe first"); + return ESP_FAIL; + } + audio_pipeline_terminate(player_pipeline->audio_pipeline); + + audio_pipeline_unregister(player_pipeline->audio_pipeline, player_pipeline->raw_writer); + audio_pipeline_unregister(player_pipeline->audio_pipeline, player_pipeline->i2s_stream_writer); + audio_pipeline_unregister(player_pipeline->audio_pipeline, player_pipeline->audio_decoder); + audio_pipeline_unregister(player_pipeline->audio_pipeline, player_pipeline->player_rsp); + + /* Release all resources */ + audio_pipe_safe_free(player_pipeline->player_rsp, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->raw_writer, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->i2s_stream_writer, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->audio_decoder, audio_element_deinit); + audio_pipe_safe_free(player_pipeline->audio_pipeline, audio_pipeline_deinit); + audio_pipe_safe_free(player_pipeline, audio_free); + return ESP_OK; +}; + +// Just create a placeholder function +int player_pipeline_get_default_read_size(player_pipeline_handle_t player_pipeline) +{ + return 0; +}; + +audio_element_handle_t player_pipeline_get_raw_write(player_pipeline_handle_t player_pipeline) +{ + return player_pipeline->raw_writer; +} + +static int input_cb_for_afe(int16_t *buffer, int buf_sz, void *user_ctx, TickType_t ticks) +{ +#if CONFIG_ENABLE_RECORDER_DEBUG + aec_debug_data_write((char *)buffer, buf_sz); +#endif // ENABLE_AEC_DEBUG + return raw_stream_read(s_recorder_pipeline->raw_reader, (char *)buffer, buf_sz); +} + +void * audio_record_engine_init(recorder_pipeline_handle_t pipeline, rec_event_cb_t cb) +{ + recorder_sr_cfg_t recorder_sr_cfg = get_default_audio_record_config(); +#if defined (CONFIG_CONTINUOUS_CONVERSATION_MODE) + recorder_sr_cfg.afe_cfg->wakenet_init = false; + recorder_sr_cfg.afe_cfg->se_init = false; + recorder_sr_cfg.afe_cfg->vad_init = false; +#endif // CONFIG_CONTINUOUS_CONVERSATION_MODE + + recorder_encoder_cfg_t recorder_encoder_cfg = { 0 }; + +#if defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; + filter_cfg.dest_ch = 1; + filter_cfg.dest_rate = 8000; + filter_cfg.complexity = 0; + filter_cfg.stack_in_ext = true; + filter_cfg.max_indata_bytes = 1024; + recorder_encoder_cfg.resample = rsp_filter_init(&filter_cfg); +#endif // CONFIG_AUDIO_SUPPORT_G711A_DECODER + + recorder_encoder_cfg.encoder = create_record_encoder_stream(); + + audio_rec_cfg_t cfg = AUDIO_RECORDER_DEFAULT_CFG(); + cfg.read = (recorder_data_read_t)&input_cb_for_afe; + cfg.sr_handle = recorder_sr_create(&recorder_sr_cfg, &cfg.sr_iface); + cfg.event_cb = cb; + cfg.vad_off = 1000; + cfg.wakeup_end = 120000; + +#if defined (CONFIG_CONTINUOUS_CONVERSATION_MODE) + cfg.vad_start = 0; +#endif // CONFIG_CONTINUOUS_CONVERSATION_MODE + cfg.encoder_handle = recorder_encoder_create(&recorder_encoder_cfg, &cfg.encoder_iface); + pipeline->recorder_engine = audio_recorder_create(&cfg); +#if defined (CONFIG_CONTINUOUS_CONVERSATION_MODE) + vTaskDelay(pdMS_TO_TICKS(200)); + audio_recorder_trigger_start(pipeline->recorder_engine); +#endif // CONFIG_CONTINUOUS_CONVERSATION_MODE + return pipeline->recorder_engine; +} + +static void audio_player_state_task(void *arg) +{ + audio_event_iface_handle_t evt = (audio_event_iface_handle_t) arg; + + s_audio_player->running = true; + while (s_audio_player->running) { + audio_event_iface_msg_t msg; + esp_err_t ret = audio_event_iface_listen(evt, &msg, portMAX_DELAY); + if (ret != ESP_OK) { + ESP_LOGE(TAG, "[ * ] Event interface error : %d", ret); + continue; + } + if (msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT + && msg.source == (void *) s_audio_player->audio_decoder + && msg.cmd == AEL_MSG_CMD_REPORT_MUSIC_INFO) { + audio_element_info_t music_info = {0}; + audio_element_getinfo(s_audio_player->audio_decoder, &music_info); + + ESP_LOGI(TAG, "[ * ] Receive music info from wav decoder, sample_rates=%d, bits=%d, ch=%d", + music_info.sample_rates, music_info.bits, music_info.channels); + continue; + } + /* Stop when the last pipeline element (i2s_stream_writer in this case) receives stop event */ + if (msg.source_type == AUDIO_ELEMENT_TYPE_ELEMENT && msg.source == (void *) s_audio_player->i2s_stream_writer + && msg.cmd == AEL_MSG_CMD_REPORT_STATUS + && (((int)msg.data == AEL_STATUS_STATE_STOPPED) || ((int)msg.data == AEL_STATUS_STATE_FINISHED))) { + ESP_LOGI(TAG, "[ * ] Stop event received"); + audio_tone_stop(); + s_audio_player->tone_cb(AEL_STATUS_STATE_FINISHED); + s_audio_player->player_state = PIPE_STATE_IDLE; + } + } +} + +esp_err_t audio_tone_init(tone_play_callback_t callback) +{ + s_audio_player = (audio_player_t *)audio_calloc(1, sizeof(audio_player_t)); + AUDIO_MEM_CHECK(TAG, s_audio_player, goto _exit_open); + + ESP_LOGI(TAG, "Create audio pipeline for audio player"); + audio_pipeline_cfg_t pipeline_cfg = DEFAULT_AUDIO_PIPELINE_CONFIG(); + s_audio_player->pipeline = audio_pipeline_init(&pipeline_cfg); + AUDIO_MEM_CHECK(TAG, s_audio_player->pipeline, goto _exit_open); + + ESP_LOGI(TAG, "Create audio player audio stream"); + s_audio_player->spiffs_stream = create_audio_player_spiffs_stream(); + AUDIO_MEM_CHECK(TAG, s_audio_player->spiffs_stream, goto _exit_open); + s_audio_player->i2s_stream_writer = create_player_i2s_stream(true); + AUDIO_MEM_CHECK(TAG, s_audio_player->i2s_stream_writer, goto _exit_open); + s_audio_player->audio_decoder = create_player_wav_decoder_stream(); + AUDIO_MEM_CHECK(TAG, s_audio_player->audio_decoder, goto _exit_open); + + ESP_LOGI(TAG, "Register all elements to playback pipeline"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->spiffs_stream, "audio_player_spiffs"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->audio_decoder, "audio_player_dec"); + audio_pipeline_register(s_audio_player->pipeline, s_audio_player->i2s_stream_writer, "audio_player_i2s"); + + ESP_LOGI(TAG, "Link playback element together raw-->audio_decoder-->i2s_stream-->[codec_chip]"); + const char *link_tag[3] = {"audio_player_spiffs", "audio_player_dec", "audio_player_i2s"}; + audio_pipeline_link(s_audio_player->pipeline, &link_tag[0], 3); + + esp_periph_config_t periph_cfg = DEFAULT_ESP_PERIPH_SET_CONFIG(); + esp_periph_set_handle_t set = esp_periph_set_init(&periph_cfg); + audio_event_iface_cfg_t evt_cfg = AUDIO_EVENT_IFACE_DEFAULT_CFG(); + audio_event_iface_handle_t evt = audio_event_iface_init(&evt_cfg); + audio_pipeline_set_listener(s_audio_player->pipeline, evt); + audio_event_iface_set_listener(esp_periph_set_get_event_iface(set), evt); + + s_audio_player->tone_cb = callback; + audio_thread_create(NULL, "audio_player_state_task", audio_player_state_task, (void *)evt, 5 * 1024, 15, true, 1); + + return ESP_OK; + +_exit_open: + audio_pipe_safe_free(s_audio_player->spiffs_stream, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->i2s_stream_writer, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->audio_decoder, audio_element_deinit); + audio_pipe_safe_free(s_audio_player->pipeline, audio_pipeline_deinit); + audio_pipe_safe_free(s_audio_player, audio_free); + return ESP_FAIL; +} + +esp_err_t audio_tone_play(const char *uri) +{ + ESP_RETURN_ON_FALSE(s_audio_player != NULL, ESP_FAIL, TAG, "audio tone not initialized"); + if (s_audio_player->player_state == PIPE_STATE_RUNNING) { + return ESP_FAIL; + } + ESP_LOGI(TAG, "audio_tone_play: %s", uri); + + audio_element_set_uri(s_audio_player->spiffs_stream, uri); + audio_pipeline_run(s_audio_player->pipeline); + s_audio_player->player_state = PIPE_STATE_RUNNING; + return ESP_OK; +} + +esp_err_t audio_tone_stop(void) +{ + ESP_RETURN_ON_FALSE(s_audio_player != NULL, ESP_FAIL, TAG, "audio tone not initialized"); + if (s_audio_player->player_state == PIPE_STATE_IDLE) { + return ESP_FAIL; + } + audio_pipeline_stop(s_audio_player->pipeline); + audio_pipeline_wait_for_stop(s_audio_player->pipeline); + audio_pipeline_terminate(s_audio_player->pipeline); + audio_pipeline_reset_ringbuffer(s_audio_player->pipeline); + audio_pipeline_reset_elements(s_audio_player->pipeline); + s_audio_player->player_state = PIPE_STATE_IDLE; + return ESP_OK; +} diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_dual_microphones.c b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_dual_microphones.c new file mode 100644 index 0000000..cc3188d --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_dual_microphones.c @@ -0,0 +1,213 @@ +#include "esp_log.h" + +#include "audio_stream.h" +#include "filter_resample.h" +#include "raw_stream.h" +#include "spiffs_stream.h" +#include "audio_common.h" +#include "i2s_stream.h" +#include "wav_decoder.h" +#include "es7210.h" +#include "board.h" +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#include "raw_opus_encoder.h" +#include "raw_opus_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) +#include "aac_encoder.h" +#include "aac_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) +#include "g711_encoder.h" +#include "g711_decoder.h" +#endif +#include "audio_stream.h" + +static const char *TAG = "DUAL_MICROPHONES"; + +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#define SAMPLE_RATE 16000 +#define BIT_RATE 32000 +#define COMPLEXITY 0 +#define FRAME_TIME_MS 20 +#endif + +#if defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) +#define SAMPLE_RATE 16000 +#define BIT_RATE 80000 +#endif + +#if defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) +#define SAMPLE_RATE 8000 +#endif + +#define CHANNEL 1 + +audio_element_handle_t create_record_encoder_stream(void) +{ + audio_element_handle_t encoder_stream = NULL; +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + raw_opus_enc_config_t opus_cfg = RAW_OPUS_ENC_CONFIG_DEFAULT(); + opus_cfg.sample_rate = SAMPLE_RATE; + opus_cfg.channel = CHANNEL; + opus_cfg.bitrate = BIT_RATE; + opus_cfg.complexity = COMPLEXITY; + encoder_stream = raw_opus_encoder_init(&opus_cfg); +#elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) + aac_encoder_cfg_t aac_cfg = DEFAULT_AAC_ENCODER_CONFIG(); + aac_cfg.sample_rate = SAMPLE_RATE; + aac_cfg.channel = CHANNEL; + aac_cfg.bitrate = BIT_RATE; + encoder_stream = aac_encoder_init(&aac_cfg); +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + g711_encoder_cfg_t g711_cfg = DEFAULT_G711_ENCODER_CONFIG(); + encoder_stream = g711_encoder_init(&g711_cfg); +#endif + return encoder_stream; +} + +audio_element_handle_t create_record_raw_stream(void) +{ + audio_element_handle_t raw_stream = NULL; + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + raw_cfg.type = AUDIO_STREAM_WRITER; + raw_cfg.out_rb_size = 2 * 1024; + raw_stream = raw_stream_init(&raw_cfg); + audio_element_set_output_timeout(raw_stream, portMAX_DELAY); + return raw_stream; +} + +audio_element_handle_t create_record_i2s_stream(void) +{ + es7210_adc_set_gain(ES7210_INPUT_MIC1 | ES7210_INPUT_MIC2, GAIN_34_5DB); +#if defined (CONFIG_ESP32_S3_BOX_3_BOARD) + es7210_adc_set_gain(ES7210_INPUT_MIC3, GAIN_6DB); +#else + es7210_adc_set_gain(ES7210_INPUT_MIC3, GAIN_27DB); +#endif + audio_element_handle_t i2s_stream = NULL; + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(I2S_NUM_0, 16000, 32, AUDIO_STREAM_READER); + i2s_stream = i2s_stream_init(&i2s_cfg); + return i2s_stream; +} + +audio_element_handle_t create_player_decoder_stream(void) +{ + audio_element_handle_t decoder_stream = NULL; +#ifdef CONFIG_AUDIO_SUPPORT_OPUS_DECODER + ESP_LOGI(TAG, "Create opus decoder"); + raw_opus_dec_cfg_t opus_dec_cfg = RAW_OPUS_DEC_CONFIG_DEFAULT(); + opus_dec_cfg.enable_frame_length_prefix = true; + opus_dec_cfg.sample_rate = 16000; + opus_dec_cfg.channels = 1; + decoder_stream = raw_opus_decoder_init(&opus_dec_cfg); +#elif CONFIG_AUDIO_SUPPORT_AAC_DECODER + ESP_LOGI(TAG, "Create aac decoder"); + aac_decoder_cfg_t aac_dec_cfg = DEFAULT_AAC_DECODER_CONFIG(); + aac_dec_cfg.task_core = 1; + decoder_stream = aac_decoder_init(&aac_dec_cfg); +#elif CONFIG_AUDIO_SUPPORT_G711A_DECODER + ESP_LOGI(TAG, "Create g711a decoder"); + g711_decoder_cfg_t g711_dec_cfg = DEFAULT_G711_DECODER_CONFIG(); + g711_dec_cfg.out_rb_size = 8 * 1024; + g711_dec_cfg.task_core = 1; + g711_dec_cfg.task_prio = 4; + decoder_stream = g711_decoder_init(&g711_dec_cfg); +#endif + return decoder_stream; +} + +audio_element_handle_t create_player_wav_decoder_stream() +{ + audio_element_handle_t decoder_stream = NULL; + wav_decoder_cfg_t wav_cfg = DEFAULT_WAV_DECODER_CONFIG(); + decoder_stream = wav_decoder_init(&wav_cfg); + return decoder_stream; +} + +audio_element_handle_t create_player_raw_stream(void) +{ + audio_element_handle_t raw_stream = NULL; + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + raw_cfg.type = AUDIO_STREAM_READER; + raw_cfg.out_rb_size = 8 * 1024; + raw_stream = raw_stream_init(&raw_cfg); + return raw_stream; +} + +audio_element_handle_t create_player_i2s_stream(bool enable_task) +{ + audio_element_handle_t i2s_stream = NULL; + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(I2S_NUM_0, 16000, CODEC_ADC_BITS_PER_SAMPLE, AUDIO_STREAM_WRITER); + if (enable_task == false) { + i2s_cfg.task_stack = -1; + } + i2s_cfg.out_rb_size = 8 * 1024; + i2s_cfg.need_expand = true; + i2s_cfg.expand_src_bits = 16; + i2s_cfg.buffer_len = 1416; + i2s_cfg.chan_cfg.dma_desc_num = 6; + i2s_stream = i2s_stream_init(&i2s_cfg); + return i2s_stream; +} + +audio_element_handle_t create_8k_ch1_to_16k_ch2_rsp_stream() +{ + audio_element_handle_t filter = NULL; + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 8000; + filter_cfg.dest_ch = 2; + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 5; + filter = rsp_filter_init(&filter_cfg); + return filter; +} + +audio_element_handle_t create_ch1_to_ch2_rsp_stream() // create_audio_player_rsp_stream +{ + audio_element_handle_t filter = NULL; + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; + filter_cfg.dest_ch = 2; + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 5; + filter = rsp_filter_init(&filter_cfg); + return filter; +} + +audio_element_handle_t create_audio_player_spiffs_stream(void) +{ + audio_element_handle_t spiffs_stream = NULL; + spiffs_stream_cfg_t spiffs_cfg = SPIFFS_STREAM_CFG_DEFAULT(); + spiffs_cfg.type = AUDIO_STREAM_READER; + spiffs_stream = spiffs_stream_init(&spiffs_cfg); + return spiffs_stream; +} + +audio_element_handle_t create_tone_i2s_stream(void) +{ + audio_element_handle_t i2s_stream = NULL; + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(I2S_NUM_0, 16000, CODEC_ADC_BITS_PER_SAMPLE, AUDIO_STREAM_WRITER); + i2s_cfg.out_rb_size = 8 * 1024; + i2s_cfg.need_expand = false; + i2s_cfg.buffer_len = 1416; + i2s_cfg.chan_cfg.dma_desc_num = 6; + i2s_stream = i2s_stream_init(&i2s_cfg); + return i2s_stream; +} + +recorder_sr_cfg_t get_default_audio_record_config(void) +{ + recorder_sr_cfg_t recorder_sr_cfg = DEFAULT_RECORDER_SR_CFG(AUDIO_ADC_INPUT_CH_FORMAT, "model", AFE_TYPE_SR, AFE_MODE_LOW_COST); + recorder_sr_cfg.multinet_init = false; + recorder_sr_cfg.fetch_task_core = 0; + recorder_sr_cfg.feed_task_core = 1; + recorder_sr_cfg.afe_cfg->vad_mode = VAD_MODE_3; + recorder_sr_cfg.afe_cfg->memory_alloc_mode = AFE_MEMORY_ALLOC_MORE_PSRAM; + recorder_sr_cfg.afe_cfg->agc_mode = AFE_MN_PEAK_NO_AGC; + return recorder_sr_cfg; +} diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_single_microphone.c b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_single_microphone.c new file mode 100644 index 0000000..a96c481 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/audio_stream_single_microphone.c @@ -0,0 +1,196 @@ +#include "esp_log.h" + +#include "audio_stream.h" +#include "filter_resample.h" +#include "raw_stream.h" +#include "spiffs_stream.h" +#include "audio_common.h" +#include "i2s_stream.h" +#include "wav_decoder.h" +#include "es8311.h" +#include "board.h" +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#include "raw_opus_encoder.h" +#include "raw_opus_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) +#include "aac_encoder.h" +#include "aac_decoder.h" +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) +#include "g711_encoder.h" +#include "g711_decoder.h" +#endif +#include "audio_stream.h" + +static const char *TAG = "SINGLE_MICROPHONE"; + +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#define SAMPLE_RATE 16000 +#define BIT_RATE 32000 +#define COMPLEXITY 0 +#define FRAME_TIME_MS 20 +#endif + +#if defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) +#define SAMPLE_RATE 16000 +#define BIT_RATE 80000 +#endif + +#if defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) +#define SAMPLE_RATE 8000 +#endif + +#define CHANNEL 1 + +audio_element_handle_t create_record_encoder_stream(void) +{ + audio_element_handle_t encoder_stream = NULL; +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + raw_opus_enc_config_t opus_cfg = RAW_OPUS_ENC_CONFIG_DEFAULT(); + opus_cfg.sample_rate = SAMPLE_RATE; + opus_cfg.channel = CHANNEL; + opus_cfg.bitrate = BIT_RATE; + opus_cfg.complexity = COMPLEXITY; + encoder_stream = raw_opus_encoder_init(&opus_cfg); +#elif defined (CONFIG_AUDIO_SUPPORT_AAC_DECODER) + aac_encoder_cfg_t aac_cfg = DEFAULT_AAC_ENCODER_CONFIG(); + aac_cfg.sample_rate = SAMPLE_RATE; + aac_cfg.channel = CHANNEL; + aac_cfg.bitrate = BIT_RATE; + encoder_stream = aac_encoder_init(&aac_cfg); +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + g711_encoder_cfg_t g711_cfg = DEFAULT_G711_ENCODER_CONFIG(); + encoder_stream = g711_encoder_init(&g711_cfg); +#endif + return encoder_stream; +} + +audio_element_handle_t create_record_raw_stream(void) +{ + audio_element_handle_t raw_stream = NULL; + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + raw_cfg.type = AUDIO_STREAM_WRITER; + raw_cfg.out_rb_size = 2 * 1024; + raw_stream = raw_stream_init(&raw_cfg); + audio_element_set_output_timeout(raw_stream, portMAX_DELAY); + return raw_stream; +} + +audio_element_handle_t create_record_i2s_stream(void) +{ + audio_element_handle_t i2s_stream = NULL; + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(I2S_NUM_0, 16000, 16, AUDIO_STREAM_READER); + i2s_stream = i2s_stream_init(&i2s_cfg); + es8311_set_mic_gain(ES8311_MIC_GAIN_0DB); + return i2s_stream; +} + +audio_element_handle_t create_player_decoder_stream(void) +{ + audio_element_handle_t decoder_stream = NULL; +#ifdef CONFIG_AUDIO_SUPPORT_OPUS_DECODER + ESP_LOGI(TAG, "Create opus decoder"); + raw_opus_dec_cfg_t opus_dec_cfg = RAW_OPUS_DEC_CONFIG_DEFAULT(); + opus_dec_cfg.enable_frame_length_prefix = true; + opus_dec_cfg.sample_rate = 16000; + opus_dec_cfg.channels = 1; + opus_dec_cfg.task_core = 1; + decoder_stream = raw_opus_decoder_init(&opus_dec_cfg); +#elif CONFIG_AUDIO_SUPPORT_AAC_DECODER + ESP_LOGI(TAG, "Create aac decoder"); + aac_decoder_cfg_t aac_dec_cfg = DEFAULT_AAC_DECODER_CONFIG(); + aac_dec_cfg.task_core = 1; + decoder_stream = aac_decoder_init(&aac_dec_cfg); +#elif CONFIG_AUDIO_SUPPORT_G711A_DECODER + ESP_LOGI(TAG, "Create g711a decoder"); + g711_decoder_cfg_t g711_dec_cfg = DEFAULT_G711_DECODER_CONFIG(); + g711_dec_cfg.out_rb_size = 8 * 1024; + g711_dec_cfg.task_core = 1; + decoder_stream = g711_decoder_init(&g711_dec_cfg); +#endif + return decoder_stream; +} + +audio_element_handle_t create_player_wav_decoder_stream() +{ + audio_element_handle_t decoder_stream = NULL; + wav_decoder_cfg_t wav_cfg = DEFAULT_WAV_DECODER_CONFIG(); + decoder_stream = wav_decoder_init(&wav_cfg); + return decoder_stream; +} + +audio_element_handle_t create_player_raw_stream(void) +{ + audio_element_handle_t raw_stream = NULL; + raw_stream_cfg_t raw_cfg = RAW_STREAM_CFG_DEFAULT(); + raw_cfg.type = AUDIO_STREAM_READER; + raw_cfg.out_rb_size = 8 * 1024; + raw_stream = raw_stream_init(&raw_cfg); + return raw_stream; +} + +audio_element_handle_t create_player_i2s_stream(bool enable_task) +{ + audio_element_handle_t i2s_stream = NULL; + i2s_stream_cfg_t i2s_cfg = I2S_STREAM_CFG_DEFAULT_WITH_PARA(I2S_NUM_0, 16000, 16, AUDIO_STREAM_WRITER); + i2s_cfg.out_rb_size = 8 * 1024; + i2s_cfg.buffer_len = 708; + if (enable_task == false) { + i2s_cfg.task_stack = -1; + } + i2s_cfg.chan_cfg.dma_desc_num = 6; + i2s_stream = i2s_stream_init(&i2s_cfg); + return i2s_stream; +} + +audio_element_handle_t create_ch1_to_ch2_rsp_stream() +{ + audio_element_handle_t filter = NULL; + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 16000; + filter_cfg.dest_ch = 2; + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 1; + filter = rsp_filter_init(&filter_cfg); + return filter; +} + +audio_element_handle_t create_8k_ch1_to_16k_ch2_rsp_stream() +{ + audio_element_handle_t filter = NULL; + rsp_filter_cfg_t filter_cfg = DEFAULT_RESAMPLE_FILTER_CONFIG(); + filter_cfg.src_ch = 1; + filter_cfg.src_rate = 8000; + filter_cfg.dest_ch = 2; + filter_cfg.dest_rate = 16000; + filter_cfg.stack_in_ext = true; + filter_cfg.task_core = 1; + filter_cfg.complexity = 2; + filter = rsp_filter_init(&filter_cfg); + return filter; +} + +audio_element_handle_t create_audio_player_spiffs_stream(void) +{ + audio_element_handle_t spiffs_stream = NULL; + spiffs_stream_cfg_t spiffs_cfg = SPIFFS_STREAM_CFG_DEFAULT(); + spiffs_cfg.type = AUDIO_STREAM_READER; + spiffs_stream = spiffs_stream_init(&spiffs_cfg); + return spiffs_stream; +} + +recorder_sr_cfg_t get_default_audio_record_config(void) +{ + recorder_sr_cfg_t recorder_sr_cfg = DEFAULT_RECORDER_SR_CFG("MR", "model", AFE_TYPE_SR, AFE_MODE_LOW_COST); + recorder_sr_cfg.afe_cfg->aec_init = true; + recorder_sr_cfg.multinet_init = false; + recorder_sr_cfg.afe_cfg->memory_alloc_mode = AFE_MEMORY_ALLOC_MORE_PSRAM; + recorder_sr_cfg.afe_cfg->agc_mode = AFE_MN_PEAK_NO_AGC; + recorder_sr_cfg.afe_cfg->pcm_config.mic_num = 1; + recorder_sr_cfg.afe_cfg->pcm_config.ref_num = 1; + recorder_sr_cfg.afe_cfg->pcm_config.total_ch_num = 2; + recorder_sr_cfg.afe_cfg->wakenet_mode = DET_MODE_90; + return recorder_sr_cfg; +} \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_processor.h b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_processor.h new file mode 100644 index 0000000..285b13c --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_processor.h @@ -0,0 +1,264 @@ +/* + * ESPRESSIF MIT License + * + * Copyright (c) 2025 + * + * Permission is hereby granted for use on all ESPRESSIF SYSTEMS products, in which case, + * it is free of charge, to any person obtaining a copy of this software and associated + * documentation files (the "Software"), to deal in the Software without restriction, including + * without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, + * and/or sell copies of the Software, and to permit persons to whom the Software is furnished + * to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all copies or + * substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + * + */ + + +#pragma once + +#include +#include +#include +#include "audio_pipeline.h" + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void (*tone_play_callback_t)(audio_element_status_t evt); + +/** + * @brief Enumeration of player pipeline states + */ +typedef enum { + PIPE_STATE_IDLE, /**< The pipeline is idle and not processing any audio */ + PIPE_STATE_RUNNING, /**< The pipeline is actively processing and playing audio */ +} pipe_player_state_e; + +/** + * @brief A handle to the recorder pipeline structure + */ +typedef struct recorder_pipeline_t *recorder_pipeline_handle_t; + +/** + * @brief A handle to the player pipeline structure + */ +typedef struct player_pipeline_t *player_pipeline_handle_t; + +/** + * @brief Opens and initializes the recorder pipeline + * + * This function sets up the necessary resources and configurations for the recorder pipeline + * + * @return + * - A handle to the initialized recorder pipeline + * - NULL if the operation fails + */ +recorder_pipeline_handle_t recorder_pipeline_open(void); + +/** + * @brief Starts the recorder pipeline + * + * This function begins recording audio using the specified pipeline. It must be called after `recorder_pipeline_open() + * + * @param[in] recorder_pipeline The handle to the recorder pipeline + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t recorder_pipeline_run(recorder_pipeline_handle_t recorder_pipeline); + +/** + * @brief Stops the recorder pipeline. + * + * This function halts the audio recording and ensures that all resources used by the pipeline are properly released + * + * @param[in] recorder_pipeline The handle to the recorder pipeline + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t recorder_pipeline_stop(recorder_pipeline_handle_t recorder_pipeline); + +/** + * @brief Closes the recorder pipeline and releases resources. + * + * This function cleans up all resources associated with the recorder pipeline. It should be called after `recorder_pipeline_stop() + * + * @param[in] recorder_pipeline The handle to the recorder pipeline + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t recorder_pipeline_close(recorder_pipeline_handle_t recorder_pipeline); + +/** + * @brief Gets the default buffer size for reading data from the recorder pipeline + * + * This function returns the recommended buffer size for optimal data reading from the recorder pipeline + * + * @param[in] recorder_pipeline The handle to the recorder pipeline + * + * @return + * - > 0 The specific length of data being read + * - <= 0 Error code + */ +int recorder_pipeline_get_default_read_size(recorder_pipeline_handle_t recorder_pipeline); + +/** + * @brief Reads audio data from the recorder pipeline + * + * @param[in] recorder_pipeline The handle to the recorder pipeline. + * @param[in] buffer The buffer to store the audio data. + * @param[in] buf_size The size of the buffer in bytes. + * + * @return + * - > 0 The specific length of data being read + * - <= 0 Error code + */ +int recorder_pipeline_read(recorder_pipeline_handle_t recorder_pipeline, char *buffer, int buf_size); + +/** + * @brief Opens and initializes the player pipeline + * + * @return A handle to the initialized player pipeline, or NULL if the operation fails + */ +player_pipeline_handle_t player_pipeline_open(void); + +/** + * @brief Starts the player pipeline + * + * @param[in] player_pipeline The handle to the player pipeline + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t player_pipeline_run(player_pipeline_handle_t player_pipeline); + +/** + * @brief Closes the player pipeline and releases resources + * + * @param[in] player_pipeline The handle to the player pipeline + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t player_pipeline_close(player_pipeline_handle_t player_pipeline); + +/** + * @brief Stop the player pipeline + * + * @param[in] player_pipeline handle to the player pipeline instance + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t player_pipeline_stop(player_pipeline_handle_t player_pipeline); + +/** + * @brief Get the current state of the player pipeline + * + * @param[in] player_pipeline The handle to the player pipeline instance + * @param[out] state A pointer to a variable where the pipeline state will be stored + * The state will be set to one of the values defined in the + * `pipe_player_state_e` enumeration + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t player_pipeline_get_state(player_pipeline_handle_t player_pipeline, pipe_player_state_e *state); + +/** + * @brief Gets the default buffer size for writing data to the player pipeline + * + * @param[in] player_pipeline The handle to the player pipeline + * + * @return + * - The default buffer size in bytes + */ +int player_pipeline_get_default_read_size(player_pipeline_handle_t player_pipeline); + +/** + * @brief Writes audio data to the player pipeline + * + * This function sends audio data to the pipeline for playback + * + * @param[in] player_pipeline The handle to the player pipeline + * @param[in] buffer The buffer containing the audio data to be written + * @param[in] buf_size The size of the audio data in bytes + * + * @return + * - >= 0 The number of bytes successfully written + * - < 0 If an error occurs + */ +int player_pipeline_write(player_pipeline_handle_t player_pipeline, char *buffer, int buf_size); + +/** + * @brief Gets the raw write element handle from the player pipeline + * + * @param[in] player_pipeline The handle to the player pipeline + * + * @return + - The raw write element handle + - NULL, if the operation fails + */ +audio_element_handle_t player_pipeline_get_raw_write(player_pipeline_handle_t player_pipeline); + +/** + * @brief Initializes the audio recording engine with the specified pipeline and callback + * + * @param[in] pipeline The handle to the recorder pipeline to be used by the recording engine + * @param[in] cb The callback function to handle recording events + * + * @return A pointer to the initialized audio recording engine instance, or NULL if the initialization fails. + * The returned pointer must be managed appropriately, including cleanup when no longer needed. + */ +void *audio_record_engine_init(recorder_pipeline_handle_t pipeline, rec_event_cb_t cb); + +/** + * @brief Initialize the audio tone. + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t audio_tone_init(tone_play_callback_t callbaclk); + +/** + * @brief Play audio tone from a specified URI + * + * @param[in] uri The URI of the audio source to play. This can be a file path + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t audio_tone_play(const char *uri); + +/** + * @brief Stop audio tone playback + * + * @return + * - ESP_OK if the operation is successful + * - ESP_FAIL if the operation fails + */ +esp_err_t audio_tone_stop(void); + +#ifdef __cplusplus +} +#endif diff --git a/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_stream.h b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_stream.h new file mode 100644 index 0000000..9986168 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/components/audio_processor/include/audio_stream.h @@ -0,0 +1,102 @@ +#pragma once + +#include "audio_element.h" +#include "recorder_sr.h" + +/** + * @brief Create an encoder stream for recording audio + * + * @return + * - Handle to the audio element representing the encoder stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_record_encoder_stream(void); + +/** + * @brief Create a raw audio stream for recording + * + * @return + * - Handle to the audio element representing the raw stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_record_raw_stream(void); + +/** + * @brief Create an I2S stream for recording audio + * + * @return + * - Handle to the audio element representing the I2S stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_record_i2s_stream(void); + +/** + * @brief Create a decoder stream for audio playback + * + * @return + * - Handle to the audio element representing the decoder stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_player_decoder_stream(void); + +/** + * @brief Create a WAV decoder stream for audio playback + * + * @return + * - Handle to the audio element representing the WAV decoder stream +* - NULL if the creation fails. + */ +audio_element_handle_t create_player_wav_decoder_stream(void); + +/** + * @brief Create a raw audio stream for playback + * + * @return + * - Handle to the audio element representing the raw stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_player_raw_stream(void); + +/** + * @brief Create an I2S stream for audio playback + * + * @return + * - Handle to the audio element representing the I2S stream + * - NULL if the creation fails. + */ +audio_element_handle_t create_player_i2s_stream(bool enable_task); + +/** + * @brief Create a stream element to convert mono-channel (1 channel) audio data to stereo-channel (2 channels). + * + * @return + * - A handle to the audio element on success. + * - NULL if the creation fails. + */ +audio_element_handle_t create_ch1_to_ch2_rsp_stream(void); + +/** + * @brief Create a stream element to convert mono-channel 8 kHz audio to stereo-channel 16 kHz audio. + * + * @return + * - A handle to the audio element on success. + * - NULL if the creation fails. + */ +audio_element_handle_t create_8k_ch1_to_16k_ch2_rsp_stream(void); + +/** + * @brief Create a SPIFFS stream for audio player file input + * + * @return + * - Handle to the audio element representing the SPIFFS stream +* - NULL if the creation fails. + */ +audio_element_handle_t create_audio_player_spiffs_stream(void); + +/** + * @brief Get the default audio recording configuration. + * + * @return Default audio recording configuration + */ +recorder_sr_cfg_t get_default_audio_record_config(void); + diff --git a/esp-spot/example/adf/volc_rtc_spot/main/CMakeLists.txt b/esp-spot/example/adf/volc_rtc_spot/main/CMakeLists.txt new file mode 100644 index 0000000..54e1323 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/CMakeLists.txt @@ -0,0 +1,5 @@ +set(COMPONENT_SRCS "main.c" "volc_rtc.c" "coze_http_request.c" "volc_rtc_message.c" "http_client_request.c") +set(COMPONENT_ADD_INCLUDEDIRS .) +register_component() + +spiffs_create_partition_image(spiffs_data ../spiffs FLASH_IN_PROJECT) diff --git a/esp-spot/example/adf/volc_rtc_spot/main/Kconfig.projbuild b/esp-spot/example/adf/volc_rtc_spot/main/Kconfig.projbuild new file mode 100644 index 0000000..5ff77ef --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/Kconfig.projbuild @@ -0,0 +1,100 @@ +menu "Example Configuration" + +config WIFI_SSID + string "WiFi SSID" + default "myssid" + help + SSID (network name) for the example to connect to. + +config WIFI_PASSWORD + string "WiFi Password" + default "mypasswod" + help + WiFi password (WPA or WPA2) for the example to use. + + Can be left blank if the network has no security set. + +choice AUDIO_DECODER_SUPPORT + prompt "Audio Decoder" + default AUDIO_SUPPORT_OPUS_DECODER + +config AUDIO_SUPPORT_G711A_DECODER + bool "AUDIUO-G711-DECODER" + +config AUDIO_SUPPORT_OPUS_DECODER + bool "AUDIO-OPUS-DECODER" + +config AUDIO_SUPPORT_AAC_DECODER + bool "AUDIO-AAC-DECODER" + +endchoice + +choice AUDIO_CONVERSATION_MODE_SUPPORT + prompt "Conversion Mode" + default CONTINUOUS_CONVERSATION_MODE + +config LANGUAGE_WAKEUP_MODE + select USE_WAKENET + select SR_WN_WN9_HILEXIN + bool "LANGUAGE-WAKEUP-MODE" + +config CONTINUOUS_CONVERSATION_MODE + bool "CONTINUOUS-CONVERSATION-MODE" + +endchoice + +choice RTC_MODE_CHOICE + prompt "rtc mode choice" + default COZE_REQUEST + help + Select rtc request mode + + config COZE_REQUEST + bool "enable coze request" + + config RTC_TOKEN_REQUEST + bool "enable rtc token request" +endchoice + + +config COZE_URL + string "COZE-URL" + default "COZE-URL" + depends on COZE_REQUEST + + config COZE_AUTHORIZATION + string "COZE-AUTHORIZATION" + default "COZE-AUTHORIZATION" + depends on COZE_REQUEST + + config COZE_BOTID + string "COZE-BOTID" + default "COZE-BOTID" + depends on COZE_REQUEST + + +config RTC_TOKEN + depends on RTC_TOKEN_REQUEST + config APPID + string "appid" + depends on RTC_TOKEN_REQUEST + + config ROOMID + string "roomid" + depends on RTC_TOKEN_REQUEST + + config UID + string "userid" + depends on RTC_TOKEN_REQUEST + + config TOKEN + string "token" + depends on RTC_TOKEN_REQUEST + +config ENABLE_RECORDER_DEBUG + bool "enable recorder debug save to sdcard" + default "n" + help + Save the data processed by AEC to the SD card. The saved file name is "rec.pcm", and by default, 30 seconds' worth of data will be saved. + +endmenu diff --git a/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.c b/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.c new file mode 100644 index 0000000..494dd96 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.c @@ -0,0 +1,253 @@ +/* Coze http request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#include +#include + +#include "esp_log.h" +#include "esp_http_client.h" +#include "esp_heap_caps.h" +#include "cJSON.h" +#include "audio_error.h" +#include "http_client_request.h" +#include "coze_http_request.h" + + +#define HTTP_FINSH_BIT (1 << 0) + +#define HTTP_REQ_MEM_CHECK(ptr, action) do { \ + if (ptr == NULL) { \ + ESP_LOGE(TAG, "<%s | %d> malloc failed", __func__, __LINE__); \ + action; \ + } \ +} while (0) + +#define http_req_safe_free(ptr, free_fn) do { \ + if (ptr != NULL) { \ + free_fn(ptr); \ + ptr = NULL; \ + } \ +} while (0) + +static const char *TAG = "COZE_HTTP_REQUEST"; + +static void *impl_malloc_fn(size_t size) +{ + uint32_t allocate_caps = MALLOC_CAP_8BIT; +#if CONFIG_PSRAM + allocate_caps |= MALLOC_CAP_SPIRAM; +#else + allocate_caps |= MALLOC_CAP_INTERNAL; +#endif + return heap_caps_malloc(size, allocate_caps); +} + +static void *impl_strdup_fn(const char *s) +{ + int size = strlen(s) + 1; + char *ptr = impl_malloc_fn(size); + if (ptr != NULL) + { + memset(ptr, 0, size); + strcpy(ptr, s); + } + return ptr; +} + +static void impl_free_fn(void *ptr) +{ + heap_caps_free(ptr); +} + +const char *audio_type_2_str(coze_http_req_audio_type_e audio_type) +{ + switch (audio_type) { + case COZE_HTTP_REQ_AUDIO_TYPE_G711A: + return "G711A"; + case COZE_HTTP_REQ_AUDIO_TYPE_OPUS: + return "OPUS"; + default: + return "UNKNOWN"; + } +} + +const char *video_type_2_str(coze_http_req_video_type_e video_type) +{ + switch (video_type) + { + case COZE_HTTP_REQ_VIDEO_TYPE_MJPEG: + return "MJPEG"; + case COZE_HTTP_REQ_VIDEO_TYPE_H264: + return "H264"; + default: + return "UNKNOWN"; + } +} + +static coze_http_req_result_t *cjson_unpkg_2_str_fmt(const char *rsp_string) { + + coze_http_req_result_t *result = (coze_http_req_result_t *)impl_malloc_fn(sizeof(coze_http_req_result_t)); + cJSON *root = cJSON_Parse(rsp_string); + if (root == NULL) { + ESP_LOGW(TAG, "Error parsing JSON\n"); + return NULL; + } + + cJSON *data = cJSON_GetObjectItem(root, "data"); + if (data != NULL) { + cJSON *token = cJSON_GetObjectItem(data, "token"); + if (token != NULL && cJSON_IsString(token)) { + ESP_LOGD(TAG, "Token: %s\n", token->valuestring); + result->token = impl_strdup_fn(token->valuestring); + HTTP_REQ_MEM_CHECK( result->token, goto _exit); + } + + cJSON *uid = cJSON_GetObjectItem(data, "uid"); + if (uid != NULL && cJSON_IsString(uid)) { + ESP_LOGD(TAG, "UID: %s\n", uid->valuestring); + result->uid = impl_strdup_fn(uid->valuestring); + HTTP_REQ_MEM_CHECK( result->uid, goto _exit); + } + + cJSON *room_id = cJSON_GetObjectItem(data, "room_id"); + if (room_id != NULL && cJSON_IsString(room_id)) { + ESP_LOGD(TAG, "Room ID: %s\n", room_id->valuestring); + result->room_id = impl_strdup_fn(room_id->valuestring); + HTTP_REQ_MEM_CHECK( result->uid, goto _exit); + } + + cJSON *app_id = cJSON_GetObjectItem(data, "app_id"); + if (app_id != NULL && cJSON_IsString(app_id)) { + ESP_LOGD(TAG, "App ID: %s\n", app_id->valuestring); + result->app_id = impl_strdup_fn(app_id->valuestring); + HTTP_REQ_MEM_CHECK(result->app_id, goto _exit); + } + } + + cJSON *code = cJSON_GetObjectItem(root, "code"); + if (code != NULL && cJSON_IsNumber(code)) { + ESP_LOGD(TAG, "Code: %d\n", code->valueint); + } else { + ESP_LOGE(TAG, "Code not found in response"); + goto _exit; + } + + cJSON_Delete(root); + return result; + +_exit: + coze_http_request_free(result); + cJSON_Delete(root); + return NULL; +} + +static char *str_pkg_to_cjson_fmt(coze_http_req_config_t *cfg, req_svc_type_t type) +{ + cJSON *root = cJSON_CreateObject(); + if (root == NULL) { + ESP_LOGE(TAG, "cJSON_CreateObject failed"); + return NULL; + } + + cJSON_AddStringToObject(root, "bot_id", cfg->bot_id); + + if (type == COZE_HTTP_REQ_SVC_TYPE_COZE) { + + cJSON_AddStringToObject(root, "voice_id", cfg->voice_id); + cJSON *config = cJSON_CreateObject(); + if (config == NULL) { + ESP_LOGE(TAG, "cJSON_CreateObject failed"); + goto _exit; + } + cJSON_AddItemToObject(root, "config", config); + + cJSON *audio_config = cJSON_CreateObject(); + if (audio_config == NULL) { + ESP_LOGE(TAG, "cJSON_CreateObject failed"); + goto _exit; + } + cJSON_AddStringToObject(audio_config, "audio_codec", audio_type_2_str(cfg->audio_type)); + cJSON_AddItemToObject(config, "audio_config", audio_config); + + cJSON *video_config = cJSON_CreateObject(); + if (video_config == NULL) { + ESP_LOGE(TAG, "cJSON_CreateObject failed"); + goto _exit; + } + cJSON_AddStringToObject(video_config, "video_codec", video_type_2_str(cfg->video_type)); + cJSON_AddItemToObject(config, "video_config", video_config); + + } else { + cJSON_AddStringToObject(root, "audio_codec", audio_type_2_str(cfg->audio_type)); + } + char *json_string = cJSON_Print(root); + if (json_string == NULL) { + ESP_LOGE(TAG, "cJSON_Print failed"); + goto _exit; + } else { + ESP_LOGI(TAG, "request json: %s", json_string); + } + + cJSON_Delete(root); + return json_string; + +_exit: + cJSON_Delete(root); + return NULL; +} + +coze_http_req_result_t *coze_http_request(coze_http_req_config_t *config, req_svc_type_t type) +{ + if (config == NULL) { + ESP_LOGE(TAG, "Invalid parameters: config"); + return NULL; + } + char *req_body = NULL; + coze_http_req_result_t *req_result = NULL; + http_response_t http_response; + + req_body = str_pkg_to_cjson_fmt(config, type); + + http_req_header_t header[] = { + { "Content-Type", "application/json" }, + { "Authorization", config->authorization }, + { NULL, NULL }, + }; + + esp_err_t err = http_client_post(config->uri, header, req_body, &http_response); + if (err != ESP_OK) { + ESP_LOGE(TAG, "HTTP POST request failed: %s", esp_err_to_name(err)); + goto _clean_buffer; + } + + ESP_LOGD(TAG, "http_response.body: \n%s", http_response.body); + req_result = cjson_unpkg_2_str_fmt(http_response.body); + AUDIO_MEM_CHECK(TAG, req_result, goto _clean_buffer); + + ESP_LOGI(TAG, "room_id: %s", req_result->room_id); + ESP_LOGI(TAG, "app_id: %s", req_result->app_id); + ESP_LOGI(TAG, "token: %s", req_result->token); + ESP_LOGI(TAG, "uid: %s", req_result->uid); + +_clean_buffer: + // clear up + http_req_safe_free(http_response.body, free); + http_req_safe_free(req_body, free); + + return req_result; +} + +void coze_http_request_free(coze_http_req_result_t *result) +{ + http_req_safe_free(result->room_id, free); + http_req_safe_free(result->app_id, free); + http_req_safe_free(result->token, free); + http_req_safe_free(result->uid, free); + http_req_safe_free(result, free); +} \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.h b/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.h new file mode 100644 index 0000000..ac4f927 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/coze_http_request.h @@ -0,0 +1,72 @@ +/* Coze http request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#pragma once + +typedef enum { + COZE_HTTP_REQ_SVC_TYPE_COZE, + COZE_HTTP_REQ_SVC_TYPE_RTC, +} req_svc_type_t; + +typedef enum { + COZE_HTTP_REQ_AUDIO_TYPE_G711A, + COZE_HTTP_REQ_AUDIO_TYPE_OPUS, +} coze_http_req_audio_type_e; + +typedef enum { + COZE_HTTP_REQ_VIDEO_TYPE_MJPEG, + COZE_HTTP_REQ_VIDEO_TYPE_H264, +} coze_http_req_video_type_e; + +#define COZE_HTTP_DEFAULT_CONFIG() { \ + .uri = "https://api.coze.cn/v1/audio/rooms", \ + .authorization = "", \ + .bot_id = "", \ + .voice_id = "", \ + .audio_type = COZE_HTTP_REQ_AUDIO_TYPE_G711A, \ + .video_type = COZE_HTTP_REQ_VIDEO_TYPE_H264, \ +} + +typedef struct { + char *room_id; + char *app_id; + char *token; + char *uid; +} coze_http_req_result_t; + +typedef struct { + const char *uri; + const char *authorization; + const char *bot_id; + const char *voice_id; + coze_http_req_audio_type_e audio_type; + coze_http_req_video_type_e video_type; +} coze_http_req_config_t; + +/** + * @brief Make a request to the ByteRTC service. + * + * @param[in] config Pointer to the configuration structure specifying the request parameters. + * The structure should be initialized before calling this function. + * + * @return + - A pointer to a `coze_http_req_result_t` structure containing the result of the request, + * - NULL if the request failed + */ +coze_http_req_result_t *coze_http_request(coze_http_req_config_t *config, req_svc_type_t type); + +/** + * @brief Free the result of a ByteRTC request. + * + * @param[in] result Pointer to the result structure to be freed. Passing NULL is safe and has no effect. + * + * @return + * - None + */ +void coze_http_request_free(coze_http_req_result_t *result); \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.c b/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.c new file mode 100644 index 0000000..2e27200 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.c @@ -0,0 +1,115 @@ +/* http client request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#include +#include "freertos/FreeRTOS.h" +#include "freertos/event_groups.h" +#include "esp_log.h" +#include "esp_http_client.h" +#include "audio_mem.h" +#include "http_client_request.h" + +static char *TAG = "HTTP_CLIENT_REQ"; + +#define http_client_free(x, fn) do { \ + if (x) { \ + fn(x); \ + x = NULL; \ + } \ +} while (0) + +#define HTTP_FINSH_BIT (1 << 0) + +typedef struct { + http_response_t *resp; + EventGroupHandle_t eg; +} http_client_ctx_t; + +static esp_err_t _http_event_handler(esp_http_client_event_t *evt) +{ + http_client_ctx_t *ctx = (http_client_ctx_t *)evt->user_data; + static int output_len = 0; + switch(evt->event_id) { + case HTTP_EVENT_ERROR: + ESP_LOGD(TAG, "HTTP_EVENT_ERROR"); + break; + case HTTP_EVENT_ON_CONNECTED: + ESP_LOGD(TAG, "HTTP_EVENT_ON_CONNECTED"); + break; + case HTTP_EVENT_HEADER_SENT: + ESP_LOGD(TAG, "HTTP_EVENT_HEADER_SENT"); + break; + case HTTP_EVENT_ON_HEADER: + ESP_LOGD(TAG, "HTTP_EVENT_ON_HEADER, key=%s, value=%s", evt->header_key, evt->header_value); + break; + case HTTP_EVENT_ON_DATA: + memcpy(ctx->resp->body + output_len, evt->data , evt->data_len); + output_len += evt->data_len; + break; + case HTTP_EVENT_ON_FINISH: + ESP_LOGD(TAG, "HTTP_EVENT_ON_FINISH"); + output_len = 0; + xEventGroupSetBits(ctx->eg, HTTP_FINSH_BIT); + break; + case HTTP_EVENT_DISCONNECTED: + ESP_LOGD(TAG, "HTTP_EVENT_DISCONNECTED"); + break; + case HTTP_EVENT_REDIRECT: + break; + } + return ESP_OK; +} + +esp_err_t http_client_post(const char *url, http_req_header_t *header, char *body, http_response_t *response) +{ + http_response_t rsp_data = { 0 }; + rsp_data.body = (char *)audio_calloc(1, 1024); + + http_client_ctx_t _ctx = { + .resp = &rsp_data, + .eg = xEventGroupCreate(), + }; + esp_http_client_config_t http_client_config = { + .url = url, + .query = "esp", + .event_handler = _http_event_handler, + .user_data = &_ctx, + .disable_auto_redirect = true, + }; + esp_http_client_handle_t client = NULL; + client = esp_http_client_init(&http_client_config); + esp_http_client_set_method(client, HTTP_METHOD_POST); + int header_index = 0; + while (header[header_index].key && header[header_index].value) { + ESP_LOGD(TAG, "key: %s, value: %s\n", header[header_index].key, header[header_index].value); + esp_http_client_set_header(client, header[header_index].key, header[header_index].value); + header_index++; + } + esp_http_client_set_post_field(client, body, strlen(body)); + + esp_err_t err = esp_http_client_perform(client); + + EventBits_t uxBits = xEventGroupWaitBits(_ctx.eg, HTTP_FINSH_BIT , pdTRUE, pdFALSE, pdMS_TO_TICKS(10000)); // wait 10s + if (( uxBits & HTTP_FINSH_BIT ) != 0) { + } else { + ESP_LOGE(TAG, "HTTP POST request failed: %s", esp_err_to_name(err)); + goto _exit; + } + *response = rsp_data; + esp_http_client_close(client); + esp_http_client_cleanup(client); + http_client_free(_ctx.eg, vEventGroupDelete); + return ESP_OK; +_exit: + http_client_free(client, esp_http_client_close); + http_client_free(client, esp_http_client_cleanup); + http_client_free(_ctx.eg, vEventGroupDelete); + http_client_free(rsp_data.body, audio_free); + return ESP_FAIL; +} \ No newline at end of file diff --git a/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.h b/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.h new file mode 100644 index 0000000..07e9b8c --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/http_client_request.h @@ -0,0 +1,22 @@ +/* http client request example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#pragma once + +typedef struct { + char *body; + int body_len; +} http_response_t; + +typedef struct { + const char *key; + const char *value; +} http_req_header_t; + +int http_client_post(const char *url, http_req_header_t *header , char *body, http_response_t *response); diff --git a/esp-spot/example/adf/volc_rtc_spot/main/main.c b/esp-spot/example/adf/volc_rtc_spot/main/main.c new file mode 100644 index 0000000..89144b0 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/main.c @@ -0,0 +1,127 @@ +/* volc rtc example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#include +#include +#include + +#include "freertos/idf_additions.h" +#include "nvs_flash.h" +#include "esp_event.h" +#include "esp_netif.h" +#include "esp_netif_sntp.h" +#include "esp_log.h" +#include "esp_timer.h" + +#include "audio_sys.h" +#include "audio_thread.h" +#include "esp_peripherals.h" +#include "periph_wifi.h" +#include "periph_spiffs.h" +#include "periph_sdcard.h" +#include "audio_mem.h" +#include "es8311.h" +#include "board.h" + +#include "volc_rtc.h" + +// #define ENABLE_TASK_MONITOR + +static char *TAG = "main"; + +#if defined(ENABLE_TASK_MONITOR) +static void monitor_task(void *arg) +{ + while (1) { + audio_sys_get_real_time_stats(); + AUDIO_MEM_SHOW(TAG); + vTaskDelay(10000 / portTICK_RATE_MS); + } +} +#endif + +// Pullup to fix the leakage issue of the ES8311 in standby mode. +static void set_gpio6_high(void) +{ + gpio_config_t io_conf = { + .pin_bit_mask = 1ULL << GPIO_NUM_6, + .mode = GPIO_MODE_OUTPUT, + .pull_up_en = 0, + .pull_down_en = 0, + .intr_type = GPIO_INTR_DISABLE + }; + gpio_config(&io_conf); + + gpio_set_level(GPIO_NUM_6, 1); +} + +void app_main() +{ + ESP_ERROR_CHECK(nvs_flash_init()); + ESP_ERROR_CHECK(esp_netif_init()); + + ESP_LOGI(TAG, "Initialize board peripherals"); + esp_periph_config_t periph_cfg = DEFAULT_ESP_PERIPH_SET_CONFIG(); + esp_periph_set_handle_t set = esp_periph_set_init(&periph_cfg); + +#if CONFIG_ENABLE_RECORDER_DEBUG + // Initialize SD Card peripheral + audio_board_sdcard_init(set, SD_MODE_1_LINE); +#endif + + periph_spiffs_cfg_t spiffs_cfg = { + .root = "/spiffs", + .partition_label = "spiffs_data", + .max_files = 5, + .format_if_mount_failed = true}; + esp_periph_handle_t spiffs_handle = periph_spiffs_init(&spiffs_cfg); + esp_periph_start(set, spiffs_handle); + + // Wait until spiffs is mounted + while (!periph_spiffs_is_mounted(spiffs_handle)) { + vTaskDelay(500 / portTICK_PERIOD_MS); + } + + periph_wifi_cfg_t wifi_cfg = { + .wifi_config.sta.ssid = CONFIG_WIFI_SSID, + .wifi_config.sta.password = CONFIG_WIFI_PASSWORD, + }; + esp_periph_handle_t wifi_handle = periph_wifi_init(&wifi_cfg); + esp_periph_start(set, wifi_handle); + periph_wifi_wait_for_connected(wifi_handle, portMAX_DELAY); + + esp_sntp_config_t config = ESP_NETIF_SNTP_DEFAULT_CONFIG("pool.ntp.org"); + esp_netif_sntp_init(&config); + + // Wait for time to be set + int retry = 0; + const int retry_count = 5; + while (esp_netif_sntp_sync_wait(1000 / portTICK_PERIOD_MS) == ESP_ERR_TIMEOUT && ++retry < retry_count) { + ESP_LOGI(TAG, "Waiting for system time to be set... (%d/%d)", retry, retry_count); + } + // Set timezone to China Standard Time + time_t now = 0; + struct tm timeinfo = { 0 }; + setenv("TZ", "CST-8", 1); + tzset(); + localtime_r(&now, &timeinfo); + + set_gpio6_high(); + audio_board_handle_t board_handle = audio_board_init(); + audio_hal_ctrl_codec(board_handle->audio_hal, AUDIO_HAL_CODEC_MODE_BOTH, AUDIO_HAL_CTRL_START); + audio_hal_set_volume(board_handle->audio_hal, 80); + es8311_set_mic_gain(ES8311_MIC_GAIN_0DB); + +#if defined(ENABLE_TASK_MONITOR) + audio_thread_create(NULL, "monitor_task", monitor_task, NULL, 5 * 1024, 13, true, 0); +#endif + + // Init byte rtc engine + volc_rtc_init(); +} diff --git a/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.c b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.c new file mode 100644 index 0000000..efbd44d --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.c @@ -0,0 +1,440 @@ +/* volc rtc example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ +#include + +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "freertos/semphr.h" +#include "freertos/queue.h" + +#include "esp_log.h" +#include "sdkconfig.h" + +#include "esp_timer.h" +#include "audio_recorder.h" +#include "recorder_sr.h" +#include "audio_pipeline.h" +#include "audio_thread.h" +#include "raw_stream.h" +#include "audio_mem.h" +#include "esp_delegate.h" +#include "esp_dispatcher.h" + +#include "VolcEngineRTCLite.h" +#include "coze_http_request.h" +#include "audio_processor.h" +#include "volc_rtc_message.h" + +static const char* TAG = "volc_rtc"; + +#define STATS_TASK_PRIO (5) +#define JOIN_EVENT_BIT (1 << 0) +#define WAIT_DESTORY_EVENT_BIT (1 << 0) +#define WAKEUP_REC_READING (1 << 0) +#define DEFAULT_MAX_QUEUE_NUM (30) +#define DEFAULT_QUEUE_CACHE_NUM (10) + +#define volc_rtc_safe_free(x, fn) do { \ + if (x) { \ + fn(x); \ + x = NULL; \ + } \ +} while (0) + +typedef struct { + char *frame_ptr; + int frame_len; +} frame_package_t; + +typedef enum { + RTC_JOIN_IDLE = 0, + RTC_JOIN, + RTC_LEAVE, +} rtc_join_state_t; + +struct volc_rtc_t { + recorder_pipeline_handle_t record_pipeline; + player_pipeline_handle_t player_pipeline; + void *recorder_engine; + byte_rtc_engine_t engine; + EventGroupHandle_t join_event; + EventGroupHandle_t wait_destory_event; + EventGroupHandle_t wakeup_event; + QueueHandle_t frame_q; + coze_http_req_result_t *user_info; + esp_dispatcher_handle_t esp_dispatcher; + rtc_join_state_t join_state; + bool byte_rtc_running; + bool data_proc_running; + esp_timer_handle_t int_timer_hd; +#if defined(CONFIG_AUDIO_SUPPORT_OPUS_DECODER) +#define DEALULT_OPUS_DATA_CHACHE_SIZE (512) + char *opus_data_cache; + int opus_data_cache_len; +#endif // CONFIF_AUDIO_SUPPORT_OPUS_DECODER +}; + +static struct volc_rtc_t s_volc_rtc; + +static void audio_pull_data_process(char *ptr, int len); + +// byte rtc lite callbacks +static void byte_rtc_on_join_room_success(byte_rtc_engine_t engine, const char* channel, int elapsed_ms, bool ms) +{ + ESP_LOGI(TAG, "join channel success %s elapsed %d ms now %d ms\n", channel, elapsed_ms, elapsed_ms); + xEventGroupSetBits(s_volc_rtc.join_event, JOIN_EVENT_BIT); +}; + +static __attribute__((unused)) void byte_rtc_on_rejoin_room_success(byte_rtc_engine_t engine, const char* channel, int elapsed_ms) +{ + ESP_LOGI(TAG, "rejoin channel success %s\n", channel); +}; + +static void byte_rtc_on_user_joined(byte_rtc_engine_t engine, const char* channel, const char* user_name, int elapsed_ms) +{ + ESP_LOGI(TAG, "remote user joined %s:%s", channel, user_name); +}; + +static void byte_rtc_on_user_offline(byte_rtc_engine_t engine, const char* channel, const char* user_name, int reason) +{ + ESP_LOGI(TAG, "remote user offline %s:%s", channel, user_name); +}; + +static void byte_rtc_on_user_mute_audio(byte_rtc_engine_t engine, const char* channel, const char* user_name, int muted) +{ + ESP_LOGI(TAG, "remote user mute audio %s:%s %d", channel, user_name, muted); +}; + +static void byte_rtc_on_user_mute_video(byte_rtc_engine_t engine, const char* channel, const char* user_name, int muted) +{ + ESP_LOGI(TAG, "remote user mute video %s:%s %d", channel, user_name, muted); +}; + +static __attribute__((unused)) void byte_rtc_on_connection_lost(byte_rtc_engine_t engine, const char* channel) +{ + ESP_LOGI(TAG, "connection Lost %s", channel); +}; + +static void byte_rtc_on_room_error(byte_rtc_engine_t engine, const char* channel, int code, const char* msg) +{ + ESP_LOGE(TAG, "error occur %s %d %s", channel, code, msg ? msg : "UnKown"); +}; + +// remote audio +static void byte_rtc_on_audio_data(byte_rtc_engine_t engine, const char* room, const char* uid , uint16_t sent_ts, + audio_data_type_e codec, const void* data_ptr, size_t data_len) +{ + ESP_LOGD(TAG, "remote audio data %s %s %d %d %p %zu", room, uid, sent_ts, codec, data_ptr, data_len); + + pipe_player_state_e state; + player_pipeline_get_state(s_volc_rtc.player_pipeline, &state); + if (state == PIPE_STATE_IDLE) { + return; + } + frame_package_t frame = { 0 }; + frame.frame_ptr = audio_calloc(1, data_len); + memcpy(frame.frame_ptr, data_ptr, data_len); + frame.frame_len = data_len; + if (xQueueSend(s_volc_rtc.frame_q, &frame, pdMS_TO_TICKS(10)) != pdPASS) { + ESP_LOGW(TAG, "audio frame queue full"); + } +} + +// remote video +static void byte_rtc_on_video_data(byte_rtc_engine_t engine, const char* channel, const char* uid, uint16_t sent_ts, + video_data_type_e codec, int is_key_frame, + const void * data_ptr, size_t data_len){ } + +// remote message +void on_message_received(byte_rtc_engine_t engine, const char* room, const char* uid, const uint8_t* message, int size, bool binary) +{ + ESP_LOGD(TAG, "on_message_received uid: %s, message: %s, message size: %d", uid, message, size); + volc_rtc_message_process(message, size); +} + +static void on_key_frame_gen_req(byte_rtc_engine_t engine, const char* channel, const char* uid) {} +// byte rtc lite callbacks end. + +static void audio_pull_data_process(char *ptr, int len) +{ + char *data_ptr = ptr; + int data_len = len; + /* Since OPUS is in VBR mode, it needs to be packaged into a length + data format first then to decoder*/ +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + +#define frame_length_prefix (2) + if (s_volc_rtc.opus_data_cache_len + frame_length_prefix < len) { + s_volc_rtc.opus_data_cache = audio_realloc(s_volc_rtc.opus_data_cache, len + frame_length_prefix); + s_volc_rtc.opus_data_cache_len = len; + } + s_volc_rtc.opus_data_cache[0] = (len >> 8) & 0xFF; + s_volc_rtc.opus_data_cache[1] = len & 0xFF; + memcpy(s_volc_rtc.opus_data_cache + frame_length_prefix, ptr, len); + data_ptr = s_volc_rtc.opus_data_cache; + data_len += frame_length_prefix; +#else + data_ptr = ptr; + data_len = len; +#endif // CONFIG_AUDIO_SUPPORT_OPUS_DECODER + raw_stream_write(player_pipeline_get_raw_write(s_volc_rtc.player_pipeline), data_ptr, data_len); +} + +static void audio_tone_player_event_cb(audio_element_status_t evt) +{ + if (evt == AEL_STATUS_STATE_FINISHED) { + player_pipeline_run(s_volc_rtc.player_pipeline); + } +} + +static esp_err_t dispatcher_audio_play(void *instance, action_arg_t *arg, action_result_t *result) +{ + audio_tone_play((char *)arg->data); + return ESP_OK; +}; + +static esp_err_t rec_engine_cb(audio_rec_evt_t *event, void *user_data) +{ + if (AUDIO_REC_WAKEUP_START == event->type) { +#if CONFIG_LANGUAGE_WAKEUP_MODE + ESP_LOGI(TAG, "rec_engine_cb - AUDIO_REC_WAKEUP_START"); + player_pipeline_stop(s_volc_rtc.player_pipeline); + action_arg_t action_arg = {0}; + action_arg.data = (void *)"spiffs://spiffs/dingding.wav"; + + action_result_t result = {0}; + esp_dispatcher_execute_with_func(s_volc_rtc.esp_dispatcher, dispatcher_audio_play, NULL, &action_arg, &result); + xEventGroupSetBits(s_volc_rtc.wakeup_event, WAKEUP_REC_READING); +#endif // CONFIG_LANGUAGE_WAKEUP_MODE + } else if (AUDIO_REC_VAD_START == event->type) { + } else if (AUDIO_REC_VAD_END == event->type) { + } else if (AUDIO_REC_WAKEUP_END == event->type) { + #if CONFIG_LANGUAGE_WAKEUP_MODE + xEventGroupClearBits(s_volc_rtc.wakeup_event, WAKEUP_REC_READING); + player_pipeline_stop(s_volc_rtc.player_pipeline); + ESP_LOGI(TAG, "rec_engine_cb - AUDIO_REC_WAKEUP_END"); + #endif // CONFIG_LANGUAGE_WAKEUP_MODE + } else { + } + + return ESP_OK; +} + +static esp_err_t open_audio_pipeline() +{ + s_volc_rtc.record_pipeline = recorder_pipeline_open(); + s_volc_rtc.player_pipeline = player_pipeline_open(); + recorder_pipeline_run(s_volc_rtc.record_pipeline); + player_pipeline_run(s_volc_rtc.player_pipeline); + return ESP_OK; +} + +static void byte_rtc_engine_destroy() +{ + if (s_volc_rtc.engine) { + byte_rtc_fini(s_volc_rtc.engine); + vTaskDelay(pdMS_TO_TICKS(1000)); + byte_rtc_destory(s_volc_rtc.engine); + s_volc_rtc.engine = NULL; + } +} + +static esp_err_t byte_rtc_engine_create() +{ +#ifdef CONFIG_COZE_REQUEST + #ifdef CONFIG_AUDIO_SUPPORT_OPUS_DECODER + coze_http_req_audio_type_e audio_type = COZE_HTTP_REQ_AUDIO_TYPE_OPUS; + #else + coze_http_req_audio_type_e audio_type = COZE_HTTP_REQ_AUDIO_TYPE_G711A; + #endif + coze_http_req_config_t http_config = COZE_HTTP_DEFAULT_CONFIG(); + http_config.uri = CONFIG_COZE_URL"/startvoicechat"; + http_config.authorization = CONFIG_COZE_AUTHORIZATION; + http_config.bot_id = CONFIG_COZE_BOTID; + http_config.audio_type = audio_type, + s_volc_rtc.user_info = coze_http_request(&http_config, COZE_HTTP_REQ_SVC_TYPE_RTC); + +#else + s_volc_rtc.user_info = audio_calloc(1, sizeof(coze_http_req_result_t)); + s_volc_rtc.user_info->app_id = CONFIG_APPID; + s_volc_rtc.user_info->room_id = CONFIG_ROOMID; + s_volc_rtc.user_info->uid = CONFIG_UID; + s_volc_rtc.user_info->token = CONFIG_TOKEN; +#endif + + ESP_LOGI(TAG, "app_id: %s, room_id: %s, uid: %s, token: %s", + s_volc_rtc.user_info->app_id, s_volc_rtc.user_info->room_id, s_volc_rtc.user_info->uid, s_volc_rtc.user_info->token); +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + s_volc_rtc.opus_data_cache_len = DEALULT_OPUS_DATA_CHACHE_SIZE; + s_volc_rtc.opus_data_cache = audio_calloc(1, s_volc_rtc.opus_data_cache_len); +#endif // CONFIG_AUDIO_SUPPORT_OPUS_DECODER + + byte_rtc_event_handler_t handler = { + .on_join_room_success = byte_rtc_on_join_room_success, + .on_room_error = byte_rtc_on_room_error, + .on_user_joined = byte_rtc_on_user_joined, + .on_user_offline = byte_rtc_on_user_offline, + .on_user_mute_audio = byte_rtc_on_user_mute_audio, + .on_user_mute_video = byte_rtc_on_user_mute_video, + .on_audio_data = byte_rtc_on_audio_data, + .on_video_data = byte_rtc_on_video_data, + .on_key_frame_gen_req = on_key_frame_gen_req, + .on_message_received = on_message_received, + }; + s_volc_rtc.join_state = RTC_JOIN_IDLE; + s_volc_rtc.engine = byte_rtc_create(s_volc_rtc.user_info->app_id, &handler); + + byte_rtc_set_log_level(s_volc_rtc.engine, BYTE_RTC_LOG_LEVEL_ERROR); +#ifdef RTC_TEST_ENV + byte_rtc_set_params(s_volc_rtc.engine, "{\"env\":2}"); // test env +#endif + byte_rtc_set_params(s_volc_rtc.engine, "{\"debug\":{\"log_to_console\":1}}"); + byte_rtc_set_params(s_volc_rtc.engine,"{\"rtc\":{\"thread\":{\"pinned_to_core\":1}}}"); + byte_rtc_set_params(s_volc_rtc.engine,"{\"rtc\":{\"thread\":{\"priority\":6}}}"); + // byte_rtc_set_params(s_volc_rtc.engine,"{\"rtc\":{\"network\":{\"audio_jitter_buffer_target_delay_min_ms\":200}}}"); + // byte_rtc_set_params(s_volc_rtc.engine,"{\"rtc\":{\"license\":{\"enable\":1}}}"); + + byte_rtc_init(s_volc_rtc.engine); +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + byte_rtc_set_audio_codec(s_volc_rtc.engine, AUDIO_CODEC_TYPE_OPUS); +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + byte_rtc_set_audio_codec(s_volc_rtc.engine, AUDIO_CODEC_TYPE_G711A); +#else // AACLC Encoder + byte_rtc_set_audio_codec(s_volc_rtc.engine, AUDIO_CODEC_TYPE_AACLC); +#endif // CONFIG_AUDIO_SUPPORT_OPUS_DECODER + open_audio_pipeline(); + + ESP_LOGI(TAG, "start join room\n"); + byte_rtc_room_options_t options = { 0 }; + options.auto_subscribe_audio = 1; + options.auto_subscribe_video = 0; + options.auto_publish_audio = 1; + options.auto_publish_video = 0; + byte_rtc_join_room(s_volc_rtc.engine, s_volc_rtc.user_info->room_id, s_volc_rtc.user_info->uid, s_volc_rtc.user_info->token, &options); + // Default wait time is forever + xEventGroupWaitBits(s_volc_rtc.join_event, JOIN_EVENT_BIT, pdTRUE, pdTRUE, portMAX_DELAY); + ESP_LOGI(TAG, "join room success\n"); + s_volc_rtc.join_state = RTC_JOIN; + return ESP_OK; +} + +static void audio_data_process_task(void *args) +{ + frame_package_t frame = { 0 }; + s_volc_rtc.data_proc_running = true; + while (s_volc_rtc.data_proc_running) { + xQueueReceive(s_volc_rtc.frame_q, &frame, portMAX_DELAY); + if (frame.frame_ptr) { + audio_pull_data_process(frame.frame_ptr, frame.frame_len); + audio_free(frame.frame_ptr); + } + } + vTaskDelete(NULL); +} + +static void voice_read_task(void *args) +{ + const int voice_data_read_sz = recorder_pipeline_get_default_read_size(s_volc_rtc.record_pipeline); + uint8_t *voice_data = audio_calloc(1, voice_data_read_sz); + bool runing = true; + +#if defined (CONFIG_AUDIO_SUPPORT_OPUS_DECODER) + audio_frame_info_t audio_frame_info = {.data_type = AUDIO_DATA_TYPE_OPUS}; +#elif defined (CONFIG_AUDIO_SUPPORT_G711A_DECODER) + audio_frame_info_t audio_frame_info = {.data_type = AUDIO_DATA_TYPE_PCMA}; +#else + audio_frame_info_t audio_frame_info = {.data_type = AUDIO_DATA_TYPE_AACLC}; +#endif // CONFIG_AUDIO_SUPPORT_OPUS_DECODER + +#if defined (CONFIG_LANGUAGE_WAKEUP_MODE) + TickType_t wait_tm = portMAX_DELAY; +#else + TickType_t wait_tm = 0; +#endif // CONFIG_LANGUAGE_WAKEUP_MODE + while (runing) { + #if defined (CONFIG_LANGUAGE_WAKEUP_MODE) + EventBits_t bits = xEventGroupWaitBits(s_volc_rtc.wakeup_event, WAKEUP_REC_READING , false, true, wait_tm); + if (bits & WAKEUP_REC_READING) { + int ret = audio_recorder_data_read(s_volc_rtc.recorder_engine, voice_data, voice_data_read_sz, portMAX_DELAY); + if (ret == 0 || ret == -1) { + if (ret == 0) { + vTaskDelay(15 / portTICK_PERIOD_MS); + continue; + } + ESP_LOGE(TAG, "audio_recorder_data_read failed, ret: %d\n", ret); + xEventGroupClearBits(s_volc_rtc.wakeup_event, WAKEUP_REC_READING); + } else { + byte_rtc_send_audio_data(s_volc_rtc.engine, s_volc_rtc.user_info->room_id, voice_data, voice_data_read_sz, &audio_frame_info); + } + } + #else + int read_len = audio_recorder_data_read(s_volc_rtc.recorder_engine, voice_data, voice_data_read_sz, portMAX_DELAY); + if (read_len == voice_data_read_sz) { + byte_rtc_send_audio_data(s_volc_rtc.engine, s_volc_rtc.user_info->room_id, voice_data, voice_data_read_sz, &audio_frame_info); + } + #endif + } + xEventGroupClearBits(s_volc_rtc.wakeup_event, WAKEUP_REC_READING); + audio_free(voice_data); + vTaskDelete(NULL); +} + +static void log_clear(void) +{ + esp_log_level_set("*", ESP_LOG_INFO); + esp_log_level_set("AUDIO_THREAD", ESP_LOG_ERROR); + esp_log_level_set("i2c_bus_v2", ESP_LOG_ERROR); + esp_log_level_set("AUDIO_HAL", ESP_LOG_ERROR); + esp_log_level_set("AUDIO_PIPELINE", ESP_LOG_ERROR); + esp_log_level_set("AUDIO_ELEMENT", ESP_LOG_ERROR); + esp_log_level_set("I2S_STREAM_IDF5.x", ESP_LOG_ERROR); + esp_log_level_set("RSP_FILTER", ESP_LOG_ERROR); + esp_log_level_set("AUDIO_EVT", ESP_LOG_ERROR); +} + +esp_err_t volc_rtc_init(void) +{ + log_clear(); + audio_tone_init(audio_tone_player_event_cb); + +#if CONFIG_LANGUAGE_WAKEUP_MODE + esp_timer_create_args_t timer_cfg = { + .callback = rtc_int_timer_expired, + .arg = NULL, + .dispatch_method = ESP_TIMER_TASK, + .name = "int_timer", + }; + esp_timer_create(&timer_cfg, &s_volc_rtc.int_timer_hd); +#endif + + s_volc_rtc.join_event = xEventGroupCreate(); + s_volc_rtc.wait_destory_event = xEventGroupCreate(); + s_volc_rtc.wakeup_event = xEventGroupCreate(); + s_volc_rtc.frame_q = xQueueCreate(30, sizeof(frame_package_t)); + s_volc_rtc.esp_dispatcher = esp_dispatcher_get_delegate_handle(); + + // volc_rtc_message_init(VOLC_RTC_MESSAGE_TYPE_SUBTITLE | VOLC_RTC_MESSAGE_TYPE_FUNCTION_CALL); + byte_rtc_engine_create(); + s_volc_rtc.recorder_engine = audio_record_engine_init(s_volc_rtc.record_pipeline, rec_engine_cb); + vTaskDelay(pdMS_TO_TICKS(200)); + audio_thread_create(NULL, "voice_read_task", voice_read_task, (void *)NULL, 5 * 1024, 5, true, 0); + audio_thread_create(NULL, "audio_data_process_task", audio_data_process_task, (void *)NULL, 5 * 1024, 10, true, 0); + return ESP_OK; +} + +esp_err_t volc_rtc_deinit(void) +{ + s_volc_rtc.byte_rtc_running = false; + s_volc_rtc.data_proc_running = false; + xEventGroupWaitBits(s_volc_rtc.wait_destory_event, WAIT_DESTORY_EVENT_BIT, pdTRUE, pdTRUE, portMAX_DELAY); + byte_rtc_engine_destroy(); + volc_rtc_safe_free(s_volc_rtc.join_event, vEventGroupDelete); + volc_rtc_safe_free(s_volc_rtc.wait_destory_event, vEventGroupDelete); + volc_rtc_safe_free(s_volc_rtc.int_timer_hd, esp_timer_delete); + return ESP_OK; +} diff --git a/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.h b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.h new file mode 100644 index 0000000..69ca58e --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc.h @@ -0,0 +1,37 @@ +/* volc rtc example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#pragma once + +#include "esp_err.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Initialize the Volc RTC module. + * + * @return + * - ESP_OK: Initialization was successful. + * - Other: Appropriate erro code indicating the failure reason. + */ +esp_err_t volc_rtc_init(void); + +/** + * @brief Deinitialize the Volc RTC module.. + * + * @return + * - ESP_OK: Deinitialization was successful. + */ +esp_err_t volc_rtc_deinit(void); + +#ifdef __cplusplus +} +#endif diff --git a/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.c b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.c new file mode 100644 index 0000000..a22945b --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.c @@ -0,0 +1,188 @@ +/* volc rtc message parse example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#include "string.h" +#include "esp_log.h" +#include "esp_err.h" +#include "audio_mem.h" +#include "volc_rtc_message.h" + +static const char* TAG = "volc_rtc_message"; + +#define DEF_MESSAGE_SIZE (512) + +struct volc_rtc_message_s { + uint8_t *message; + int size; + uint32_t msg_type; +}; + +static struct volc_rtc_message_s s_rtc_message = {0}; + +static void on_subtitle_message_received(const cJSON* root); +static int on_function_calling_message_received(const cJSON* root, const char* json_str); + +// remote message +// ref https://www.volcengine.com/docs/6348/1337284 +static void on_subtitle_message_received(const cJSON* root) +{ + // Json format: + // { + // "data" : + // [ + // { + // "definite" : false, + // "language" : "zh", + // "mode" : 1, + // "paragraph" : false, + // "sequence" : 0, + // "text" : "\\u4f60\\u597d", + // "userId" : "voiceChat_xxxxx" + // } + // ], + // "type" : "subtitle" + // } + cJSON * type_obj = cJSON_GetObjectItem(root, "type"); + if (type_obj != NULL && strcmp("subtitle", cJSON_GetStringValue(type_obj)) == 0) { + cJSON* data_obj_arr = cJSON_GetObjectItem(root, "data"); + cJSON* obji = NULL; + cJSON_ArrayForEach(obji, data_obj_arr) { + cJSON* user_id_obj = cJSON_GetObjectItem(obji, "userId"); + cJSON* text_obj = cJSON_GetObjectItem(obji, "text"); + if (user_id_obj && text_obj) { + ESP_LOGI(TAG, "subtitle:%s:%s", cJSON_GetStringValue(user_id_obj), cJSON_GetStringValue(text_obj)); + } + } + } +} + +// // function calling +// // ref https://www.volcengine.com/docs/6348/1359441 +static int on_function_calling_message_received(const cJSON* root, const char* json_str) +{ + +// Json format: +// { +// "subscriber_user_id": "", +// "tool_calls": [{ +// "function": { +// "arguments": "{\"命令\": \"打开\", \"亮度\": 50, \"颜色\": \"蓝色\"}", +// "name": "led_on_off" +// }, +// "id": "call_3t9gonmmtx8r2s89xlrxltmb", +// "type": "function" +// }] +// } + + // cJSON *tool_calls = cJSON_GetObjectItem(root, "tool_calls"); + // if (!cJSON_IsArray(tool_calls)) { + // ESP_LOGE(TAG , "`tool_calls` is not array") + // return 1; + // } + + // cJSON *tool_call_item = cJSON_GetArrayItem(tool_calls, 0); + // if (!tool_call_item) { + // ESP_LOGE(TAG, "Get tool_call_item falied") + // return ; + // } + + // cJSON *function_obj = cJSON_GetObjectItem(tool_call_item, "function"); + // if (!function_obj) { + // ESP_LOGE(TAG, "Get function_obj falied"); + // return ; + // } + + // cJSON *arguments = cJSON_GetObjectItem(function_obj, "arguments"); + // if (!cJSON_IsString(arguments)) { + // ESP_LOGE(TAG, "Get arguments falied"); + // return 1; + // } + + // cJSON *arguments_json = cJSON_Parse(arguments->valuestring); + // if (!arguments_json) { + // ESP_LOGE(TAG, "Parse arguments_json falied"); + // return 1; + // } + + // cJSON *cmd = cJSON_GetObjectItem(arguments_json, "命令"); + // cJSON *brightness = cJSON_GetObjectItem(arguments_json, "亮度"); + // cJSON *color = cJSON_GetObjectItem(arguments_json, "颜色"); + // if (cmd) { + // ESP_LOGI(TAG, "命令: %s", cmd->valuestring); + // } + // if (brightness) { + // ESP_LOGI(TAG, "亮度: %d", brightness->valueint); + // } + + // if (color) { + // ESP_LOGI(TAG, "颜色: %s", color->valuestring); + // } + // cJSON_Delete(arguments_json); + return 0; +} + +esp_err_t volc_rtc_message_init(uint32_t msg_type) +{ + if (msg_type == VOLC_RTC_MESSAGE_TYPE_NONE) { + return ESP_OK; + } + s_rtc_message.message = (uint8_t*)audio_malloc(DEF_MESSAGE_SIZE); + s_rtc_message.size = DEF_MESSAGE_SIZE; + if (s_rtc_message.message == NULL) { + ESP_LOGE(TAG, "Allocate memory failed"); + return ESP_ERR_NO_MEM; + } + s_rtc_message.msg_type = msg_type; + return ESP_OK; +} + +esp_err_t volc_rtc_message_process(const uint8_t* message, int size) +{ + if (s_rtc_message.msg_type == VOLC_RTC_MESSAGE_TYPE_NONE) { + return ESP_OK; + } + if (message == NULL || size <= 0) { + ESP_LOGE(TAG, "Invalid message"); + return ESP_ERR_INVALID_ARG; + } + esp_err_t ret = ESP_OK; + if (size > 8) { + if (size > s_rtc_message.size) { + ESP_LOGW(TAG, "Message size too large(size: %d)", size); + s_rtc_message.message = (uint8_t*)audio_realloc(s_rtc_message.message, size); + if (s_rtc_message.message == NULL) { + ESP_LOGE(TAG, "Realloc message buffer failed"); + return ESP_ERR_NO_MEM; + } + s_rtc_message.size = size; + } + memset(s_rtc_message.message, 0, size); + memcpy(s_rtc_message.message, message, size); + + cJSON *root = cJSON_Parse((char *)s_rtc_message.message + 8); + if (root) { + if (message[0] == 's' && message[1] == 'u' && message[2] == 'b' && message[3] == 'v') { + if (s_rtc_message.msg_type & VOLC_RTC_MESSAGE_TYPE_SUBTITLE) { + on_subtitle_message_received(root); + } + } else if (message[0] == 't' && message[1] == 'o' && message[2] == 'o' && message[3] == 'l') { + if (s_rtc_message.msg_type & VOLC_RTC_MESSAGE_TYPE_FUNCTION_CALL) { + on_function_calling_message_received(root, (char *)s_rtc_message.message + 8); + } + } else { + ESP_LOGE(TAG, "Unknown json message: %s", (char *)s_rtc_message.message + 8); + } + } else { + ESP_LOGE(TAG, "Parse json message failed"); + ret = ESP_FAIL; + } + cJSON_Delete(root); + } + return ret; +} diff --git a/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.h b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.h new file mode 100644 index 0000000..18ab468 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/main/volc_rtc_message.h @@ -0,0 +1,22 @@ +/* volc rtc message parse example code + + This example code is in the Public Domain (or CC0 licensed, at your option.) + + Unless required by applicable law or agreed to in writing, this + software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR + CONDITIONS OF ANY KIND, either express or implied. +*/ + +#pragma once + +#include "cJSON.h" + +typedef enum { + VOLC_RTC_MESSAGE_TYPE_SUBTITLE = 1, + VOLC_RTC_MESSAGE_TYPE_FUNCTION_CALL = 2, + VOLC_RTC_MESSAGE_TYPE_NONE = 0, +} volc_rtc_message_type_t; + +esp_err_t volc_rtc_message_init(uint32_t msg_type); +esp_err_t volc_rtc_message_process(const uint8_t* message, int size); + diff --git a/esp-spot/example/adf/volc_rtc_spot/partitions.csv b/esp-spot/example/adf/volc_rtc_spot/partitions.csv new file mode 100644 index 0000000..04c3d6d --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/partitions.csv @@ -0,0 +1,6 @@ +# Name, Type, SubType, Offset, Size +nvs, data, nvs, 0x9000, 0x4000 +phy_init, data, phy, 0xd000, 0x1000 +factory, app, factory, 0x10000, 3M, +model, data, spiffs, , 4152K, +spiffs_data, data, spiffs, , 64k, diff --git a/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults b/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults new file mode 100644 index 0000000..4a45349 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults @@ -0,0 +1,37 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.1 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_BOOTLOADER_FLASH_DC_AWARE=y +CONFIG_ESPTOOLPY_FLASHMODE_QIO=y +CONFIG_ESPTOOLPY_FLASHFREQ_120M=y +CONFIG_ESPTOOLPY_FLASHSIZE_8MB=y +CONFIG_ESPTOOLPY_HEADER_FLASHSIZE_UPDATE=y +CONFIG_PARTITION_TABLE_CUSTOM=y +CONFIG_ESP32_S3_SPOT_BOARD=y +CONFIG_COMPILER_OPTIMIZATION_PERF=y +CONFIG_ESP_TLS_INSECURE=y +CONFIG_ESP_TLS_SKIP_SERVER_CERT_VERIFY=y +CONFIG_SPIRAM=y +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_XIP_FROM_PSRAM=y +CONFIG_SPIRAM_SPEED_120M=y +CONFIG_SPIRAM_MALLOC_ALWAYSINTERNAL=4096 +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_ESP_DEFAULT_CPU_FREQ_MHZ_240=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_32KB=y +CONFIG_ESP32S3_DATA_CACHE_64KB=y +CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y +CONFIG_ESP_MAIN_TASK_STACK_SIZE=4096 +CONFIG_ESP_CONSOLE_USB_SERIAL_JTAG=y +CONFIG_ESP_TASK_WDT_TIMEOUT_S=10 +CONFIG_FREERTOS_HZ=1000 +CONFIG_FREERTOS_ENABLE_BACKWARD_COMPATIBILITY=y +CONFIG_FREERTOS_TIMER_TASK_STACK_DEPTH=4096 +CONFIG_FREERTOS_VTASKLIST_INCLUDE_COREID=y +CONFIG_FREERTOS_GENERATE_RUN_TIME_STATS=y +CONFIG_MBEDTLS_EXTERNAL_MEM_ALLOC=y +CONFIG_MBEDTLS_HARDWARE_AES=n +CONFIG_MBEDTLS_HARDWARE_SHA=n +CONFIG_SPI_FLASH_HPM_ENA=y +CONFIG_IDF_EXPERIMENTAL_FEATURES=y diff --git a/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults.esp32s3 b/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults.esp32s3 new file mode 100644 index 0000000..10dbfb9 --- /dev/null +++ b/esp-spot/example/adf/volc_rtc_spot/sdkconfig.defaults.esp32s3 @@ -0,0 +1,115 @@ +CONFIG_IDF_CMAKE=y +CONFIG_IDF_TARGET_ARCH_XTENSA=y +CONFIG_IDF_TARGET="esp32s3" +CONFIG_IDF_TARGET_ESP32S3=y +CONFIG_IDF_FIRMWARE_CHIP_ID=0x0009 + +# +# Make experimental features visible +# +CONFIG_IDF_EXPERIMENTAL_FEATURES=y + +# +# Serial flasher config +# +CONFIG_BOOTLOADER_FLASH_DC_AWARE=y +CONFIG_ESPTOOLPY_FLASHFREQ_120M=y +CONFIG_SPI_FLASH_HPM_ENA=y +# end of Serial flasher config + +# +# Audio HAL +# +CONFIG_ESP32_S3_KORVO2_V3_BOARD=y +# end of Audio HAL + +# +# Audio Recorder +# +CONFIG_AFE_MIC_NUM=2 +# end of Audio Recorder + +# +# ESP Speech Recognition +# +CONFIG_MODEL_IN_FLASH=y +CONFIG_USE_AFE=y +CONFIG_AFE_INTERFACE_V1=y +CONFIG_USE_WAKENET=n +CONFIG_SR_WN_WN9_HILEXIN=n +CONFIG_USE_MULTINET=n +# end of ESP Speech Recognition + +# +# Component config +# + +# +# Driver configurations +# + +# +# mbedTLS +# +# CONFIG_MBEDTLS_INTERNAL_MEM_ALLOC is not set +CONFIG_MBEDTLS_EXTERNAL_MEM_ALLOC=y +# CONFIG_MBEDTLS_HARDWARE_AES is not set +# CONFIG_MBEDTLS_HARDWARE_SHA is not set +# end of mbedTLS + + +# +# ESP32s3-PSRAM +# +CONFIG_SPIRAM_XIP_FROM_PSRAM=y + +# +# ESP32S3-Specific +# +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_80 is not set +# CONFIG_ESP32S3_DEFAULT_CPU_FREQ_160 is not set +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_240=y +CONFIG_ESP32S3_DEFAULT_CPU_FREQ_MHZ=240 + +# +# Cache config +# +# CONFIG_ESP32S3_INSTRUCTION_CACHE_16KB is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_32KB=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_SIZE=0x8000 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_4WAYS is not set +CONFIG_ESP32S3_INSTRUCTION_CACHE_8WAYS=y +CONFIG_ESP32S3_ICACHE_ASSOCIATED_WAYS=8 +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_32B=y +CONFIG_ESP32S3_INSTRUCTION_CACHE_LINE_SIZE=32 +# CONFIG_ESP32S3_INSTRUCTION_CACHE_WRAP is not set +# CONFIG_ESP32S3_DATA_CACHE_16KB is not set +# CONFIG_ESP32S3_DATA_CACHE_32KB is not set +CONFIG_ESP32S3_DATA_CACHE_64KB=y +CONFIG_ESP32S3_DATA_CACHE_SIZE=0x10000 +# CONFIG_ESP32S3_DATA_CACHE_4WAYS is not set +CONFIG_ESP32S3_DATA_CACHE_8WAYS=y +CONFIG_ESP32S3_DCACHE_ASSOCIATED_WAYS=8 +# CONFIG_ESP32S3_DATA_CACHE_LINE_32B is not set +CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y +CONFIG_ESP32S3_DATA_CACHE_LINE_SIZE=64 +# CONFIG_ESP32S3_DATA_CACHE_WRAP is not set +# end of Cache config + +CONFIG_ESP32S3_SPIRAM_SUPPORT=y + +# +# SPI RAM config +# +CONFIG_SPIRAM_MODE_OCT=y +CONFIG_SPIRAM_TYPE_AUTO=y +CONFIG_SPIRAM_SPEED_120M=y +CONFIG_SPIRAM=y +CONFIG_SPIRAM_BOOT_INIT=y +CONFIG_SPIRAM_USE_MALLOC=y +CONFIG_SPIRAM_MEMTEST=y +CONFIG_SPIRAM_MALLOC_ALWAYSINTERNAL=4096 +CONFIG_SPIRAM_TRY_ALLOCATE_WIFI_LWIP=y +CONFIG_SPIRAM_MALLOC_RESERVE_INTERNAL=32768 +# end of SPI RAM config +# end of ESP32S3-Specific diff --git a/esp-spot/example/adf/volc_rtc_spot/spiffs/dingding.wav b/esp-spot/example/adf/volc_rtc_spot/spiffs/dingding.wav new file mode 100644 index 0000000..a6e1f41 Binary files /dev/null and b/esp-spot/example/adf/volc_rtc_spot/spiffs/dingding.wav differ diff --git a/esp-spot/example/imu_led/CMakeLists.txt b/esp-spot/example/imu_led/CMakeLists.txt new file mode 100644 index 0000000..dc9af98 --- /dev/null +++ b/esp-spot/example/imu_led/CMakeLists.txt @@ -0,0 +1,9 @@ +# For more information about build system see +# https://docs.espressif.com/projects/esp-idf/en/latest/api-guides/build-system.html +# The following five lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.16) + +set(EXTRA_COMPONENT_DIRS "$ENV{IDF_PATH}/tools/unit-test-app/components") +include($ENV{IDF_PATH}/tools/cmake/project.cmake) +project(IMU_Wrist) \ No newline at end of file diff --git a/esp-spot/example/imu_led/README.md b/esp-spot/example/imu_led/README.md new file mode 100644 index 0000000..e69de29 diff --git a/esp-spot/example/imu_led/main/CMakeLists.txt b/esp-spot/example/imu_led/main/CMakeLists.txt new file mode 100644 index 0000000..48c3da3 --- /dev/null +++ b/esp-spot/example/imu_led/main/CMakeLists.txt @@ -0,0 +1,6 @@ +idf_component_register( + SRC_DIRS "." + INCLUDE_DIRS ".") + +include(package_manager) +cu_pkg_define_version(${CMAKE_CURRENT_LIST_DIR}) \ No newline at end of file diff --git a/esp-spot/example/imu_led/main/Kconfig.projbuild b/esp-spot/example/imu_led/main/Kconfig.projbuild new file mode 100644 index 0000000..71512cd --- /dev/null +++ b/esp-spot/example/imu_led/main/Kconfig.projbuild @@ -0,0 +1,15 @@ +menu "Example Configuration" + + config I2C_MASTER_SCL + int "SCL GPIO Num" + default 1 + help + GPIO number for I2C Master clock line. + + config I2C_MASTER_SDA + int "SDA GPIO Num" + default 2 + help + GPIO number for I2C Master data line. + +endmenu \ No newline at end of file diff --git a/esp-spot/example/imu_led/main/idf_component.yml b/esp-spot/example/imu_led/main/idf_component.yml new file mode 100644 index 0000000..fe54e1a --- /dev/null +++ b/esp-spot/example/imu_led/main/idf_component.yml @@ -0,0 +1,6 @@ +## IDF Component Manager Manifest File +dependencies: + idf: '>=5.0' + + espressif2022/bmi270: ^1.1.0 + espressif/led_strip: "^3.0.0" diff --git a/esp-spot/example/imu_led/main/main.c b/esp-spot/example/imu_led/main/main.c new file mode 100644 index 0000000..ac50fc8 --- /dev/null +++ b/esp-spot/example/imu_led/main/main.c @@ -0,0 +1,176 @@ +#include +#include "esp_system.h" +#include "esp_log.h" +#include "driver/rmt_tx.h" +#include "led_strip.h" +#include "driver/gpio.h" +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "bmi270.h" +#include "common/common.h" + +#define WS2812_GPIO 27 +#define LED_COUNT 1 + + +#define HW_ESP_SPOT_C5 0 +#define HW_ESP_SPOT_S3 1 +#define HW_ESP_ASTOM_S3 0 + +#if HW_ESP_SPOT_C5 +#define I2C_INT_IO 3 +#define I2C_MASTER_SCL_IO 26 +#define I2C_MASTER_SDA_IO 25 +#elif HW_ESP_SPOT_S3 +#define I2C_INT_IO 5 +#define I2C_MASTER_SCL_IO 1 +#define I2C_MASTER_SDA_IO 2 +#elif HW_ESP_ASTOM_S3 +#define I2C_INT_IO 16 +#define I2C_MASTER_SCL_IO 0 +#define I2C_MASTER_SDA_IO 45 +#endif + +#define I2C_MASTER_FREQ_HZ (100 * 1000) + +static bmi270_handle_t bmi_handle = NULL; +static i2c_bus_handle_t i2c_bus = NULL; +static led_strip_handle_t led_strip; + +static const char *TAG = "gesture_led"; + +static led_strip_handle_t configure_led(void) +{ + led_strip_config_t strip_config = { + .strip_gpio_num = WS2812_GPIO, + .max_leds = 1, + .led_model = LED_MODEL_WS2812, + .color_component_format = { + .format = { + .r_pos = 1, // GRB排列 + .g_pos = 0, + .b_pos = 2, + .num_components = 3, + }, + }, + .flags = {.invert_out = false}, + }; + + led_strip_spi_config_t spi_config = { + .clk_src = SPI_CLK_SRC_DEFAULT, + .spi_bus = SPI2_HOST, + .flags = {.with_dma = true} + }; + + led_strip_handle_t led_strip; + ESP_ERROR_CHECK(led_strip_new_spi_device(&strip_config, &spi_config, &led_strip)); + ESP_LOGI(TAG, "LED strip initialized (SPI)"); + return led_strip; +} + +// 设置LED颜色 +static void set_led_color(led_strip_handle_t led_strip, uint8_t r, uint8_t g, uint8_t b) +{ + ESP_ERROR_CHECK(led_strip_set_pixel(led_strip, 0, r, g, b)); + ESP_ERROR_CHECK(led_strip_refresh(led_strip)); +} + +// BMI270 初始化 +static esp_err_t i2c_sensor_bmi270_init(void) +{ + const i2c_config_t i2c_bus_conf = { + .mode = I2C_MODE_MASTER, + .sda_io_num = I2C_MASTER_SDA_IO, + .sda_pullup_en = GPIO_PULLUP_ENABLE, + .scl_io_num = I2C_MASTER_SCL_IO, + .scl_pullup_en = GPIO_PULLUP_ENABLE, + .master.clk_speed = I2C_MASTER_FREQ_HZ + }; + i2c_bus = i2c_bus_create(I2C_NUM_0, &i2c_bus_conf); + if (!i2c_bus) { + ESP_LOGE(TAG, "I2C bus create failed"); + return ESP_FAIL; + } + + bmi270_i2c_config_t i2c_bmi270_conf = { + .i2c_handle = i2c_bus, + .i2c_addr = BMI270_I2C_ADDRESS, + }; + if (bmi270_sensor_create(&i2c_bmi270_conf, &bmi_handle) != ESP_OK || !bmi_handle) { + ESP_LOGE(TAG, "BMI270 create failed"); + return ESP_FAIL; + } + + return ESP_OK; +} + +void gesture_task(void *arg) +{ + uint16_t int_status = 0; + struct bmi2_feat_sensor_data sens_data = { .type = BMI2_WRIST_GESTURE }; + const char *gesture_output[6] = { + "unknown_gesture", "push_arm_down", "pivot_up", + "wrist_shake_jiggle", "flick_in", "flick_out" + }; + + // 配置并启动手势识别 + struct bmi2_sens_config config = {.type = BMI2_WRIST_GESTURE}; + uint8_t sens_list[] = {BMI2_ACCEL, BMI2_WRIST_GESTURE}; + bmi270_sensor_enable(sens_list, 2, bmi_handle); + bmi270_get_sensor_config(&config, 1, bmi_handle); + config.cfg.wrist_gest.wearable_arm = BMI2_ARM_LEFT; + bmi270_set_sensor_config(&config, 1, bmi_handle); + + struct bmi2_sens_int_config sens_int = { + .type = BMI2_WRIST_GESTURE, + .hw_int_pin = BMI2_INT1 + }; + bmi270_map_feat_int(&sens_int, 1, bmi_handle); + + ESP_LOGI(TAG, "Gesture detection started"); + + while (1) { + bmi2_get_int_status(&int_status, bmi_handle); + + if (int_status & BMI270_WRIST_GEST_STATUS_MASK) { + bmi270_get_feature_data(&sens_data, 1, bmi_handle); + int gesture = sens_data.sens_data.wrist_gesture_output; + ESP_LOGI(TAG, "Detected gesture: %s", gesture_output[gesture]); + + switch (gesture) { + case 0: // unknown + set_led_color(led_strip, 0, 0, 0); + break; + case 1: // push_arm_down 红色 + set_led_color(led_strip, 255, 0, 0); + break; + case 2: // pivot_up 绿色 + set_led_color(led_strip, 0, 255, 0); + break; + case 3: // wrist_shake_jiggle 蓝色 + set_led_color(led_strip, 0, 0, 255); + break; + case 4: // flick_in 黄色 + set_led_color(led_strip, 255, 255, 0); + break; + case 5: // flick_out 紫色 + set_led_color(led_strip, 128, 0, 128); + break; + default: + set_led_color(led_strip, 0, 0, 0); + break; + } + vTaskDelay(pdMS_TO_TICKS(500)); + set_led_color(led_strip, 0, 0, 0); + } + vTaskDelay(pdMS_TO_TICKS(100)); + } +} + +void app_main(void) +{ + ESP_ERROR_CHECK(i2c_sensor_bmi270_init()); + led_strip = configure_led(); + + xTaskCreate(gesture_task, "gesture_task", 4096, NULL, 5, NULL); +} diff --git a/esp-spot/example/imu_led/sdkconfig.defaults b/esp-spot/example/imu_led/sdkconfig.defaults new file mode 100644 index 0000000..4ded6a9 --- /dev/null +++ b/esp-spot/example/imu_led/sdkconfig.defaults @@ -0,0 +1,7 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.1 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESPTOOLPY_FLASHSIZE_16MB=y +CONFIG_ESP_CONSOLE_USB_SERIAL_JTAG=y +CONFIG_ESP_TASK_WDT_EN=n diff --git a/esp-spot/example/s3_factory_bin/s3_spot_factory.bin b/esp-spot/example/s3_factory_bin/s3_spot_factory.bin new file mode 100644 index 0000000..1f0e801 Binary files /dev/null and b/esp-spot/example/s3_factory_bin/s3_spot_factory.bin differ diff --git a/esp-spot/example/simple_touch/CMakeLists.txt b/esp-spot/example/simple_touch/CMakeLists.txt new file mode 100644 index 0000000..d6bb63b --- /dev/null +++ b/esp-spot/example/simple_touch/CMakeLists.txt @@ -0,0 +1,6 @@ +# The following lines of boilerplate have to be in your project's +# CMakeLists in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.16) + +include($ENV{IDF_PATH}/tools/cmake/project.cmake) +project(simple_touch) diff --git a/esp-spot/example/simple_touch/README.md b/esp-spot/example/simple_touch/README.md new file mode 100644 index 0000000..6d8866f --- /dev/null +++ b/esp-spot/example/simple_touch/README.md @@ -0,0 +1,11 @@ +| Supported Targets | ESP32-P4 | ESP32-S2 | ESP32-S3 | +| ----------------- | -------- | -------- | -------- | + +# 简单触摸例程 + +配置 ``channel_threshold`` 调整对应触摸 IO 的灵敏度，阈值越低越灵敏 + +TOUCH_CHANNEL_1 + +``LIGHT_TOUCH_THRESHOLD`` 配置轻按 +``HEAVY_TOUCH_THRESHOLD`` \ No newline at end of file diff --git a/esp-spot/example/simple_touch/components/touch_button/CHANGELOG.md b/esp-spot/example/simple_touch/components/touch_button/CHANGELOG.md new file mode 100644 index 0000000..802cc43 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/CHANGELOG.md @@ -0,0 +1,5 @@ +# ChangeLog + +## v0.1.0 - 2025-03-18 + +* Add Initial version diff --git a/esp-spot/example/simple_touch/components/touch_button/CMakeLists.txt b/esp-spot/example/simple_touch/components/touch_button/CMakeLists.txt new file mode 100644 index 0000000..be47ea4 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/CMakeLists.txt @@ -0,0 +1,5 @@ +idf_component_register(SRC_DIRS "." + INCLUDE_DIRS ".") + +include(package_manager) +cu_pkg_define_version(${CMAKE_CURRENT_LIST_DIR}) diff --git a/esp-spot/example/simple_touch/components/touch_button/README.md b/esp-spot/example/simple_touch/components/touch_button/README.md new file mode 100644 index 0000000..ed31df6 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/README.md @@ -0,0 +1,25 @@ +[![Component Registry](https://components.espressif.com/components/espressif/touch_button/badge.svg)](https://components.espressif.com/components/espressif/touch_button) + +# Touch Button + +Supports a series of operations on touch buttons, such as press, release, long press, and short press. + +**Note:** This component is for developers testing only. It is not intended for production use. + +ESP32/ESP32-S2/ESP32-S3 touch-related components are intended for testing or demo purposes only. Due to the poor anti-interference capability of the touch functionality, it may not pass EMS testing, and therefore, it is not recommended for mass production products. + +This component is currently applicable to ESP32, ESP32-S2, and ESP32-S3, and requires an IDF version greater than or equal to v5.3. + +## Dependencies + +- [touch_sensor_fsm](https://components.espressif.com/components/espressif/touch_sensor_fsm) +- [touch_sensor_lowlevel](https://components.espressif.com/components/espressif/touch_sensor_lowlevel) +- [touch_button_sensor](https://components.espressif.com/components/espressif/touch_button_sensor) + +## Add component to your project + +Please use the component manager command `add-dependency` to add the `touch_button` to your project's dependency, during the `CMake` step the component will be downloaded automatically + +``` +idf.py add-dependency "espressif/touch_button=*" +``` diff --git a/esp-spot/example/simple_touch/components/touch_button/idf_component.yml b/esp-spot/example/simple_touch/components/touch_button/idf_component.yml new file mode 100644 index 0000000..4339e2d --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/idf_component.yml @@ -0,0 +1,19 @@ +dependencies: + cmake_utilities: '*' + espressif/button: + public: true + version: ^4 + espressif/touch_button_sensor: + public: true + version: ^0.2.1 + idf: '>=4.4' +description: Provide a complete set of button detection methods based on `iot_button` + to drive the touch FSM. +issues: https://github.com/espressif/esp-iot-solution/issues +repository: https://github.com/espressif/esp-iot-solution.git +targets: +- esp32s2 +- esp32s3 +- esp32p4 +url: https://github.com/espressif/esp-iot-solution/tree/master/components/touch/ +version: 0.1.1 diff --git a/esp-spot/example/simple_touch/components/touch_button/license.txt b/esp-spot/example/simple_touch/components/touch_button/license.txt new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/license.txt @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/esp-spot/example/simple_touch/components/touch_button/test_apps/CMakeLists.txt b/esp-spot/example/simple_touch/components/touch_button/test_apps/CMakeLists.txt new file mode 100644 index 0000000..81e5d2d --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/test_apps/CMakeLists.txt @@ -0,0 +1,9 @@ + +# The following lines of boilerplate have to be in your project's CMakeLists +# in this exact order for cmake to work correctly +cmake_minimum_required(VERSION 3.5) + +set(EXTRA_COMPONENT_DIRS "$ENV{IDF_PATH}/tools/unit-test-app/components" + "../../touch_button") +include($ENV{IDF_PATH}/tools/cmake/project.cmake) +project(button_test) diff --git a/esp-spot/example/simple_touch/components/touch_button/test_apps/main/CMakeLists.txt b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/CMakeLists.txt new file mode 100644 index 0000000..0b90b03 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/CMakeLists.txt @@ -0,0 +1,3 @@ +idf_component_register(SRC_DIRS "." + PRIV_INCLUDE_DIRS "." + WHOLE_ARCHIVE) diff --git a/esp-spot/example/simple_touch/components/touch_button/test_apps/main/test_main.c b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/test_main.c new file mode 100644 index 0000000..7d9cdf6 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/test_main.c @@ -0,0 +1,41 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "unity.h" +#include "unity_test_runner.h" +#include "unity_test_utils_memory.h" +#include "esp_heap_caps.h" +#include "sdkconfig.h" + +#define LEAKS (400) + +void setUp(void) +{ + unity_utils_record_free_mem(); +} + +void tearDown(void) +{ + unity_utils_evaluate_leaks_direct(LEAKS); +} + +void app_main(void) +{ + + /* _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ + *|_ _| _ | | | / __ \| | | | | ___ \ | | |_ _|_ _| _ | \ | | + * | | | | | | | | | / \/| |_| | | |_/ / | | | | | | | | | | | \| | + * | | | | | | | | | | | _ | | ___ \ | | | | | | | | | | | . ` | + * | | \ \_/ / |_| | \__/\| | | | | |_/ / |_| | | | | | \ \_/ / |\ | + * \_/ \___/ \___/ \____/\_| |_/ \____/ \___/ \_/ \_/ \___/\_| \_/ + */ + printf(" _____ _____ _ _ _____ _ _ ______ _ _ _____ _____ _____ _ _ \n"); + printf("|_ _| _ | | | / __ \\| | | | | ___ \\ | | |_ _|_ _| _ | \\ | |\n"); + printf(" | | | | | | | | | / \\/| |_| | | |_/ / | | | | | | | | | | | \\| |\n"); + printf(" | | | | | | | | | | | _ | | ___ \\ | | | | | | | | | | | . ` |\n"); + printf(" | | \\ \\_/ / |_| | \\__/\\| | | | | |_/ / |_| | | | | | \\ \\_/ / |\\ |\n"); + printf(" \\_/ \\___/ \\___/ \\____/\\_| |_/ \\____/ \\___/ \\_/ \\_/ \\___/\\_| \\_/\n"); + unity_run_menu(); +} diff --git a/esp-spot/example/simple_touch/components/touch_button/test_apps/main/touch_button_test.c b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/touch_button_test.c new file mode 100644 index 0000000..193c962 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/test_apps/main/touch_button_test.c @@ -0,0 +1,234 @@ +/* SPDX-FileCopyrightText: 2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_log.h" +#include "unity.h" +#include "iot_button.h" +#include "touch_button.h" +#include "touch_sensor_lowlevel.h" + +static const char *TAG = "GPIO BUTTON TEST"; + +#define TOUCH_CHANNEL_1 1 +#define TOUCH_CHANNEL_2 2 + +static void button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "%s", iot_button_get_event_str(event)); + if (BUTTON_PRESS_REPEAT == event || BUTTON_PRESS_REPEAT_DONE == event) { + ESP_LOGI(TAG, "\tREPEAT[%d]", iot_button_get_repeat(arg)); + } + + if (BUTTON_PRESS_UP == event || BUTTON_LONG_PRESS_HOLD == event || BUTTON_LONG_PRESS_UP == event) { + ESP_LOGI(TAG, "\tTICKS[%"PRIu32"]", iot_button_get_ticks_time(arg)); + } + + if (BUTTON_MULTIPLE_CLICK == event) { + ESP_LOGI(TAG, "\tMULTIPLE[%d]", (int)data); + } +} + +TEST_CASE("touch button test", "[button][touch]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + uint8_t level = 0; + level = iot_button_get_key_level(btn); + ESP_LOGI(TAG, "button level is %d", level); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn); +} + +static void multiple_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%d]: %s", (int)data, iot_button_get_event_str(event)); +} + +TEST_CASE("touch multiple button test", "[button][touch][multiple]") +{ + touch_lowlevel_type_t *channel_type = calloc(2, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 2; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2}; + touch_lowlevel_config_t low_config = { + .channel_num = 2, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, multiple_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, multiple_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +static void light_and_heavy_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "touch button[%s]: %s", (int)data == 1 ? "light" : "heavy", iot_button_get_event_str(event)); +} + +TEST_CASE("touch button light and heavy press test", "[button][touch][light and heavy]") +{ + touch_lowlevel_type_t *channel_type = calloc(1, sizeof(touch_lowlevel_type_t)); + TEST_ASSERT_NOT_NULL(channel_type); + for (int i = 0; i < 1; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1}; + touch_lowlevel_config_t low_config = { + .channel_num = 1, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + TEST_ASSERT(ret == ESP_OK); + free(channel_type); + + const button_config_t btn_cfg = {0}; + button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = true, + }; + + button_handle_t btn_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_1); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_1); + + touch_cfg.touch_channel = TOUCH_CHANNEL_1; + touch_cfg.channel_threshold = 0.2; + button_handle_t btn_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn_2); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn_2); + touch_sensor_lowlevel_start(); + + iot_button_register_cb(btn_1, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_1, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)1); + iot_button_register_cb(btn_2, BUTTON_PRESS_DOWN, NULL, light_and_heavy_button_event_cb, (void *)2); + iot_button_register_cb(btn_2, BUTTON_PRESS_UP, NULL, light_and_heavy_button_event_cb, (void *)2); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } + + iot_button_delete(btn_1); + iot_button_delete(btn_2); +} + +TEST_CASE("touch button test memory leak", "[button][touch][memory leak]") +{ + const button_config_t btn_cfg = {0}; + const button_touch_config_t touch_cfg = { + .touch_channel = TOUCH_CHANNEL_1, + .channel_threshold = 0.1, + .skip_lowlevel_init = false, + }; + + button_handle_t btn = NULL; + esp_err_t ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg, &btn); + TEST_ASSERT(ret == ESP_OK); + TEST_ASSERT_NOT_NULL(btn); + + iot_button_register_cb(btn, BUTTON_PRESS_DOWN, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_REPEAT_DONE, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_SINGLE_CLICK, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_DOUBLE_CLICK, NULL, button_event_cb, NULL); + + /*!< Multiple Click must provide button_event_args_t */ + /*!< Double Click */ + button_event_args_t args = { + .multiple_clicks.clicks = 2, + }; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)2); + /*!< Triple Click */ + args.multiple_clicks.clicks = 3; + iot_button_register_cb(btn, BUTTON_MULTIPLE_CLICK, &args, button_event_cb, (void *)3); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_START, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_HOLD, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_LONG_PRESS_UP, NULL, button_event_cb, NULL); + iot_button_register_cb(btn, BUTTON_PRESS_END, NULL, button_event_cb, NULL); + + iot_button_delete(btn); +} diff --git a/esp-spot/example/simple_touch/components/touch_button/test_apps/sdkconfig.defaults b/esp-spot/example/simple_touch/components/touch_button/test_apps/sdkconfig.defaults new file mode 100644 index 0000000..9b28759 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/test_apps/sdkconfig.defaults @@ -0,0 +1,13 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.0 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_ESP_DEFAULT_CPU_FREQ_MHZ_240=y +CONFIG_ESP_TASK_WDT_EN=n +CONFIG_FREERTOS_HZ=1000 +CONFIG_TOUCH_BUTTON_SENSOR_BASELINE_COEF_X1000=1 +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_P_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_NOISE_N_SNR=100 +CONFIG_TOUCH_BUTTON_SENSOR_RESET_N=10 diff --git a/esp-spot/example/simple_touch/components/touch_button/touch_button.c b/esp-spot/example/simple_touch/components/touch_button/touch_button.c new file mode 100644 index 0000000..851b1a7 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/touch_button.c @@ -0,0 +1,75 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "esp_check.h" +#include "iot_button.h" +#include "touch_button_sensor.h" +#include "touch_button.h" + +static const char *TAG = "button_touch"; +typedef struct { + button_driver_t base; + int32_t touch_channel; + touch_button_handle_t touch_handle; /**< touch handle */ +} button_touch_obj; + +static uint8_t button_touch_get_key_level(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + touch_button_sensor_handle_events(touch_btn->touch_handle); + touch_state_t state; + touch_button_sensor_get_state(touch_btn->touch_handle, touch_btn->touch_channel, &state); + return state == TOUCH_STATE_ACTIVE ? BUTTON_ACTIVE : BUTTON_INACTIVE; +} + +static esp_err_t button_touch_delete(button_driver_t *button_driver) +{ + button_touch_obj *touch_btn = __containerof(button_driver, button_touch_obj, base); + esp_err_t ret = touch_button_sensor_delete(touch_btn->touch_handle); + free(touch_btn); + return ret; +} + +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button) +{ + ESP_LOGI(TAG, "Touch Button Version: %d.%d.%d", TOUCH_BUTTON_VER_MAJOR, TOUCH_BUTTON_VER_MINOR, TOUCH_BUTTON_VER_PATCH); + button_touch_obj *touch_btn = NULL; + esp_err_t ret = ESP_OK; + ESP_GOTO_ON_FALSE(button_config && touch_config && ret_button, ESP_ERR_INVALID_ARG, err, TAG, "Invalid argument"); + + touch_btn = (button_touch_obj *)calloc(1, sizeof(button_touch_obj)); + ESP_GOTO_ON_FALSE(touch_btn, ESP_ERR_NO_MEM, err, TAG, "No memory for gpio button"); + + touch_btn->touch_channel = touch_config->touch_channel; + + uint32_t channel_list[1] = {touch_config->touch_channel}; + float button_threshold[1] = {touch_config->channel_threshold}; + + touch_button_config_t config = { + .channel_num = 1, + .channel_list = channel_list, + .channel_threshold = button_threshold, + .skip_lowlevel_init = touch_config->skip_lowlevel_init, + .debounce_times = 1, + }; + ret = touch_button_sensor_create(&config, &touch_btn->touch_handle, NULL, NULL); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create touch button sensor failed"); + ret = iot_button_create(button_config, &touch_btn->base, ret_button); + ESP_GOTO_ON_FALSE(ret == ESP_OK, ESP_FAIL, err, TAG, "Create button failed"); + + touch_btn->base.get_key_level = button_touch_get_key_level; + touch_btn->base.del = button_touch_delete; + + return ESP_OK; +err: + if (touch_btn) { + free(touch_btn); + } + return ret; +} diff --git a/esp-spot/example/simple_touch/components/touch_button/touch_button.h b/esp-spot/example/simple_touch/components/touch_button/touch_button.h new file mode 100644 index 0000000..e3ccc89 --- /dev/null +++ b/esp-spot/example/simple_touch/components/touch_button/touch_button.h @@ -0,0 +1,48 @@ +/* + * SPDX-FileCopyrightText: 2024-2025 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: Apache-2.0 + */ +#pragma once + +#include "esp_err.h" +#include "button_types.h" + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * @brief Configuration structure for a touch button. + * + * This structure defines the configuration parameters required to initialize a touch button. + * It specifies the touch channel, activation threshold, and whether to skip low-level initialization. + */ +typedef struct { + int32_t touch_channel; /*!< The touch sensor channel assigned to the button */ + float channel_threshold; /*!< Activation threshold for the touch channel (range: 0.0 - 1.0) */ + bool skip_lowlevel_init; /*!< If true, skips low-level initialization (useful for pre-configured setups) */ +} button_touch_config_t; + +/** + * @brief Create a new touch button device. + * + * This function initializes a new touch button device based on the given configurations. + * It sets up the touch channel, configures the touch button sensor, and integrates it + * with the iot_button framework. + * + * @param[in] button_config Pointer to the general button configuration. + * @param[in] touch_config Pointer to the touch-specific button configuration. + * @param[out] ret_button Pointer to store the created button handle. + * + * @return + * - ESP_OK on success + * - ESP_ERR_INVALID_ARG if any argument is NULL + * - ESP_ERR_NO_MEM if memory allocation fails + * - ESP_FAIL if touch button sensor or button creation fails + */ +esp_err_t iot_button_new_touch_button_device(const button_config_t *button_config, const button_touch_config_t *touch_config, button_handle_t *ret_button); + +#ifdef __cplusplus +} +#endif diff --git a/esp-spot/example/simple_touch/main/CMakeLists.txt b/esp-spot/example/simple_touch/main/CMakeLists.txt new file mode 100644 index 0000000..d65c117 --- /dev/null +++ b/esp-spot/example/simple_touch/main/CMakeLists.txt @@ -0,0 +1,6 @@ +set(COMPONENT_REQUIRES touch_button) + +set(COMPONENT_SRCS "main.c") +set(COMPONENT_ADD_INCLUDEDIRS .) + +register_component() \ No newline at end of file diff --git a/esp-spot/example/simple_touch/main/main.c b/esp-spot/example/simple_touch/main/main.c new file mode 100644 index 0000000..6164a84 --- /dev/null +++ b/esp-spot/example/simple_touch/main/main.c @@ -0,0 +1,143 @@ +/* + * SPDX-FileCopyrightText: 2010-2022 Espressif Systems (Shanghai) CO LTD + * + * SPDX-License-Identifier: CC0-1.0 + */ + +#include +#include +#include "sdkconfig.h" +#include "freertos/FreeRTOS.h" +#include "freertos/task.h" +#include "driver/gpio.h" + +#include "touch_button.h" +#include "iot_button.h" +#include "touch_sensor_lowlevel.h" + +static const char *TAG = "main"; + +#define TOUCH_CHANNEL_1 (3) +#define TOUCH_CHANNEL_2 (9) +#define TOUCH_CHANNEL_3 (13) +#define TOUCH_CHANNEL_4 (14) + +#define LIGHT_TOUCH_THRESHOLD (0.15) +#define HEAVY_TOUCH_THRESHOLD (0.4) + +static void light_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "Light Button 1: %s", iot_button_get_event_str(event)); +} + +static void heavy_button_event_cb(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "Heavy Button 1: %s", iot_button_get_event_str(event)); +} + +static void touch_event_light_2(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "Light Button 2: %s", iot_button_get_event_str(event)); +} + +static void touch_event_light_3(void *arg, void *data) +{ + button_event_t event = iot_button_get_event(arg); + ESP_LOGI(TAG, "Light Button 3: %s", iot_button_get_event_str(event)); +} + +static void touch_task(void *arg) +{ + // Register all touch channel + uint32_t touch_channel_list[] = {TOUCH_CHANNEL_1, TOUCH_CHANNEL_2, TOUCH_CHANNEL_3, TOUCH_CHANNEL_4}; + int total_channel_num = sizeof(touch_channel_list) / sizeof(touch_channel_list[0]); + + // calloc channel_type for every button from the list + touch_lowlevel_type_t *channel_type = calloc(total_channel_num, sizeof(touch_lowlevel_type_t)); + assert(channel_type); + for (int i = 0; i < total_channel_num; i++) { + channel_type[i] = TOUCH_LOWLEVEL_TYPE_TOUCH; + } + + touch_lowlevel_config_t low_config = { + .channel_num = total_channel_num, + .channel_list = touch_channel_list, + .channel_type = channel_type, + }; + esp_err_t ret = touch_sensor_lowlevel_create(&low_config); + assert(ret == ESP_OK); + free(channel_type); + + /* ============================= Init touch IO3 ============================= */ + const button_config_t btn_cfg = { + .short_press_time = 300, + .long_press_time = 2000, + }; + button_touch_config_t touch_cfg_1 = { + .touch_channel = touch_channel_list[0], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + // Create light press button + button_handle_t btn_light_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_1, &btn_light_1); + assert(ret == ESP_OK); + + // Create heavy press button + touch_cfg_1.channel_threshold = HEAVY_TOUCH_THRESHOLD; + button_handle_t btn_heavy_1 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_1, &btn_heavy_1); + assert(ret == ESP_OK); + + /* ============================= Init touch IO9 ============================= */ + button_touch_config_t touch_cfg_2 = { + .touch_channel = touch_channel_list[1], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + // Create light press button + button_handle_t btn_light_2 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_2, &btn_light_2); + assert(ret == ESP_OK); + + /* ============================= Init touch IO13 ============================= */ + button_touch_config_t touch_cfg_3 = { + .touch_channel = touch_channel_list[2], + .channel_threshold = LIGHT_TOUCH_THRESHOLD, + .skip_lowlevel_init = true, + }; + + // Create light press button + button_handle_t btn_light_3 = NULL; + ret = iot_button_new_touch_button_device(&btn_cfg, &touch_cfg_3, &btn_light_3); + assert(ret == ESP_OK); + + /* ========================== Register touch callback ========================== */ + // Register touch IO3 callback + iot_button_register_cb(btn_light_1, BUTTON_PRESS_DOWN, NULL, light_button_event_cb, NULL); + iot_button_register_cb(btn_light_1, BUTTON_PRESS_UP, NULL, light_button_event_cb, NULL); + iot_button_register_cb(btn_heavy_1, BUTTON_PRESS_DOWN, NULL, heavy_button_event_cb, NULL); + iot_button_register_cb(btn_heavy_1, BUTTON_PRESS_UP, NULL, heavy_button_event_cb, NULL); + + // Register touch IO9 callback + iot_button_register_cb(btn_light_2, BUTTON_LONG_PRESS_START, NULL, touch_event_light_2, NULL); + + // Register touch IO13 callback + iot_button_register_cb(btn_light_3, BUTTON_PRESS_DOWN, NULL, touch_event_light_3, NULL); + + touch_sensor_lowlevel_start(); + + while (1) { + vTaskDelay(pdMS_TO_TICKS(1000)); + } +} + +void app_main(void) +{ + xTaskCreate(touch_task, "touch_task", 1024 * 5, NULL, 5, NULL); +} diff --git a/esp-spot/example/simple_touch/sdkconfig.defaults b/esp-spot/example/simple_touch/sdkconfig.defaults new file mode 100644 index 0000000..09c26b5 --- /dev/null +++ b/esp-spot/example/simple_touch/sdkconfig.defaults @@ -0,0 +1,7 @@ +# This file was generated using idf.py save-defconfig. It can be edited manually. +# Espressif IoT Development Framework (ESP-IDF) 5.3.1 Project Minimal Configuration +# +CONFIG_IDF_TARGET="esp32s3" +CONFIG_FREERTOS_ENABLE_BACKWARD_COMPATIBILITY=y +CONFIG_TOUCH_BUTTON_SENSOR_MAX_P_X1000=0 +CONFIG_TOUCH_BUTTON_SENSOR_MIN_N_X1000=0 diff --git a/esp-spot/example/xiaozhi/README.md b/esp-spot/example/xiaozhi/README.md new file mode 100644 index 0000000..e69de29 diff --git a/managed_components/.DS_Store b/managed_components/.DS_Store new file mode 100644 index 0000000..7373b35 Binary files /dev/null and b/managed_components/.DS_Store differ diff --git a/managed_components/78__esp-ml307/.gitignore b/managed_components/78__esp-ml307/.gitignore new file mode 100644 index 0000000..98c718a --- /dev/null +++ b/managed_components/78__esp-ml307/.gitignore @@ -0,0 +1,2 @@ +dist/ +.component_hash diff --git a/managed_components/78__esp-ml307/CHECKSUMS.json b/managed_components/78__esp-ml307/CHECKSUMS.json new file mode 100644 index 0000000..39d1e02 --- 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a/managed_components/78__esp-ml307/CMakeLists.txt b/managed_components/78__esp-ml307/CMakeLists.txt new file mode 100644 index 0000000..a971083 --- /dev/null +++ b/managed_components/78__esp-ml307/CMakeLists.txt @@ -0,0 +1,24 @@ +idf_component_register( + SRCS + "ml307_at_modem.cc" + "ml307_ssl_transport.cc" + "ml307_http.cc" + "ml307_mqtt.cc" + "ml307_udp.cc" + "web_socket.cc" + "tls_transport.cc" + "tcp_transport.cc" + "esp_http.cc" + "esp_mqtt.cc" + "esp_udp.cc" + INCLUDE_DIRS + "include" + PRIV_INCLUDE_DIRS + "." + REQUIRES + "esp_driver_gpio" + "esp_driver_uart" + "esp-tls" + "esp_http_client" + "mqtt" +) diff --git a/managed_components/78__esp-ml307/LICENSE b/managed_components/78__esp-ml307/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/managed_components/78__esp-ml307/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/managed_components/78__esp-ml307/README.md b/managed_components/78__esp-ml307/README.md new file mode 100644 index 0000000..81e1ab1 --- /dev/null +++ b/managed_components/78__esp-ml307/README.md @@ -0,0 +1,121 @@ +# ML307 Series Cat.1 AT Modem + +This is a component for the ML307R / ML307A Cat.1 Module. +This project is initially created for https://github.com/78/xiaozhi-esp32 + +## Features + +- AT Command +- MQTT / MQTTS +- HTTP / HTTPS +- SSLTCP +- WebSocket + +## Supported Modules + +- ML307R +- ML307A + +## Sample Code + +```cpp +#include "esp_log.h" +#include "Ml307AtModem.h" +#include "Ml307SslTransport.h" +#include "Ml307Http.h" +#include "Ml307Mqtt.h" + +static const char *TAG = "ML307"; + +void TestHttp(Ml307AtModem& modem) { + ESP_LOGI(TAG, "Starting HTTP test"); + + Ml307Http http(modem); + http.SetHeader("User-Agent", "Xiaozhi/1.0.0"); + http.Open("GET", "https://xiaozhi.me/"); + + // print body length & body + ESP_LOGI(TAG, "Response body: %zu bytes", http.GetBodyLength()); + ESP_LOGI(TAG, "Response body: %s", http.GetBody().c_str()); + http.Close(); +} + +void TestMqtt(Ml307AtModem& modem) { + ESP_LOGI(TAG, "Starting MQTT test"); + + Ml307Mqtt mqtt(modem, 0); + if (!mqtt.Connect("broker.emqx.io", 1883, "emqx", "public", "")) { + ESP_LOGE(TAG, "Failed to connect to MQTT broker"); + return; + } + mqtt.OnMessage([](const std::string& topic, const std::string& payload) { + ESP_LOGI(TAG, "Received message: %s, %s", topic.c_str(), payload.c_str()); + }); + mqtt.Subscribe("test/clientid/event"); + mqtt.Publish("test", "Hello, MQTT!"); + vTaskDelay(pdMS_TO_TICKS(5000)); + mqtt.Disconnect(); +} + +void TestWebSocket(Ml307AtModem& modem) { + ESP_LOGI(TAG, "Starting WebSocket test"); + + WebSocket ws(new Ml307SslTransport(modem, 0)); + ws.SetHeader("Protocol-Version", "2"); + + ws.OnConnected([]() { + ESP_LOGI(TAG, "Connected to server"); + }); + + ws.OnData([](const char* data, size_t length, bool binary) { + ESP_LOGI(TAG, "Received data: %.*s", length, data); + }); + + ws.OnDisconnected([]() { + ESP_LOGI(TAG, "Disconnected from server"); + }); + + ws.OnError([](int error) { + ESP_LOGE(TAG, "WebSocket error: %d", error); + }); + + if (!ws.Connect("wss://api.tenclass.net/xiaozhi/v1/")) { + ESP_LOGE(TAG, "Failed to connect to server"); + return; + } + + for (int i = 0; i < 10; i++) { + ws.Send("{\"type\": \"hello\"}"); + vTaskDelay(pdMS_TO_TICKS(1000)); + } + ws.Close(); +} + + +extern "C" void app_main(void) { + Ml307AtModem modem(GPIO_NUM_13, GPIO_NUM_14, 2048); + modem.SetDebug(true); + modem.SetBaudRate(921600); + + modem.WaitForNetworkReady(); + + // Print IP Address + ESP_LOGI(TAG, "IP Address: %s", modem.ip_address().c_str()); + // Print IMEI, ICCID, Product ID, Carrier Name + ESP_LOGI(TAG, "IMEI: %s", modem.GetImei().c_str()); + ESP_LOGI(TAG, "ICCID: %s", modem.GetIccid().c_str()); + ESP_LOGI(TAG, "Product ID: %s", modem.GetModuleName().c_str()); + ESP_LOGI(TAG, "Carrier Name: %s", modem.GetCarrierName().c_str()); + // Print CSQ + ESP_LOGI(TAG, "CSQ: %d", modem.GetCsq()); + + TestMqtt(modem); + TestHttp(modem); + TestWebSocket(modem); +} + +``` + +## Author + +- Terrence (terrence@tenclass.com) diff --git a/managed_components/78__esp-ml307/esp_http.cc b/managed_components/78__esp-ml307/esp_http.cc new file mode 100644 index 0000000..0f9ad3b --- /dev/null +++ b/managed_components/78__esp-ml307/esp_http.cc @@ -0,0 +1,112 @@ +#include "esp_http.h" +#include +#include +#include +#include + +static const char* TAG = "EspHttp"; + +EspHttp::EspHttp() : client_(nullptr), status_code_(0) {} + +EspHttp::~EspHttp() { + Close(); +} + +void EspHttp::SetHeader(const std::string& key, const std::string& value) { + headers_[key] = value; +} + +bool EspHttp::Open(const std::string& method, const std::string& url, const std::string& content) { + esp_http_client_config_t config = {}; + config.url = url.c_str(); + config.crt_bundle_attach = esp_crt_bundle_attach; + + ESP_LOGI(TAG, "Opening HTTP connection to %s", url.c_str()); + + client_ = esp_http_client_init(&config); + if (!client_) { + ESP_LOGE(TAG, "Failed to initialize HTTP client"); + return false; + } + + esp_http_client_set_method(client_, + method == "GET" ? HTTP_METHOD_GET : + method == "POST" ? HTTP_METHOD_POST : + method == "PUT" ? HTTP_METHOD_PUT : + method == "DELETE" ? HTTP_METHOD_DELETE : HTTP_METHOD_GET); + + for (const auto& header : headers_) { + esp_http_client_set_header(client_, header.first.c_str(), header.second.c_str()); + } + + esp_err_t err = esp_http_client_open(client_, content.length()); + if (err != ESP_OK) { + ESP_LOGE(TAG, "Failed to perform HTTP request: %s", esp_err_to_name(err)); + Close(); + return false; + } + + auto written = esp_http_client_write(client_, content.data(), content.length()); + if (written < 0) { + ESP_LOGE(TAG, "Failed to write request body: %s", esp_err_to_name(err)); + Close(); + return false; + } + content_length_ = esp_http_client_fetch_headers(client_); + if (content_length_ <= 0) { + ESP_LOGE(TAG, "Failed to fetch headers"); + Close(); + return false; + } + return true; +} + +void EspHttp::Close() { + if (client_) { + esp_http_client_cleanup(client_); + client_ = nullptr; + } +} + +int EspHttp::GetStatusCode() const { + return status_code_; +} + +std::string EspHttp::GetResponseHeader(const std::string& key) const { + if (!client_) return ""; + char* value = nullptr; + esp_http_client_get_header(client_, key.c_str(), &value); + if (!value) return ""; + std::string result(value); + free(value); + return result; +} + +size_t EspHttp::GetBodyLength() const { + return content_length_; +} + +const std::string& EspHttp::GetBody() { + response_body_.resize(content_length_); + assert(Read(const_cast(response_body_.data()), content_length_) == content_length_); + return response_body_; +} + +int EspHttp::Read(char* buffer, size_t buffer_size) { + if (!client_) return -1; + return esp_http_client_read(client_, buffer, buffer_size); +} + +esp_err_t EspHttp::HttpEventHandler(esp_http_client_event_t *evt) { + EspHttp* http = static_cast(evt->user_data); + switch (evt->event_id) { + case HTTP_EVENT_ON_DATA: + if (evt->data_len > 0) { + http->response_body_.append((char*)evt->data, evt->data_len); + } + break; + default: + break; + } + return ESP_OK; +} diff --git a/managed_components/78__esp-ml307/esp_mqtt.cc b/managed_components/78__esp-ml307/esp_mqtt.cc new file mode 100644 index 0000000..ae7ec9a --- /dev/null +++ b/managed_components/78__esp-ml307/esp_mqtt.cc @@ -0,0 +1,121 @@ +#include "esp_mqtt.h" +#include +#include + +static const char *TAG = "esp_mqtt"; + +EspMqtt::EspMqtt() { + event_group_handle_ = xEventGroupCreate(); +} + +EspMqtt::~EspMqtt() { + if (event_group_handle_ != nullptr) { + Disconnect(); + } + + vEventGroupDelete(event_group_handle_); +} + +bool EspMqtt::Connect(const std::string broker_address, int broker_port, const std::string client_id, const std::string username, const std::string password) { + if (mqtt_client_handle_ != nullptr) { + Disconnect(); + } + + esp_mqtt_client_config_t mqtt_config = {}; + mqtt_config.broker.address.hostname = broker_address.c_str(); + mqtt_config.broker.address.port = broker_port; + if (broker_port == 8883) { + mqtt_config.broker.address.transport = MQTT_TRANSPORT_OVER_SSL; + mqtt_config.broker.verification.crt_bundle_attach = esp_crt_bundle_attach; + } else { + mqtt_config.broker.address.transport = MQTT_TRANSPORT_OVER_TCP; + } + mqtt_config.credentials.client_id = client_id.c_str(); + mqtt_config.credentials.username = username.c_str(); + mqtt_config.credentials.authentication.password = password.c_str(); + mqtt_config.session.keepalive = keep_alive_seconds_; + + mqtt_client_handle_ = esp_mqtt_client_init(&mqtt_config); + esp_mqtt_client_register_event(mqtt_client_handle_, MQTT_EVENT_ANY, [](void *handler_args, esp_event_base_t base, int32_t event_id, void *event_data) { + ((EspMqtt*)handler_args)->MqttEventCallback(base, event_id, event_data); + }, this); + esp_mqtt_client_start(mqtt_client_handle_); + + auto bits = xEventGroupWaitBits(event_group_handle_, MQTT_CONNECTED_EVENT | MQTT_DISCONNECTED_EVENT | MQTT_ERROR_EVENT, + pdTRUE, pdFALSE, pdMS_TO_TICKS(MQTT_CONNECT_TIMEOUT_MS)); + connected_ = (bits & MQTT_CONNECTED_EVENT) != 0; + return connected_; +} + +void EspMqtt::MqttEventCallback(esp_event_base_t base, int32_t event_id, void *event_data) { + auto event = (esp_mqtt_event_t*)event_data; + switch (event_id) { + case MQTT_EVENT_CONNECTED: + xEventGroupSetBits(event_group_handle_, MQTT_CONNECTED_EVENT); + break; + case MQTT_EVENT_DISCONNECTED: + connected_ = false; + xEventGroupSetBits(event_group_handle_, MQTT_DISCONNECTED_EVENT); + break; + case MQTT_EVENT_DATA: { + auto topic = std::string(event->topic, event->topic_len); + auto payload = std::string(event->data, event->data_len); + if (event->data_len == event->total_data_len) { + if (on_message_callback_) { + on_message_callback_(topic, payload); + } + } else { + message_payload_.append(payload); + if (message_payload_.size() >= event->total_data_len && on_message_callback_) { + on_message_callback_(topic, message_payload_); + message_payload_.clear(); + } + } + break; + } + case MQTT_EVENT_BEFORE_CONNECT: + break; + case MQTT_EVENT_SUBSCRIBED: + break; + case MQTT_EVENT_ERROR: + xEventGroupSetBits(event_group_handle_, MQTT_ERROR_EVENT); + ESP_LOGI(TAG, "MQTT error occurred: %s", esp_err_to_name(event->error_handle->esp_tls_last_esp_err)); + break; + default: + ESP_LOGI(TAG, "Unhandled event id %ld", event_id); + break; + } +} + +void EspMqtt::Disconnect() { + esp_mqtt_client_stop(mqtt_client_handle_); + esp_mqtt_client_destroy(mqtt_client_handle_); + mqtt_client_handle_ = nullptr; + connected_ = false; + xEventGroupClearBits(event_group_handle_, MQTT_CONNECTED_EVENT | MQTT_DISCONNECTED_EVENT | MQTT_ERROR_EVENT); +} + +bool EspMqtt::Publish(const std::string topic, const std::string payload, int qos) { + if (!connected_) { + return false; + } + return esp_mqtt_client_publish(mqtt_client_handle_, topic.c_str(), payload.data(), payload.size(), qos, 0) == 0; +} + +bool EspMqtt::Subscribe(const std::string topic, int qos) { + if (!connected_) { + return false; + } + return esp_mqtt_client_subscribe_single(mqtt_client_handle_, topic.c_str(), qos) == 0; +} + +bool EspMqtt::Unsubscribe(const std::string topic) { + if (!connected_) { + return false; + } + return esp_mqtt_client_unsubscribe(mqtt_client_handle_, topic.c_str()) == 0; +} + +bool EspMqtt::IsConnected() { + return connected_; +} diff --git a/managed_components/78__esp-ml307/esp_udp.cc b/managed_components/78__esp-ml307/esp_udp.cc new file mode 100644 index 0000000..f933f44 --- /dev/null +++ b/managed_components/78__esp-ml307/esp_udp.cc @@ -0,0 +1,84 @@ +#include "esp_udp.h" + +#include +#include +#include +#include +#include +#include + +static const char *TAG = "EspUdp"; + +EspUdp::EspUdp() : udp_fd_(-1) { +} + +EspUdp::~EspUdp() { + Disconnect(); +} + +bool EspUdp::Connect(const std::string& host, int port) { + struct sockaddr_in server_addr; + bzero(&server_addr, sizeof(server_addr)); + server_addr.sin_family = AF_INET; + server_addr.sin_port = htons(port); + // host is domain + struct hostent *server = gethostbyname(host.c_str()); + if (server == NULL) { + ESP_LOGE(TAG, "Failed to get host by name"); + return false; + } + memcpy(&server_addr.sin_addr, server->h_addr, server->h_length); + + udp_fd_ = socket(AF_INET, SOCK_DGRAM, 0); + if (udp_fd_ < 0) { + ESP_LOGE(TAG, "Failed to create socket"); + return false; + } + + int ret = connect(udp_fd_, (struct sockaddr*)&server_addr, sizeof(server_addr)); + if (ret < 0) { + ESP_LOGE(TAG, "Failed to connect to %s:%d", host.c_str(), port); + close(udp_fd_); + udp_fd_ = -1; + return false; + } + + connected_ = true; + receive_thread_ = std::thread(&EspUdp::ReceiveTask, this); + return true; +} + +void EspUdp::Disconnect() { + if (udp_fd_ != -1) { + close(udp_fd_); + udp_fd_ = -1; + } + connected_ = false; + if (receive_thread_.joinable()) { + receive_thread_.join(); + } +} + +int EspUdp::Send(const std::string& data) { + int ret = send(udp_fd_, data.data(), data.size(), 0); + if (ret <= 0) { + connected_ = false; + ESP_LOGE(TAG, "Send failed: ret=%d", ret); + } + return ret; +} + +void EspUdp::ReceiveTask() { + while (true) { + std::string data; + data.resize(1500); + int ret = recv(udp_fd_, data.data(), data.size(), 0); + if (ret <= 0) { + break; + } + data.resize(ret); + if (message_callback_) { + message_callback_(data); + } + } +} diff --git a/managed_components/78__esp-ml307/idf_component.yml b/managed_components/78__esp-ml307/idf_component.yml new file mode 100644 index 0000000..81b5f71 --- /dev/null +++ b/managed_components/78__esp-ml307/idf_component.yml @@ -0,0 +1,11 @@ +dependencies: + idf: ^5.3 +description: ESP32 ML307R/ML307A Cat.1 Cellular Module +files: + exclude: + - .git + - dist +license: MIT +repository: https://github.com/78/esp-ml307 +url: https://github.com/78/esp-ml307 +version: 1.7.3 diff --git a/managed_components/78__esp-ml307/include/esp_http.h b/managed_components/78__esp-ml307/include/esp_http.h new file mode 100644 index 0000000..cc8448f --- /dev/null +++ b/managed_components/78__esp-ml307/include/esp_http.h @@ -0,0 +1,35 @@ +#ifndef ESP_HTTP_H +#define ESP_HTTP_H + +#include "http.h" +#include + +#include +#include + +class EspHttp : public Http { +public: + EspHttp(); + virtual ~EspHttp(); + + void SetHeader(const std::string& key, const std::string& value) override; + bool Open(const std::string& method, const std::string& url, const std::string& content = "") override; + void Close() override; + + int GetStatusCode() const override; + std::string GetResponseHeader(const std::string& key) const override; + size_t GetBodyLength() const override; + const std::string& GetBody() override; + int Read(char* buffer, size_t buffer_size) override; + +private: + esp_http_client_handle_t client_; + std::map headers_; + std::string response_body_; + int status_code_; + int64_t content_length_; + + static esp_err_t HttpEventHandler(esp_http_client_event_t *evt); +}; + +#endif // ESP_HTTP_H diff --git a/managed_components/78__esp-ml307/include/esp_mqtt.h b/managed_components/78__esp-ml307/include/esp_mqtt.h new file mode 100644 index 0000000..36bf176 --- /dev/null +++ b/managed_components/78__esp-ml307/include/esp_mqtt.h @@ -0,0 +1,46 @@ +#ifndef ESP_MQTT_H +#define ESP_MQTT_H + +#include "mqtt.h" + +#include +#include +#include +#include +#include +#include + +#define MQTT_CONNECT_TIMEOUT_MS 10000 + +#define MQTT_INITIALIZED_EVENT BIT0 +#define MQTT_CONNECTED_EVENT BIT1 +#define MQTT_DISCONNECTED_EVENT BIT2 +#define MQTT_ERROR_EVENT BIT3 + +class EspMqtt : public Mqtt { +public: + EspMqtt(); + ~EspMqtt(); + + bool Connect(const std::string broker_address, int broker_port, const std::string client_id, const std::string username, const std::string password); + void Disconnect(); + bool Publish(const std::string topic, const std::string payload, int qos = 0); + bool Subscribe(const std::string topic, int qos = 0); + bool Unsubscribe(const std::string topic); + bool IsConnected(); + +private: + bool connected_ = false; + EventGroupHandle_t event_group_handle_; + std::string broker_address_; + int broker_port_ = 1883; + std::string client_id_; + std::string username_; + std::string password_; + std::string message_payload_; + esp_mqtt_client_handle_t mqtt_client_handle_ = nullptr; + + void MqttEventCallback(esp_event_base_t base, int32_t event_id, void *event_data); +}; + +#endif \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/esp_udp.h b/managed_components/78__esp-ml307/include/esp_udp.h new file mode 100644 index 0000000..4ffcc6a --- /dev/null +++ b/managed_components/78__esp-ml307/include/esp_udp.h @@ -0,0 +1,26 @@ +#ifndef ESP_UDP_H +#define ESP_UDP_H + +#include "udp.h" + +#include +#include +#include + +class EspUdp : public Udp { +public: + EspUdp(); + ~EspUdp(); + + bool Connect(const std::string& host, int port) override; + void Disconnect() override; + int Send(const std::string& data) override; + +private: + int udp_fd_; + std::thread receive_thread_; + + void ReceiveTask(); +}; + +#endif // ESP_UDP_H diff --git a/managed_components/78__esp-ml307/include/http.h b/managed_components/78__esp-ml307/include/http.h new file mode 100644 index 0000000..c757c5c --- /dev/null +++ b/managed_components/78__esp-ml307/include/http.h @@ -0,0 +1,37 @@ +#ifndef HTTP_H +#define HTTP_H + +#include +#include +#include + +class Http { +public: + virtual ~Http() = default; + + // 设置 HTTP 请求头 + virtual void SetHeader(const std::string& key, const std::string& value) = 0; + + // 打开 HTTP 连接并发送请求 + virtual bool Open(const std::string& method, const std::string& url, const std::string& content = "") = 0; + + // 关闭 HTTP 连接 + virtual void Close() = 0; + + // 获取 HTTP 响应状态码 + virtual int GetStatusCode() const = 0; + + // 获取指定 key 的 HTTP 响应头 + virtual std::string GetResponseHeader(const std::string& key) const = 0; + + // 获取 HTTP 响应体长度 + virtual size_t GetBodyLength() const = 0; + + // 获取 HTTP 响应体 + virtual const std::string& GetBody() = 0; + + // 读取 HTTP 响应数据 + virtual int Read(char* buffer, size_t buffer_size) = 0; +}; + +#endif // HTTP_H \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/ml307_at_modem.h b/managed_components/78__esp-ml307/include/ml307_at_modem.h new file mode 100644 index 0000000..4ea93d3 --- /dev/null +++ b/managed_components/78__esp-ml307/include/ml307_at_modem.h @@ -0,0 +1,107 @@ +#ifndef _ML307_AT_MODEM_H_ +#define _ML307_AT_MODEM_H_ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define AT_EVENT_DATA_AVAILABLE BIT1 +#define AT_EVENT_COMMAND_DONE BIT2 +#define AT_EVENT_COMMAND_ERROR BIT3 +#define AT_EVENT_NETWORK_READY BIT4 + +#define DEFAULT_COMMAND_TIMEOUT 3000 +#define DEFAULT_BAUD_RATE 115200 +#define DEFAULT_UART_NUM UART_NUM_1 + +struct AtArgumentValue { + enum class Type { + String, + Int, + Double + }; + Type type; + std::string string_value; + int int_value; + double double_value; +}; + +typedef std::function& arguments)> CommandResponseCallback; + +class Ml307AtModem { +public: + Ml307AtModem(int tx_pin = GPIO_NUM_17, int rx_pin = GPIO_NUM_18, size_t rx_buffer_size = 2048); + ~Ml307AtModem(); + + std::string EncodeHex(const std::string& data); + std::string DecodeHex(const std::string& data); + void EncodeHexAppend(std::string& dest, const char* data, size_t length); + void DecodeHexAppend(std::string& dest, const char* data, size_t length); + + bool Command(const std::string command, int timeout_ms = DEFAULT_COMMAND_TIMEOUT); + std::list::iterator RegisterCommandResponseCallback(CommandResponseCallback callback); + void UnregisterCommandResponseCallback(std::list::iterator iterator); + + void OnMaterialReady(std::function callback); + void Reset(); + void ResetConnections(); + void SetDebug(bool debug); + bool SetBaudRate(int new_baud_rate); + int WaitForNetworkReady(); + + std::string GetImei(); + std::string GetIccid(); + std::string GetModuleName(); + std::string GetCarrierName(); + int GetCsq(); + + const std::string& ip_address() const { return ip_address_; } + bool network_ready() const { return network_ready_; } + int registration_state() const { return registration_state_; } + int pin_ready() const { return pin_ready_; } +private: + std::mutex mutex_; + std::mutex command_mutex_; + bool debug_ = false; + bool network_ready_ = false; + std::string ip_address_; + std::string iccid_; + std::string carrier_name_; + int csq_ = -1; + int registration_state_ = 0; + int pin_ready_ = 0; + + std::string rx_buffer_; + size_t rx_buffer_size_; + uart_port_t uart_num_; + int tx_pin_; + int rx_pin_; + int baud_rate_; + TaskHandle_t event_task_handle_ = nullptr; + TaskHandle_t receive_task_handle_ = nullptr; + QueueHandle_t event_queue_handle_ = nullptr; + EventGroupHandle_t event_group_handle_ = nullptr; + std::string last_command_; + std::string response_; + + void EventTask(); + void ReceiveTask(); + bool ParseResponse(); + bool DetectBaudRate(); + void NotifyCommandResponse(const std::string& command, const std::vector& arguments); + + std::list on_data_received_; + std::function on_material_ready_; +}; + + +#endif // _ML307_AT_MODEM_H_ \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/ml307_http.h b/managed_components/78__esp-ml307/include/ml307_http.h new file mode 100644 index 0000000..82597f1 --- /dev/null +++ b/managed_components/78__esp-ml307/include/ml307_http.h @@ -0,0 +1,64 @@ +#ifndef ML307_HTTP_TRANSPORT_H +#define ML307_HTTP_TRANSPORT_H + +#include "ml307_at_modem.h" +#include "http.h" +#include +#include + +#include +#include +#include +#include +#include + +#define HTTP_CONNECT_TIMEOUT_MS 30000 + +#define ML307_HTTP_EVENT_INITIALIZED (1 << 0) +#define ML307_HTTP_EVENT_ERROR (1 << 2) +#define ML307_HTTP_EVENT_HEADERS_RECEIVED (1 << 3) + +class Ml307Http : public Http { +public: + Ml307Http(Ml307AtModem& modem); + ~Ml307Http(); + + void SetHeader(const std::string& key, const std::string& value) override; + bool Open(const std::string& method, const std::string& url, const std::string& content = "") override; + void Close() override; + + int GetStatusCode() const override { return status_code_; } + std::string GetResponseHeader(const std::string& key) const override; + size_t GetBodyLength() const override; + const std::string& GetBody() override; + int Read(char* buffer, size_t buffer_size) override; + +private: + Ml307AtModem& modem_; + EventGroupHandle_t event_group_handle_; + std::mutex mutex_; + std::condition_variable cv_; + + int http_id_ = -1; + int status_code_ = -1; + int error_code_ = -1; + std::string rx_buffer_; + std::list::iterator command_callback_it_; + std::map headers_; + std::string url_; + std::string method_; + std::string protocol_; + std::string host_; + std::string path_; + std::map response_headers_; + std::string body_; + size_t body_offset_ = 0; + size_t content_length_ = 0; + bool eof_ = false; + bool connected_ = false; + + void ParseResponseHeaders(const std::string& headers); + std::string ErrorCodeToString(int error_code); +}; + +#endif \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/ml307_mqtt.h b/managed_components/78__esp-ml307/include/ml307_mqtt.h new file mode 100644 index 0000000..a21cd03 --- /dev/null +++ b/managed_components/78__esp-ml307/include/ml307_mqtt.h @@ -0,0 +1,48 @@ +#ifndef ML307_MQTT_H +#define ML307_MQTT_H + +#include "mqtt.h" + +#include "ml307_at_modem.h" +#include +#include +#include +#include +#include + +#define MQTT_CONNECT_TIMEOUT_MS 10000 + +#define MQTT_INITIALIZED_EVENT BIT0 +#define MQTT_CONNECTED_EVENT BIT1 +#define MQTT_DISCONNECTED_EVENT BIT2 + +class Ml307Mqtt : public Mqtt { +public: + Ml307Mqtt(Ml307AtModem& modem, int mqtt_id); + ~Ml307Mqtt(); + + bool Connect(const std::string broker_address, int broker_port, const std::string client_id, const std::string username, const std::string password); + void Disconnect(); + bool Publish(const std::string topic, const std::string payload, int qos = 0); + bool Subscribe(const std::string topic, int qos = 0); + bool Unsubscribe(const std::string topic); + bool IsConnected(); + +private: + Ml307AtModem& modem_; + int mqtt_id_; + bool connected_ = false; + EventGroupHandle_t event_group_handle_; + std::string broker_address_; + int broker_port_ = 1883; + std::string client_id_; + std::string username_; + std::string password_; + std::string message_payload_; + + std::list::iterator command_callback_it_; + + std::string ErrorToString(int error_code); +}; + +#endif \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/ml307_ssl_transport.h b/managed_components/78__esp-ml307/include/ml307_ssl_transport.h new file mode 100644 index 0000000..8bf15d0 --- /dev/null +++ b/managed_components/78__esp-ml307/include/ml307_ssl_transport.h @@ -0,0 +1,42 @@ +#ifndef ML307_SSL_TRANSPORT_H +#define ML307_SSL_TRANSPORT_H + +#include +#include +#include +#include +#include "transport.h" +#include "ml307_at_modem.h" + +#include +#include + +#define ML307_SSL_TRANSPORT_CONNECTED BIT0 +#define ML307_SSL_TRANSPORT_DISCONNECTED BIT1 +#define ML307_SSL_TRANSPORT_ERROR BIT2 +#define ML307_SSL_TRANSPORT_RECEIVE BIT3 +#define ML307_SSL_TRANSPORT_SEND_COMPLETE BIT4 +#define ML307_SSL_TRANSPORT_INITIALIZED BIT5 + +#define SSL_CONNECT_TIMEOUT_MS 10000 + +class Ml307SslTransport : public Transport { +public: + Ml307SslTransport(Ml307AtModem& modem, int tcp_id); + ~Ml307SslTransport(); + + bool Connect(const char* host, int port) override; + void Disconnect() override; + int Send(const char* data, size_t length) override; + int Receive(char* buffer, size_t bufferSize) override; + +private: + std::mutex mutex_; + Ml307AtModem& modem_; + EventGroupHandle_t event_group_handle_; + int tcp_id_ = 0; + std::string rx_buffer_; + std::list::iterator command_callback_it_; +}; + +#endif // ML307_SSL_TRANSPORT_H diff --git a/managed_components/78__esp-ml307/include/ml307_udp.h b/managed_components/78__esp-ml307/include/ml307_udp.h new file mode 100644 index 0000000..cd27fc9 --- /dev/null +++ b/managed_components/78__esp-ml307/include/ml307_udp.h @@ -0,0 +1,35 @@ +#ifndef ML307_UDP_H +#define ML307_UDP_H + +#include "udp.h" +#include "ml307_at_modem.h" + +#include +#include + +#define ML307_UDP_CONNECTED BIT0 +#define ML307_UDP_DISCONNECTED BIT1 +#define ML307_UDP_ERROR BIT2 +#define ML307_UDP_RECEIVE BIT3 +#define ML307_UDP_SEND_COMPLETE BIT4 +#define ML307_UDP_INITIALIZED BIT5 + +#define UDP_CONNECT_TIMEOUT_MS 10000 + +class Ml307Udp : public Udp { +public: + Ml307Udp(Ml307AtModem& modem, int udp_id); + ~Ml307Udp(); + + bool Connect(const std::string& host, int port) override; + void Disconnect() override; + int Send(const std::string& data) override; + +private: + Ml307AtModem& modem_; + int udp_id_; + EventGroupHandle_t event_group_handle_; + std::list::iterator command_callback_it_; +}; + +#endif // ML307_UDP_H diff --git a/managed_components/78__esp-ml307/include/mqtt.h b/managed_components/78__esp-ml307/include/mqtt.h new file mode 100644 index 0000000..3883d68 --- /dev/null +++ b/managed_components/78__esp-ml307/include/mqtt.h @@ -0,0 +1,30 @@ +#ifndef MQTT_INTERFACE_H +#define MQTT_INTERFACE_H + +#include +#include + +class Mqtt { +public: + virtual ~Mqtt() {} + + void SetKeepAlive(int keep_alive_seconds) { keep_alive_seconds_ = keep_alive_seconds; } + virtual bool Connect(const std::string broker_address, int broker_port, const std::string client_id, const std::string username, const std::string password) = 0; + virtual void Disconnect() = 0; + virtual bool Publish(const std::string topic, const std::string payload, int qos = 0) = 0; + virtual bool Subscribe(const std::string topic, int qos = 0) = 0; + virtual bool Unsubscribe(const std::string topic) = 0; + virtual bool IsConnected() = 0; + + virtual void OnConnected(std::function callback) { on_connected_callback_ = callback; } + virtual void OnDisconnected(std::function callback) { on_disconnected_callback_ = callback; } + virtual void OnMessage(std::function callback) { on_message_callback_ = callback; } + +protected: + int keep_alive_seconds_ = 60; + std::function on_message_callback_; + std::function on_connected_callback_; + std::function on_disconnected_callback_; +}; + +#endif // MQTT_INTERFACE_H diff --git a/managed_components/78__esp-ml307/include/tcp_transport.h b/managed_components/78__esp-ml307/include/tcp_transport.h new file mode 100644 index 0000000..87d4f9a --- /dev/null +++ b/managed_components/78__esp-ml307/include/tcp_transport.h @@ -0,0 +1,20 @@ +#ifndef _TCP_TRANSPORT_H_ +#define _TCP_TRANSPORT_H_ + +#include "transport.h" + +class TcpTransport : public Transport { +public: + TcpTransport(); + ~TcpTransport(); + + bool Connect(const char* host, int port) override; + void Disconnect() override; + int Send(const char* data, size_t length) override; + int Receive(char* buffer, size_t bufferSize) override; + +private: + int fd_; +}; + +#endif // _TCP_TRANSPORT_H_ \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/tls_transport.h b/managed_components/78__esp-ml307/include/tls_transport.h new file mode 100644 index 0000000..ddb135d --- /dev/null +++ b/managed_components/78__esp-ml307/include/tls_transport.h @@ -0,0 +1,21 @@ +#ifndef _TLS_TRANSPORT_H_ +#define _TLS_TRANSPORT_H_ + +#include "transport.h" +#include + +class TlsTransport : public Transport { +public: + TlsTransport(); + ~TlsTransport(); + + bool Connect(const char* host, int port) override; + void Disconnect() override; + int Send(const char* data, size_t length) override; + int Receive(char* buffer, size_t bufferSize) override; + +private: + esp_tls_t* tls_client_; +}; + +#endif // _TLS_TRANSPORT_H_ \ No newline at end of file diff --git a/managed_components/78__esp-ml307/include/transport.h b/managed_components/78__esp-ml307/include/transport.h new file mode 100644 index 0000000..2415e57 --- /dev/null +++ b/managed_components/78__esp-ml307/include/transport.h @@ -0,0 +1,20 @@ +#ifndef _TRANSPORT_H_ +#define _TRANSPORT_H_ + +#include + +class Transport { +public: + virtual ~Transport() = default; + virtual bool Connect(const char* host, int port) = 0; + virtual void Disconnect() = 0; + virtual int Send(const char* data, size_t length) = 0; + virtual int Receive(char* buffer, size_t bufferSize) = 0; + + bool connected() const { return connected_; } + +protected: + bool connected_ = false; +}; + +#endif // _TRANSPORT_H_ diff --git a/managed_components/78__esp-ml307/include/udp.h b/managed_components/78__esp-ml307/include/udp.h new file mode 100644 index 0000000..be5f463 --- /dev/null +++ b/managed_components/78__esp-ml307/include/udp.h @@ -0,0 +1,25 @@ +#ifndef UDP_H +#define UDP_H + + +#include +#include + +class Udp { +public: + virtual ~Udp() = default; + virtual bool Connect(const std::string& host, int port) = 0; + virtual void Disconnect() = 0; + virtual int Send(const std::string& data) = 0; + + virtual void OnMessage(std::function callback) { + message_callback_ = callback; + } + bool connected() const { return connected_; } + +protected: + std::function message_callback_; + bool connected_ = false; +}; + +#endif // UDP_H diff --git a/managed_components/78__esp-ml307/include/web_socket.h b/managed_components/78__esp-ml307/include/web_socket.h new file mode 100644 index 0000000..80c988c --- /dev/null +++ b/managed_components/78__esp-ml307/include/web_socket.h @@ -0,0 +1,47 @@ +#ifndef WEBSOCKET_H +#define WEBSOCKET_H + +#include +#include +#include +#include +#include "transport.h" + + +class WebSocket { +public: + WebSocket(Transport *transport); + ~WebSocket(); + + void SetHeader(const char* key, const char* value); + void SetReceiveBufferSize(size_t size); + bool IsConnected() const; + bool Connect(const char* uri); + bool Send(const std::string& data); + bool Send(const void* data, size_t len, bool binary = false, bool fin = true); + void Ping(); + void Close(); + + void OnConnected(std::function callback); + void OnDisconnected(std::function callback); + void OnData(std::function callback); + void OnError(std::function callback); + +private: + Transport *transport_; + std::thread receive_thread_; + bool continuation_ = false; + size_t receive_buffer_size_ = 2048; + + std::map headers_; + std::function on_data_; + std::function on_error_; + std::function on_connected_; + std::function on_disconnected_; + + void ReceiveTask(); + bool SendAllRaw(const void* data, size_t len); + bool SendControlFrame(uint8_t opcode, const void* data, size_t len); +}; + +#endif // WEBSOCKET_H diff --git a/managed_components/78__esp-ml307/ml307_at_modem.cc b/managed_components/78__esp-ml307/ml307_at_modem.cc new file mode 100644 index 0000000..b2a812d --- /dev/null +++ b/managed_components/78__esp-ml307/ml307_at_modem.cc @@ -0,0 +1,395 @@ +#include "ml307_at_modem.h" +#include +#include +#include +#include +#include + +static const char* TAG = "Ml307AtModem"; + + +static bool is_number(const std::string& s) { + return !s.empty() && std::all_of(s.begin(), s.end(), ::isdigit) && s.length() < 10; +} + +Ml307AtModem::Ml307AtModem(int tx_pin, int rx_pin, size_t rx_buffer_size) + : rx_buffer_size_(rx_buffer_size), uart_num_(DEFAULT_UART_NUM), tx_pin_(tx_pin), rx_pin_(rx_pin), baud_rate_(DEFAULT_BAUD_RATE) { + event_group_handle_ = xEventGroupCreate(); + + uart_config_t uart_config = {}; + uart_config.baud_rate = baud_rate_; + uart_config.data_bits = UART_DATA_8_BITS; + uart_config.parity = UART_PARITY_DISABLE; + uart_config.stop_bits = UART_STOP_BITS_1; + uart_config.source_clk = UART_SCLK_DEFAULT; + + ESP_ERROR_CHECK(uart_driver_install(uart_num_, rx_buffer_size_ * 2, 0, 100, &event_queue_handle_, 0)); + ESP_ERROR_CHECK(uart_param_config(uart_num_, &uart_config)); + ESP_ERROR_CHECK(uart_set_pin(uart_num_, tx_pin_, rx_pin_, UART_PIN_NO_CHANGE, UART_PIN_NO_CHANGE)); + + xTaskCreate([](void* arg) { + auto ml307_at_modem = (Ml307AtModem*)arg; + ml307_at_modem->EventTask(); + vTaskDelete(NULL); + }, "modem_event", 4096, this, 15, &event_task_handle_); + + xTaskCreate([](void* arg) { + auto ml307_at_modem = (Ml307AtModem*)arg; + ml307_at_modem->ReceiveTask(); + vTaskDelete(NULL); + }, "modem_receive", 4096 * 2, this, 15, &receive_task_handle_); +} + +Ml307AtModem::~Ml307AtModem() { + vTaskDelete(event_task_handle_); + vTaskDelete(receive_task_handle_); + vEventGroupDelete(event_group_handle_); + uart_driver_delete(uart_num_); +} + +bool Ml307AtModem::DetectBaudRate() { + // Write and Read AT command to detect the current baud rate + std::vector baud_rates = {115200, 921600, 460800, 230400, 57600, 38400, 19200, 9600}; + while (true) { + ESP_LOGI(TAG, "Detecting baud rate..."); + for (int rate : baud_rates) { + uart_set_baudrate(uart_num_, rate); + if (Command("AT", 20)) { + ESP_LOGI(TAG, "Detected baud rate: %d", rate); + baud_rate_ = rate; + return true; + } + } + vTaskDelay(pdMS_TO_TICKS(1000)); + } + return false; +} + +bool Ml307AtModem::SetBaudRate(int new_baud_rate) { + if (!DetectBaudRate()) { + ESP_LOGE(TAG, "Failed to detect baud rate"); + return false; + } + if (new_baud_rate == baud_rate_) { + return true; + } + // Set new baud rate + if (Command(std::string("AT+IPR=") + std::to_string(new_baud_rate))) { + uart_set_baudrate(uart_num_, new_baud_rate); + baud_rate_ = new_baud_rate; + ESP_LOGI(TAG, "Set baud rate to %d", new_baud_rate); + return true; + } + ESP_LOGI(TAG, "Failed to set baud rate to %d", new_baud_rate); + return false; +} + +int Ml307AtModem::WaitForNetworkReady() { + ESP_LOGI(TAG, "Waiting for network ready..."); + Command("AT+CEREG=1", 1000); + while (!network_ready_) { + if (pin_ready_ == 2) { + ESP_LOGE(TAG, "PIN is not ready"); + return -1; + } + if (registration_state_ == 3) { + ESP_LOGI(TAG, "Registration denied"); + return -2; + } + Command("AT+MIPCALL?"); + xEventGroupWaitBits(event_group_handle_, AT_EVENT_NETWORK_READY, pdTRUE, pdFALSE, pdMS_TO_TICKS(1000)); + } + return 0; +} + +std::string Ml307AtModem::GetImei() { + if (Command("AT+CIMI")) { + return response_; + } + return ""; +} + +std::string Ml307AtModem::GetIccid() { + if (Command("AT+ICCID")) { + return iccid_; + } + return ""; +} + +std::string Ml307AtModem::GetModuleName() { + if (Command("AT+CGMR")) { + return response_; + } + return ""; +} + +std::string Ml307AtModem::GetCarrierName() { + if (Command("AT+COPS?")) { + return carrier_name_; + } + return ""; +} + +int Ml307AtModem::GetCsq() { + if (Command("AT+CSQ")) { + return csq_; + } + return -1; +} + +void Ml307AtModem::SetDebug(bool debug) { + debug_ = debug; +} + +std::list::iterator Ml307AtModem::RegisterCommandResponseCallback(CommandResponseCallback callback) { + std::lock_guard lock(mutex_ ); + return on_data_received_.insert(on_data_received_.end(), callback); +} + +void Ml307AtModem::UnregisterCommandResponseCallback(std::list::iterator iterator) { + std::lock_guard lock(mutex_); + on_data_received_.erase(iterator); +} + +bool Ml307AtModem::Command(const std::string command, int timeout_ms) { + std::lock_guard lock(command_mutex_); + if (debug_) { + ESP_LOGI(TAG, ">> %.64s", command.c_str()); + } + response_.clear(); + { + std::lock_guard lock(mutex_); + last_command_ = command + "\r\n"; + int ret = uart_write_bytes(uart_num_, last_command_.c_str(), last_command_.length()); + if (ret < 0) { + ESP_LOGE(TAG, "uart_write_bytes failed: %d", ret); + return false; + } + } + + if (timeout_ms > 0) { + auto bits = xEventGroupWaitBits(event_group_handle_, AT_EVENT_COMMAND_DONE | AT_EVENT_COMMAND_ERROR, pdTRUE, pdFALSE, pdMS_TO_TICKS(timeout_ms)); + if (bits & AT_EVENT_COMMAND_DONE) { + return true; + } else if (bits & AT_EVENT_COMMAND_ERROR) { + ESP_LOGE(TAG, "command error: %s", command.c_str()); + return false; + } + } + return false; +} + +void Ml307AtModem::EventTask() { + uart_event_t event; + while (true) { + if (xQueueReceive(event_queue_handle_, &event, portMAX_DELAY) == pdTRUE) { + switch (event.type) + { + case UART_DATA: + xEventGroupSetBits(event_group_handle_, AT_EVENT_DATA_AVAILABLE); + break; + case UART_BREAK: + ESP_LOGI(TAG, "break"); + break; + case UART_BUFFER_FULL: + ESP_LOGE(TAG, "buffer full"); + break; + case UART_FIFO_OVF: + ESP_LOGE(TAG, "FIFO overflow"); + NotifyCommandResponse("FIFO_OVERFLOW", {}); + break; + default: + ESP_LOGE(TAG, "unknown event type: %d", event.type); + break; + } + } + } +} + +void Ml307AtModem::ReceiveTask() { + while (true) { + auto bits = xEventGroupWaitBits(event_group_handle_, AT_EVENT_DATA_AVAILABLE, pdTRUE, pdFALSE, portMAX_DELAY); + if (bits & AT_EVENT_DATA_AVAILABLE) { + size_t available; + uart_get_buffered_data_len(uart_num_, &available); + if (available > 0) { + // Extend rx_buffer_ and read into buffer + rx_buffer_.resize(rx_buffer_.size() + available); + char* rx_buffer_ptr = &rx_buffer_[rx_buffer_.size() - available]; + uart_read_bytes(uart_num_, rx_buffer_ptr, available, portMAX_DELAY); + while (ParseResponse()) {} + } + } + } +} + +bool Ml307AtModem::ParseResponse() { + auto end_pos = rx_buffer_.find("\r\n"); + if (end_pos == std::string::npos) { + return false; + } + + // Ignore empty lines + if (end_pos == 0) { + rx_buffer_.erase(0, 2); + return true; + } + if (debug_) { + ESP_LOGI(TAG, "<< %.64s", rx_buffer_.substr(0, end_pos).c_str()); + // print last 64 bytes before end_pos if available + // if (end_pos > 64) { + // ESP_LOGI(TAG, "<< LAST: %.64s", rx_buffer_.c_str() + end_pos - 64); + // } + } + + // Parse "+CME ERROR: 123,456,789" + if (rx_buffer_[0] == '+') { + std::string command, values; + auto pos = rx_buffer_.find(": "); + if (pos == std::string::npos || pos > end_pos) { + command = rx_buffer_.substr(1, end_pos - 1); + } else { + command = rx_buffer_.substr(1, pos - 1); + values = rx_buffer_.substr(pos + 2, end_pos - pos - 2); + } + rx_buffer_.erase(0, end_pos + 2); + + // Parse "string", int, int, ... into AtArgumentValue + std::vector arguments; + std::istringstream iss(values); + std::string item; + while (std::getline(iss, item, ',')) { + AtArgumentValue argument; + if (item.front() == '"') { + argument.type = AtArgumentValue::Type::String; + argument.string_value = item.substr(1, item.size() - 2); + } else if (item.find(".") != std::string::npos) { + argument.type = AtArgumentValue::Type::Double; + argument.double_value = std::stod(item); + } else if (is_number(item)) { + argument.type = AtArgumentValue::Type::Int; + argument.int_value = std::stoi(item); + argument.string_value = std::move(item); + } else { + argument.type = AtArgumentValue::Type::String; + argument.string_value = std::move(item); + } + arguments.push_back(argument); + } + + NotifyCommandResponse(command, arguments); + return true; + } else if (rx_buffer_.size() >= 4 && rx_buffer_[0] == 'O' && rx_buffer_[1] == 'K' && rx_buffer_[2] == '\r' && rx_buffer_[3] == '\n') { + rx_buffer_.erase(0, 4); + xEventGroupSetBits(event_group_handle_, AT_EVENT_COMMAND_DONE); + return true; + } else if (rx_buffer_.size() >= 1 && rx_buffer_[0] == '>') { + rx_buffer_.erase(0, 1); + xEventGroupSetBits(event_group_handle_, AT_EVENT_COMMAND_DONE); + return true; + } else if (rx_buffer_.size() >= 7 && rx_buffer_[0] == 'E' && rx_buffer_[1] == 'R' && rx_buffer_[2] == 'R' && rx_buffer_[3] == 'O' && rx_buffer_[4] == 'R' && rx_buffer_[5] == '\r' && rx_buffer_[6] == '\n') { + rx_buffer_.erase(0, 7); + xEventGroupSetBits(event_group_handle_, AT_EVENT_COMMAND_ERROR); + return true; + } else { + response_ = rx_buffer_.substr(0, end_pos); + rx_buffer_.erase(0, end_pos + 2); + return true; + } + return false; +} + +void Ml307AtModem::OnMaterialReady(std::function callback) { + on_material_ready_ = callback; +} + +void Ml307AtModem::NotifyCommandResponse(const std::string& command, const std::vector& arguments) { + if (command == "CME ERROR") { + xEventGroupSetBits(event_group_handle_, AT_EVENT_COMMAND_ERROR); + return; + } + if (command == "MIPCALL" && arguments.size() >= 3) { + if (arguments[1].int_value == 1) { + ip_address_ = arguments[2].string_value; + network_ready_ = true; + xEventGroupSetBits(event_group_handle_, AT_EVENT_NETWORK_READY); + } + } else if (command == "ICCID" && arguments.size() >= 1) { + iccid_ = arguments[0].string_value; + } else if (command == "COPS" && arguments.size() >= 4) { + carrier_name_ = arguments[2].string_value; + } else if (command == "CSQ" && arguments.size() >= 1) { + csq_ = arguments[0].int_value; + } else if (command == "MATREADY") { + network_ready_ = false; + if (on_material_ready_) { + on_material_ready_(); + } + } else if (command == "CEREG" && arguments.size() >= 1) { + if (arguments.size() == 1) { + registration_state_ = arguments[0].int_value; + } else { + registration_state_ = arguments[1].int_value; + } + } else if (command == "CPIN" && arguments.size() >= 1) { + if (arguments[0].string_value == "READY") { + pin_ready_ = 1; + } else { + pin_ready_ = 2; + } + } + + std::lock_guard lock(mutex_); + for (auto& callback : on_data_received_) { + callback(command, arguments); + } +} + +static const char hex_chars[] = "0123456789ABCDEF"; +// 辅助函数，将单个十六进制字符转换为对应的数值 +inline uint8_t CharToHex(char c) { + if (c >= '0' && c <= '9') return c - '0'; + if (c >= 'A' && c <= 'F') return c - 'A' + 10; + if (c >= 'a' && c <= 'f') return c - 'a' + 10; + return 0; // 对于无效输入，返回0 +} + +void Ml307AtModem::EncodeHexAppend(std::string& dest, const char* data, size_t length) { + dest.reserve(dest.size() + length * 2); // 预分配空间 + for (size_t i = 0; i < length; i++) { + dest.push_back(hex_chars[(data[i] & 0xF0) >> 4]); + dest.push_back(hex_chars[data[i] & 0x0F]); + } +} + +void Ml307AtModem::DecodeHexAppend(std::string& dest, const char* data, size_t length) { + dest.reserve(dest.size() + length / 2); // 预分配空间 + for (size_t i = 0; i < length; i += 2) { + char byte = (CharToHex(data[i]) << 4) | CharToHex(data[i + 1]); + dest.push_back(byte); + } +} + +std::string Ml307AtModem::EncodeHex(const std::string& data) { + std::string encoded; + EncodeHexAppend(encoded, data.c_str(), data.size()); + return encoded; +} + +std::string Ml307AtModem::DecodeHex(const std::string& data) { + std::string decoded; + DecodeHexAppend(decoded, data.c_str(), data.size()); + return decoded; +} + +void Ml307AtModem::Reset() { + Command("AT+MREBOOT=0"); +} + +void Ml307AtModem::ResetConnections() { + // Reset HTTP instances + Command("AT+MHTTPDEL=0"); + Command("AT+MHTTPDEL=1"); + Command("AT+MHTTPDEL=2"); + Command("AT+MHTTPDEL=3"); +} \ No newline at end of file diff --git a/managed_components/78__esp-ml307/ml307_http.cc b/managed_components/78__esp-ml307/ml307_http.cc new file mode 100644 index 0000000..aee9e15 --- /dev/null +++ b/managed_components/78__esp-ml307/ml307_http.cc @@ -0,0 +1,266 @@ +#include "ml307_http.h" +#include +#include +#include +#include + +static const char *TAG = "Ml307Http"; + +Ml307Http::Ml307Http(Ml307AtModem& modem) : modem_(modem) { + event_group_handle_ = xEventGroupCreate(); + + command_callback_it_ = modem_.RegisterCommandResponseCallback([this](const std::string& command, const std::vector& arguments) { + if (command == "MHTTPURC") { + if (arguments[1].int_value == http_id_) { + auto& type = arguments[0].string_value; + if (type == "header") { + body_.clear(); + status_code_ = arguments[2].int_value; + ParseResponseHeaders(modem_.DecodeHex(arguments[4].string_value)); + xEventGroupSetBits(event_group_handle_, ML307_HTTP_EVENT_HEADERS_RECEIVED); + } else if (type == "content") { + // +MHTTPURC: "content",,,,, + std::string decoded_data; + modem_.DecodeHexAppend(decoded_data, arguments[5].string_value.c_str(), arguments[5].string_value.length()); + + std::lock_guard lock(mutex_); + body_.append(decoded_data); + if (arguments[3].int_value >= arguments[2].int_value) { + eof_ = true; + } + body_offset_ += arguments[4].int_value; + if (arguments[3].int_value > body_offset_) { + ESP_LOGE(TAG, "body_offset_: %zu, arguments[3].int_value: %d", body_offset_, arguments[3].int_value); + Close(); + return; + } + cv_.notify_one(); // 使用条件变量通知 + } else if (type == "err") { + error_code_ = arguments[2].int_value; + xEventGroupSetBits(event_group_handle_, ML307_HTTP_EVENT_ERROR); + } + } + } else if (command == "MHTTPCREATE") { + http_id_ = arguments[0].int_value; + xEventGroupSetBits(event_group_handle_, ML307_HTTP_EVENT_INITIALIZED); + } else if (command == "FIFO_OVERFLOW") { + xEventGroupSetBits(event_group_handle_, ML307_HTTP_EVENT_ERROR); + Close(); + } + }); +} + +int Ml307Http::Read(char* buffer, size_t buffer_size) { + std::unique_lock lock(mutex_); + + if (eof_ && body_.empty()) { + return 0; + } + + // 使用条件变量等待数据 + auto timeout = std::chrono::milliseconds(HTTP_CONNECT_TIMEOUT_MS); + bool received = cv_.wait_for(lock, timeout, [this] { + return !body_.empty() || eof_; + }); + + if (!received) { + ESP_LOGE(TAG, "等待HTTP内容接收超时"); + return -1; + } + + size_t bytes_to_read = std::min(body_.size(), buffer_size); + std::memcpy(buffer, body_.data(), bytes_to_read); + body_.erase(0, bytes_to_read); + + return bytes_to_read; +} + +Ml307Http::~Ml307Http() { + if (connected_) { + Close(); + } + modem_.UnregisterCommandResponseCallback(command_callback_it_); + vEventGroupDelete(event_group_handle_); +} + +void Ml307Http::SetHeader(const std::string& key, const std::string& value) { + headers_[key] = value; +} + +void Ml307Http::ParseResponseHeaders(const std::string& headers) { + std::istringstream iss(headers); + std::string line; + while (std::getline(iss, line)) { + std::istringstream line_iss(line); + std::string key, value; + std::getline(line_iss, key, ':'); + std::getline(line_iss, value); + response_headers_[key] = value; + } +} + +bool Ml307Http::Open(const std::string& method, const std::string& url, const std::string& content) { + method_ = method; + url_ = url; + // 解析URL + size_t protocol_end = url.find("://"); + if (protocol_end != std::string::npos) { + protocol_ = url.substr(0, protocol_end); + size_t host_start = protocol_end + 3; + size_t path_start = url.find("/", host_start); + if (path_start != std::string::npos) { + host_ = url.substr(host_start, path_start - host_start); + path_ = url.substr(path_start); + } else { + host_ = url.substr(host_start); + path_ = "/"; + } + } else { + // URL格式不正确 + ESP_LOGE(TAG, "无效的URL格式"); + return false; + } + + // 创建HTTP连接 + char command[256]; + sprintf(command, "AT+MHTTPCREATE=\"%s://%s\"", protocol_.c_str(), host_.c_str()); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "创建HTTP连接失败"); + return false; + } + + auto bits = xEventGroupWaitBits(event_group_handle_, ML307_HTTP_EVENT_INITIALIZED, pdTRUE, pdFALSE, pdMS_TO_TICKS(HTTP_CONNECT_TIMEOUT_MS)); + if (!(bits & ML307_HTTP_EVENT_INITIALIZED)) { + ESP_LOGE(TAG, "等待HTTP连接创建超时"); + return false; + } + connected_ = true; + ESP_LOGI(TAG, "HTTP 连接已创建，ID: %d", http_id_); + + if (protocol_ == "https") { + sprintf(command, "AT+MHTTPCFG=\"ssl\",%d,1,0", http_id_); + modem_.Command(command); + } + + // Set HEX encoding OFF + sprintf(command, "AT+MHTTPCFG=\"encoding\",%d,0,0", http_id_); + modem_.Command(command); + + // Flow control to 1024 bytes per 100ms + sprintf(command, "AT+MHTTPCFG=\"fragment\",%d,1024,100", http_id_); + modem_.Command(command); + + // Set headers + for (const auto& header : headers_) { + auto line = header.first + ": " + header.second; + sprintf(command, "AT+MHTTPCFG=\"header\",%d,%s", http_id_, line.c_str()); + modem_.Command(command); + } + + if (!content.empty() && method_ == "POST") { + sprintf(command, "AT+MHTTPCONTENT=%d,0,%zu", http_id_, content.size()); + modem_.Command(command); + modem_.Command(content); + } + + // Set HEX encoding ON + sprintf(command, "AT+MHTTPCFG=\"encoding\",%d,1,1", http_id_); + modem_.Command(command); + + // Send request + // method to value: 1. GET 2. POST 3. PUT 4. DELETE 5. HEAD + const char* methods[6] = {"UNKNOWN", "GET", "POST", "PUT", "DELETE", "HEAD"}; + int method_value = 1; + for (int i = 0; i < 6; i++) { + if (strcmp(methods[i], method_.c_str()) == 0) { + method_value = i; + break; + } + } + sprintf(command, "AT+MHTTPREQUEST=%d,%d,0,", http_id_, method_value); + modem_.Command(std::string(command) + modem_.EncodeHex(path_)); + + // Wait for headers + bits = xEventGroupWaitBits(event_group_handle_, ML307_HTTP_EVENT_HEADERS_RECEIVED | ML307_HTTP_EVENT_ERROR, pdTRUE, pdFALSE, pdMS_TO_TICKS(HTTP_CONNECT_TIMEOUT_MS)); + if (bits & ML307_HTTP_EVENT_ERROR) { + ESP_LOGE(TAG, "HTTP请求错误: %s", ErrorCodeToString(error_code_).c_str()); + return false; + } + if (!(bits & ML307_HTTP_EVENT_HEADERS_RECEIVED)) { + ESP_LOGE(TAG, "等待HTTP头部接收超时"); + return false; + } + + if (status_code_ >= 400) { + ESP_LOGE(TAG, "HTTP请求失败，状态码: %d", status_code_); + return false; + } + + auto it = response_headers_.find("Content-Length"); + if (it != response_headers_.end()) { + content_length_ = std::stoul(it->second); + } + eof_ = false; + body_offset_ = 0; + ESP_LOGI(TAG, "HTTP请求成功，状态码: %d", status_code_); + return true; +} + +size_t Ml307Http::GetBodyLength() const { + return content_length_; +} + +const std::string& Ml307Http::GetBody() { + std::unique_lock lock(mutex_); + + auto timeout = std::chrono::milliseconds(HTTP_CONNECT_TIMEOUT_MS); + bool received = cv_.wait_for(lock, timeout, [this] { + return eof_; + }); + + if (!received) { + ESP_LOGE(TAG, "等待HTTP内容接收完成超时"); + return body_; + } + + return body_; +} + +void Ml307Http::Close() { + if (!connected_) { + return; + } + char command[32]; + sprintf(command, "AT+MHTTPDEL=%d", http_id_); + modem_.Command(command); + + connected_ = false; + eof_ = true; + cv_.notify_one(); + ESP_LOGI(TAG, "HTTP连接已关闭，ID: %d", http_id_); +} + +std::string Ml307Http::ErrorCodeToString(int error_code) { + switch (error_code) { + case 1: return "域名解析失败"; + case 2: return "连接服务器失败"; + case 3: return "连接服务器超时"; + case 4: return "SSL握手失败"; + case 5: return "连接异常断开"; + case 6: return "请求响应超时"; + case 7: return "接收数据解析失败"; + case 8: return "缓存空间不足"; + case 9: return "数据丢包"; + case 10: return "写文件失败"; + case 255: return "未知错误"; + default: return "未定义错误"; + } +} + +std::string Ml307Http::GetResponseHeader(const std::string& key) const { + auto it = response_headers_.find(key); + if (it != response_headers_.end()) { + return it->second; + } + return ""; +} \ No newline at end of file diff --git a/managed_components/78__esp-ml307/ml307_mqtt.cc b/managed_components/78__esp-ml307/ml307_mqtt.cc new file mode 100644 index 0000000..a053f32 --- /dev/null +++ b/managed_components/78__esp-ml307/ml307_mqtt.cc @@ -0,0 +1,185 @@ +#include "ml307_mqtt.h" +#include + +static const char *TAG = "Ml307Mqtt"; + +Ml307Mqtt::Ml307Mqtt(Ml307AtModem& modem, int mqtt_id) : modem_(modem), mqtt_id_(mqtt_id) { + event_group_handle_ = xEventGroupCreate(); + + command_callback_it_ = modem_.RegisterCommandResponseCallback([this](const std::string command, const std::vector& arguments) { + if (command == "MQTTURC" && arguments.size() >= 2) { + if (arguments[1].int_value == mqtt_id_) { + auto type = arguments[0].string_value; + if (type == "conn") { + if (arguments[2].int_value == 0) { + xEventGroupSetBits(event_group_handle_, MQTT_CONNECTED_EVENT); + } else { + if (connected_) { + connected_ = false; + if (on_disconnected_callback_) { + on_disconnected_callback_(); + } + } + xEventGroupSetBits(event_group_handle_, MQTT_DISCONNECTED_EVENT); + } + ESP_LOGI(TAG, "MQTT connection state: %s", ErrorToString(arguments[2].int_value).c_str()); + } else if (type == "suback") { + } else if (type == "publish" && arguments.size() >= 7) { + auto topic = arguments[3].string_value; + if (arguments[4].int_value == arguments[5].int_value) { + if (on_message_callback_) { + on_message_callback_(topic, modem_.DecodeHex(arguments[6].string_value)); + } + } else { + message_payload_.append(modem_.DecodeHex(arguments[6].string_value)); + if (message_payload_.size() >= arguments[4].int_value && on_message_callback_) { + on_message_callback_(topic, message_payload_); + message_payload_.clear(); + } + } + } else { + ESP_LOGI(TAG, "unhandled MQTT event: %s", type.c_str()); + } + } + } else if (command == "MQTTSTATE" && arguments.size() == 1) { + connected_ = arguments[0].int_value != 3; + xEventGroupSetBits(event_group_handle_, MQTT_INITIALIZED_EVENT); + } + }); +} + +Ml307Mqtt::~Ml307Mqtt() { + modem_.UnregisterCommandResponseCallback(command_callback_it_); + vEventGroupDelete(event_group_handle_); +} + +bool Ml307Mqtt::Connect(const std::string broker_address, int broker_port, const std::string client_id, const std::string username, const std::string password) { + broker_address_ = broker_address; + broker_port_ = broker_port; + client_id_ = client_id; + username_ = username; + password_ = password; + + EventBits_t bits; + if (IsConnected()) { + // 断开之前的连接 + Disconnect(); + bits = xEventGroupWaitBits(event_group_handle_, MQTT_DISCONNECTED_EVENT, pdTRUE, pdFALSE, pdMS_TO_TICKS(MQTT_CONNECT_TIMEOUT_MS)); + if (!(bits & MQTT_DISCONNECTED_EVENT)) { + ESP_LOGE(TAG, "Failed to disconnect from previous connection"); + return false; + } + } + + if (broker_port_ == 8883) { + if (!modem_.Command(std::string("AT+MQTTCFG=\"ssl\",") + std::to_string(mqtt_id_) + ",1")) { + ESP_LOGE(TAG, "Failed to set MQTT to use SSL"); + return false; + } + } + + // Set clean session + if (!modem_.Command(std::string("AT+MQTTCFG=\"clean\",") + std::to_string(mqtt_id_) + ",1")) { + ESP_LOGE(TAG, "Failed to set MQTT clean session"); + return false; + } + + // Set keep alive + if (!modem_.Command(std::string("AT+MQTTCFG=\"pingreq\",") + std::to_string(mqtt_id_) + "," + std::to_string(keep_alive_seconds_))) { + ESP_LOGE(TAG, "Failed to set MQTT keep alive"); + return false; + } + + // Set HEX encoding + if (!modem_.Command("AT+MQTTCFG=\"encoding\"," + std::to_string(mqtt_id_) + ",1,1")) { + ESP_LOGE(TAG, "Failed to set MQTT to use HEX encoding"); + return false; + } + + // 创建MQTT连接 + std::string command = "AT+MQTTCONN=" + std::to_string(mqtt_id_) + ",\"" + broker_address_ + "\"," + std::to_string(broker_port_) + ",\"" + client_id_ + "\",\"" + username_ + "\",\"" + password_ + "\""; + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to create MQTT connection"); + return false; + } + + // 等待连接完成 + bits = xEventGroupWaitBits(event_group_handle_, MQTT_CONNECTED_EVENT | MQTT_DISCONNECTED_EVENT, pdTRUE, pdFALSE, pdMS_TO_TICKS(MQTT_CONNECT_TIMEOUT_MS)); + if (!(bits & MQTT_CONNECTED_EVENT)) { + ESP_LOGE(TAG, "Failed to connect to MQTT broker"); + return false; + } + + connected_ = true; + if (on_connected_callback_) { + on_connected_callback_(); + } + return true; +} + +bool Ml307Mqtt::IsConnected() { + // 检查这个 id 是否已经连接 + modem_.Command(std::string("AT+MQTTSTATE=") + std::to_string(mqtt_id_)); + auto bits = xEventGroupWaitBits(event_group_handle_, MQTT_INITIALIZED_EVENT, pdTRUE, pdFALSE, pdMS_TO_TICKS(MQTT_CONNECT_TIMEOUT_MS)); + if (!(bits & MQTT_INITIALIZED_EVENT)) { + ESP_LOGE(TAG, "Failed to initialize MQTT connection"); + return false; + } + return connected_; +} + +void Ml307Mqtt::Disconnect() { + if (!connected_) { + return; + } + modem_.Command(std::string("AT+MQTTDISC=") + std::to_string(mqtt_id_)); +} + +bool Ml307Mqtt::Publish(const std::string topic, const std::string payload, int qos) { + if (!connected_) { + return false; + } + std::string command = "AT+MQTTPUB=" + std::to_string(mqtt_id_) + ",\"" + topic + "\","; + command += std::to_string(qos) + ",0,0,"; + command += std::to_string(payload.size()) + "," + modem_.EncodeHex(payload); + return modem_.Command(command); +} + +bool Ml307Mqtt::Subscribe(const std::string topic, int qos) { + if (!connected_) { + return false; + } + std::string command = "AT+MQTTSUB=" + std::to_string(mqtt_id_) + ",\"" + topic + "\"," + std::to_string(qos); + return modem_.Command(command); +} + +bool Ml307Mqtt::Unsubscribe(const std::string topic) { + if (!connected_) { + return false; + } + std::string command = "AT+MQTTUNSUB=" + std::to_string(mqtt_id_) + ",\"" + topic + "\""; + return modem_.Command(command); +} + +std::string Ml307Mqtt::ErrorToString(int error_code) { + switch (error_code) { + case 0: + return "连接成功"; + case 1: + return "正在重连"; + case 2: + return "断开：用户主动断开"; + case 3: + return "断开：拒绝连接（协议版本、标识符、用户名或密码错误）"; + case 4: + return "断开：服务器断开"; + case 5: + return "断开：Ping包超时断开"; + case 6: + return "断开：网络异常断开"; + case 255: + return "断开：未知错误"; + default: + return "未知错误"; + } +} diff --git a/managed_components/78__esp-ml307/ml307_ssl_transport.cc b/managed_components/78__esp-ml307/ml307_ssl_transport.cc new file mode 100644 index 0000000..e9cff95 --- /dev/null +++ b/managed_components/78__esp-ml307/ml307_ssl_transport.cc @@ -0,0 +1,183 @@ +#include "ml307_ssl_transport.h" +#include +#include + +static const char *TAG = "Ml307SslTransport"; + + +Ml307SslTransport::Ml307SslTransport(Ml307AtModem& modem, int tcp_id) : modem_(modem), tcp_id_(tcp_id) { + event_group_handle_ = xEventGroupCreate(); + + command_callback_it_ = modem_.RegisterCommandResponseCallback([this](const std::string& command, const std::vector& arguments) { + if (command == "MIPOPEN" && arguments.size() == 2) { + if (arguments[0].int_value == tcp_id_) { + if (arguments[1].int_value == 0) { + connected_ = true; + xEventGroupClearBits(event_group_handle_, ML307_SSL_TRANSPORT_DISCONNECTED | ML307_SSL_TRANSPORT_ERROR); + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_CONNECTED); + } else { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_ERROR); + } + } + } else if (command == "MIPCLOSE" && arguments.size() == 1) { + if (arguments[0].int_value == tcp_id_) { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_DISCONNECTED); + } + } else if (command == "MIPSEND" && arguments.size() == 2) { + if (arguments[0].int_value == tcp_id_) { + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_SEND_COMPLETE); + } + } else if (command == "MIPURC" && arguments.size() == 4) { + if (arguments[1].int_value == tcp_id_) { + if (arguments[0].string_value == "rtcp") { + std::lock_guard lock(mutex_); + modem_.DecodeHexAppend(rx_buffer_, arguments[3].string_value.c_str(), arguments[3].string_value.size()); + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_RECEIVE); + } else if (arguments[0].string_value == "disconn") { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_DISCONNECTED); + } else { + ESP_LOGE(TAG, "Unknown MIPURC command: %s", arguments[0].string_value.c_str()); + } + } + } else if (command == "MIPSTATE" && arguments.size() == 5) { + if (arguments[0].int_value == tcp_id_) { + if (arguments[4].string_value == "INITIAL") { + connected_ = false; + } else { + connected_ = true; + } + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_INITIALIZED); + } + } else if (command == "FIFO_OVERFLOW") { + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_ERROR); + Disconnect(); + } + }); +} + +Ml307SslTransport::~Ml307SslTransport() { + modem_.UnregisterCommandResponseCallback(command_callback_it_); +} + +bool Ml307SslTransport::Connect(const char* host, int port) { + char command[64]; + + // Clear bits + xEventGroupClearBits(event_group_handle_, ML307_SSL_TRANSPORT_CONNECTED | ML307_SSL_TRANSPORT_DISCONNECTED | ML307_SSL_TRANSPORT_ERROR); + + // 检查这个 id 是否已经连接 + sprintf(command, "AT+MIPSTATE=%d", tcp_id_); + modem_.Command(command); + auto bits = xEventGroupWaitBits(event_group_handle_, ML307_SSL_TRANSPORT_INITIALIZED, pdTRUE, pdFALSE, pdMS_TO_TICKS(SSL_CONNECT_TIMEOUT_MS)); + if (!(bits & ML307_SSL_TRANSPORT_INITIALIZED)) { + ESP_LOGE(TAG, "Failed to initialize TCP connection"); + return false; + } + + // 断开之前的连接 + if (connected_) { + Disconnect(); + } + + // 设置 SSL 配置 + sprintf(command, "AT+MSSLCFG=\"auth\",0,0"); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to set SSL configuration"); + return false; + } + + sprintf(command, "AT+MIPCFG=\"ssl\",%d,1,0", tcp_id_); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to set TCP SSL configuration"); + return false; + } + + // 打开 TCP 连接 + sprintf(command, "AT+MIPOPEN=%d,\"TCP\",\"%s\",%d,,0", tcp_id_, host, port); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to open TCP connection"); + return false; + } + + // 使用 HEX 编码 + sprintf(command, "AT+MIPCFG=\"encoding\",%d,1,1", tcp_id_); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to set HEX encoding"); + return false; + } + + // 等待连接完成 + bits = xEventGroupWaitBits(event_group_handle_, ML307_SSL_TRANSPORT_CONNECTED | ML307_SSL_TRANSPORT_ERROR, pdTRUE, pdFALSE, SSL_CONNECT_TIMEOUT_MS / portTICK_PERIOD_MS); + if (bits & ML307_SSL_TRANSPORT_ERROR) { + ESP_LOGE(TAG, "Failed to connect to %s:%d", host, port); + return false; + } + return true; +} + +void Ml307SslTransport::Disconnect() { + if (!connected_) { + return; + } + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_DISCONNECTED); + std::string command = "AT+MIPCLOSE=" + std::to_string(tcp_id_); + modem_.Command(command); +} + +int Ml307SslTransport::Send(const char* data, size_t length) { + const size_t MAX_PACKET_SIZE = 1460 / 2; + size_t total_sent = 0; + + // 在循环外预先分配command + std::string command; + command.reserve(32 + MAX_PACKET_SIZE * 2); // 预分配最大可能需要的空间 + + while (total_sent < length) { + size_t chunk_size = std::min(length - total_sent, MAX_PACKET_SIZE); + + // 重置command并构建新的命令 + command.clear(); + command = "AT+MIPSEND=" + std::to_string(tcp_id_) + "," + std::to_string(chunk_size) + ","; + + // 直接在command字符串上进行十六进制编码 + modem_.EncodeHexAppend(command, data + total_sent, chunk_size); + + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "发送数据块失败"); + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_SSL_TRANSPORT_DISCONNECTED); + return -1; + } + + auto bits = xEventGroupWaitBits(event_group_handle_, ML307_SSL_TRANSPORT_SEND_COMPLETE, pdTRUE, pdFALSE, pdMS_TO_TICKS(SSL_CONNECT_TIMEOUT_MS)); + if (!(bits & ML307_SSL_TRANSPORT_SEND_COMPLETE)) { + ESP_LOGE(TAG, "未收到发送确认"); + return -1; + } + + total_sent += chunk_size; + } + return length; +} + +int Ml307SslTransport::Receive(char* buffer, size_t bufferSize) { + while (rx_buffer_.empty()) { + auto bits = xEventGroupWaitBits(event_group_handle_, ML307_SSL_TRANSPORT_RECEIVE | ML307_SSL_TRANSPORT_DISCONNECTED, pdTRUE, pdFALSE, portMAX_DELAY); + if (bits & ML307_SSL_TRANSPORT_DISCONNECTED) { + return 0; + } + if (!(bits & ML307_SSL_TRANSPORT_RECEIVE)) { + ESP_LOGE(TAG, "Failed to receive data"); + return -1; + } + } + std::lock_guard lock(mutex_); + size_t length = std::min(bufferSize, rx_buffer_.size()); + memcpy(buffer, rx_buffer_.data(), length); + rx_buffer_.erase(0, length); + return length; +} diff --git a/managed_components/78__esp-ml307/ml307_udp.cc b/managed_components/78__esp-ml307/ml307_udp.cc new file mode 100644 index 0000000..452b73e --- /dev/null +++ b/managed_components/78__esp-ml307/ml307_udp.cc @@ -0,0 +1,142 @@ +#include "ml307_udp.h" + +#include + +#define TAG "Ml307Udp" + + +Ml307Udp::Ml307Udp(Ml307AtModem& modem, int udp_id) : modem_(modem), udp_id_(udp_id) { + event_group_handle_ = xEventGroupCreate(); + + command_callback_it_ = modem_.RegisterCommandResponseCallback([this](const std::string& command, const std::vector& arguments) { + if (command == "MIPOPEN" && arguments.size() == 2) { + if (arguments[0].int_value == udp_id_) { + if (arguments[1].int_value == 0) { + connected_ = true; + xEventGroupClearBits(event_group_handle_, ML307_UDP_DISCONNECTED | ML307_UDP_ERROR); + xEventGroupSetBits(event_group_handle_, ML307_UDP_CONNECTED); + } else { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_UDP_ERROR); + } + } + } else if (command == "MIPCLOSE" && arguments.size() == 1) { + if (arguments[0].int_value == udp_id_) { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_UDP_DISCONNECTED); + } + } else if (command == "MIPSEND" && arguments.size() == 2) { + if (arguments[0].int_value == udp_id_) { + xEventGroupSetBits(event_group_handle_, ML307_UDP_SEND_COMPLETE); + } + } else if (command == "MIPURC" && arguments.size() == 4) { + if (arguments[1].int_value == udp_id_) { + if (arguments[0].string_value == "rudp") { + if (message_callback_) { + message_callback_(modem_.DecodeHex(arguments[3].string_value)); + } + } else if (arguments[0].string_value == "disconn") { + connected_ = false; + xEventGroupSetBits(event_group_handle_, ML307_UDP_DISCONNECTED); + } else { + ESP_LOGE(TAG, "Unknown MIPURC command: %s", arguments[0].string_value.c_str()); + } + } + } else if (command == "MIPSTATE" && arguments.size() == 5) { + if (arguments[0].int_value == udp_id_) { + if (arguments[4].string_value == "INITIAL") { + connected_ = false; + } else { + connected_ = true; + } + xEventGroupSetBits(event_group_handle_, ML307_UDP_INITIALIZED); + } + } else if (command == "FIFO_OVERFLOW") { + xEventGroupSetBits(event_group_handle_, ML307_UDP_ERROR); + Disconnect(); + } + }); +} + +Ml307Udp::~Ml307Udp() { + Disconnect(); + modem_.UnregisterCommandResponseCallback(command_callback_it_); +} + +bool Ml307Udp::Connect(const std::string& host, int port) { + char command[64]; + + // Clear bits + xEventGroupClearBits(event_group_handle_, ML307_UDP_CONNECTED | ML307_UDP_DISCONNECTED | ML307_UDP_ERROR); + + // 检查这个 id 是否已经连接 + sprintf(command, "AT+MIPSTATE=%d", udp_id_); + modem_.Command(command); + auto bits = xEventGroupWaitBits(event_group_handle_, ML307_UDP_INITIALIZED, pdTRUE, pdFALSE, pdMS_TO_TICKS(UDP_CONNECT_TIMEOUT_MS)); + if (!(bits & ML307_UDP_INITIALIZED)) { + ESP_LOGE(TAG, "Failed to initialize TCP connection"); + return false; + } + + // 断开之前的连接 + if (connected_) { + Disconnect(); + } + + // 打开 TCP 连接 + sprintf(command, "AT+MIPOPEN=%d,\"UDP\",\"%s\",%d,,0", udp_id_, host.c_str(), port); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to open UDP connection"); + return false; + } + + // 使用 HEX 编码 + sprintf(command, "AT+MIPCFG=\"encoding\",%d,1,1", udp_id_); + if (!modem_.Command(command)) { + ESP_LOGE(TAG, "Failed to set HEX encoding"); + return false; + } + + // 等待连接完成 + bits = xEventGroupWaitBits(event_group_handle_, ML307_UDP_CONNECTED | ML307_UDP_ERROR, pdTRUE, pdFALSE, UDP_CONNECT_TIMEOUT_MS / portTICK_PERIOD_MS); + if (bits & ML307_UDP_ERROR) { + ESP_LOGE(TAG, "Failed to connect to %s:%d", host.c_str(), port); + return false; + } + return true; +} + + +void Ml307Udp::Disconnect() { + if (!connected_) { + return; + } + connected_ = false; + modem_.Command("AT+MIPCLOSE=" + std::to_string(udp_id_)); +} + +int Ml307Udp::Send(const std::string& data) { + const size_t MAX_PACKET_SIZE = 1460 / 2; + + if (!connected_) { + ESP_LOGE(TAG, "未连接"); + return -1; + } + + if (data.size() > MAX_PACKET_SIZE) { + ESP_LOGE(TAG, "数据块超过最大限制"); + return -1; + } + + // 在循环外预先分配command + std::string command = "AT+MIPSEND=" + std::to_string(udp_id_) + "," + std::to_string(data.size()) + ","; + + // 直接在command字符串上进行十六进制编码 + modem_.EncodeHexAppend(command, data.c_str(), data.size()); + + if (!modem_.Command(command, 100)) { + ESP_LOGE(TAG, "发送数据块失败"); + return -1; + } + return data.size(); +} diff --git a/managed_components/78__esp-ml307/tcp_transport.cc b/managed_components/78__esp-ml307/tcp_transport.cc new file mode 100644 index 0000000..d44804e --- /dev/null +++ b/managed_components/78__esp-ml307/tcp_transport.cc @@ -0,0 +1,77 @@ +#include "tcp_transport.h" +#include +#include +#include +#include +#include +#include +#define TAG "TcpTransport" + +TcpTransport::TcpTransport() : fd_(-1) {} + +TcpTransport::~TcpTransport() { + if (fd_ != -1) { + close(fd_); + } +} + +bool TcpTransport::Connect(const char* host, int port) { + struct sockaddr_in server_addr; + bzero(&server_addr, sizeof(server_addr)); + server_addr.sin_family = AF_INET; + server_addr.sin_port = htons(port); + // host is domain + struct hostent *server = gethostbyname(host); + if (server == NULL) { + ESP_LOGE(TAG, "Failed to get host by name"); + return false; + } + memcpy(&server_addr.sin_addr, server->h_addr, server->h_length); + + fd_ = socket(AF_INET, SOCK_STREAM, 0); + if (fd_ < 0) { + ESP_LOGE(TAG, "Failed to create socket"); + return false; + } + + int ret = connect(fd_, (struct sockaddr*)&server_addr, sizeof(server_addr)); + if (ret < 0) { + ESP_LOGE(TAG, "Failed to connect to %s:%d", host, port); + close(fd_); + fd_ = -1; + return false; + } + + connected_ = true; + return true; +} + +void TcpTransport::Disconnect() { + if (fd_ != -1) { + close(fd_); + fd_ = -1; + } + connected_ = false; +} + +int TcpTransport::Send(const char* data, size_t length) { + int ret = send(fd_, data, length, 0); + if (ret <= 0) { + connected_ = false; + ESP_LOGE(TAG, "Send failed: %d", ret); + } + return ret; +} + +int TcpTransport::Receive(char* buffer, size_t bufferSize) { + int ret = recv(fd_, buffer, bufferSize, 0); + if (ret == 0) { + connected_ = false; + } else if (ret < 0) { + ESP_LOGE(TAG, "Receive failed: %d", ret); + } + return ret; +} + + + diff --git a/managed_components/78__esp-ml307/tls_transport.cc b/managed_components/78__esp-ml307/tls_transport.cc new file mode 100644 index 0000000..f35d022 --- /dev/null +++ b/managed_components/78__esp-ml307/tls_transport.cc @@ -0,0 +1,67 @@ +#include "tls_transport.h" +#include +#include +#include +#include +#include + +#define TAG "TlsTransport" + +TlsTransport::TlsTransport() { + tls_client_ = esp_tls_init(); +} + +TlsTransport::~TlsTransport() { + if (tls_client_) { + esp_tls_conn_destroy(tls_client_); + } +} + +bool TlsTransport::Connect(const char* host, int port) { + esp_tls_cfg_t cfg = {}; + cfg.crt_bundle_attach = esp_crt_bundle_attach; + + int ret = esp_tls_conn_new_sync(host, strlen(host), port, &cfg, tls_client_); + if (ret != 1) { + ESP_LOGE(TAG, "Failed to connect to %s:%d", host, port); + return false; + } + + connected_ = true; + return true; +} + +void TlsTransport::Disconnect() { + if (tls_client_) { + esp_tls_conn_destroy(tls_client_); + tls_client_ = nullptr; + } + connected_ = false; +} + +int TlsTransport::Send(const char* data, size_t length) { + int ret = esp_tls_conn_write(tls_client_, data, length); + if (ret == ESP_TLS_ERR_SSL_WANT_WRITE) { + vTaskDelay(1); + return 0; + } + if (ret <= 0) { + connected_ = false; + ESP_LOGE(TAG, "TLS发送失败: %d", ret); + } + return ret; +} + +int TlsTransport::Receive(char* buffer, size_t bufferSize) { + int ret = 0; + do { + ret = esp_tls_conn_read(tls_client_, buffer, bufferSize); + } while (ret == ESP_TLS_ERR_SSL_WANT_READ); + + if (ret == 0) { + connected_ = false; + } else if (ret < 0) { + ESP_LOGE(TAG, "TLS读取失败: %d", ret); + } + return ret; +} diff --git a/managed_components/78__esp-ml307/web_socket.cc b/managed_components/78__esp-ml307/web_socket.cc new file mode 100644 index 0000000..a085344 --- /dev/null +++ b/managed_components/78__esp-ml307/web_socket.cc @@ -0,0 +1,412 @@ +#include "web_socket.h" +#include +#include +#include + +static const char *TAG = "WebSocket"; + +static std::string base64_encode(const unsigned char *data, size_t len) { + const char *base64_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; + std::string encoded; + unsigned char char_array_3[3]; + unsigned char char_array_4[4]; + + size_t i = 0; + while (i < len) { + size_t chunk_size = std::min((size_t)3, len - i); + + for (size_t j = 0; j < 3; j++) { + char_array_3[j] = (j < chunk_size) ? data[i + j] : 0; + } + + char_array_4[0] = (char_array_3[0] & 0xfc) >> 2; + char_array_4[1] = ((char_array_3[0] & 0x03) << 4) + ((char_array_3[1] & 0xf0) >> 4); + char_array_4[2] = ((char_array_3[1] & 0x0f) << 2) + ((char_array_3[2] & 0xc0) >> 6); + char_array_4[3] = char_array_3[2] & 0x3f; + + for (size_t j = 0; j < 4; j++) { + if (j <= chunk_size) { + encoded.push_back(base64_chars[char_array_4[j]]); + } else { + encoded.push_back('='); + } + } + + i += chunk_size; + } + return encoded; +} + + +WebSocket::WebSocket(Transport *transport) : transport_(transport) { +} + +WebSocket::~WebSocket() { + if (transport_->connected()) { + transport_->Disconnect(); + } + + if (receive_thread_.joinable()) { + receive_thread_.join(); + } + delete transport_; +} + +void WebSocket::SetHeader(const char* key, const char* value) { + headers_[key] = value; +} + +void WebSocket::SetReceiveBufferSize(size_t size) { + receive_buffer_size_ = size; +} + +bool WebSocket::IsConnected() const { + return transport_->connected(); +} + +bool WebSocket::Connect(const char* uri) { + std::string uri_str(uri); + std::string protocol, host, port, path; + size_t pos = 0; + size_t next_pos = 0; + + // 解析协议 + next_pos = uri_str.find("://"); + if (next_pos == std::string::npos) { + ESP_LOGE(TAG, "Invalid URI format"); + return false; + } + protocol = uri_str.substr(0, next_pos); + pos = next_pos + 3; + + // 解析主机 + next_pos = uri_str.find(':', pos); + if (next_pos == std::string::npos) { + next_pos = uri_str.find('/', pos); + if (next_pos == std::string::npos) { + host = uri_str.substr(pos); + path = "/"; + } else { + host = uri_str.substr(pos, next_pos - pos); + path = uri_str.substr(next_pos); + } + port = (protocol == "wss") ? "443" : "80"; + } else { + host = uri_str.substr(pos, next_pos - pos); + pos = next_pos + 1; + + // 解析端口 + next_pos = uri_str.find('/', pos); + if (next_pos == std::string::npos) { + port = uri_str.substr(pos); + path = "/"; + } else { + port = uri_str.substr(pos, next_pos - pos); + path = uri_str.substr(next_pos); + } + } + + ESP_LOGI(TAG, "Connecting to %s://%s:%s%s", protocol.c_str(), host.c_str(), port.c_str(), path.c_str()); + + // 设置 WebSocket 特定的头部 + SetHeader("Upgrade", "websocket"); + SetHeader("Connection", "Upgrade"); + SetHeader("Sec-WebSocket-Version", "13"); + + // 生成随机的 Sec-WebSocket-Key + char key[25]; + for (int i = 0; i < 16; ++i) { + key[i] = rand() % 256; + } + std::string base64_key = base64_encode(reinterpret_cast(key), 16); + SetHeader("Sec-WebSocket-Key", base64_key.c_str()); + + // 使用 transport 建立连接 + if (!transport_->Connect(host.c_str(), std::stoi(port))) { + ESP_LOGE(TAG, "Failed to connect to server"); + return false; + } + + // 发送 WebSocket 握手请求 + std::string request = "GET " + path + " HTTP/1.1\r\n"; + if (headers_.find("Host") == headers_.end()) { + request += "Host: " + host + "\r\n"; + } + for (const auto& header : headers_) { + request += header.first + ": " + header.second + "\r\n"; + } + request += "\r\n"; + + if (!SendAllRaw(request.c_str(), request.length())) { + ESP_LOGE(TAG, "Failed to send WebSocket handshake request"); + return false; + } + + std::string buffer; + // Read byte by byte until \r\n\r\n + while (transport_->connected()) { + char c = 0; + if (transport_->Receive(&c, 1) == 1) { + buffer.push_back(c); + if (buffer.size() >= 4 && buffer.substr(buffer.size() - 4) == "\r\n\r\n") { + break; + } + } + } + + if (buffer.find("HTTP/1.1 101") == std::string::npos) { + ESP_LOGE(TAG, "WebSocket handshake failed"); + return false; + } + + if (on_connected_) { + on_connected_(); + } + + // Start a task to receive data + receive_thread_ = std::thread([this]() { + ReceiveTask(); + }); + return true; +} + + +bool WebSocket::Send(const std::string& data) { + return Send(data.data(), data.size(), false); +} + +bool WebSocket::Send(const void* data, size_t len, bool binary, bool fin) { + if (len > 65535) { + ESP_LOGE(TAG, "Data too large, maximum supported size is 65535 bytes"); + return false; + } + + std::vector frame; + frame.reserve(len + 8); // 最大可能的帧大小（2字节帧头 + 2字节长度 + 4字节mask） + + // 第一个字节：FIN 位 + 操作码 + uint8_t first_byte = (fin ? 0x80 : 0x00); + if (binary) { + first_byte |= 0x02; // 二进制帧 + } else if (!continuation_) { + first_byte |= 0x01; // 文本帧 + } // 否则，操作码为0（延续帧） + + frame.push_back(first_byte); + + // 第二个字节：MASK 位 + 有效载荷长度 + if (len < 126) { + frame.push_back(0x80 | len); // 设置MASK位 + } else { + frame.push_back(0x80 | 126); // 设置MASK位 + frame.push_back((len >> 8) & 0xFF); + frame.push_back(len & 0xFF); + } + + // 生成随机的4字节mask + uint8_t mask[4]; + for (int i = 0; i < 4; ++i) { + mask[i] = rand() & 0xFF; + } + frame.insert(frame.end(), mask, mask + 4); + + // 添加并mask处理有效载荷 + const uint8_t* payload = static_cast(data); + for (size_t i = 0; i < len; ++i) { + frame.push_back(payload[i] ^ mask[i % 4]); + } + + // 更新continuation_状态 + continuation_ = !fin; + + // 发送帧 + return SendAllRaw(frame.data(), frame.size()); +} + +void WebSocket::Ping() { + SendControlFrame(0x9, nullptr, 0); +} + +void WebSocket::Close() { + if (transport_->connected()) { + SendControlFrame(0x8, nullptr, 0); + } +} + +void WebSocket::OnConnected(std::function callback) { + on_connected_ = callback; +} + +void WebSocket::OnDisconnected(std::function callback) { + on_disconnected_ = callback; +} + +void WebSocket::OnData(std::function callback) { + on_data_ = callback; +} + +void WebSocket::OnError(std::function callback) { + on_error_ = callback; +} + +void WebSocket::ReceiveTask() { + size_t buffer_offset = 0; + char* buffer = new char[receive_buffer_size_]; + + std::vector current_message; + bool is_fragmented = false; + bool is_binary = false; + + while (transport_->connected()) { + int ret = transport_->Receive(buffer + buffer_offset, receive_buffer_size_ - buffer_offset); + if (ret < 0) { + if (on_error_) { + on_error_(ret); + } + break; + } + if (ret > 0) { + buffer_offset += ret; + size_t frame_start = 0; + + while (frame_start < buffer_offset) { + if (buffer_offset - frame_start < 2) break; // 需要更多数据 + + uint8_t opcode = buffer[frame_start] & 0x0F; + bool fin = (buffer[frame_start] & 0x80) != 0; + uint8_t mask = buffer[frame_start + 1] & 0x80; + uint64_t payload_length = buffer[frame_start + 1] & 0x7F; + + size_t header_length = 2; + if (payload_length == 126) { + if (buffer_offset - frame_start < 4) break; // 需要更多数据 + payload_length = (buffer[frame_start + 2] << 8) | buffer[frame_start + 3]; + header_length += 2; + } else if (payload_length == 127) { + if (buffer_offset - frame_start < 10) break; // 需要更多数据 + payload_length = 0; + for (int i = 0; i < 8; ++i) { + payload_length = (payload_length << 8) | buffer[frame_start + 2 + i]; + } + header_length += 8; + } + + uint8_t mask_key[4] = {0}; + if (mask) { + if (buffer_offset - frame_start < header_length + 4) break; // 需要更多数据 + memcpy(mask_key, buffer + frame_start + header_length, 4); + header_length += 4; + } + + if (buffer_offset - frame_start < header_length + payload_length) break; // 需要更多数据 + + // 解码有效载荷 + char* payload = buffer + frame_start + header_length; + if (mask) { + for (size_t i = 0; i < payload_length; ++i) { + payload[i] ^= mask_key[i % 4]; + } + } + + // 处理帧 + switch (opcode) { + case 0x0: // 延续帧 + case 0x1: // 文本帧 + case 0x2: // 二进制帧 + if (opcode != 0x0 && is_fragmented) { + ESP_LOGE(TAG, "Received new message frame while still fragmenting"); + break; + } + if (opcode != 0x0) { + is_fragmented = !fin; + is_binary = (opcode == 0x2); + current_message.clear(); + } + current_message.insert(current_message.end(), payload, payload + payload_length); + if (fin) { + on_data_(current_message.data(), current_message.size(), is_binary); + current_message.clear(); + is_fragmented = false; + } + break; + case 0x8: // 关闭帧 + transport_->Disconnect(); + break; + case 0x9: // Ping + // 发送 Pong + SendControlFrame(0xA, payload, payload_length); + break; + case 0xA: // Pong + // 可以在这里处理 Pong + break; + default: + ESP_LOGE(TAG, "Unknown opcode: %d", opcode); + break; + } + + frame_start += header_length + payload_length; + } + + // 移动未处理的数据到缓冲区开始 + if (frame_start < buffer_offset) { + memmove(buffer, buffer + frame_start, buffer_offset - frame_start); + } + buffer_offset -= frame_start; + + if (buffer_offset >= receive_buffer_size_) { + ESP_LOGE(TAG, "Receive buffer overflow"); + transport_->Disconnect(); + } + } + } + + if (on_disconnected_) { + on_disconnected_(); + } + delete[] buffer; +} + +bool WebSocket::SendAllRaw(const void* data, size_t len) { + auto ptr = (char*)data; + while (transport_->connected() && len > 0) { + int sent = transport_->Send(ptr, len); + if (sent < 0) { + return false; + } + ptr += sent; + len -= sent; + } + return true; +} + +bool WebSocket::SendControlFrame(uint8_t opcode, const void* data, size_t len) { + if (len > 125) { + ESP_LOGE(TAG, "控制帧有效载荷过大"); + return false; + } + + std::vector frame; + frame.reserve(len + 6); // 帧头 + 掩码 + 有效载荷 + + // 第一个字节：FIN 位 + 操作码 + frame.push_back(0x80 | opcode); + + // 第二个字节：MASK 位 + 有效载荷长度 + frame.push_back(0x80 | len); + + // 生成随机的4字节掩码 + uint8_t mask[4]; + for (int i = 0; i < 4; ++i) { + mask[i] = rand() & 0xFF; + } + frame.insert(frame.end(), mask, mask + 4); + + // 添加并掩码处理有效载荷 + const uint8_t* payload = static_cast(data); + for (size_t i = 0; i < len; ++i) { + frame.push_back(payload[i] ^ mask[i % 4]); + } + + // 发送帧 + return SendAllRaw(frame.data(), frame.size()); +} \ No newline at end of file diff --git a/managed_components/78__esp-opus/.component_hash b/managed_components/78__esp-opus/.component_hash new file mode 100644 index 0000000..6996b29 --- /dev/null +++ b/managed_components/78__esp-opus/.component_hash @@ -0,0 +1 @@ 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src/opus_multistream_decoder.c + src/repacketizer.c + src/opus_projection_encoder.c + src/opus_projection_decoder.c + src/mapping_matrix.c +) + +# 定义SILK源文件列表 +set(SILK_SOURCES + silk/CNG.c + silk/code_signs.c + silk/init_decoder.c + silk/decode_core.c + silk/decode_frame.c + silk/decode_parameters.c + silk/decode_indices.c + silk/decode_pulses.c + silk/decoder_set_fs.c + silk/dec_API.c + silk/enc_API.c + silk/encode_indices.c + silk/encode_pulses.c + silk/gain_quant.c + silk/interpolate.c + silk/LP_variable_cutoff.c + silk/NLSF_decode.c + silk/NSQ.c + silk/NSQ_del_dec.c + silk/PLC.c + silk/shell_coder.c + silk/tables_gain.c + silk/tables_LTP.c + silk/tables_NLSF_CB_NB_MB.c + silk/tables_NLSF_CB_WB.c + silk/tables_other.c + silk/tables_pitch_lag.c + silk/tables_pulses_per_block.c + silk/VAD.c + silk/control_audio_bandwidth.c + silk/quant_LTP_gains.c + silk/VQ_WMat_EC.c + silk/HP_variable_cutoff.c + silk/NLSF_encode.c + silk/NLSF_VQ.c + silk/NLSF_unpack.c + silk/NLSF_del_dec_quant.c + silk/process_NLSFs.c + silk/stereo_LR_to_MS.c + silk/stereo_MS_to_LR.c + silk/check_control_input.c + silk/control_SNR.c + silk/init_encoder.c + silk/control_codec.c + silk/A2NLSF.c + silk/ana_filt_bank_1.c + silk/biquad_alt.c + silk/bwexpander_32.c + silk/bwexpander.c + silk/debug.c + silk/decode_pitch.c + silk/inner_prod_aligned.c + silk/lin2log.c + silk/log2lin.c + silk/LPC_analysis_filter.c + silk/LPC_inv_pred_gain.c + silk/table_LSF_cos.c + silk/NLSF2A.c + silk/NLSF_stabilize.c + silk/NLSF_VQ_weights_laroia.c + silk/pitch_est_tables.c + silk/resampler.c + silk/resampler_down2_3.c + silk/resampler_down2.c + silk/resampler_private_AR2.c + silk/resampler_private_down_FIR.c + silk/resampler_private_IIR_FIR.c + silk/resampler_private_up2_HQ.c + silk/resampler_rom.c + silk/sigm_Q15.c + silk/sort.c + silk/sum_sqr_shift.c + silk/stereo_decode_pred.c + silk/stereo_encode_pred.c + silk/stereo_find_predictor.c + silk/stereo_quant_pred.c + silk/LPC_fit.c +) + +# 定义SILK ARM RTCD源文件列表 +set(SILK_SOURCES_ARM_RTCD + silk/arm/arm_silk_map.c +) + +# 定义SILK ARM NEON Intrinsics源文件列表 +set(SILK_SOURCES_ARM_NEON_INTR + silk/arm/biquad_alt_neon_intr.c + silk/arm/LPC_inv_pred_gain_neon_intr.c + silk/arm/NSQ_del_dec_neon_intr.c + silk/arm/NSQ_neon.c +) + +# 定义SILK固定点源文件列表 +set(SILK_SOURCES_FIXED + silk/fixed/LTP_analysis_filter_FIX.c + silk/fixed/LTP_scale_ctrl_FIX.c + silk/fixed/corrMatrix_FIX.c + silk/fixed/encode_frame_FIX.c + silk/fixed/find_LPC_FIX.c + silk/fixed/find_LTP_FIX.c + silk/fixed/find_pitch_lags_FIX.c + silk/fixed/find_pred_coefs_FIX.c + silk/fixed/noise_shape_analysis_FIX.c + silk/fixed/process_gains_FIX.c + silk/fixed/regularize_correlations_FIX.c + silk/fixed/residual_energy16_FIX.c + silk/fixed/residual_energy_FIX.c + silk/fixed/warped_autocorrelation_FIX.c + silk/fixed/apply_sine_window_FIX.c + silk/fixed/autocorr_FIX.c + silk/fixed/burg_modified_FIX.c + silk/fixed/k2a_FIX.c + silk/fixed/k2a_Q16_FIX.c + silk/fixed/pitch_analysis_core_FIX.c + silk/fixed/vector_ops_FIX.c + silk/fixed/schur64_FIX.c + silk/fixed/schur_FIX.c +) + +# 定义SILK固定点ARM NEON Intrinsics源文件列表 +set(SILK_SOURCES_FIXED_ARM_NEON_INTR + silk/fixed/arm/warped_autocorrelation_FIX_neon_intr.c +) + +list(APPEND srcs ${CELT_SOURCES} + ${OPUS_SOURCES} + ${SILK_SOURCES} + ${SILK_SOURCES_FIXED} + ${SILK_SOURCES_ARM_RTCD} + ) + +set(PRIV_INCLUDES "celt/" "silk/" "silk/fixed" "src/" ".") + +# 定义编译选项 +set(OPUS_COMPILE_OPTIONS + -DHAVE_ALLOCA_H + -DHAVE_LRINT + -DHAVE_LRINTF + -DFIXED_POINT=1 + -DDISABLE_FLOAT_API + -DHAVE_MEMORY_H + -DUSE_ALLOCA + -DOPUS_BUILD + -O2 + -Wno-maybe-uninitialized + -Wno-unused-variable +) + +# 根据芯片类型添加特定选项 +if(${IDF_TARGET} STREQUAL "esp32s2" OR ${IDF_TARGET} STREQUAL "esp32s3") + list(APPEND OPUS_COMPILE_OPTIONS -DOPUS_XTENSA_LX7) +endif() + +idf_component_register(SRCS ${srcs} + INCLUDE_DIRS "include" + PRIV_INCLUDE_DIRS ${PRIV_INCLUDES}) + +target_compile_options(${COMPONENT_LIB} PRIVATE ${OPUS_COMPILE_OPTIONS}) diff --git a/managed_components/78__esp-opus/COPYING b/managed_components/78__esp-opus/COPYING new file mode 100644 index 0000000..7571146 --- /dev/null +++ b/managed_components/78__esp-opus/COPYING @@ -0,0 +1,44 @@ +Copyright 2001-2023 Xiph.Org, Skype Limited, Octasic, + Jean-Marc Valin, Timothy B. Terriberry, + CSIRO, Gregory Maxwell, Mark Borgerding, + Erik de Castro Lopo, Mozilla, Amazon + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +- Redistributions of source code must retain the above copyright +notice, this list of conditions and the following disclaimer. + +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. + +- Neither the name of Internet Society, IETF or IETF Trust, nor the +names of specific contributors, may be used to endorse or promote +products derived from this software without specific prior written +permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Opus is subject to the royalty-free patent licenses which are +specified at: + +Xiph.Org Foundation: +https://datatracker.ietf.org/ipr/1524/ + +Microsoft Corporation: +https://datatracker.ietf.org/ipr/1914/ + +Broadcom Corporation: +https://datatracker.ietf.org/ipr/1526/ diff --git a/managed_components/78__esp-opus/ChangeLog b/managed_components/78__esp-opus/ChangeLog new file mode 100644 index 0000000..e69de29 diff --git a/managed_components/78__esp-opus/LICENSE_PLEASE_READ.txt b/managed_components/78__esp-opus/LICENSE_PLEASE_READ.txt new file mode 100644 index 0000000..bc88efa --- /dev/null +++ b/managed_components/78__esp-opus/LICENSE_PLEASE_READ.txt @@ -0,0 +1,22 @@ +Contributions to the collaboration shall not be considered confidential. + +Each contributor represents and warrants that it has the right and +authority to license copyright in its contributions to the collaboration. + +Each contributor agrees to license the copyright in the contributions +under the Modified (2-clause or 3-clause) BSD License or the Clear BSD License. + +Please see the IPR statements submitted to the IETF for the complete +patent licensing details: + +Xiph.Org Foundation: +https://datatracker.ietf.org/ipr/1524/ + +Microsoft Corporation: +https://datatracker.ietf.org/ipr/1914/ + +Skype Limited: +https://datatracker.ietf.org/ipr/1602/ + +Broadcom Corporation: +https://datatracker.ietf.org/ipr/1526/ diff --git a/managed_components/78__esp-opus/Makefile.am b/managed_components/78__esp-opus/Makefile.am new file mode 100644 index 0000000..0bbcabf --- /dev/null +++ b/managed_components/78__esp-opus/Makefile.am @@ -0,0 +1,497 @@ +# Provide the full test output for failed tests when using the parallel +# test suite (which is enabled by default with automake 1.13+). +export VERBOSE = yes + +AUTOMAKE_OPTIONS = subdir-objects +ACLOCAL_AMFLAGS = -I m4 + +lib_LTLIBRARIES = libopus.la + +DIST_SUBDIRS = doc + +AM_CPPFLAGS = -I$(top_srcdir)/include -I$(top_srcdir)/celt -I$(top_srcdir)/silk \ + -I$(top_srcdir)/silk/float -I$(top_srcdir)/silk/fixed $(NE10_CFLAGS) \ + -I$(top_srcdir)/dnn + +include celt_sources.mk +include lpcnet_sources.mk +include silk_sources.mk +include opus_sources.mk + +LPCNET_SOURCES = +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DEEP_PLC_SOURCES) +endif +if ENABLE_DRED +LPCNET_SOURCES += $(DRED_SOURCES) +endif +if ENABLE_OSCE +LPCNET_SOURCES += $(OSCE_SOURCES) +endif + +if FIXED_POINT +SILK_SOURCES += $(SILK_SOURCES_FIXED) +if HAVE_SSE4_1 +SILK_SOURCES += $(SILK_SOURCES_SSE4_1) $(SILK_SOURCES_FIXED_SSE4_1) +endif +if HAVE_ARM_NEON_INTR +SILK_SOURCES += $(SILK_SOURCES_FIXED_ARM_NEON_INTR) +endif +else +SILK_SOURCES += $(SILK_SOURCES_FLOAT) +if HAVE_SSE4_1 +SILK_SOURCES += $(SILK_SOURCES_SSE4_1) +endif +if HAVE_AVX2 +SILK_SOURCES += $(SILK_SOURCES_FLOAT_AVX2) +endif +endif + +if DISABLE_FLOAT_API +else +OPUS_SOURCES += $(OPUS_SOURCES_FLOAT) +endif + +if CPU_X86 +if HAVE_RTCD +CELT_SOURCES += $(CELT_SOURCES_X86_RTCD) +SILK_SOURCES += $(SILK_SOURCES_X86_RTCD) +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DNN_SOURCES_X86_RTCD) +endif +endif +if HAVE_SSE +CELT_SOURCES += $(CELT_SOURCES_SSE) +endif +if HAVE_SSE2 +CELT_SOURCES += $(CELT_SOURCES_SSE2) +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DNN_SOURCES_SSE2) +endif +endif +if HAVE_SSE4_1 +CELT_SOURCES += $(CELT_SOURCES_SSE4_1) +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DNN_SOURCES_SSE4_1) +endif +endif +if HAVE_AVX2 +SILK_SOURCES += $(SILK_SOURCES_AVX2) +CELT_SOURCES += $(CELT_SOURCES_AVX2) +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DNN_SOURCES_AVX2) +endif +endif +endif + +if CPU_ARM +if HAVE_RTCD +CELT_SOURCES += $(CELT_SOURCES_ARM_RTCD) +SILK_SOURCES += $(SILK_SOURCES_ARM_RTCD) +if ENABLE_DEEP_PLC +LPCNET_SOURCES += $(DNN_SOURCES_ARM_RTCD) +endif +endif + +if ENABLE_DEEP_PLC +if HAVE_ARM_DOTPROD +LPCNET_SOURCES += $(DNN_SOURCES_DOTPROD) +endif +if HAVE_ARM_NEON_INTR +LPCNET_SOURCES += $(DNN_SOURCES_NEON) +endif +endif + +if HAVE_ARM_NEON_INTR +CELT_SOURCES += $(CELT_SOURCES_ARM_NEON_INTR) +SILK_SOURCES += $(SILK_SOURCES_ARM_NEON_INTR) +endif + +if HAVE_ARM_NE10 +CELT_SOURCES += $(CELT_SOURCES_ARM_NE10) +endif + +if OPUS_ARM_EXTERNAL_ASM +noinst_LTLIBRARIES = libarmasm.la +libarmasm_la_SOURCES = $(CELT_SOURCES_ARM_ASM:.s=-gnu.S) +BUILT_SOURCES = $(CELT_SOURCES_ARM_ASM:.s=-gnu.S) \ + $(CELT_AM_SOURCES_ARM_ASM:.s.in=.s) \ + $(CELT_AM_SOURCES_ARM_ASM:.s.in=-gnu.S) +endif +endif + +CLEANFILES = $(CELT_SOURCES_ARM_ASM:.s=-gnu.S) \ + $(CELT_AM_SOURCES_ARM_ASM:.s.in=-gnu.S) + +include celt_headers.mk +include lpcnet_headers.mk +include silk_headers.mk +include opus_headers.mk + +LPCNET_HEAD = +if ENABLE_DEEP_PLC +LPCNET_HEAD += $(DEEP_PLC_HEAD) +endif +if ENABLE_DRED +LPCNET_HEAD += $(DRED_HEAD) +endif +if ENABLE_OSCE +LPCNET_HEAD += $(OSCE_HEAD) +endif +if ENABLE_LOSSGEN +LPCNET_HEAD += $(LOSSGEN_HEAD) +endif + +libopus_la_SOURCES = $(CELT_SOURCES) $(SILK_SOURCES) $(LPCNET_SOURCES) $(OPUS_SOURCES) +libopus_la_LDFLAGS = -no-undefined -version-info @OPUS_LT_CURRENT@:@OPUS_LT_REVISION@:@OPUS_LT_AGE@ +libopus_la_LIBADD = $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +libopus_la_LIBADD += libarmasm.la +endif + +pkginclude_HEADERS = include/opus.h include/opus_multistream.h include/opus_types.h include/opus_defines.h include/opus_projection.h + +noinst_HEADERS = $(OPUS_HEAD) $(SILK_HEAD) $(CELT_HEAD) $(LPCNET_HEAD) + +if EXTRA_PROGRAMS +noinst_PROGRAMS = celt/tests/test_unit_cwrs32 \ + celt/tests/test_unit_dft \ + celt/tests/test_unit_entropy \ + celt/tests/test_unit_laplace \ + celt/tests/test_unit_mathops \ + celt/tests/test_unit_mdct \ + celt/tests/test_unit_rotation \ + celt/tests/test_unit_types \ + opus_compare \ + opus_demo \ + repacketizer_demo \ + silk/tests/test_unit_LPC_inv_pred_gain \ + tests/test_opus_api \ + tests/test_opus_decode \ + tests/test_opus_dred \ + tests/test_opus_encode \ + tests/test_opus_extensions \ + tests/test_opus_padding \ + tests/test_opus_projection \ + trivial_example + +TESTS = celt/tests/test_unit_cwrs32 \ + celt/tests/test_unit_dft \ + celt/tests/test_unit_entropy \ + celt/tests/test_unit_laplace \ + celt/tests/test_unit_mathops \ + celt/tests/test_unit_mdct \ + celt/tests/test_unit_rotation \ + celt/tests/test_unit_types \ + silk/tests/test_unit_LPC_inv_pred_gain \ + tests/test_opus_api \ + tests/test_opus_decode \ + tests/test_opus_encode \ + tests/test_opus_extensions \ + tests/test_opus_padding \ + tests/test_opus_projection + +opus_demo_SOURCES = src/opus_demo.c +if ENABLE_LOSSGEN +opus_demo_SOURCES += $(LOSSGEN_SOURCES) +endif + +opus_demo_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +repacketizer_demo_SOURCES = src/repacketizer_demo.c + +repacketizer_demo_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +opus_compare_SOURCES = src/opus_compare.c +opus_compare_LDADD = $(LIBM) + +trivial_example_SOURCES = doc/trivial_example.c +trivial_example_LDADD = libopus.la $(LIBM) + +tests_test_opus_api_SOURCES = tests/test_opus_api.c tests/test_opus_common.h +tests_test_opus_api_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +tests_test_opus_encode_SOURCES = tests/test_opus_encode.c tests/opus_encode_regressions.c tests/test_opus_common.h +tests_test_opus_encode_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +tests_test_opus_decode_SOURCES = tests/test_opus_decode.c tests/test_opus_common.h +tests_test_opus_decode_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +tests_test_opus_padding_SOURCES = tests/test_opus_padding.c tests/test_opus_common.h +tests_test_opus_padding_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +tests_test_opus_dred_SOURCES = tests/test_opus_dred.c tests/test_opus_common.h +tests_test_opus_dred_LDADD = libopus.la $(NE10_LIBS) $(LIBM) + +if CUSTOM_MODES +tests_test_opus_custom_SOURCES = tests/test_opus_custom.c tests/test_opus_common.h +tests_test_opus_custom_LDADD = libopus.la $(NE10_LIBS) $(LIBM) +endif + +CELT_OBJ = $(CELT_SOURCES:.c=.lo) +SILK_OBJ = $(SILK_SOURCES:.c=.lo) +LPCNET_OBJ = $(LPCNET_SOURCES:.c=.lo) +OPUS_OBJ = $(OPUS_SOURCES:.c=.lo) + +tests_test_opus_extensions_SOURCES = tests/test_opus_extensions.c tests/test_opus_common.h +tests_test_opus_extensions_LDADD = $(OPUS_OBJ) $(SILK_OBJ) $(LPCNET_OBJ) $(CELT_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +tests_test_opus_extensions_LDADD += libarmasm.la +endif + +tests_test_opus_projection_SOURCES = tests/test_opus_projection.c tests/test_opus_common.h +tests_test_opus_projection_LDADD = $(OPUS_OBJ) $(SILK_OBJ) $(LPCNET_OBJ) $(CELT_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +tests_test_opus_projection_LDADD += libarmasm.la +endif + +silk_tests_test_unit_LPC_inv_pred_gain_SOURCES = silk/tests/test_unit_LPC_inv_pred_gain.c +silk_tests_test_unit_LPC_inv_pred_gain_LDADD = $(SILK_OBJ) $(LPCNET_OBJ) $(CELT_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +silk_tests_test_unit_LPC_inv_pred_gain_LDADD += libarmasm.la +endif + +celt_tests_test_unit_cwrs32_SOURCES = celt/tests/test_unit_cwrs32.c +celt_tests_test_unit_cwrs32_LDADD = $(LIBM) + +celt_tests_test_unit_dft_SOURCES = celt/tests/test_unit_dft.c +celt_tests_test_unit_dft_LDADD = $(CELT_OBJ) $(LPCNET_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +celt_tests_test_unit_dft_LDADD += libarmasm.la +endif + +celt_tests_test_unit_entropy_SOURCES = celt/tests/test_unit_entropy.c +celt_tests_test_unit_entropy_LDADD = $(LIBM) + +celt_tests_test_unit_laplace_SOURCES = celt/tests/test_unit_laplace.c +celt_tests_test_unit_laplace_LDADD = $(LIBM) + +celt_tests_test_unit_mathops_SOURCES = celt/tests/test_unit_mathops.c +celt_tests_test_unit_mathops_LDADD = $(CELT_OBJ) $(LPCNET_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +celt_tests_test_unit_mathops_LDADD += libarmasm.la +endif + +celt_tests_test_unit_mdct_SOURCES = celt/tests/test_unit_mdct.c +celt_tests_test_unit_mdct_LDADD = $(CELT_OBJ) $(LPCNET_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +celt_tests_test_unit_mdct_LDADD += libarmasm.la +endif + +celt_tests_test_unit_rotation_SOURCES = celt/tests/test_unit_rotation.c +celt_tests_test_unit_rotation_LDADD = $(CELT_OBJ) $(LPCNET_OBJ) $(NE10_LIBS) $(LIBM) +if OPUS_ARM_EXTERNAL_ASM +celt_tests_test_unit_rotation_LDADD += libarmasm.la +endif + +celt_tests_test_unit_types_SOURCES = celt/tests/test_unit_types.c +celt_tests_test_unit_types_LDADD = $(LIBM) +endif + +if CUSTOM_MODES +pkginclude_HEADERS += include/opus_custom.h +if EXTRA_PROGRAMS +noinst_PROGRAMS += opus_custom_demo +opus_custom_demo_SOURCES = celt/opus_custom_demo.c +opus_custom_demo_LDADD = libopus.la $(LIBM) + +TESTS += tests/test_opus_custom +noinst_PROGRAMS += tests/test_opus_custom +endif +endif + +if EXTRA_PROGRAMS +if ENABLE_DEEP_PLC +noinst_PROGRAMS += fargan_demo dump_data dump_weights_blob +fargan_demo_SOURCES = dnn/fargan_demo.c +fargan_demo_LDADD = $(LPCNET_OBJ) $(CELT_OBJ) $(LIBM) + +dump_data_SOURCES = dnn/dump_data.c +dump_data_LDADD = $(LPCNET_OBJ) $(CELT_OBJ) $(LIBM) + +dump_weights_blob_SOURCES = dnn/write_lpcnet_weights.c +dump_weights_blob_LDADD = $(LIBM) +dump_weights_blob_CFLAGS = $(AM_CFLAGS) -DDUMP_BINARY_WEIGHTS +endif +if ENABLE_DRED +TESTS += tests/test_opus_dred +endif + +if ENABLE_LOSSGEN +noinst_PROGRAMS += lossgen_demo +lossgen_demo_SOURCES = dnn/lossgen_demo.c $(LOSSGEN_SOURCES) +lossgen_demo_LDADD = $(LIBM) +endif + +endif + + +EXTRA_DIST = opus.pc.in \ + opus-uninstalled.pc.in \ + opus.m4 \ + Makefile.mips \ + Makefile.unix \ + CMakeLists.txt \ + cmake/CFeatureCheck.cmake \ + cmake/OpusBuildtype.cmake \ + cmake/OpusConfig.cmake \ + cmake/OpusConfig.cmake.in \ + cmake/OpusFunctions.cmake \ + cmake/OpusPackageVersion.cmake \ + cmake/OpusSources.cmake \ + cmake/README.md \ + cmake/RunTest.cmake \ + cmake/config.h.cmake.in \ + cmake/vla.c \ + cmake/cpu_info_by_asm.c \ + cmake/cpu_info_by_c.c \ + meson/get-version.py \ + meson/read-sources-list.py \ + meson/README.md \ + meson.build \ + meson_options.txt \ + include/meson.build \ + celt/meson.build \ + celt/tests/meson.build \ + dnn/meson.build \ + dnn/README.md \ + silk/meson.build \ + silk/tests/meson.build \ + src/meson.build \ + tests/meson.build \ + doc/meson.build \ + tests/run_vectors.sh \ + celt/arm/arm2gnu.pl \ + celt/arm/celt_pitch_xcorr_arm.s + +pkgconfigdir = $(libdir)/pkgconfig +pkgconfig_DATA = opus.pc + +m4datadir = $(datadir)/aclocal +m4data_DATA = opus.m4 + +# Targets to build and install just the library without the docs +opus check-opus install-opus: export NO_DOXYGEN = 1 + +opus: all +check-opus: check +install-opus: install + + +# Or just the docs +docs: + ( cd doc && $(MAKE) $(AM_MAKEFLAGS) ) + +install-docs: + ( cd doc && $(MAKE) $(AM_MAKEFLAGS) install ) + + +# Or everything (by default) +all-local: + @[ -n "$(NO_DOXYGEN)" ] || ( cd doc && $(MAKE) $(AM_MAKEFLAGS) ) + +install-data-local: + @[ -n "$(NO_DOXYGEN)" ] || ( cd doc && $(MAKE) $(AM_MAKEFLAGS) install ) + +clean-local: + -( cd doc && $(MAKE) $(AM_MAKEFLAGS) clean ) + +uninstall-local: + ( cd doc && $(MAKE) $(AM_MAKEFLAGS) uninstall ) + + +# We check this every time make is run, with configure.ac being touched to +# trigger an update of the build system files if update_version changes the +# current PACKAGE_VERSION (or if package_version was modified manually by a +# user with either AUTO_UPDATE=no or no update_version script present - the +# latter being the normal case for tarball releases). +# +# We can't just add the package_version file to CONFIGURE_DEPENDENCIES since +# simply running autoconf will not actually regenerate configure for us when +# the content of that file changes (due to autoconf dependency checking not +# knowing about that without us creating yet another file for it to include). +# +# The MAKECMDGOALS check is a gnu-make'ism, but will degrade 'gracefully' for +# makes that don't support it. The only loss of functionality is not forcing +# an update of package_version for `make dist` if AUTO_UPDATE=no, but that is +# unlikely to be a real problem for any real user. +$(top_srcdir)/configure.ac: force + @case "$(MAKECMDGOALS)" in \ + dist-hook) exit 0 ;; \ + dist-* | dist | distcheck | distclean) _arg=release ;; \ + esac; \ + if ! $(top_srcdir)/update_version $$_arg 2> /dev/null; then \ + if [ ! -e $(top_srcdir)/package_version ]; then \ + echo 'PACKAGE_VERSION="unknown"' > $(top_srcdir)/package_version; \ + fi; \ + . $(top_srcdir)/package_version || exit 1; \ + [ "$(PACKAGE_VERSION)" != "$$PACKAGE_VERSION" ] || exit 0; \ + fi; \ + touch $@ + +force: + +# Create a minimal package_version file when make dist is run. +dist-hook: + echo 'PACKAGE_VERSION="$(PACKAGE_VERSION)"' > $(top_distdir)/package_version + + +.PHONY: opus check-opus install-opus docs install-docs + +# automake doesn't do dependency tracking for asm files, that I can tell +$(CELT_SOURCES_ARM_ASM:%.s=%-gnu.S): celt/arm/armopts-gnu.S +$(CELT_SOURCES_ARM_ASM:%.s=%-gnu.S): $(top_srcdir)/celt/arm/arm2gnu.pl + +# convert ARM asm to GNU as format +%-gnu.S: $(top_srcdir)/%.s + $(top_srcdir)/celt/arm/arm2gnu.pl @ARM2GNU_PARAMS@ < $< > $@ +# For autoconf-modified sources (e.g., armopts.s) +%-gnu.S: %.s + $(top_srcdir)/celt/arm/arm2gnu.pl @ARM2GNU_PARAMS@ < $< > $@ + +OPT_UNIT_TEST_OBJ = $(celt_tests_test_unit_mathops_SOURCES:.c=.o) \ + $(celt_tests_test_unit_rotation_SOURCES:.c=.o) \ + $(celt_tests_test_unit_mdct_SOURCES:.c=.o) \ + $(celt_tests_test_unit_dft_SOURCES:.c=.o) \ + $(silk_tests_test_unit_LPC_inv_pred_gain_SOURCES:.c=.o) + +if HAVE_SSE +SSE_OBJ = $(CELT_SOURCES_SSE:.c=.lo) +$(SSE_OBJ): CFLAGS += $(OPUS_X86_SSE_CFLAGS) +endif + +if HAVE_SSE2 +SSE2_OBJ = $(CELT_SOURCES_SSE2:.c=.lo) \ + $(DNN_SOURCES_SSE2:.c=.lo) +$(SSE2_OBJ): CFLAGS += $(OPUS_X86_SSE2_CFLAGS) +endif + +if HAVE_SSE4_1 +SSE4_1_OBJ = $(CELT_SOURCES_SSE4_1:.c=.lo) \ + $(DNN_SOURCES_SSE4_1:.c=.lo) \ + $(SILK_SOURCES_SSE4_1:.c=.lo) \ + $(SILK_SOURCES_FIXED_SSE4_1:.c=.lo) +$(SSE4_1_OBJ): CFLAGS += $(OPUS_X86_SSE4_1_CFLAGS) +endif + +if HAVE_AVX2 +AVX2_OBJ = $(CELT_SOURCES_AVX2:.c=.lo) \ + $(SILK_SOURCES_AVX2:.c=.lo) \ + $(SILK_SOURCES_FLOAT_AVX2:.c=.lo) \ + $(DNN_SOURCES_AVX2:.c=.lo) +$(AVX2_OBJ): CFLAGS += $(OPUS_X86_AVX2_CFLAGS) +endif + +if HAVE_ARM_NEON_INTR +ARM_NEON_INTR_OBJ = $(CELT_SOURCES_ARM_NEON_INTR:.c=.lo) \ + $(SILK_SOURCES_ARM_NEON_INTR:.c=.lo) \ + $(DNN_SOURCES_NEON:.c=.lo) \ + $(SILK_SOURCES_FIXED_ARM_NEON_INTR:.c=.lo) +$(ARM_NEON_INTR_OBJ): CFLAGS += \ + $(OPUS_ARM_NEON_INTR_CFLAGS) $(NE10_CFLAGS) +endif + +if HAVE_ARM_DOTPROD +ARM_DOTPROD_OBJ = $(DNN_SOURCES_DOTPROD:.c=.lo) +$(ARM_DOTPROD_OBJ): CFLAGS += $(ARM_DOTPROD_INTR_CFLAGS) +endif diff --git a/managed_components/78__esp-opus/Makefile.mips b/managed_components/78__esp-opus/Makefile.mips new file mode 100644 index 0000000..8e8f837 --- /dev/null +++ b/managed_components/78__esp-opus/Makefile.mips @@ -0,0 +1,169 @@ +#################### COMPILE OPTIONS ####################### + +# Uncomment this for fixed-point build +FIXED_POINT=1 + +# It is strongly recommended to uncomment one of these +# VAR_ARRAYS: Use C99 variable-length arrays for stack allocation +# USE_ALLOCA: Use alloca() for stack allocation +# If none is defined, then the fallback is a non-threadsafe global array +CFLAGS := -DUSE_ALLOCA $(CFLAGS) +#CFLAGS := -DVAR_ARRAYS $(CFLAGS) + +# These options affect performance +# HAVE_LRINTF: Use C99 intrinsics to speed up float-to-int conversion +CFLAGS := -DHAVE_LRINTF $(CFLAGS) + +###################### END OF OPTIONS ###################### + +-include package_version + +include silk_sources.mk +include celt_sources.mk +include opus_sources.mk + +ifdef FIXED_POINT +SILK_SOURCES += $(SILK_SOURCES_FIXED) +else +SILK_SOURCES += $(SILK_SOURCES_FLOAT) +OPUS_SOURCES += $(OPUS_SOURCES_FLOAT) +endif + +EXESUFFIX = +LIBPREFIX = lib +LIBSUFFIX = .a +OBJSUFFIX = .o + +CC = $(TOOLCHAIN_PREFIX)cc$(TOOLCHAIN_SUFFIX) +AR = $(TOOLCHAIN_PREFIX)ar +RANLIB = $(TOOLCHAIN_PREFIX)ranlib +CP = $(TOOLCHAIN_PREFIX)cp + +cppflags-from-defines = $(addprefix -D,$(1)) +cppflags-from-includes = $(addprefix -I,$(1)) +ldflags-from-ldlibdirs = $(addprefix -L,$(1)) +ldlibs-from-libs = $(addprefix -l,$(1)) + +WARNINGS = -Wall -W -Wstrict-prototypes -Wextra -Wcast-align -Wnested-externs -Wshadow + +CFLAGS += -mips32r2 -mno-mips16 -std=gnu99 -O2 -g $(WARNINGS) -DENABLE_ASSERTIONS -DMIPSr1_ASM -DOPUS_BUILD -mdspr2 -march=74kc -mtune=74kc -mmt -mgp32 + +CINCLUDES = include silk celt + +ifdef FIXED_POINT +CFLAGS += -DFIXED_POINT=1 -DDISABLE_FLOAT_API +CINCLUDES += silk/fixed +else +CINCLUDES += silk/float +endif + + +LIBS = m + +LDLIBDIRS = ./ + +CFLAGS += $(call cppflags-from-defines,$(CDEFINES)) +CFLAGS += $(call cppflags-from-includes,$(CINCLUDES)) +LDFLAGS += $(call ldflags-from-ldlibdirs,$(LDLIBDIRS)) +LDLIBS += $(call ldlibs-from-libs,$(LIBS)) + +COMPILE.c.cmdline = $(CC) -c $(CFLAGS) -o $@ $< +LINK.o = $(CC) $(LDPREFLAGS) $(LDFLAGS) +LINK.o.cmdline = $(LINK.o) $^ $(LDLIBS) -o $@$(EXESUFFIX) + +ARCHIVE.cmdline = $(AR) $(ARFLAGS) $@ $^ && $(RANLIB) $@ + +%$(OBJSUFFIX):%.c + $(COMPILE.c.cmdline) + +%$(OBJSUFFIX):%.cpp + $(COMPILE.cpp.cmdline) + +# Directives + + +# Variable definitions +LIB_NAME = opus +TARGET = $(LIBPREFIX)$(LIB_NAME)$(LIBSUFFIX) + +SRCS_C = $(SILK_SOURCES) $(CELT_SOURCES) $(OPUS_SOURCES) + +OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(SRCS_C)) + +OPUSDEMO_SRCS_C = src/opus_demo.c +OPUSDEMO_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(OPUSDEMO_SRCS_C)) + +TESTOPUSAPI_SRCS_C = tests/test_opus_api.c +TESTOPUSAPI_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSAPI_SRCS_C)) + +TESTOPUSDECODE_SRCS_C = tests/test_opus_decode.c +TESTOPUSDECODE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSDECODE_SRCS_C)) + +TESTOPUSENCODE_SRCS_C = tests/test_opus_encode.c tests/opus_encode_regressions.c +TESTOPUSENCODE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSENCODE_SRCS_C)) + +TESTOPUSEXTENSIONS_SRCS_C = tests/test_opus_extensions.c +TESTOPUSEXTENSIONS_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSEXTENSIONS_SRCS_C)) + +TESTOPUSPADDING_SRCS_C = tests/test_opus_padding.c +TESTOPUSPADDING_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSPADDING_SRCS_C)) + +OPUSCOMPARE_SRCS_C = src/opus_compare.c +OPUSCOMPARE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(OPUSCOMPARE_SRCS_C)) + +TESTS := test_opus_api test_opus_decode test_opus_encode test_opus_extensions test_opus_padding + +# Rules +all: lib opus_demo opus_compare $(TESTS) + +lib: $(TARGET) + +check: all + for test in $(TESTS); do ./$$test; done + +$(TARGET): $(OBJS) + $(ARCHIVE.cmdline) + +opus_demo$(EXESUFFIX): $(OPUSDEMO_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_api$(EXESUFFIX): $(TESTOPUSAPI_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_decode$(EXESUFFIX): $(TESTOPUSDECODE_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_encode$(EXESUFFIX): $(TESTOPUSENCODE_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_extensions$(EXESUFFIX): $(TESTOPUSEXTENSIONS_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_padding$(EXESUFFIX): $(TESTOPUSPADDING_OBJS) $(TARGET) + $(LINK.o.cmdline) + +opus_compare$(EXESUFFIX): $(OPUSCOMPARE_OBJS) + $(LINK.o.cmdline) + +celt/celt.o: CFLAGS += -DPACKAGE_VERSION='$(PACKAGE_VERSION)' +celt/celt.o: package_version + +package_version: force + @if [ -x ./update_version ]; then \ + ./update_version || true; \ + elif [ ! -e ./package_version ]; then \ + echo 'PACKAGE_VERSION="unknown"' > ./package_version; \ + fi + +force: + +clean: + rm -f opus_demo$(EXESUFFIX) opus_compare$(EXESUFFIX) $(TARGET) \ + test_opus_api$(EXESUFFIX) test_opus_decode$(EXESUFFIX) \ + test_opus_encode$(EXESUFFIX) test_opus_extensions$(EXESUFFIX) \ + test_opus_padding$(EXESUFFIX) + $(OBJS) $(OPUSDEMO_OBJS) $(OPUSCOMPARE_OBJS) $(TESTOPUSAPI_OBJS) \ + $(TESTOPUSDECODE_OBJS) $(TESTOPUSENCODE_OBJS) \ + $(TESTOPUSEXTENSIONS_OBJS) $(TESTOPUSPADDING_OBJS) + +.PHONY: all lib clean force check diff --git a/managed_components/78__esp-opus/Makefile.unix b/managed_components/78__esp-opus/Makefile.unix new file mode 100644 index 0000000..5a09194 --- /dev/null +++ b/managed_components/78__esp-opus/Makefile.unix @@ -0,0 +1,167 @@ +#################### COMPILE OPTIONS ####################### + +# Uncomment this for fixed-point build +#FIXED_POINT=1 + +# It is strongly recommended to uncomment one of these +# VAR_ARRAYS: Use C99 variable-length arrays for stack allocation +# USE_ALLOCA: Use alloca() for stack allocation +# If none is defined, then the fallback is a non-threadsafe global array +CFLAGS := -DUSE_ALLOCA $(CFLAGS) +#CFLAGS := -DVAR_ARRAYS $(CFLAGS) + +# These options affect performance +# HAVE_LRINTF: Use C99 intrinsics to speed up float-to-int conversion +#CFLAGS := -DHAVE_LRINTF $(CFLAGS) + +###################### END OF OPTIONS ###################### + +-include package_version + +include silk_sources.mk +include celt_sources.mk +include opus_sources.mk + +ifdef FIXED_POINT +SILK_SOURCES += $(SILK_SOURCES_FIXED) +else +SILK_SOURCES += $(SILK_SOURCES_FLOAT) +OPUS_SOURCES += $(OPUS_SOURCES_FLOAT) +endif + +EXESUFFIX = +LIBPREFIX = lib +LIBSUFFIX = .a +OBJSUFFIX = .o + +CC = $(TOOLCHAIN_PREFIX)cc$(TOOLCHAIN_SUFFIX) +AR = $(TOOLCHAIN_PREFIX)ar +RANLIB = $(TOOLCHAIN_PREFIX)ranlib +CP = $(TOOLCHAIN_PREFIX)cp + +cppflags-from-defines = $(addprefix -D,$(1)) +cppflags-from-includes = $(addprefix -I,$(1)) +ldflags-from-ldlibdirs = $(addprefix -L,$(1)) +ldlibs-from-libs = $(addprefix -l,$(1)) + +WARNINGS = -Wall -W -Wstrict-prototypes -Wextra -Wcast-align -Wnested-externs -Wshadow +CFLAGS += -O2 -g $(WARNINGS) -DOPUS_BUILD +CINCLUDES = include silk celt + +ifdef FIXED_POINT +CFLAGS += -DFIXED_POINT=1 -DDISABLE_FLOAT_API +CINCLUDES += silk/fixed +else +CINCLUDES += silk/float +endif + + +LIBS = m + +LDLIBDIRS = ./ + +CFLAGS += $(call cppflags-from-defines,$(CDEFINES)) +CFLAGS += $(call cppflags-from-includes,$(CINCLUDES)) +LDFLAGS += $(call ldflags-from-ldlibdirs,$(LDLIBDIRS)) +LDLIBS += $(call ldlibs-from-libs,$(LIBS)) + +COMPILE.c.cmdline = $(CC) -c $(CFLAGS) -o $@ $< +LINK.o = $(CC) $(LDPREFLAGS) $(LDFLAGS) +LINK.o.cmdline = $(LINK.o) $^ $(LDLIBS) -o $@$(EXESUFFIX) + +ARCHIVE.cmdline = $(AR) $(ARFLAGS) $@ $^ && $(RANLIB) $@ + +%$(OBJSUFFIX):%.c + $(COMPILE.c.cmdline) + +%$(OBJSUFFIX):%.cpp + $(COMPILE.cpp.cmdline) + +# Directives + + +# Variable definitions +LIB_NAME = opus +TARGET = $(LIBPREFIX)$(LIB_NAME)$(LIBSUFFIX) + +SRCS_C = $(SILK_SOURCES) $(CELT_SOURCES) $(OPUS_SOURCES) + +OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(SRCS_C)) + +OPUSDEMO_SRCS_C = src/opus_demo.c +OPUSDEMO_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(OPUSDEMO_SRCS_C)) + +TESTOPUSAPI_SRCS_C = tests/test_opus_api.c +TESTOPUSAPI_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSAPI_SRCS_C)) + +TESTOPUSDECODE_SRCS_C = tests/test_opus_decode.c +TESTOPUSDECODE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSDECODE_SRCS_C)) + +TESTOPUSENCODE_SRCS_C = tests/test_opus_encode.c tests/opus_encode_regressions.c +TESTOPUSENCODE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSENCODE_SRCS_C)) + +TESTOPUSEXTENSIONS_SRCS_C = tests/test_opus_extensions.c +TESTOPUSEXTENSIONS_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSEXTENSIONS_SRCS_C)) + +TESTOPUSPADDING_SRCS_C = tests/test_opus_padding.c +TESTOPUSPADDING_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(TESTOPUSPADDING_SRCS_C)) + +OPUSCOMPARE_SRCS_C = src/opus_compare.c +OPUSCOMPARE_OBJS := $(patsubst %.c,%$(OBJSUFFIX),$(OPUSCOMPARE_SRCS_C)) + +TESTS := test_opus_api test_opus_decode test_opus_encode test_opus_extensions test_opus_padding + +# Rules +all: lib opus_demo opus_compare $(TESTS) + +lib: $(TARGET) + +check: all + for test in $(TESTS); do ./$$test; done + +$(TARGET): $(OBJS) + $(ARCHIVE.cmdline) + +opus_demo$(EXESUFFIX): $(OPUSDEMO_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_api$(EXESUFFIX): $(TESTOPUSAPI_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_decode$(EXESUFFIX): $(TESTOPUSDECODE_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_encode$(EXESUFFIX): $(TESTOPUSENCODE_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_extensions$(EXESUFFIX): $(TESTOPUSEXTENSIONS_OBJS) $(TARGET) + $(LINK.o.cmdline) + +test_opus_padding$(EXESUFFIX): $(TESTOPUSPADDING_OBJS) $(TARGET) + $(LINK.o.cmdline) + +opus_compare$(EXESUFFIX): $(OPUSCOMPARE_OBJS) + $(LINK.o.cmdline) + +celt/celt.o: CFLAGS += -DPACKAGE_VERSION='$(PACKAGE_VERSION)' +celt/celt.o: package_version + +package_version: force + @if [ -x ./update_version ]; then \ + ./update_version || true; \ + elif [ ! -e ./package_version ]; then \ + echo 'PACKAGE_VERSION="unknown"' > ./package_version; \ + fi + +force: + +clean: + rm -f opus_demo$(EXESUFFIX) opus_compare$(EXESUFFIX) $(TARGET) \ + test_opus_api$(EXESUFFIX) test_opus_decode$(EXESUFFIX) \ + test_opus_encode$(EXESUFFIX) test_opus_extensions$(EXESUFFIX) \ + test_opus_padding$(EXESUFFIX) + $(OBJS) $(OPUSDEMO_OBJS) $(OPUSCOMPARE_OBJS) $(TESTOPUSAPI_OBJS) \ + $(TESTOPUSDECODE_OBJS) $(TESTOPUSENCODE_OBJS) \ + $(TESTOPUSEXTENSIONS_OBJS) $(TESTOPUSPADDING_OBJS) + +.PHONY: all lib clean force check diff --git a/managed_components/78__esp-opus/NEWS b/managed_components/78__esp-opus/NEWS new file mode 100644 index 0000000..e69de29 diff --git a/managed_components/78__esp-opus/README.draft b/managed_components/78__esp-opus/README.draft new file mode 100644 index 0000000..9c31bd0 --- /dev/null +++ b/managed_components/78__esp-opus/README.draft @@ -0,0 +1,54 @@ +To build this source code, simply type: + +% make + +If this does not work, or if you want to change the default configuration +(e.g., to compile for a fixed-point architecture), simply edit the options +in the Makefile. + +An up-to-date implementation conforming to this standard is available in a +Git repository at https://gitlab.xiph.org/xiph/opus.git or on a website at: +https://opus-codec.org/ +However, although that implementation is expected to remain conformant +with the standard, it is the code in this RFC that shall remain normative. +To build from the git repository instead of using this RFC, follow these +steps: + +1) Clone the repository (latest implementation of this standard at the time +of publication) + +% git clone https://gitlab.xiph.org/xiph/opus.git +% cd opus + +2) Compile + +% ./autogen.sh +% ./configure +% make + +Once you have compiled the codec, there will be a opus_demo executable in +the top directory. + +Usage: opus_demo [-e] + [options] + opus_demo -d [options] + + +mode: voip | audio | restricted-lowdelay +options: +-e : only runs the encoder (output the bit-stream) +-d : only runs the decoder (reads the bit-stream as input) +-cbr : enable constant bitrate; default: variable bitrate +-cvbr : enable constrained variable bitrate; default: unconstrained +-bandwidth : audio bandwidth (from narrowband to fullband); + default: sampling rate +-framesize <2.5|5|10|20|40|60> : frame size in ms; default: 20 +-max_payload : maximum payload size in bytes, default: 1024 +-complexity : complexity, 0 (lowest) ... 10 (highest); default: 10 +-inbandfec : enable SILK inband FEC +-forcemono : force mono encoding, even for stereo input +-dtx : enable SILK DTX +-loss : simulate packet loss, in percent (0-100); default: 0 + +input and output are little endian signed 16-bit PCM files or opus bitstreams +with simple opus_demo proprietary framing. diff --git a/managed_components/78__esp-opus/README.md b/managed_components/78__esp-opus/README.md new file mode 100644 index 0000000..7cd2f62 --- /dev/null +++ b/managed_components/78__esp-opus/README.md @@ -0,0 +1,222 @@ +## ESP32 OPUS + +Only modified the CMakeLists.txt for compilation under the ESP-IDF 5.x SDK. + +只修改了 CMakeLists.txt，用于在 ESP-IDF 5.x SDK 下编译。 + +Clone the repos into the components directory and it’s ready to use. + +放置到 components 目录下即可使用。 + +### Example + +```cpp +#include "opus.h" + +... + +auto encoder = opus_encoder_create(16000, 1, OPUS_APPLICATION_VOIP, nullptr); +assert(encoder != nullptr); +opus_encoder_ctl(encoder, OPUS_SET_COMPLEXITY(5)); + +int frame_size = 960; +int16_t pcm[frame_size]; +uint8_t opus[1500]; + +int len = opus_encode(encoder, pcm, frame_size, opus, sizeof(opus)); +printf("len: %d\n", len); + +opus_encoder_destroy(encoder); +``` + +## Original README + +== Opus audio codec == + +Opus is a codec for interactive speech and audio transmission over the Internet. + + Opus can handle a wide range of interactive audio applications, including +Voice over IP, videoconferencing, in-game chat, and even remote live music +performances. It can scale from low bit-rate narrowband speech to very high +quality stereo music. + + Opus, when coupled with an appropriate container format, is also suitable +for non-realtime stored-file applications such as music distribution, game +soundtracks, portable music players, jukeboxes, and other applications that +have historically used high latency formats such as MP3, AAC, or Vorbis. + + Opus is specified by IETF RFC 6716: + https://tools.ietf.org/html/rfc6716 + + The Opus format and this implementation of it are subject to the royalty- +free patent and copyright licenses specified in the file COPYING. + +This package implements a shared library for encoding and decoding raw Opus +bitstreams. Raw Opus bitstreams should be used over RTP according to + https://tools.ietf.org/html/rfc7587 + +The package also includes a number of test tools used for testing the +correct operation of the library. The bitstreams read/written by these +tools should not be used for Opus file distribution: They include +additional debugging data and cannot support seeking. + +Opus stored in files should use the Ogg encapsulation for Opus which is +described at: + https://tools.ietf.org/html/rfc7845 + +An opus-tools package is available which provides encoding and decoding of +Ogg encapsulated Opus files and includes a number of useful features. + +Opus-tools can be found at: + https://gitlab.xiph.org/xiph/opus-tools.git +or on the main Opus website: + https://opus-codec.org/ + +== Deep Learning and Opus == + +Lossy networks continue to be a challenge for real-time communications. +While the original implementation of Opus provides an excellent packet loss +concealment mechanism, the team has continued to advance the methodology used +to improve audio quality in challenge network environments. + +In Opus 1.5, we added a deep learning based redundancy encoder that enhances +audio in lossy networks by embedding one second of recovery data in the padding +data of each packet. The underlying algorithm behind encoding and decoding the +recovery data is called the deep redundancy (DRED) algorithm. By leveraging +the padding data within the packet, Opus 1.5 is fully backward compatible with +prior revisions of Opus. Please see the README under the "dnn" subdirectory to +understand DRED. + +DRED was developed by a team that Amazon Web Services initially sponsored, +who open-sourced the implementation as well as began the +standardization process at the IETF: + https://datatracker.ietf.org/doc/draft-ietf-mlcodec-opus-extension/ +The license behind Opus or the intellectual property position of Opus does +not change with Opus 1.5. + +== Compiling libopus == + +To build from a distribution tarball, you only need to do the following: + + % ./configure + % make + +To build from the git repository, the following steps are necessary: + +0) Set up a development environment: + +On an Ubuntu or Debian family Linux distribution: + + % sudo apt-get install git autoconf automake libtool gcc make + +On a Fedora/Redhat based Linux: + + % sudo dnf install git autoconf automake libtool gcc make + +Or for older Redhat/Centos Linux releases: + + % sudo yum install git autoconf automake libtool gcc make + +On Apple macOS, install Xcode and brew.sh, then in the Terminal enter: + + % brew install autoconf automake libtool + +1) Clone the repository: + + % git clone https://gitlab.xiph.org/xiph/opus.git + % cd opus + +2) Compiling the source + + % ./autogen.sh + % ./configure + % make + +On x86, it's a good idea to use a -march= option that allows the use of AVX2. + +3) Install the codec libraries (optional) + + % sudo make install + +Once you have compiled the codec, there will be a opus_demo executable +in the top directory. + +Usage: opus_demo [-e] + [options] + opus_demo -d [options] + + +mode: voip | audio | restricted-lowdelay +options: + -e : only runs the encoder (output the bit-stream) + -d : only runs the decoder (reads the bit-stream as input) + -cbr : enable constant bitrate; default: variable bitrate + -cvbr : enable constrained variable bitrate; default: + unconstrained + -bandwidth + : audio bandwidth (from narrowband to fullband); + default: sampling rate + -framesize <2.5|5|10|20|40|60> + : frame size in ms; default: 20 + -max_payload + : maximum payload size in bytes, default: 1024 + -complexity + : complexity, 0 (lowest) ... 10 (highest); default: 10 + -inbandfec : enable SILK inband FEC + -forcemono : force mono encoding, even for stereo input + -dtx : enable SILK DTX + -loss : simulate packet loss, in percent (0-100); default: 0 + +input and output are little-endian signed 16-bit PCM files or opus +bitstreams with simple opus_demo proprietary framing. + +== Testing == + +This package includes a collection of automated unit and system tests +which SHOULD be run after compiling the package especially the first +time it is run on a new platform. + +To run the integrated tests: + + % make check + +There is also collection of standard test vectors which are not +included in this package for size reasons but can be obtained from: +https://opus-codec.org/docs/opus_testvectors-rfc8251.tar.gz + +To run compare the code to these test vectors: + + % curl -OL https://opus-codec.org/docs/opus_testvectors-rfc8251.tar.gz + % tar -zxf opus_testvectors-rfc8251.tar.gz + % ./tests/run_vectors.sh ./ opus_newvectors 48000 + +== Compiling libopus for Windows and alternative build systems == + +See cmake/README.md or meson/README.md. + +== Portability notes == + +This implementation uses floating-point by default but can be compiled to +use only fixed-point arithmetic by setting --enable-fixed-point (if using +autoconf) or by defining the FIXED_POINT macro (if building manually). +The fixed point implementation has somewhat lower audio quality and is +slower on platforms with fast FPUs, it is normally only used in embedded +environments. + +The implementation can be compiled with either a C89 or a C99 compiler. +While it does not rely on any _undefined behavior_ as defined by C89 or +C99, it relies on common _implementation-defined behavior_ for two's +complement architectures: + +o Right shifts of negative values are consistent with two's + complement arithmetic, so that a>>b is equivalent to + floor(a/(2^b)), + +o For conversion to a signed integer of N bits, the value is reduced + modulo 2^N to be within range of the type, + +o The result of integer division of a negative value is truncated + towards zero, and + +o The compiler provides a 64-bit integer type (a C99 requirement + which is supported by most C89 compilers). diff --git a/managed_components/78__esp-opus/autogen.bat b/managed_components/78__esp-opus/autogen.bat new file mode 100644 index 0000000..b7c735b --- /dev/null +++ b/managed_components/78__esp-opus/autogen.bat @@ -0,0 +1,13 @@ +@echo off +REM Run this to set up the build system: configure, makefiles, etc. + +setlocal enabledelayedexpansion + +REM Parse the real autogen.sh script for version +for /F "tokens=2 delims= " %%A in ('findstr "dnn/download_model.sh" autogen.sh') do ( + set "model=%%A" +) + +call dnn\download_model.bat %model% + +echo Updating build configuration files, please wait.... diff --git a/managed_components/78__esp-opus/autogen.sh b/managed_components/78__esp-opus/autogen.sh new file mode 100644 index 0000000..0bfad9e --- /dev/null +++ b/managed_components/78__esp-opus/autogen.sh @@ -0,0 +1,16 @@ +#!/bin/sh +# Copyright (c) 2010-2015 Xiph.Org Foundation and contributors. +# Use of this source code is governed by a BSD-style license that can be +# found in the COPYING file. + +# Run this to set up the build system: configure, makefiles, etc. +set -e + +srcdir=`dirname $0` +test -n "$srcdir" && cd "$srcdir" + +dnn/download_model.sh "a86f0a9db852691d4335608733ec8384a407e585801ab9e4b490e0be297ac382" + +echo "Updating build configuration files, please wait...." + +autoreconf -isf diff --git a/managed_components/78__esp-opus/celt/_kiss_fft_guts.h b/managed_components/78__esp-opus/celt/_kiss_fft_guts.h new file mode 100644 index 0000000..bb43572 --- /dev/null +++ b/managed_components/78__esp-opus/celt/_kiss_fft_guts.h @@ -0,0 +1,188 @@ +/*Copyright (c) 2003-2004, Mark Borgerding + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.*/ + +#ifndef KISS_FFT_GUTS_H +#define KISS_FFT_GUTS_H + +#define MIN(a,b) ((a)<(b) ? (a):(b)) +#define MAX(a,b) ((a)>(b) ? (a):(b)) + +/* kiss_fft.h + defines kiss_fft_scalar as either short or a float type + and defines + typedef struct { kiss_fft_scalar r; kiss_fft_scalar i; }kiss_fft_cpx; */ +#include "kiss_fft.h" + +/* + Explanation of macros dealing with complex math: + + C_MUL(m,a,b) : m = a*b + C_FIXDIV( c , div ) : if a fixed point impl., c /= div. noop otherwise + C_SUB( res, a,b) : res = a - b + C_SUBFROM( res , a) : res -= a + C_ADDTO( res , a) : res += a + * */ +#ifdef FIXED_POINT +#include "arch.h" + + +#define SAMP_MAX 2147483647 +#define TWID_MAX 32767 +#define TRIG_UPSCALE 1 + +#define SAMP_MIN -SAMP_MAX + +#ifdef ENABLE_QEXT +# define S_MUL(a,b) MULT32_32_Q31(b, a) +# define S_MUL2(a,b) MULT32_32_Q31(b, a) +#else +# define S_MUL(a,b) MULT16_32_Q15(b, a) +# define S_MUL2(a,b) MULT16_32_Q16(b, a) +#endif + +# define C_MUL(m,a,b) \ + do{ (m).r = SUB32_ovflw(S_MUL((a).r,(b).r) , S_MUL((a).i,(b).i)); \ + (m).i = ADD32_ovflw(S_MUL((a).r,(b).i) , S_MUL((a).i,(b).r)); }while(0) + +# define C_MULC(m,a,b) \ + do{ (m).r = ADD32_ovflw(S_MUL((a).r,(b).r) , S_MUL((a).i,(b).i)); \ + (m).i = SUB32_ovflw(S_MUL((a).i,(b).r) , S_MUL((a).r,(b).i)); }while(0) + +# define C_MULBYSCALAR( c, s ) \ + do{ (c).r = S_MUL( (c).r , s ) ;\ + (c).i = S_MUL( (c).i , s ) ; }while(0) + +# define DIVSCALAR(x,k) \ + (x) = S_MUL( x, (TWID_MAX-((k)>>1))/(k)+1 ) + +# define C_FIXDIV(c,div) \ + do { DIVSCALAR( (c).r , div); \ + DIVSCALAR( (c).i , div); }while (0) + +#define C_ADD( res, a,b)\ + do {(res).r=ADD32_ovflw((a).r,(b).r); (res).i=ADD32_ovflw((a).i,(b).i); \ + }while(0) +#define C_SUB( res, a,b)\ + do {(res).r=SUB32_ovflw((a).r,(b).r); (res).i=SUB32_ovflw((a).i,(b).i); \ + }while(0) +#define C_ADDTO( res , a)\ + do {(res).r = ADD32_ovflw((res).r, (a).r); (res).i = ADD32_ovflw((res).i,(a).i);\ + }while(0) + +#define C_SUBFROM( res , a)\ + do {(res).r = ADD32_ovflw((res).r,(a).r); (res).i = SUB32_ovflw((res).i,(a).i); \ + }while(0) + +#if defined(OPUS_ARM_INLINE_ASM) +#include "arm/kiss_fft_armv4.h" +#endif + +#if defined(OPUS_ARM_INLINE_EDSP) +#include "arm/kiss_fft_armv5e.h" +#endif +#if defined(MIPSr1_ASM) +#include "mips/kiss_fft_mipsr1.h" +#endif + +#else /* not FIXED_POINT*/ + +# define S_MUL(a,b) ( (a)*(b) ) +# define S_MUL2(a,b) ( (a)*(b) ) +#define C_MUL(m,a,b) \ + do{ (m).r = (a).r*(b).r - (a).i*(b).i;\ + (m).i = (a).r*(b).i + (a).i*(b).r; }while(0) +#define C_MULC(m,a,b) \ + do{ (m).r = (a).r*(b).r + (a).i*(b).i;\ + (m).i = (a).i*(b).r - (a).r*(b).i; }while(0) + +#define C_MUL4(m,a,b) C_MUL(m,a,b) + +# define C_FIXDIV(c,div) /* NOOP */ +# define C_MULBYSCALAR( c, s ) \ + do{ (c).r *= (s);\ + (c).i *= (s); }while(0) +#endif + +#ifndef CHECK_OVERFLOW_OP +# define CHECK_OVERFLOW_OP(a,op,b) /* noop */ +#endif + +#ifndef C_ADD +#define C_ADD( res, a,b)\ + do { \ + CHECK_OVERFLOW_OP((a).r,+,(b).r)\ + CHECK_OVERFLOW_OP((a).i,+,(b).i)\ + (res).r=(a).r+(b).r; (res).i=(a).i+(b).i; \ + }while(0) +#define C_SUB( res, a,b)\ + do { \ + CHECK_OVERFLOW_OP((a).r,-,(b).r)\ + CHECK_OVERFLOW_OP((a).i,-,(b).i)\ + (res).r=(a).r-(b).r; (res).i=(a).i-(b).i; \ + }while(0) +#define C_ADDTO( res , a)\ + do { \ + CHECK_OVERFLOW_OP((res).r,+,(a).r)\ + CHECK_OVERFLOW_OP((res).i,+,(a).i)\ + (res).r += (a).r; (res).i += (a).i;\ + }while(0) + +#define C_SUBFROM( res , a)\ + do {\ + CHECK_OVERFLOW_OP((res).r,-,(a).r)\ + CHECK_OVERFLOW_OP((res).i,-,(a).i)\ + (res).r -= (a).r; (res).i -= (a).i; \ + }while(0) +#endif /* C_ADD defined */ + +#ifdef FIXED_POINT +/*# define KISS_FFT_COS(phase) TRIG_UPSCALE*floor(MIN(32767,MAX(-32767,.5+32768 * cos (phase)))) +# define KISS_FFT_SIN(phase) TRIG_UPSCALE*floor(MIN(32767,MAX(-32767,.5+32768 * sin (phase))))*/ +# define KISS_FFT_COS(phase) floor(.5+TWID_MAX*cos (phase)) +# define KISS_FFT_SIN(phase) floor(.5+TWID_MAX*sin (phase)) +# define HALF_OF(x) ((x)>>1) +#elif defined(USE_SIMD) +# define KISS_FFT_COS(phase) _mm_set1_ps( cos(phase) ) +# define KISS_FFT_SIN(phase) _mm_set1_ps( sin(phase) ) +# define HALF_OF(x) ((x)*_mm_set1_ps(.5f)) +#else +# define KISS_FFT_COS(phase) (kiss_fft_scalar) cos(phase) +# define KISS_FFT_SIN(phase) (kiss_fft_scalar) sin(phase) +# define HALF_OF(x) ((x)*.5f) +#endif + +#define kf_cexp(x,phase) \ + do{ \ + (x)->r = KISS_FFT_COS(phase);\ + (x)->i = KISS_FFT_SIN(phase);\ + }while(0) + +#define kf_cexp2(x,phase) \ + do{ \ + (x)->r = TRIG_UPSCALE*celt_cos_norm((phase));\ + (x)->i = TRIG_UPSCALE*celt_cos_norm((phase)-32768);\ +}while(0) + +#endif /* KISS_FFT_GUTS_H */ diff --git a/managed_components/78__esp-opus/celt/arch.h b/managed_components/78__esp-opus/celt/arch.h new file mode 100644 index 0000000..a843d1b --- /dev/null +++ b/managed_components/78__esp-opus/celt/arch.h @@ -0,0 +1,396 @@ +/* Copyright (c) 2003-2008 Jean-Marc Valin + Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/** + @file arch.h + @brief Various architecture definitions for CELT +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef ARCH_H +#define ARCH_H + +#include "opus_types.h" +#include "opus_defines.h" + +# if !defined(__GNUC_PREREQ) +# if defined(__GNUC__)&&defined(__GNUC_MINOR__) +# define __GNUC_PREREQ(_maj,_min) \ + ((__GNUC__<<16)+__GNUC_MINOR__>=((_maj)<<16)+(_min)) +# else +# define __GNUC_PREREQ(_maj,_min) 0 +# endif +# endif + +#if OPUS_GNUC_PREREQ(3, 0) +#define opus_likely(x) (__builtin_expect(!!(x), 1)) +#define opus_unlikely(x) (__builtin_expect(!!(x), 0)) +#else +#define opus_likely(x) (!!(x)) +#define opus_unlikely(x) (!!(x)) +#endif + +#define CELT_SIG_SCALE 32768.f + +#define CELT_FATAL(str) celt_fatal(str, __FILE__, __LINE__) + +#if defined(ENABLE_ASSERTIONS) || defined(ENABLE_HARDENING) +#ifdef __GNUC__ +__attribute__((noreturn)) +#endif +void celt_fatal(const char *str, const char *file, int line); + +#if defined(CELT_C) && !defined(OVERRIDE_celt_fatal) +#include +#include +#ifdef __GNUC__ +__attribute__((noreturn)) +#endif +void celt_fatal(const char *str, const char *file, int line) +{ + fprintf (stderr, "Fatal (internal) error in %s, line %d: %s\n", file, line, str); +#if defined(_MSC_VER) + _set_abort_behavior( 0, _WRITE_ABORT_MSG); +#endif + abort(); +} +#endif + +#define celt_assert(cond) {if (!(cond)) {CELT_FATAL("assertion failed: " #cond);}} +#define celt_assert2(cond, message) {if (!(cond)) {CELT_FATAL("assertion failed: " #cond "\n" message);}} +#define MUST_SUCCEED(call) celt_assert((call) == OPUS_OK) +#else +#define celt_assert(cond) +#define celt_assert2(cond, message) +#define MUST_SUCCEED(call) do {if((call) != OPUS_OK) {RESTORE_STACK; return OPUS_INTERNAL_ERROR;} } while (0) +#endif + +#if defined(ENABLE_ASSERTIONS) +#define celt_sig_assert(cond) {if (!(cond)) {CELT_FATAL("signal assertion failed: " #cond);}} +#else +#define celt_sig_assert(cond) +#endif + +#define IMUL32(a,b) ((a)*(b)) + +#define MIN16(a,b) ((a) < (b) ? (a) : (b)) /**< Minimum 16-bit value. */ +#define MAX16(a,b) ((a) > (b) ? (a) : (b)) /**< Maximum 16-bit value. */ +#define MIN32(a,b) ((a) < (b) ? (a) : (b)) /**< Minimum 32-bit value. */ +#define MAX32(a,b) ((a) > (b) ? (a) : (b)) /**< Maximum 32-bit value. */ +#define IMIN(a,b) ((a) < (b) ? (a) : (b)) /**< Minimum int value. */ +#define IMAX(a,b) ((a) > (b) ? (a) : (b)) /**< Maximum int value. */ +#define FMIN(a,b) ((a) < (b) ? (a) : (b)) /**< Minimum float value. */ +#define FMAX(a,b) ((a) > (b) ? (a) : (b)) /**< Maximum float value. */ +#define UADD32(a,b) ((a)+(b)) +#define USUB32(a,b) ((a)-(b)) +#define MAXG(a,b) MAX32(a, b) +#define MING(a,b) MIN32(a, b) + +/* Throughout the code, we use the following scaling for signals: + FLOAT: used for float API, normalized to +/-1. + INT16: used for 16-bit API, normalized to +/- 32768 + RES: internal Opus resolution, defined as +/-1. in float builds, or either 16-bit or 24-bit int for fixed-point builds + SIG: internal CELT resolution: defined as +/- 32768. in float builds, or Q27 in fixed-point builds (int16 shifted by 12) +*/ + + +/* Set this if opus_int64 is a native type of the CPU. */ +/* Assume that all LP64 architectures have fast 64-bit types; also x86_64 + (which can be ILP32 for x32) and Win64 (which is LLP64). */ +#if defined(__x86_64__) || defined(__LP64__) || defined(_WIN64) +#define OPUS_FAST_INT64 1 +#else +#define OPUS_FAST_INT64 0 +#endif + +#ifdef FIXED_POINT +#define ARG_FIXED(arg) , arg +#else +#define ARG_FIXED(arg) +#endif + +#define PRINT_MIPS(file) + +#ifdef FIXED_POINT + +typedef opus_int16 opus_val16; +typedef opus_int32 opus_val32; +typedef opus_int64 opus_val64; + +typedef opus_val32 celt_sig; +typedef opus_val16 celt_norm; +typedef opus_val32 celt_ener; +typedef opus_val32 celt_glog; + +#ifdef ENABLE_RES24 +typedef opus_val32 opus_res; +#define RES_SHIFT 8 +#define SIG2RES(a) PSHR32(a, SIG_SHIFT-RES_SHIFT) +#define RES2INT16(a) SAT16(PSHR32(a, RES_SHIFT)) +#define RES2INT24(a) (a) +#define RES2FLOAT(a) ((1.f/32768.f/256.)*(a)) +#define INT16TORES(a) SHL32(EXTEND32(a), RES_SHIFT) +#define INT24TORES(a) (a) +#define ADD_RES(a, b) ADD32(a, b) +#define FLOAT2RES(a) float2int(32768.f*256.f*(a)) +#define RES2SIG(a) SHL32((a), SIG_SHIFT-RES_SHIFT) +#define MULT16_RES_Q15(a,b) MULT16_32_Q15(a,b) +#define MAX_ENCODING_DEPTH 24 +#else +typedef opus_val16 opus_res; +#define RES_SHIFT 0 +#define SIG2RES(a) SIG2WORD16(a) +#define RES2INT16(a) (a) +#define RES2INT24(a) SHL32(EXTEND32(a), 8) +#define RES2FLOAT(a) ((1.f/32768.f)*(a)) +#define INT16TORES(a) (a) +#define INT24TORES(a) SAT16(PSHR32(a, 8)) +#define ADD_RES(a, b) SAT16(ADD32((a), (b))); +#define FLOAT2RES(a) FLOAT2INT16(a) +#define RES2SIG(a) SHL32(EXTEND32(a), SIG_SHIFT) +#define MULT16_RES_Q15(a,b) MULT16_16_Q15(a,b) +#define MAX_ENCODING_DEPTH 16 +#endif + +#define RES2VAL16(a) RES2INT16(a) +#define FLOAT2SIG(a) float2int(((opus_int32)32768< 32767 ? 32767 : x < -32768 ? -32768 : (opus_int16)x; +} + +#ifdef FIXED_DEBUG +#include "fixed_debug.h" +#else + +#include "fixed_generic.h" + +#ifdef OPUS_ARM_PRESUME_AARCH64_NEON_INTR +#include "arm/fixed_arm64.h" +#elif defined (OPUS_ARM_INLINE_EDSP) +#include "arm/fixed_armv5e.h" +#elif defined (OPUS_ARM_INLINE_ASM) +#include "arm/fixed_armv4.h" +#elif defined (BFIN_ASM) +#include "fixed_bfin.h" +#elif defined (TI_C5X_ASM) +#include "fixed_c5x.h" +#elif defined (TI_C6X_ASM) +#include "fixed_c6x.h" +#endif + +#endif + +#else /* FIXED_POINT */ + +typedef float opus_val16; +typedef float opus_val32; +typedef float opus_val64; + +typedef float celt_sig; +typedef float celt_norm; +typedef float celt_ener; +typedef float celt_glog; + +typedef float opus_res; +typedef float celt_coef; + +#ifdef FLOAT_APPROX +/* This code should reliably detect NaN/inf even when -ffast-math is used. + Assumes IEEE 754 format. */ +static OPUS_INLINE int celt_isnan(float x) +{ + union {float f; opus_uint32 i;} in; + in.f = x; + return ((in.i>>23)&0xFF)==0xFF && (in.i&0x007FFFFF)!=0; +} +#else +#ifdef __FAST_MATH__ +#error Cannot build libopus with -ffast-math unless FLOAT_APPROX is defined. This could result in crashes on extreme (e.g. NaN) input +#endif +#define celt_isnan(x) ((x)!=(x)) +#endif + +#define Q15ONE 1.0f +#define Q31ONE 1.0f +#define COEF_ONE 1.0f +#define COEF2VAL16(x) (x) + +#define NORM_SCALING 1.f + +#define EPSILON 1e-15f +#define VERY_SMALL 1e-30f +#define VERY_LARGE16 1e15f +#define Q15_ONE ((opus_val16)1.f) + +/* This appears to be the same speed as C99's fabsf() but it's more portable. */ +#define ABS16(x) ((float)fabs(x)) +#define ABS32(x) ((float)fabs(x)) + +#define QCONST16(x,bits) (x) +#define QCONST32(x,bits) (x) +#define GCONST(x) (x) + +#define NEG16(x) (-(x)) +#define NEG32(x) (-(x)) +#define NEG32_ovflw(x) (-(x)) +#define EXTRACT16(x) (x) +#define EXTEND32(x) (x) +#define SHR16(a,shift) (a) +#define SHL16(a,shift) (a) +#define SHR32(a,shift) (a) +#define SHL32(a,shift) (a) +#define PSHR32(a,shift) (a) +#define VSHR32(a,shift) (a) + +#define PSHR(a,shift) (a) +#define SHR(a,shift) (a) +#define SHL(a,shift) (a) +#define SATURATE(x,a) (x) +#define SATURATE16(x) (x) + +#define ROUND16(a,shift) (a) +#define SROUND16(a,shift) (a) +#define HALF16(x) (.5f*(x)) +#define HALF32(x) (.5f*(x)) + +#define ADD16(a,b) ((a)+(b)) +#define SUB16(a,b) ((a)-(b)) +#define ADD32(a,b) ((a)+(b)) +#define SUB32(a,b) ((a)-(b)) +#define ADD32_ovflw(a,b) ((a)+(b)) +#define SUB32_ovflw(a,b) ((a)-(b)) +#define SHL32_ovflw(a,shift) (a) +#define PSHR32_ovflw(a,shift) (a) + +#define MULT16_16_16(a,b) ((a)*(b)) +#define MULT16_16(a,b) ((opus_val32)(a)*(opus_val32)(b)) +#define MAC16_16(c,a,b) ((c)+(opus_val32)(a)*(opus_val32)(b)) + +#define MULT16_32_Q15(a,b) ((a)*(b)) +#define MULT16_32_Q16(a,b) ((a)*(b)) + +#define MULT32_32_Q16(a,b) ((a)*(b)) +#define MULT32_32_Q31(a,b) ((a)*(b)) + +#define MAC16_32_Q15(c,a,b) ((c)+(a)*(b)) +#define MAC16_32_Q16(c,a,b) ((c)+(a)*(b)) +#define MAC_COEF_32_ARM(c,a,b) ((c)+(a)*(b)) + +#define MULT16_16_Q11_32(a,b) ((a)*(b)) +#define MULT16_16_Q11(a,b) ((a)*(b)) +#define MULT16_16_Q13(a,b) ((a)*(b)) +#define MULT16_16_Q14(a,b) ((a)*(b)) +#define MULT16_16_Q15(a,b) ((a)*(b)) +#define MULT16_16_P15(a,b) ((a)*(b)) +#define MULT16_16_P13(a,b) ((a)*(b)) +#define MULT16_16_P14(a,b) ((a)*(b)) +#define MULT16_32_P16(a,b) ((a)*(b)) + +#define MULT_COEF_32(a, b) ((a)*(b)) +#define MULT_COEF(a, b) ((a)*(b)) +#define MULT_COEF_TAPS(a, b) ((a)*(b)) + +#define DIV32_16(a,b) (((opus_val32)(a))/(opus_val16)(b)) +#define DIV32(a,b) (((opus_val32)(a))/(opus_val32)(b)) + +#define SIG2RES(a) ((1/CELT_SIG_SCALE)*(a)) +#define RES2INT16(a) FLOAT2INT16(a) +#define RES2INT24(a) float2int(32768.f*256.f*(a)) +#define RES2FLOAT(a) (a) +#define INT16TORES(a) ((a)*(1/CELT_SIG_SCALE)) +#define INT24TORES(a) ((1.f/32768.f/256.)*(a)) +#define ADD_RES(a, b) ADD32(a, b) +#define FLOAT2RES(a) (a) +#define RES2SIG(a) (CELT_SIG_SCALE*(a)) +#define MULT16_RES_Q15(a,b) MULT16_16_Q15(a,b) + +#define RES2VAL16(a) (a) +#define FLOAT2SIG(a) ((a)*CELT_SIG_SCALE) +#define INT16TOSIG(a) ((float)(a)) +#define INT24TOSIG(a) ((float)(a)*(1.f/256.f)) +#define MAX_ENCODING_DEPTH 24 + +#endif /* !FIXED_POINT */ + +#ifndef GLOBAL_STACK_SIZE +#ifdef FIXED_POINT +#define GLOBAL_STACK_SIZE 120000 +#else +#define GLOBAL_STACK_SIZE 120000 +#endif +#endif + +#endif /* ARCH_H */ diff --git a/managed_components/78__esp-opus/celt/arm/arm2gnu.pl b/managed_components/78__esp-opus/celt/arm/arm2gnu.pl new file mode 100644 index 0000000..a2895f7 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/arm2gnu.pl @@ -0,0 +1,353 @@ +#!/usr/bin/perl +# Copyright (C) 2002-2013 Xiph.org Foundation +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: +# +# - Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# +# - Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +# ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +my $bigend; # little/big endian +my $nxstack; +my $apple = 0; +my $symprefix = ""; + +$nxstack = 0; + +eval 'exec /usr/local/bin/perl -S $0 ${1+"$@"}' + if $running_under_some_shell; + +while ($ARGV[0] =~ /^-/) { + $_ = shift; + last if /^--$/; + if (/^-n$/) { + $nflag++; + next; + } + if (/^--apple$/) { + $apple = 1; + $symprefix = "_"; + next; + } + die "I don't recognize this switch: $_\\n"; +} +$printit++ unless $nflag; + +$\ = "\n"; # automatically add newline on print +$n=0; + +$thumb = 0; # ARM mode by default, not Thumb. +@proc_stack = (); + +printf (" .syntax unified\n"); + +LINE: +while (<>) { + + # For ADRLs we need to add a new line after the substituted one. + $addPadding = 0; + + # First, we do not dare to touch *anything* inside double quotes, do we? + # Second, if you want a dollar character in the string, + # insert two of them -- that's how ARM C and assembler treat strings. + s/^([A-Za-z_]\w*)[ \t]+DCB[ \t]*\"/$1: .ascii \"/ && do { s/\$\$/\$/g; next }; + s/\bDCB\b[ \t]*\"/.ascii \"/ && do { s/\$\$/\$/g; next }; + s/^(\S+)\s+RN\s+(\S+)/$1 .req r$2/ && do { s/\$\$/\$/g; next }; + # If there's nothing on a line but a comment, don't try to apply any further + # substitutions (this is a cheap hack to avoid mucking up the license header) + s/^([ \t]*);/$1@/ && do { s/\$\$/\$/g; next }; + # If substituted -- leave immediately ! + + s/@/,:/; + s/;/@/; + while ( /@.*'/ ) { + s/(@.*)'/$1/g; + } + s/\{FALSE\}/0/g; + s/\{TRUE\}/1/g; + s/\{(\w\w\w\w+)\}/$1/g; + s/\bINCLUDE[ \t]*([^ \t\n]+)/.include \"$1\"/; + s/\bGET[ \t]*([^ \t\n]+)/.include \"${ my $x=$1; $x =~ s|\.s|-gnu.S|; \$x }\"/; + s/\bIMPORT\b/.extern/; + s/\bEXPORT\b\s*/.global $symprefix/; + s/^(\s+)\[/$1IF/; + s/^(\s+)\|/$1ELSE/; + s/^(\s+)\]/$1ENDIF/; + s/IF *:DEF:/ .ifdef/; + s/IF *:LNOT: *:DEF:/ .ifndef/; + s/ELSE/ .else/; + s/ENDIF/ .endif/; + + if( /\bIF\b/ ) { + s/\bIF\b/ .if/; + s/=/==/; + } + if ( $n == 2) { + s/\$/\\/g; + } + if ($n == 1) { + s/\$//g; + s/label//g; + $n = 2; + } + if ( /MACRO/ ) { + s/MACRO *\n/.macro/; + $n=1; + } + if ( /\bMEND\b/ ) { + s/\bMEND\b/.endm/; + $n=0; + } + + # ".rdata" doesn't work in 'as' version 2.13.2, as it is ".rodata" there. + # + if ( /\bAREA\b/ ) { + my $align; + $align = "2"; + if ( /ALIGN=(\d+)/ ) { + $align = $1; + } + if ( /CODE/ ) { + $nxstack = 1; + } + s/^(.+)CODE(.+)READONLY(.*)/ .text/; + s/^(.+)DATA(.+)READONLY(.*)/ .section .rdata/; + s/^(.+)\|\|\.data\|\|(.+)/ .data/; + s/^(.+)\|\|\.bss\|\|(.+)/ .bss/; + s/$/; .p2align $align/; + # Enable NEON instructions but don't produce a binary that requires + # ARMv7. RVCT does not have equivalent directives, so we just do this + # for all CODE areas. + if ( /.text/ ) { + # Separating .arch, .fpu, etc., by semicolons does not work (gas + # thinks the semicolon is part of the arch name, even when there's + # whitespace separating them). Sadly this means our line numbers + # won't match the original source file (we could use the .line + # directive, which is documented to be obsolete, but then gdb will + # show the wrong line in the translated source file). + s/$/; .arch armv7-a\n .fpu neon\n .object_arch armv4t/ unless ($apple); + } + } + + s/\|\|\.constdata\$(\d+)\|\|/.L_CONST$1/; # ||.constdata$3|| + s/\|\|\.bss\$(\d+)\|\|/.L_BSS$1/; # ||.bss$2|| + s/\|\|\.data\$(\d+)\|\|/.L_DATA$1/; # ||.data$2|| + s/\|\|([a-zA-Z0-9_]+)\@([a-zA-Z0-9_]+)\|\|/@ $&/; + s/^(\s+)\%(\s)/ .space $1/; + + s/\|(.+)\.(\d+)\|/\.$1_$2/; # |L80.123| -> .L80_123 + s/\bCODE32\b/.code 32/ && do {$thumb = 0}; + s/\bCODE16\b/.code 16/ && do {$thumb = 1}; + if (/\bPROC\b/) + { + my $prefix; + my $proc; + /^([A-Za-z_\.]\w+)\b/; + $proc = $1; + $prefix = ""; + if ($proc) + { + $prefix = $prefix.sprintf("\t.type\t%s, %%function", $proc) unless ($apple); + # Make sure we $prefix isn't empty here (for the $apple case). + # We handle mangling the label here, make sure it doesn't match + # the label handling below (if $prefix would be empty). + $prefix = $prefix."; "; + push(@proc_stack, $proc); + s/^[A-Za-z_\.]\w+/$symprefix$&:/; + } + $prefix = $prefix."\t.thumb_func; " if ($thumb); + s/\bPROC\b/@ $&/; + $_ = $prefix.$_; + } + s/^(\s*)(S|Q|SH|U|UQ|UH)ASX\b/$1$2ADDSUBX/; + s/^(\s*)(S|Q|SH|U|UQ|UH)SAX\b/$1$2SUBADDX/; + if (/\bENDP\b/) + { + my $proc; + s/\bENDP\b/@ $&/; + $proc = pop(@proc_stack); + $_ = "\t.size $proc, .-$proc".$_ if ($proc && !$apple); + } + s/\bSUBT\b/@ $&/; + s/\bDATA\b/@ $&/; # DATA directive is deprecated -- Asm guide, p.7-25 + s/\bKEEP\b/@ $&/; + s/\bEXPORTAS\b/@ $&/; + s/\|\|(.)+\bEQU\b/@ $&/; + s/\|\|([\w\$]+)\|\|/$1/; + s/\bENTRY\b/@ $&/; + s/\bASSERT\b/@ $&/; + s/\bGBLL\b/@ $&/; + s/\bGBLA\b/@ $&/; + s/^\W+OPT\b/@ $&/; + s/:OR:/|/g; + s/:SHL:/<>/g; + s/:AND:/&/g; + s/:LAND:/&&/g; + s/CPSR/cpsr/; + s/SPSR/spsr/; + s/ALIGN$/.balign 4/; + s/ALIGN\s+([0-9x]+)$/.balign $1/; + s/psr_cxsf/psr_all/; + s/LTORG/.ltorg/; + s/^([A-Za-z_]\w*)[ \t]+EQU/ .set $1,/; + s/^([A-Za-z_]\w*)[ \t]+SETL/ .set $1,/; + s/^([A-Za-z_]\w*)[ \t]+SETA/ .set $1,/; + s/^([A-Za-z_]\w*)[ \t]+\*/ .set $1,/; + + # {PC} + 0xdeadfeed --> . + 0xdeadfeed + s/\{PC\} \+/ \. +/; + + # Single hex constant on the line ! + # + # >>> NOTE <<< + # Double-precision floats in gcc are always mixed-endian, which means + # bytes in two words are little-endian, but words are big-endian. + # So, 0x0000deadfeed0000 would be stored as 0x0000dead at low address + # and 0xfeed0000 at high address. + # + s/\bDCFD\b[ \t]+0x([a-fA-F0-9]{8})([a-fA-F0-9]{8})/.long 0x$1, 0x$2/; + # Only decimal constants on the line, no hex ! + s/\bDCFD\b[ \t]+([0-9\.\-]+)/.double $1/; + + # Single hex constant on the line ! +# s/\bDCFS\b[ \t]+0x([a-f0-9]{8})([a-f0-9]{8})/.long 0x$1, 0x$2/; + # Only decimal constants on the line, no hex ! +# s/\bDCFS\b[ \t]+([0-9\.\-]+)/.double $1/; + s/\bDCFS[ \t]+0x/.word 0x/; + s/\bDCFS\b/.float/; + + s/^([A-Za-z_]\w*)[ \t]+DCD/$1 .word/; + s/\bDCD\b/.word/; + s/^([A-Za-z_]\w*)[ \t]+DCW/$1 .short/; + s/\bDCW\b/.short/; + s/^([A-Za-z_]\w*)[ \t]+DCB/$1 .byte/; + s/\bDCB\b/.byte/; + s/^([A-Za-z_]\w*)[ \t]+\%/.comm $1,/; + s/^[A-Za-z_\.]\w+/$&:/; + s/^(\d+)/$1:/; + s/\%(\d+)/$1b_or_f/; + s/\%[Bb](\d+)/$1b/; + s/\%[Ff](\d+)/$1f/; + s/\%[Ff][Tt](\d+)/$1f/; + s/&([\dA-Fa-f]+)/0x$1/; + if ( /\b2_[01]+\b/ ) { + s/\b2_([01]+)\b/conv$1&&&&/g; + while ( /[01][01][01][01]&&&&/ ) { + s/0000&&&&/&&&&0/g; + s/0001&&&&/&&&&1/g; + s/0010&&&&/&&&&2/g; + s/0011&&&&/&&&&3/g; + s/0100&&&&/&&&&4/g; + s/0101&&&&/&&&&5/g; + s/0110&&&&/&&&&6/g; + s/0111&&&&/&&&&7/g; + s/1000&&&&/&&&&8/g; + s/1001&&&&/&&&&9/g; + s/1010&&&&/&&&&A/g; + s/1011&&&&/&&&&B/g; + s/1100&&&&/&&&&C/g; + s/1101&&&&/&&&&D/g; + s/1110&&&&/&&&&E/g; + s/1111&&&&/&&&&F/g; + } + s/000&&&&/&&&&0/g; + s/001&&&&/&&&&1/g; + s/010&&&&/&&&&2/g; + s/011&&&&/&&&&3/g; + s/100&&&&/&&&&4/g; + s/101&&&&/&&&&5/g; + s/110&&&&/&&&&6/g; + s/111&&&&/&&&&7/g; + s/00&&&&/&&&&0/g; + s/01&&&&/&&&&1/g; + s/10&&&&/&&&&2/g; + s/11&&&&/&&&&3/g; + s/0&&&&/&&&&0/g; + s/1&&&&/&&&&1/g; + s/conv&&&&/0x/g; + } + + if ( /commandline/) + { + if( /-bigend/) + { + $bigend=1; + } + } + + if ( /\bDCDU\b/ ) + { + my $cmd=$_; + my $value; + my $prefix; + my $w1; + my $w2; + my $w3; + my $w4; + + s/\s+DCDU\b/@ $&/; + + $cmd =~ /\bDCDU\b\s+0x(\d+)/; + $value = $1; + $value =~ /(\w\w)(\w\w)(\w\w)(\w\w)/; + $w1 = $1; + $w2 = $2; + $w3 = $3; + $w4 = $4; + + if( $bigend ne "") + { + # big endian + $prefix = "\t.byte\t0x".$w1.";". + "\t.byte\t0x".$w2.";". + "\t.byte\t0x".$w3.";". + "\t.byte\t0x".$w4."; "; + } + else + { + # little endian + $prefix = "\t.byte\t0x".$w4.";". + "\t.byte\t0x".$w3.";". + "\t.byte\t0x".$w2.";". + "\t.byte\t0x".$w1."; "; + } + $_=$prefix.$_; + } + + if ( /\badrl\b/i ) + { + s/\badrl\s+(\w+)\s*,\s*(\w+)/ldr $1,=$2/i; + $addPadding = 1; + } + s/\bEND\b/@ END/; +} continue { + printf ("%s", $_) if $printit; + if ($addPadding != 0) + { + printf (" mov r0,r0\n"); + $addPadding = 0; + } +} +#If we had a code section, mark that this object doesn't need an executable +# stack. +if ($nxstack && !$apple) { + printf (" .section\t.note.GNU-stack,\"\",\%\%progbits\n"); +} diff --git a/managed_components/78__esp-opus/celt/arm/arm_celt_map.c b/managed_components/78__esp-opus/celt/arm/arm_celt_map.c new file mode 100644 index 0000000..931c011 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/arm_celt_map.c @@ -0,0 +1,193 @@ +/* Copyright (c) 2010 Xiph.Org Foundation + * Copyright (c) 2013 Parrot + * Copyright (c) 2024 Arm Limited */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "kiss_fft.h" +#include "mathops.h" +#include "mdct.h" +#include "pitch.h" + +#if defined(OPUS_HAVE_RTCD) + +# if !defined(DISABLE_FLOAT_API) +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR) +void (*const CELT_FLOAT2INT16_IMPL[OPUS_ARCHMASK+1])(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt) = { + celt_float2int16_c, /* ARMv4 */ + celt_float2int16_c, /* EDSP */ + celt_float2int16_c, /* Media */ + celt_float2int16_neon,/* NEON */ + celt_float2int16_neon /* DOTPROD */ +}; + +int (*const OPUS_LIMIT2_CHECKWITHIN1_IMPL[OPUS_ARCHMASK+1])(float * samples, int cnt) = { + opus_limit2_checkwithin1_c, /* ARMv4 */ + opus_limit2_checkwithin1_c, /* EDSP */ + opus_limit2_checkwithin1_c, /* Media */ + opus_limit2_checkwithin1_neon,/* NEON */ + opus_limit2_checkwithin1_neon /* DOTPROD */ +}; +# endif +# endif + +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR) +opus_val32 (*const CELT_INNER_PROD_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *x, const opus_val16 *y, int N) = { + celt_inner_prod_c, /* ARMv4 */ + celt_inner_prod_c, /* EDSP */ + celt_inner_prod_c, /* Media */ + celt_inner_prod_neon,/* NEON */ + celt_inner_prod_neon /* DOTPROD */ +}; + +void (*const DUAL_INNER_PROD_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2) = { + dual_inner_prod_c, /* ARMv4 */ + dual_inner_prod_c, /* EDSP */ + dual_inner_prod_c, /* Media */ + dual_inner_prod_neon,/* NEON */ + dual_inner_prod_neon /* DOTPROD */ +}; +# endif + +# if defined(FIXED_POINT) +# if ((defined(OPUS_ARM_MAY_HAVE_NEON) && !defined(OPUS_ARM_PRESUME_NEON)) || \ + (defined(OPUS_ARM_MAY_HAVE_MEDIA) && !defined(OPUS_ARM_PRESUME_MEDIA)) || \ + (defined(OPUS_ARM_MAY_HAVE_EDSP) && !defined(OPUS_ARM_PRESUME_EDSP))) +opus_val32 (*const CELT_PITCH_XCORR_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *, + const opus_val16 *, opus_val32 *, int, int, int) = { + celt_pitch_xcorr_c, /* ARMv4 */ + MAY_HAVE_EDSP(celt_pitch_xcorr), /* EDSP */ + MAY_HAVE_MEDIA(celt_pitch_xcorr), /* Media */ + MAY_HAVE_NEON(celt_pitch_xcorr), /* NEON */ + MAY_HAVE_NEON(celt_pitch_xcorr) /* DOTPROD */ +}; + +# endif +# else /* !FIXED_POINT */ +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR) +void (*const CELT_PITCH_XCORR_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *, + const opus_val16 *, opus_val32 *, int, int, int) = { + celt_pitch_xcorr_c, /* ARMv4 */ + celt_pitch_xcorr_c, /* EDSP */ + celt_pitch_xcorr_c, /* Media */ + celt_pitch_xcorr_float_neon, /* Neon */ + celt_pitch_xcorr_float_neon /* DOTPROD */ +}; +# endif +# endif /* FIXED_POINT */ + +#if defined(FIXED_POINT) && defined(OPUS_HAVE_RTCD) && \ + defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR) + +void (*const XCORR_KERNEL_IMPL[OPUS_ARCHMASK + 1])( + const opus_val16 *x, + const opus_val16 *y, + opus_val32 sum[4], + int len +) = { + xcorr_kernel_c, /* ARMv4 */ + xcorr_kernel_c, /* EDSP */ + xcorr_kernel_c, /* Media */ + xcorr_kernel_neon_fixed, /* Neon */ + xcorr_kernel_neon_fixed /* DOTPROD */ +}; + +#endif + +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +# if defined(HAVE_ARM_NE10) +# if defined(CUSTOM_MODES) +int (*const OPUS_FFT_ALLOC_ARCH_IMPL[OPUS_ARCHMASK+1])(kiss_fft_state *st) = { + opus_fft_alloc_arch_c, /* ARMv4 */ + opus_fft_alloc_arch_c, /* EDSP */ + opus_fft_alloc_arch_c, /* Media */ + opus_fft_alloc_arm_neon, /* Neon with NE10 library support */ + opus_fft_alloc_arm_neon /* DOTPROD with NE10 library support */ +}; + +void (*const OPUS_FFT_FREE_ARCH_IMPL[OPUS_ARCHMASK+1])(kiss_fft_state *st) = { + opus_fft_free_arch_c, /* ARMv4 */ + opus_fft_free_arch_c, /* EDSP */ + opus_fft_free_arch_c, /* Media */ + opus_fft_free_arm_neon, /* Neon with NE10 */ + opus_fft_free_arm_neon /* DOTPROD with NE10 */ +}; +# endif /* CUSTOM_MODES */ + +void (*const OPUS_FFT[OPUS_ARCHMASK+1])(const kiss_fft_state *cfg, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout) = { + opus_fft_c, /* ARMv4 */ + opus_fft_c, /* EDSP */ + opus_fft_c, /* Media */ + opus_fft_neon, /* Neon with NE10 */ + opus_fft_neon /* DOTPROD with NE10 */ +}; + +void (*const OPUS_IFFT[OPUS_ARCHMASK+1])(const kiss_fft_state *cfg, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout) = { + opus_ifft_c, /* ARMv4 */ + opus_ifft_c, /* EDSP */ + opus_ifft_c, /* Media */ + opus_ifft_neon, /* Neon with NE10 */ + opus_ifft_neon /* DOTPROD with NE10 */ +}; + +void (*const CLT_MDCT_FORWARD_IMPL[OPUS_ARCHMASK+1])(const mdct_lookup *l, + kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, + int overlap, int shift, + int stride, int arch) = { + clt_mdct_forward_c, /* ARMv4 */ + clt_mdct_forward_c, /* EDSP */ + clt_mdct_forward_c, /* Media */ + clt_mdct_forward_neon, /* Neon with NE10 */ + clt_mdct_forward_neon /* DOTPROD with NE10 */ +}; + +void (*const CLT_MDCT_BACKWARD_IMPL[OPUS_ARCHMASK+1])(const mdct_lookup *l, + kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, + int overlap, int shift, + int stride, int arch) = { + clt_mdct_backward_c, /* ARMv4 */ + clt_mdct_backward_c, /* EDSP */ + clt_mdct_backward_c, /* Media */ + clt_mdct_backward_neon, /* Neon with NE10 */ + clt_mdct_backward_neon /* DOTPROD with NE10 */ +}; + +# endif /* HAVE_ARM_NE10 */ +# endif /* OPUS_ARM_MAY_HAVE_NEON_INTR */ + +#endif /* OPUS_HAVE_RTCD */ diff --git a/managed_components/78__esp-opus/celt/arm/armcpu.c b/managed_components/78__esp-opus/celt/arm/armcpu.c new file mode 100644 index 0000000..6785121 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/armcpu.c @@ -0,0 +1,291 @@ +/* Copyright (c) 2010 Xiph.Org Foundation + * Copyright (c) 2013 Parrot */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* Original code from libtheora modified to suit to Opus */ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#ifdef OPUS_HAVE_RTCD + +#include "armcpu.h" +#include "cpu_support.h" +#include "os_support.h" +#include "opus_types.h" +#include "arch.h" + +#define OPUS_CPU_ARM_V4_FLAG (1< + +static OPUS_INLINE opus_uint32 opus_cpu_capabilities(void){ + opus_uint32 flags; + flags=0; + /* MSVC has no OPUS_INLINE __asm support for ARM, but it does let you __emit + * instructions via their assembled hex code. + * All of these instructions should be essentially nops. */ +# if defined(OPUS_ARM_MAY_HAVE_EDSP) || defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + __try{ + /*PLD [r13]*/ + __emit(0xF5DDF000); + flags|=OPUS_CPU_ARM_EDSP_FLAG; + } + __except(GetExceptionCode()==EXCEPTION_ILLEGAL_INSTRUCTION){ + /*Ignore exception.*/ + } +# if defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + __try{ + /*SHADD8 r3,r3,r3*/ + __emit(0xE6333F93); + flags|=OPUS_CPU_ARM_MEDIA_FLAG; + } + __except(GetExceptionCode()==EXCEPTION_ILLEGAL_INSTRUCTION){ + /*Ignore exception.*/ + } +# if defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + __try{ + /*VORR q0,q0,q0*/ + __emit(0xF2200150); + flags|=OPUS_CPU_ARM_NEON_FLAG; + } + __except(GetExceptionCode()==EXCEPTION_ILLEGAL_INSTRUCTION){ + /*Ignore exception.*/ + } +# endif +# endif +# endif + return flags; +} + +#elif defined(__linux__) +/* Linux based */ +#include + +static opus_uint32 opus_cpu_capabilities(void) +{ + opus_uint32 flags = 0; + FILE *cpuinfo; + + /* Reading /proc/self/auxv would be easier, but that doesn't work reliably on + * Android */ + cpuinfo = fopen("/proc/cpuinfo", "r"); + + if(cpuinfo != NULL) + { + /* 512 should be enough for anybody (it's even enough for all the flags that + * x86 has accumulated... so far). */ + char buf[512]; + + while(fgets(buf, 512, cpuinfo) != NULL) + { +# if defined(OPUS_ARM_MAY_HAVE_EDSP) || defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + /* Search for edsp and neon flag */ + if(memcmp(buf, "Features", 8) == 0) + { + char *p; + p = strstr(buf, " edsp"); + if(p != NULL && (p[5] == ' ' || p[5] == '\n')) + flags |= OPUS_CPU_ARM_EDSP_FLAG; + +# if defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + p = strstr(buf, " neon"); + if(p != NULL && (p[5] == ' ' || p[5] == '\n')) + flags |= OPUS_CPU_ARM_NEON_FLAG; + p = strstr(buf, " asimd"); + if(p != NULL && (p[6] == ' ' || p[6] == '\n')) + flags |= OPUS_CPU_ARM_NEON_FLAG | OPUS_CPU_ARM_MEDIA_FLAG | OPUS_CPU_ARM_EDSP_FLAG; +# endif +# if defined(OPUS_ARM_MAY_HAVE_DOTPROD) + p = strstr(buf, " asimddp"); + if(p != NULL && (p[8] == ' ' || p[8] == '\n')) + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; +# endif + } +# endif + +# if defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + /* Search for media capabilities (>= ARMv6) */ + if(memcmp(buf, "CPU architecture:", 17) == 0) + { + int version; + version = atoi(buf+17); + + if(version >= 6) + flags |= OPUS_CPU_ARM_MEDIA_FLAG; + } +# endif + } + +#if defined(OPUS_ARM_PRESUME_AARCH64_NEON_INTR) + flags |= OPUS_CPU_ARM_EDSP_FLAG | OPUS_CPU_ARM_MEDIA_FLAG | OPUS_CPU_ARM_NEON_FLAG; +# if defined(OPUS_ARM_PRESUME_DOTPROD) + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; +# endif +#endif + + fclose(cpuinfo); + } + return flags; +} + +#elif defined(__APPLE__) +#include +#include + +static opus_uint32 opus_cpu_capabilities(void) +{ + opus_uint32 flags = 0; + +#if defined(OPUS_ARM_MAY_HAVE_DOTPROD) + size_t size = sizeof(uint32_t); + uint32_t value = 0; + if (!sysctlbyname("hw.optional.arm.FEAT_DotProd", &value, &size, NULL, 0) && value) + { + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; + } +#endif + +#if defined(OPUS_ARM_PRESUME_AARCH64_NEON_INTR) + flags |= OPUS_CPU_ARM_EDSP_FLAG | OPUS_CPU_ARM_MEDIA_FLAG | OPUS_CPU_ARM_NEON_FLAG; +# if defined(OPUS_ARM_PRESUME_DOTPROD) + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; +# endif +#endif + return flags; +} + +#elif defined(__FreeBSD__) +#include + +static opus_uint32 opus_cpu_capabilities(void) +{ + long hwcap = 0; + opus_uint32 flags = 0; + +# if defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + /* FreeBSD requires armv6+, which always supports media instructions */ + flags |= OPUS_CPU_ARM_MEDIA_FLAG; +# endif + + elf_aux_info(AT_HWCAP, &hwcap, sizeof hwcap); + +# if defined(OPUS_ARM_MAY_HAVE_EDSP) || defined(OPUS_ARM_MAY_HAVE_MEDIA) \ + || defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +# ifdef HWCAP_EDSP + if (hwcap & HWCAP_EDSP) + flags |= OPUS_CPU_ARM_EDSP_FLAG; +# endif + +# if defined(OPUS_ARM_MAY_HAVE_NEON) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +# ifdef HWCAP_NEON + if (hwcap & HWCAP_NEON) + flags |= OPUS_CPU_ARM_NEON_FLAG; +# elif defined(HWCAP_ASIMD) + if (hwcap & HWCAP_ASIMD) + flags |= OPUS_CPU_ARM_NEON_FLAG | OPUS_CPU_ARM_MEDIA_FLAG | OPUS_CPU_ARM_EDSP_FLAG; +# endif +# endif +# if defined(OPUS_ARM_MAY_HAVE_DOTPROD) && defined(HWCAP_ASIMDDP) + if (hwcap & HWCAP_ASIMDDP) + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; +# endif +# endif + +#if defined(OPUS_ARM_PRESUME_AARCH64_NEON_INTR) + flags |= OPUS_CPU_ARM_EDSP_FLAG | OPUS_CPU_ARM_MEDIA_FLAG | OPUS_CPU_ARM_NEON_FLAG; +# if defined(OPUS_ARM_PRESUME_DOTPROD) + flags |= OPUS_CPU_ARM_DOTPROD_FLAG; +# endif +#endif + + return (flags); +} + +#else +/* The feature registers which can tell us what the processor supports are + * accessible in priveleged modes only, so we can't have a general user-space + * detection method like on x86.*/ +# error "Configured to use ARM asm but no CPU detection method available for " \ + "your platform. Reconfigure with --disable-rtcd (or send patches)." +#endif + +static int opus_select_arch_impl(void) +{ + opus_uint32 flags = opus_cpu_capabilities(); + int arch = 0; + + if(!(flags & OPUS_CPU_ARM_EDSP_FLAG)) { + /* Asserts ensure arch values are sequential */ + celt_assert(arch == OPUS_ARCH_ARM_V4); + return arch; + } + arch++; + + if(!(flags & OPUS_CPU_ARM_MEDIA_FLAG)) { + celt_assert(arch == OPUS_ARCH_ARM_EDSP); + return arch; + } + arch++; + + if(!(flags & OPUS_CPU_ARM_NEON_FLAG)) { + celt_assert(arch == OPUS_ARCH_ARM_MEDIA); + return arch; + } + arch++; + + if(!(flags & OPUS_CPU_ARM_DOTPROD_FLAG)) { + celt_assert(arch == OPUS_ARCH_ARM_NEON); + return arch; + } + arch++; + + celt_assert(arch == OPUS_ARCH_ARM_DOTPROD); + return arch; +} + +int opus_select_arch(void) { + int arch = opus_select_arch_impl(); +#ifdef FUZZING + arch = rand()%(arch+1); +#endif + return arch; +} +#endif diff --git a/managed_components/78__esp-opus/celt/arm/armcpu.h b/managed_components/78__esp-opus/celt/arm/armcpu.h new file mode 100644 index 0000000..6d5803d --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/armcpu.h @@ -0,0 +1,90 @@ +/* Copyright (c) 2010 Xiph.Org Foundation + * Copyright (c) 2013 Parrot */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(ARMCPU_H) +# define ARMCPU_H + +# if defined(OPUS_ARM_MAY_HAVE_EDSP) +# define MAY_HAVE_EDSP(name) name ## _edsp +# else +# define MAY_HAVE_EDSP(name) name ## _c +# endif + +# if defined(OPUS_ARM_MAY_HAVE_MEDIA) +# define MAY_HAVE_MEDIA(name) name ## _media +# else +# define MAY_HAVE_MEDIA(name) MAY_HAVE_EDSP(name) +# endif + +# if defined(OPUS_ARM_MAY_HAVE_NEON) +# define MAY_HAVE_NEON(name) name ## _neon +# else +# define MAY_HAVE_NEON(name) MAY_HAVE_MEDIA(name) +# endif + +# if defined(OPUS_ARM_MAY_HAVE_DOTPROD) +# define MAY_HAVE_DOTPROD(name) name ## _dotprod +# else +# define MAY_HAVE_DOTPROD(name) MAY_HAVE_NEON(name) +# endif + +# if defined(OPUS_ARM_PRESUME_EDSP) +# define PRESUME_EDSP(name) name ## _edsp +# else +# define PRESUME_EDSP(name) name ## _c +# endif + +# if defined(OPUS_ARM_PRESUME_MEDIA) +# define PRESUME_MEDIA(name) name ## _media +# else +# define PRESUME_MEDIA(name) PRESUME_EDSP(name) +# endif + +# if defined(OPUS_ARM_PRESUME_NEON) +# define PRESUME_NEON(name) name ## _neon +# else +# define PRESUME_NEON(name) PRESUME_MEDIA(name) +# endif + +# if defined(OPUS_ARM_PRESUME_DOTPROD) +# define PRESUME_DOTPROD(name) name ## _dotprod +# else +# define PRESUME_DOTPROD(name) PRESUME_NEON(name) +# endif + +# if defined(OPUS_HAVE_RTCD) +int opus_select_arch(void); + +#define OPUS_ARCH_ARM_V4 (0) +#define OPUS_ARCH_ARM_EDSP (1) +#define OPUS_ARCH_ARM_MEDIA (2) +#define OPUS_ARCH_ARM_NEON (3) +#define OPUS_ARCH_ARM_DOTPROD (4) + +# endif + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/armopts.s.in b/managed_components/78__esp-opus/celt/arm/armopts.s.in new file mode 100644 index 0000000..3d8aaf2 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/armopts.s.in @@ -0,0 +1,37 @@ +/* Copyright (C) 2013 Mozilla Corporation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +; Set the following to 1 if we have EDSP instructions +; (LDRD/STRD, etc., ARMv5E and later). +OPUS_ARM_MAY_HAVE_EDSP * @OPUS_ARM_MAY_HAVE_EDSP@ + +; Set the following to 1 if we have ARMv6 media instructions. +OPUS_ARM_MAY_HAVE_MEDIA * @OPUS_ARM_MAY_HAVE_MEDIA@ + +; Set the following to 1 if we have NEON (some ARMv7) +OPUS_ARM_MAY_HAVE_NEON * @OPUS_ARM_MAY_HAVE_NEON@ + +END diff --git a/managed_components/78__esp-opus/celt/arm/celt_fft_ne10.c b/managed_components/78__esp-opus/celt/arm/celt_fft_ne10.c new file mode 100644 index 0000000..ea5fd78 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/celt_fft_ne10.c @@ -0,0 +1,173 @@ +/* Copyright (c) 2015 Xiph.Org Foundation + Written by Viswanath Puttagunta */ +/** + @file celt_fft_ne10.c + @brief ARM Neon optimizations for fft using NE10 library + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef SKIP_CONFIG_H +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif +#endif + +#include +#include "os_support.h" +#include "kiss_fft.h" +#include "stack_alloc.h" + +#if !defined(FIXED_POINT) +# define NE10_FFT_ALLOC_C2C_TYPE_NEON ne10_fft_alloc_c2c_float32_neon +# define NE10_FFT_CFG_TYPE_T ne10_fft_cfg_float32_t +# define NE10_FFT_STATE_TYPE_T ne10_fft_state_float32_t +# define NE10_FFT_DESTROY_C2C_TYPE ne10_fft_destroy_c2c_float32 +# define NE10_FFT_CPX_TYPE_T ne10_fft_cpx_float32_t +# define NE10_FFT_C2C_1D_TYPE_NEON ne10_fft_c2c_1d_float32_neon +#else +# define NE10_FFT_ALLOC_C2C_TYPE_NEON(nfft) ne10_fft_alloc_c2c_int32_neon(nfft) +# define NE10_FFT_CFG_TYPE_T ne10_fft_cfg_int32_t +# define NE10_FFT_STATE_TYPE_T ne10_fft_state_int32_t +# define NE10_FFT_DESTROY_C2C_TYPE ne10_fft_destroy_c2c_int32 +# define NE10_FFT_DESTROY_C2C_TYPE ne10_fft_destroy_c2c_int32 +# define NE10_FFT_CPX_TYPE_T ne10_fft_cpx_int32_t +# define NE10_FFT_C2C_1D_TYPE_NEON ne10_fft_c2c_1d_int32_neon +#endif + +#if defined(CUSTOM_MODES) + +/* nfft lengths in NE10 that support scaled fft */ +# define NE10_FFTSCALED_SUPPORT_MAX 4 +static const int ne10_fft_scaled_support[NE10_FFTSCALED_SUPPORT_MAX] = { + 480, 240, 120, 60 +}; + +int opus_fft_alloc_arm_neon(kiss_fft_state *st) +{ + int i; + size_t memneeded = sizeof(struct arch_fft_state); + + st->arch_fft = (arch_fft_state *)opus_alloc(memneeded); + if (!st->arch_fft) + return -1; + + for (i = 0; i < NE10_FFTSCALED_SUPPORT_MAX; i++) { + if(st->nfft == ne10_fft_scaled_support[i]) + break; + } + if (i == NE10_FFTSCALED_SUPPORT_MAX) { + /* This nfft length (scaled fft) is not supported in NE10 */ + st->arch_fft->is_supported = 0; + st->arch_fft->priv = NULL; + } + else { + st->arch_fft->is_supported = 1; + st->arch_fft->priv = (void *)NE10_FFT_ALLOC_C2C_TYPE_NEON(st->nfft); + if (st->arch_fft->priv == NULL) { + return -1; + } + } + return 0; +} + +void opus_fft_free_arm_neon(kiss_fft_state *st) +{ + NE10_FFT_CFG_TYPE_T cfg; + + if (!st->arch_fft) + return; + + cfg = (NE10_FFT_CFG_TYPE_T)st->arch_fft->priv; + if (cfg) + NE10_FFT_DESTROY_C2C_TYPE(cfg); + opus_free(st->arch_fft); +} +#endif + +void opus_fft_neon(const kiss_fft_state *st, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout) +{ + NE10_FFT_STATE_TYPE_T state; + NE10_FFT_CFG_TYPE_T cfg = &state; + VARDECL(NE10_FFT_CPX_TYPE_T, buffer); + SAVE_STACK; + ALLOC(buffer, st->nfft, NE10_FFT_CPX_TYPE_T); + + if (!st->arch_fft->is_supported) { + /* This nfft length (scaled fft) not supported in NE10 */ + opus_fft_c(st, fin, fout); + } + else { + memcpy((void *)cfg, st->arch_fft->priv, sizeof(NE10_FFT_STATE_TYPE_T)); + state.buffer = (NE10_FFT_CPX_TYPE_T *)&buffer[0]; +#if !defined(FIXED_POINT) + state.is_forward_scaled = 1; + + NE10_FFT_C2C_1D_TYPE_NEON((NE10_FFT_CPX_TYPE_T *)fout, + (NE10_FFT_CPX_TYPE_T *)fin, + cfg, 0); +#else + NE10_FFT_C2C_1D_TYPE_NEON((NE10_FFT_CPX_TYPE_T *)fout, + (NE10_FFT_CPX_TYPE_T *)fin, + cfg, 0, 1); +#endif + } + RESTORE_STACK; +} + +void opus_ifft_neon(const kiss_fft_state *st, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout) +{ + NE10_FFT_STATE_TYPE_T state; + NE10_FFT_CFG_TYPE_T cfg = &state; + VARDECL(NE10_FFT_CPX_TYPE_T, buffer); + SAVE_STACK; + ALLOC(buffer, st->nfft, NE10_FFT_CPX_TYPE_T); + + if (!st->arch_fft->is_supported) { + /* This nfft length (scaled fft) not supported in NE10 */ + opus_ifft_c(st, fin, fout); + } + else { + memcpy((void *)cfg, st->arch_fft->priv, sizeof(NE10_FFT_STATE_TYPE_T)); + state.buffer = (NE10_FFT_CPX_TYPE_T *)&buffer[0]; +#if !defined(FIXED_POINT) + state.is_backward_scaled = 0; + + NE10_FFT_C2C_1D_TYPE_NEON((NE10_FFT_CPX_TYPE_T *)fout, + (NE10_FFT_CPX_TYPE_T *)fin, + cfg, 1); +#else + NE10_FFT_C2C_1D_TYPE_NEON((NE10_FFT_CPX_TYPE_T *)fout, + (NE10_FFT_CPX_TYPE_T *)fin, + cfg, 1, 0); +#endif + } + RESTORE_STACK; +} diff --git a/managed_components/78__esp-opus/celt/arm/celt_mdct_ne10.c b/managed_components/78__esp-opus/celt/arm/celt_mdct_ne10.c new file mode 100644 index 0000000..3531d02 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/celt_mdct_ne10.c @@ -0,0 +1,258 @@ +/* Copyright (c) 2015 Xiph.Org Foundation + Written by Viswanath Puttagunta */ +/** + @file celt_mdct_ne10.c + @brief ARM Neon optimizations for mdct using NE10 library + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef SKIP_CONFIG_H +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif +#endif + +#include "kiss_fft.h" +#include "_kiss_fft_guts.h" +#include "mdct.h" +#include "stack_alloc.h" + +void clt_mdct_forward_neon(const mdct_lookup *l, + kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, + int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + VARDECL(kiss_fft_scalar, f); + VARDECL(kiss_fft_cpx, f2); + const kiss_fft_state *st = l->kfft[shift]; + const kiss_twiddle_scalar *trig; + + SAVE_STACK; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + ALLOC(f, N2, kiss_fft_scalar); + ALLOC(f2, N4, kiss_fft_cpx); + + /* Consider the input to be composed of four blocks: [a, b, c, d] */ + /* Window, shuffle, fold */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in+(overlap>>1); + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+N2-1+(overlap>>1); + kiss_fft_scalar * OPUS_RESTRICT yp = f; + const opus_val16 * OPUS_RESTRICT wp1 = window+(overlap>>1); + const opus_val16 * OPUS_RESTRICT wp2 = window+(overlap>>1)-1; + for(i=0;i<((overlap+3)>>2);i++) + { + /* Real part arranged as -d-cR, Imag part arranged as -b+aR*/ + *yp++ = MULT16_32_Q15(*wp2, xp1[N2]) + MULT16_32_Q15(*wp1,*xp2); + *yp++ = MULT16_32_Q15(*wp1, *xp1) - MULT16_32_Q15(*wp2, xp2[-N2]); + xp1+=2; + xp2-=2; + wp1+=2; + wp2-=2; + } + wp1 = window; + wp2 = window+overlap-1; + for(;i>2);i++) + { + /* Real part arranged as a-bR, Imag part arranged as -c-dR */ + *yp++ = *xp2; + *yp++ = *xp1; + xp1+=2; + xp2-=2; + } + for(;ii,t[N4+i]) - S_MUL(fp->r,t[i]); + yi = S_MUL(fp->r,t[N4+i]) + S_MUL(fp->i,t[i]); + *yp1 = yr; + *yp2 = yi; + fp++; + yp1 += 2*stride; + yp2 -= 2*stride; + } + } + RESTORE_STACK; +} + +void clt_mdct_backward_neon(const mdct_lookup *l, + kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 * OPUS_RESTRICT window, + int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + VARDECL(kiss_fft_scalar, f); + const kiss_twiddle_scalar *trig; + const kiss_fft_state *st = l->kfft[shift]; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + ALLOC(f, N2, kiss_fft_scalar); + + /* Pre-rotate */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in; + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+stride*(N2-1); + kiss_fft_scalar * OPUS_RESTRICT yp = f; + const kiss_twiddle_scalar * OPUS_RESTRICT t = &trig[0]; + for(i=0;i>1)), arch); + + /* Post-rotate and de-shuffle from both ends of the buffer at once to make + it in-place. */ + { + kiss_fft_scalar * yp0 = out+(overlap>>1); + kiss_fft_scalar * yp1 = out+(overlap>>1)+N2-2; + const kiss_twiddle_scalar *t = &trig[0]; + /* Loop to (N4+1)>>1 to handle odd N4. When N4 is odd, the + middle pair will be computed twice. */ + for(i=0;i<(N4+1)>>1;i++) + { + kiss_fft_scalar re, im, yr, yi; + kiss_twiddle_scalar t0, t1; + re = yp0[0]; + im = yp0[1]; + t0 = t[i]; + t1 = t[N4+i]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = S_MUL(re,t0) + S_MUL(im,t1); + yi = S_MUL(re,t1) - S_MUL(im,t0); + re = yp1[0]; + im = yp1[1]; + yp0[0] = yr; + yp1[1] = yi; + + t0 = t[(N4-i-1)]; + t1 = t[(N2-i-1)]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = S_MUL(re,t0) + S_MUL(im,t1); + yi = S_MUL(re,t1) - S_MUL(im,t0); + yp1[0] = yr; + yp0[1] = yi; + yp0 += 2; + yp1 -= 2; + } + } + + /* Mirror on both sides for TDAC */ + { + kiss_fft_scalar * OPUS_RESTRICT xp1 = out+overlap-1; + kiss_fft_scalar * OPUS_RESTRICT yp1 = out; + const opus_val16 * OPUS_RESTRICT wp1 = window; + const opus_val16 * OPUS_RESTRICT wp2 = window+overlap-1; + + for(i = 0; i < overlap/2; i++) + { + kiss_fft_scalar x1, x2; + x1 = *xp1; + x2 = *yp1; + *yp1++ = MULT16_32_Q15(*wp2, x2) - MULT16_32_Q15(*wp1, x1); + *xp1-- = MULT16_32_Q15(*wp1, x2) + MULT16_32_Q15(*wp2, x1); + wp1++; + wp2--; + } + } + RESTORE_STACK; +} diff --git a/managed_components/78__esp-opus/celt/arm/celt_neon_intr.c b/managed_components/78__esp-opus/celt/arm/celt_neon_intr.c new file mode 100644 index 0000000..b87bebc --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/celt_neon_intr.c @@ -0,0 +1,400 @@ +/* Copyright (c) 2014-2015 Xiph.Org Foundation + Copyright (c) 2024 Arm Limited + Written by Viswanath Puttagunta */ +/** + @file celt_neon_intr.c + @brief ARM Neon Intrinsic optimizations for celt + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include "../float_cast.h" +#include "../mathops.h" +#include "../pitch.h" +#if defined(OPUS_CHECK_ASM) +#include +#endif + +#if !defined(DISABLE_FLOAT_API) && defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + +void celt_float2int16_neon(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt) +{ + int i = 0; + +#if defined(__ARM_NEON) + const int BLOCK_SIZE = 16; + const int blockedSize = cnt / BLOCK_SIZE * BLOCK_SIZE; + + for (; i < blockedSize; i += BLOCK_SIZE) + { + float32x4_t orig_a = vld1q_f32(&in[i + 0]); + float32x4_t orig_b = vld1q_f32(&in[i + 4]); + float32x4_t orig_c = vld1q_f32(&in[i + 8]); + float32x4_t orig_d = vld1q_f32(&in[i + 12]); + + int16x4_t asShort_a = vqmovn_s32(vroundf(vmulq_n_f32(orig_a, CELT_SIG_SCALE))); + int16x4_t asShort_b = vqmovn_s32(vroundf(vmulq_n_f32(orig_b, CELT_SIG_SCALE))); + int16x4_t asShort_c = vqmovn_s32(vroundf(vmulq_n_f32(orig_c, CELT_SIG_SCALE))); + int16x4_t asShort_d = vqmovn_s32(vroundf(vmulq_n_f32(orig_d, CELT_SIG_SCALE))); + + vst1_s16(&out[i + 0], asShort_a); + vst1_s16(&out[i + 4], asShort_b); + vst1_s16(&out[i + 8], asShort_c); + vst1_s16(&out[i + 12], asShort_d); +# if defined(OPUS_CHECK_ASM) + short out_c[BLOCK_SIZE]; + int j; + for(j = 0; j < BLOCK_SIZE; j++) + { + out_c[j] = FLOAT2INT16(in[i + j]); + celt_assert(abs((out_c[j] - out[i + j])) <= 1); + } +# endif + } +#endif + + for (; i < cnt; i++) + { + out[i] = FLOAT2INT16(in[i]); + } +} + +int opus_limit2_checkwithin1_neon(float *samples, int cnt) +{ + const float hardclipMin = -2.0f; + const float hardclipMax = 2.0f; + + int i = 0; + int exceeding1 = 0; + int nextIndex = 0; + +#if defined(__ARM_NEON) + const int BLOCK_SIZE = 16; + const int blockedSize = cnt / BLOCK_SIZE * BLOCK_SIZE; + + float32x4_t min_all_0 = vdupq_n_f32(0.0f); + float32x4_t min_all_1 = vdupq_n_f32(0.0f); + float32x4_t max_all_0 = vdupq_n_f32(0.0f); + float32x4_t max_all_1 = vdupq_n_f32(0.0f); + + float max, min; + + for (i = 0; i < blockedSize; i += BLOCK_SIZE) + { + const float32x4_t orig_a = vld1q_f32(&samples[i + 0]); + const float32x4_t orig_b = vld1q_f32(&samples[i + 4]); + const float32x4_t orig_c = vld1q_f32(&samples[i + 8]); + const float32x4_t orig_d = vld1q_f32(&samples[i + 12]); + max_all_0 = vmaxq_f32(max_all_0, vmaxq_f32(orig_a, orig_b)); + max_all_1 = vmaxq_f32(max_all_1, vmaxq_f32(orig_c, orig_d)); + min_all_0 = vminq_f32(min_all_0, vminq_f32(orig_a, orig_b)); + min_all_1 = vminq_f32(min_all_1, vminq_f32(orig_c, orig_d)); + } + + max = vmaxvf(vmaxq_f32(max_all_0, max_all_1)); + min = vminvf(vminq_f32(min_all_0, min_all_1)); + + if (min < hardclipMin || max > hardclipMax) + { + const float32x4_t hardclipMinReg = vdupq_n_f32(hardclipMin); + const float32x4_t hardclipMaxReg = vdupq_n_f32(hardclipMax); + for (i = 0; i < blockedSize; i += BLOCK_SIZE) + { + const float32x4_t orig_a = vld1q_f32(&samples[i + 0]); + const float32x4_t orig_b = vld1q_f32(&samples[i + 4]); + const float32x4_t orig_c = vld1q_f32(&samples[i + 8]); + const float32x4_t orig_d = vld1q_f32(&samples[i + 12]); + const float32x4_t clipped_a = vminq_f32(hardclipMaxReg, vmaxq_f32(orig_a, hardclipMinReg)); + const float32x4_t clipped_b = vminq_f32(hardclipMaxReg, vmaxq_f32(orig_b, hardclipMinReg)); + const float32x4_t clipped_c = vminq_f32(hardclipMaxReg, vmaxq_f32(orig_c, hardclipMinReg)); + const float32x4_t clipped_d = vminq_f32(hardclipMaxReg, vmaxq_f32(orig_d, hardclipMinReg)); + vst1q_f32(&samples[i + 0], clipped_a); + vst1q_f32(&samples[i + 4], clipped_b); + vst1q_f32(&samples[i + 8], clipped_c); + vst1q_f32(&samples[i + 12], clipped_d); + } + } + + nextIndex = blockedSize; + exceeding1 |= max > 1.0f || min < -1.0f; + +#endif + + for (i = nextIndex; i < cnt; i++) + { + const float origVal = samples[i]; + float clippedVal = origVal; + clippedVal = MAX16(hardclipMin, clippedVal); + clippedVal = MIN16(hardclipMax, clippedVal); + samples[i] = clippedVal; + + exceeding1 |= origVal > 1.0f || origVal < -1.0f; + } + + return !exceeding1; +} + +#endif + + +#if defined(FIXED_POINT) +#include + +void xcorr_kernel_neon_fixed(const opus_val16 * x, const opus_val16 * y, opus_val32 sum[4], int len) +{ + int j; + int32x4_t a = vld1q_s32(sum); + /* Load y[0...3] */ + /* This requires len>0 to always be valid (which we assert in the C code). */ + int16x4_t y0 = vld1_s16(y); + y += 4; + + /* This loop loads one y value more than we actually need. + Therefore we have to stop as soon as there are 8 or fewer samples left + (instead of 7), to avoid reading past the end of the array. */ + for (j = 0; j + 8 < len; j += 8) + { + /* Load x[0...7] */ + int16x8_t xx = vld1q_s16(x); + int16x4_t x0 = vget_low_s16(xx); + int16x4_t x4 = vget_high_s16(xx); + /* Load y[4...11] */ + int16x8_t yy = vld1q_s16(y); + int16x4_t y4 = vget_low_s16(yy); + int16x4_t y8 = vget_high_s16(yy); + int32x4_t a0 = vmlal_lane_s16(a, y0, x0, 0); + int32x4_t a1 = vmlal_lane_s16(a0, y4, x4, 0); + + int16x4_t y1 = vext_s16(y0, y4, 1); + int16x4_t y5 = vext_s16(y4, y8, 1); + int32x4_t a2 = vmlal_lane_s16(a1, y1, x0, 1); + int32x4_t a3 = vmlal_lane_s16(a2, y5, x4, 1); + + int16x4_t y2 = vext_s16(y0, y4, 2); + int16x4_t y6 = vext_s16(y4, y8, 2); + int32x4_t a4 = vmlal_lane_s16(a3, y2, x0, 2); + int32x4_t a5 = vmlal_lane_s16(a4, y6, x4, 2); + + int16x4_t y3 = vext_s16(y0, y4, 3); + int16x4_t y7 = vext_s16(y4, y8, 3); + int32x4_t a6 = vmlal_lane_s16(a5, y3, x0, 3); + int32x4_t a7 = vmlal_lane_s16(a6, y7, x4, 3); + + y0 = y8; + a = a7; + x += 8; + y += 8; + } + if (j + 4 < len) { + /* Load x[0...3] */ + int16x4_t x0 = vld1_s16(x); + /* Load y[4...7] */ + int16x4_t y4 = vld1_s16(y); + int32x4_t a0 = vmlal_lane_s16(a, y0, x0, 0); + int16x4_t y1 = vext_s16(y0, y4, 1); + int32x4_t a1 = vmlal_lane_s16(a0, y1, x0, 1); + int16x4_t y2 = vext_s16(y0, y4, 2); + int32x4_t a2 = vmlal_lane_s16(a1, y2, x0, 2); + int16x4_t y3 = vext_s16(y0, y4, 3); + int32x4_t a3 = vmlal_lane_s16(a2, y3, x0, 3); + y0 = y4; + a = a3; + x += 4; + y += 4; + j += 4; + } + if (j + 2 < len) { + /* Load x[0...1] */ + int16x4x2_t xx = vld2_dup_s16(x); + int16x4_t x0 = xx.val[0]; + int16x4_t x1 = xx.val[1]; + /* Load y[4...5]. + We would like to use vld1_dup_s32(), but casting the pointer would + break strict aliasing rules and potentially have alignment issues. + Fortunately the compiler seems capable of translating this memcpy() + and vdup_n_s32() into the equivalent vld1_dup_s32().*/ + int32_t yy; + memcpy(&yy, y, sizeof(yy)); + int16x4_t y4 = vreinterpret_s16_s32(vdup_n_s32(yy)); + int32x4_t a0 = vmlal_s16(a, y0, x0); + int16x4_t y1 = vext_s16(y0, y4, 1); + /* Replace bottom copy of {y[5], y[4]} in y4 with {y[3], y[2]} from y0, + using VSRI instead of VEXT, since it's a data-processing + instruction. */ + y0 = vreinterpret_s16_s64(vsri_n_s64(vreinterpret_s64_s16(y4), + vreinterpret_s64_s16(y0), 32)); + int32x4_t a1 = vmlal_s16(a0, y1, x1); + a = a1; + x += 2; + y += 2; + j += 2; + } + if (j + 1 < len) { + /* Load next x. */ + int16x4_t x0 = vld1_dup_s16(x); + int32x4_t a0 = vmlal_s16(a, y0, x0); + /* Load last y. */ + int16x4_t y4 = vld1_dup_s16(y); + y0 = vreinterpret_s16_s64(vsri_n_s64(vreinterpret_s64_s16(y4), + vreinterpret_s64_s16(y0), 16)); + a = a0; + x++; + } + /* Load last x. */ + int16x4_t x0 = vld1_dup_s16(x); + int32x4_t a0 = vmlal_s16(a, y0, x0); + vst1q_s32(sum, a0); +} + +#else + +#if defined(__ARM_FEATURE_FMA) && defined(__ARM_ARCH_ISA_A64) +/* If we can, force the compiler to use an FMA instruction rather than break + * vmlaq_f32() into fmul/fadd. */ +#ifdef vmlaq_lane_f32 +#undef vmlaq_lane_f32 +#endif +#define vmlaq_lane_f32(a,b,c,lane) vfmaq_lane_f32(a,b,c,lane) +#endif + + +/* + * Function: xcorr_kernel_neon_float + * --------------------------------- + * Computes 4 correlation values and stores them in sum[4] + */ +static void xcorr_kernel_neon_float(const float32_t *x, const float32_t *y, + float32_t sum[4], int len) { + float32x4_t YY[3]; + float32x4_t YEXT[3]; + float32x4_t XX[2]; + float32x2_t XX_2; + float32x4_t SUMM; + const float32_t *xi = x; + const float32_t *yi = y; + + celt_assert(len>0); + + YY[0] = vld1q_f32(yi); + SUMM = vdupq_n_f32(0); + + /* Consume 8 elements in x vector and 12 elements in y + * vector. However, the 12'th element never really gets + * touched in this loop. So, if len == 8, then we only + * must access y[0] to y[10]. y[11] must not be accessed + * hence make sure len > 8 and not len >= 8 + */ + while (len > 8) { + yi += 4; + YY[1] = vld1q_f32(yi); + yi += 4; + YY[2] = vld1q_f32(yi); + + XX[0] = vld1q_f32(xi); + xi += 4; + XX[1] = vld1q_f32(xi); + xi += 4; + + SUMM = vmlaq_lane_f32(SUMM, YY[0], vget_low_f32(XX[0]), 0); + YEXT[0] = vextq_f32(YY[0], YY[1], 1); + SUMM = vmlaq_lane_f32(SUMM, YEXT[0], vget_low_f32(XX[0]), 1); + YEXT[1] = vextq_f32(YY[0], YY[1], 2); + SUMM = vmlaq_lane_f32(SUMM, YEXT[1], vget_high_f32(XX[0]), 0); + YEXT[2] = vextq_f32(YY[0], YY[1], 3); + SUMM = vmlaq_lane_f32(SUMM, YEXT[2], vget_high_f32(XX[0]), 1); + + SUMM = vmlaq_lane_f32(SUMM, YY[1], vget_low_f32(XX[1]), 0); + YEXT[0] = vextq_f32(YY[1], YY[2], 1); + SUMM = vmlaq_lane_f32(SUMM, YEXT[0], vget_low_f32(XX[1]), 1); + YEXT[1] = vextq_f32(YY[1], YY[2], 2); + SUMM = vmlaq_lane_f32(SUMM, YEXT[1], vget_high_f32(XX[1]), 0); + YEXT[2] = vextq_f32(YY[1], YY[2], 3); + SUMM = vmlaq_lane_f32(SUMM, YEXT[2], vget_high_f32(XX[1]), 1); + + YY[0] = YY[2]; + len -= 8; + } + + /* Consume 4 elements in x vector and 8 elements in y + * vector. However, the 8'th element in y never really gets + * touched in this loop. So, if len == 4, then we only + * must access y[0] to y[6]. y[7] must not be accessed + * hence make sure len>4 and not len>=4 + */ + if (len > 4) { + yi += 4; + YY[1] = vld1q_f32(yi); + + XX[0] = vld1q_f32(xi); + xi += 4; + + SUMM = vmlaq_lane_f32(SUMM, YY[0], vget_low_f32(XX[0]), 0); + YEXT[0] = vextq_f32(YY[0], YY[1], 1); + SUMM = vmlaq_lane_f32(SUMM, YEXT[0], vget_low_f32(XX[0]), 1); + YEXT[1] = vextq_f32(YY[0], YY[1], 2); + SUMM = vmlaq_lane_f32(SUMM, YEXT[1], vget_high_f32(XX[0]), 0); + YEXT[2] = vextq_f32(YY[0], YY[1], 3); + SUMM = vmlaq_lane_f32(SUMM, YEXT[2], vget_high_f32(XX[0]), 1); + + YY[0] = YY[1]; + len -= 4; + } + + while (--len > 0) { + XX_2 = vld1_dup_f32(xi++); + SUMM = vmlaq_lane_f32(SUMM, YY[0], XX_2, 0); + YY[0]= vld1q_f32(++yi); + } + + XX_2 = vld1_dup_f32(xi); + SUMM = vmlaq_lane_f32(SUMM, YY[0], XX_2, 0); + + vst1q_f32(sum, SUMM); +} + +void celt_pitch_xcorr_float_neon(const opus_val16 *_x, const opus_val16 *_y, + opus_val32 *xcorr, int len, int max_pitch, int arch) { + int i; + (void)arch; + celt_assert(max_pitch > 0); + celt_sig_assert((((unsigned char *)_x-(unsigned char *)NULL)&3)==0); + + for (i = 0; i < (max_pitch-3); i += 4) { + xcorr_kernel_neon_float((const float32_t *)_x, (const float32_t *)_y+i, + (float32_t *)xcorr+i, len); + } + + /* In case max_pitch isn't a multiple of 4, do non-unrolled version. */ + for (; i < max_pitch; i++) { + xcorr[i] = celt_inner_prod_neon(_x, _y+i, len); + } +} +#endif diff --git a/managed_components/78__esp-opus/celt/arm/celt_pitch_xcorr_arm.s b/managed_components/78__esp-opus/celt/arm/celt_pitch_xcorr_arm.s new file mode 100644 index 0000000..6e873af --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/celt_pitch_xcorr_arm.s @@ -0,0 +1,551 @@ +; Copyright (c) 2007-2008 CSIRO +; Copyright (c) 2007-2009 Xiph.Org Foundation +; Copyright (c) 2013 Parrot +; Written by Aurélien Zanelli +; +; Redistribution and use in source and binary forms, with or without +; modification, are permitted provided that the following conditions +; are met: +; +; - Redistributions of source code must retain the above copyright +; notice, this list of conditions and the following disclaimer. +; +; - Redistributions in binary form must reproduce the above copyright +; notice, this list of conditions and the following disclaimer in the +; documentation and/or other materials provided with the distribution. +; +; THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +; ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +; LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +; A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +; OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +; EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +; PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +; PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +; LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +; NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +; SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + AREA |.text|, CODE, READONLY + + GET celt/arm/armopts.s + +IF OPUS_ARM_MAY_HAVE_EDSP + EXPORT celt_pitch_xcorr_edsp +ENDIF + +IF OPUS_ARM_MAY_HAVE_NEON + EXPORT celt_pitch_xcorr_neon +ENDIF + +IF OPUS_ARM_MAY_HAVE_NEON + +; Compute sum[k]=sum(x[j]*y[j+k],j=0...len-1), k=0...3 +xcorr_kernel_neon PROC +xcorr_kernel_neon_start + ; input: + ; r3 = int len + ; r4 = opus_val16 *x + ; r5 = opus_val16 *y + ; q0 = opus_val32 sum[4] + ; output: + ; q0 = opus_val32 sum[4] + ; preserved: r0-r3, r6-r11, d2, q4-q7, q9-q15 + ; internal usage: + ; r12 = int j + ; d3 = y_3|y_2|y_1|y_0 + ; q2 = y_B|y_A|y_9|y_8|y_7|y_6|y_5|y_4 + ; q3 = x_7|x_6|x_5|x_4|x_3|x_2|x_1|x_0 + ; q8 = scratch + ; + ; Load y[0...3] + ; This requires len>0 to always be valid (which we assert in the C code). + VLD1.16 {d5}, [r5]! + SUBS r12, r3, #8 + BLE xcorr_kernel_neon_process4 +; Process 8 samples at a time. +; This loop loads one y value more than we actually need. Therefore we have to +; stop as soon as there are 8 or fewer samples left (instead of 7), to avoid +; reading past the end of the array. +xcorr_kernel_neon_process8 + ; This loop has 19 total instructions (10 cycles to issue, minimum), with + ; - 2 cycles of ARM insrtuctions, + ; - 10 cycles of load/store/byte permute instructions, and + ; - 9 cycles of data processing instructions. + ; On a Cortex A8, we dual-issue the maximum amount (9 cycles) between the + ; latter two categories, meaning the whole loop should run in 10 cycles per + ; iteration, barring cache misses. + ; + ; Load x[0...7] + VLD1.16 {d6, d7}, [r4]! + ; Unlike VMOV, VAND is a data processsing instruction (and doesn't get + ; assembled to VMOV, like VORR would), so it dual-issues with the prior VLD1. + VAND d3, d5, d5 + SUBS r12, r12, #8 + ; Load y[4...11] + VLD1.16 {d4, d5}, [r5]! + VMLAL.S16 q0, d3, d6[0] + VEXT.16 d16, d3, d4, #1 + VMLAL.S16 q0, d4, d7[0] + VEXT.16 d17, d4, d5, #1 + VMLAL.S16 q0, d16, d6[1] + VEXT.16 d16, d3, d4, #2 + VMLAL.S16 q0, d17, d7[1] + VEXT.16 d17, d4, d5, #2 + VMLAL.S16 q0, d16, d6[2] + VEXT.16 d16, d3, d4, #3 + VMLAL.S16 q0, d17, d7[2] + VEXT.16 d17, d4, d5, #3 + VMLAL.S16 q0, d16, d6[3] + VMLAL.S16 q0, d17, d7[3] + BGT xcorr_kernel_neon_process8 +; Process 4 samples here if we have > 4 left (still reading one extra y value). +xcorr_kernel_neon_process4 + ADDS r12, r12, #4 + BLE xcorr_kernel_neon_process2 + ; Load x[0...3] + VLD1.16 d6, [r4]! + ; Use VAND since it's a data processing instruction again. + VAND d4, d5, d5 + SUB r12, r12, #4 + ; Load y[4...7] + VLD1.16 d5, [r5]! + VMLAL.S16 q0, d4, d6[0] + VEXT.16 d16, d4, d5, #1 + VMLAL.S16 q0, d16, d6[1] + VEXT.16 d16, d4, d5, #2 + VMLAL.S16 q0, d16, d6[2] + VEXT.16 d16, d4, d5, #3 + VMLAL.S16 q0, d16, d6[3] +; Process 2 samples here if we have > 2 left (still reading one extra y value). +xcorr_kernel_neon_process2 + ADDS r12, r12, #2 + BLE xcorr_kernel_neon_process1 + ; Load x[0...1] + VLD2.16 {d6[],d7[]}, [r4]! + ; Use VAND since it's a data processing instruction again. + VAND d4, d5, d5 + SUB r12, r12, #2 + ; Load y[4...5] + VLD1.32 {d5[]}, [r5]! + VMLAL.S16 q0, d4, d6 + VEXT.16 d16, d4, d5, #1 + ; Replace bottom copy of {y5,y4} in d5 with {y3,y2} from d4, using VSRI + ; instead of VEXT, since it's a data-processing instruction. + VSRI.64 d5, d4, #32 + VMLAL.S16 q0, d16, d7 +; Process 1 sample using the extra y value we loaded above. +xcorr_kernel_neon_process1 + ; Load next *x + VLD1.16 {d6[]}, [r4]! + ADDS r12, r12, #1 + ; y[0...3] are left in d5 from prior iteration(s) (if any) + VMLAL.S16 q0, d5, d6 + MOVLE pc, lr +; Now process 1 last sample, not reading ahead. + ; Load last *y + VLD1.16 {d4[]}, [r5]! + VSRI.64 d4, d5, #16 + ; Load last *x + VLD1.16 {d6[]}, [r4]! + VMLAL.S16 q0, d4, d6 + MOV pc, lr + ENDP + +; opus_val32 celt_pitch_xcorr_neon(opus_val16 *_x, opus_val16 *_y, +; opus_val32 *xcorr, int len, int max_pitch, int arch) +celt_pitch_xcorr_neon PROC + ; input: + ; r0 = opus_val16 *_x + ; r1 = opus_val16 *_y + ; r2 = opus_val32 *xcorr + ; r3 = int len + ; output: + ; r0 = int maxcorr + ; internal usage: + ; r4 = opus_val16 *x (for xcorr_kernel_neon()) + ; r5 = opus_val16 *y (for xcorr_kernel_neon()) + ; r6 = int max_pitch + ; r12 = int j + ; q15 = int maxcorr[4] (q15 is not used by xcorr_kernel_neon()) + ; ignored: + ; int arch + STMFD sp!, {r4-r6, lr} + LDR r6, [sp, #16] + VMOV.S32 q15, #1 + ; if (max_pitch < 4) goto celt_pitch_xcorr_neon_process4_done + SUBS r6, r6, #4 + BLT celt_pitch_xcorr_neon_process4_done +celt_pitch_xcorr_neon_process4 + ; xcorr_kernel_neon parameters: + ; r3 = len, r4 = _x, r5 = _y, q0 = {0, 0, 0, 0} + MOV r4, r0 + MOV r5, r1 + VEOR q0, q0, q0 + ; xcorr_kernel_neon only modifies r4, r5, r12, and q0...q3. + ; So we don't save/restore any other registers. + BL xcorr_kernel_neon_start + SUBS r6, r6, #4 + VST1.32 {q0}, [r2]! + ; _y += 4 + ADD r1, r1, #8 + VMAX.S32 q15, q15, q0 + ; if (max_pitch < 4) goto celt_pitch_xcorr_neon_process4_done + BGE celt_pitch_xcorr_neon_process4 +; We have less than 4 sums left to compute. +celt_pitch_xcorr_neon_process4_done + ADDS r6, r6, #4 + ; Reduce maxcorr to a single value + VMAX.S32 d30, d30, d31 + VPMAX.S32 d30, d30, d30 + ; if (max_pitch <= 0) goto celt_pitch_xcorr_neon_done + BLE celt_pitch_xcorr_neon_done +; Now compute each remaining sum one at a time. +celt_pitch_xcorr_neon_process_remaining + MOV r4, r0 + MOV r5, r1 + VMOV.I32 q0, #0 + SUBS r12, r3, #8 + BLT celt_pitch_xcorr_neon_process_remaining4 +; Sum terms 8 at a time. +celt_pitch_xcorr_neon_process_remaining_loop8 + ; Load x[0...7] + VLD1.16 {q1}, [r4]! + ; Load y[0...7] + VLD1.16 {q2}, [r5]! + SUBS r12, r12, #8 + VMLAL.S16 q0, d4, d2 + VMLAL.S16 q0, d5, d3 + BGE celt_pitch_xcorr_neon_process_remaining_loop8 +; Sum terms 4 at a time. +celt_pitch_xcorr_neon_process_remaining4 + ADDS r12, r12, #4 + BLT celt_pitch_xcorr_neon_process_remaining4_done + ; Load x[0...3] + VLD1.16 {d2}, [r4]! + ; Load y[0...3] + VLD1.16 {d3}, [r5]! + SUB r12, r12, #4 + VMLAL.S16 q0, d3, d2 +celt_pitch_xcorr_neon_process_remaining4_done + ; Reduce the sum to a single value. + VADD.S32 d0, d0, d1 + VPADDL.S32 d0, d0 + ADDS r12, r12, #4 + BLE celt_pitch_xcorr_neon_process_remaining_loop_done +; Sum terms 1 at a time. +celt_pitch_xcorr_neon_process_remaining_loop1 + VLD1.16 {d2[]}, [r4]! + VLD1.16 {d3[]}, [r5]! + SUBS r12, r12, #1 + VMLAL.S16 q0, d2, d3 + BGT celt_pitch_xcorr_neon_process_remaining_loop1 +celt_pitch_xcorr_neon_process_remaining_loop_done + VST1.32 {d0[0]}, [r2]! + VMAX.S32 d30, d30, d0 + SUBS r6, r6, #1 + ; _y++ + ADD r1, r1, #2 + ; if (--max_pitch > 0) goto celt_pitch_xcorr_neon_process_remaining + BGT celt_pitch_xcorr_neon_process_remaining +celt_pitch_xcorr_neon_done + VMOV.32 r0, d30[0] + LDMFD sp!, {r4-r6, pc} + ENDP + +ENDIF + +IF OPUS_ARM_MAY_HAVE_EDSP + +; This will get used on ARMv7 devices without NEON, so it has been optimized +; to take advantage of dual-issuing where possible. +xcorr_kernel_edsp PROC +xcorr_kernel_edsp_start + ; input: + ; r3 = int len + ; r4 = opus_val16 *_x (must be 32-bit aligned) + ; r5 = opus_val16 *_y (must be 32-bit aligned) + ; r6...r9 = opus_val32 sum[4] + ; output: + ; r6...r9 = opus_val32 sum[4] + ; preserved: r0-r5 + ; internal usage + ; r2 = int j + ; r12,r14 = opus_val16 x[4] + ; r10,r11 = opus_val16 y[4] + STMFD sp!, {r2,r4,r5,lr} + LDR r10, [r5], #4 ; Load y[0...1] + SUBS r2, r3, #4 ; j = len-4 + LDR r11, [r5], #4 ; Load y[2...3] + BLE xcorr_kernel_edsp_process4_done + LDR r12, [r4], #4 ; Load x[0...1] + ; Stall +xcorr_kernel_edsp_process4 + ; The multiplies must issue from pipeline 0, and can't dual-issue with each + ; other. Every other instruction here dual-issues with a multiply, and is + ; thus "free". There should be no stalls in the body of the loop. + SMLABB r6, r12, r10, r6 ; sum[0] = MAC16_16(sum[0],x_0,y_0) + LDR r14, [r4], #4 ; Load x[2...3] + SMLABT r7, r12, r10, r7 ; sum[1] = MAC16_16(sum[1],x_0,y_1) + SUBS r2, r2, #4 ; j-=4 + SMLABB r8, r12, r11, r8 ; sum[2] = MAC16_16(sum[2],x_0,y_2) + SMLABT r9, r12, r11, r9 ; sum[3] = MAC16_16(sum[3],x_0,y_3) + SMLATT r6, r12, r10, r6 ; sum[0] = MAC16_16(sum[0],x_1,y_1) + LDR r10, [r5], #4 ; Load y[4...5] + SMLATB r7, r12, r11, r7 ; sum[1] = MAC16_16(sum[1],x_1,y_2) + SMLATT r8, r12, r11, r8 ; sum[2] = MAC16_16(sum[2],x_1,y_3) + SMLATB r9, r12, r10, r9 ; sum[3] = MAC16_16(sum[3],x_1,y_4) + LDRGT r12, [r4], #4 ; Load x[0...1] + SMLABB r6, r14, r11, r6 ; sum[0] = MAC16_16(sum[0],x_2,y_2) + SMLABT r7, r14, r11, r7 ; sum[1] = MAC16_16(sum[1],x_2,y_3) + SMLABB r8, r14, r10, r8 ; sum[2] = MAC16_16(sum[2],x_2,y_4) + SMLABT r9, r14, r10, r9 ; sum[3] = MAC16_16(sum[3],x_2,y_5) + SMLATT r6, r14, r11, r6 ; sum[0] = MAC16_16(sum[0],x_3,y_3) + LDR r11, [r5], #4 ; Load y[6...7] + SMLATB r7, r14, r10, r7 ; sum[1] = MAC16_16(sum[1],x_3,y_4) + SMLATT r8, r14, r10, r8 ; sum[2] = MAC16_16(sum[2],x_3,y_5) + SMLATB r9, r14, r11, r9 ; sum[3] = MAC16_16(sum[3],x_3,y_6) + BGT xcorr_kernel_edsp_process4 +xcorr_kernel_edsp_process4_done + ADDS r2, r2, #4 + BLE xcorr_kernel_edsp_done + LDRH r12, [r4], #2 ; r12 = *x++ + SUBS r2, r2, #1 ; j-- + ; Stall + SMLABB r6, r12, r10, r6 ; sum[0] = MAC16_16(sum[0],x,y_0) + LDRHGT r14, [r4], #2 ; r14 = *x++ + SMLABT r7, r12, r10, r7 ; sum[1] = MAC16_16(sum[1],x,y_1) + SMLABB r8, r12, r11, r8 ; sum[2] = MAC16_16(sum[2],x,y_2) + SMLABT r9, r12, r11, r9 ; sum[3] = MAC16_16(sum[3],x,y_3) + BLE xcorr_kernel_edsp_done + SMLABT r6, r14, r10, r6 ; sum[0] = MAC16_16(sum[0],x,y_1) + SUBS r2, r2, #1 ; j-- + SMLABB r7, r14, r11, r7 ; sum[1] = MAC16_16(sum[1],x,y_2) + LDRH r10, [r5], #2 ; r10 = y_4 = *y++ + SMLABT r8, r14, r11, r8 ; sum[2] = MAC16_16(sum[2],x,y_3) + LDRHGT r12, [r4], #2 ; r12 = *x++ + SMLABB r9, r14, r10, r9 ; sum[3] = MAC16_16(sum[3],x,y_4) + BLE xcorr_kernel_edsp_done + SMLABB r6, r12, r11, r6 ; sum[0] = MAC16_16(sum[0],tmp,y_2) + CMP r2, #1 ; j-- + SMLABT r7, r12, r11, r7 ; sum[1] = MAC16_16(sum[1],tmp,y_3) + LDRH r2, [r5], #2 ; r2 = y_5 = *y++ + SMLABB r8, r12, r10, r8 ; sum[2] = MAC16_16(sum[2],tmp,y_4) + LDRHGT r14, [r4] ; r14 = *x + SMLABB r9, r12, r2, r9 ; sum[3] = MAC16_16(sum[3],tmp,y_5) + BLE xcorr_kernel_edsp_done + SMLABT r6, r14, r11, r6 ; sum[0] = MAC16_16(sum[0],tmp,y_3) + LDRH r11, [r5] ; r11 = y_6 = *y + SMLABB r7, r14, r10, r7 ; sum[1] = MAC16_16(sum[1],tmp,y_4) + SMLABB r8, r14, r2, r8 ; sum[2] = MAC16_16(sum[2],tmp,y_5) + SMLABB r9, r14, r11, r9 ; sum[3] = MAC16_16(sum[3],tmp,y_6) +xcorr_kernel_edsp_done + LDMFD sp!, {r2,r4,r5,pc} + ENDP + +celt_pitch_xcorr_edsp PROC + ; input: + ; r0 = opus_val16 *_x (must be 32-bit aligned) + ; r1 = opus_val16 *_y (only needs to be 16-bit aligned) + ; r2 = opus_val32 *xcorr + ; r3 = int len + ; output: + ; r0 = maxcorr + ; internal usage + ; r4 = opus_val16 *x + ; r5 = opus_val16 *y + ; r6 = opus_val32 sum0 + ; r7 = opus_val32 sum1 + ; r8 = opus_val32 sum2 + ; r9 = opus_val32 sum3 + ; r1 = int max_pitch + ; r12 = int j + ; ignored: + ; int arch + STMFD sp!, {r4-r11, lr} + MOV r5, r1 + LDR r1, [sp, #36] + MOV r4, r0 + TST r5, #3 + ; maxcorr = 1 + MOV r0, #1 + BEQ celt_pitch_xcorr_edsp_process1u_done +; Compute one sum at the start to make y 32-bit aligned. + SUBS r12, r3, #4 + ; r14 = sum = 0 + MOV r14, #0 + LDRH r8, [r5], #2 + BLE celt_pitch_xcorr_edsp_process1u_loop4_done + LDR r6, [r4], #4 + MOV r8, r8, LSL #16 +celt_pitch_xcorr_edsp_process1u_loop4 + LDR r9, [r5], #4 + SMLABT r14, r6, r8, r14 ; sum = MAC16_16(sum, x_0, y_0) + LDR r7, [r4], #4 + SMLATB r14, r6, r9, r14 ; sum = MAC16_16(sum, x_1, y_1) + LDR r8, [r5], #4 + SMLABT r14, r7, r9, r14 ; sum = MAC16_16(sum, x_2, y_2) + SUBS r12, r12, #4 ; j-=4 + SMLATB r14, r7, r8, r14 ; sum = MAC16_16(sum, x_3, y_3) + LDRGT r6, [r4], #4 + BGT celt_pitch_xcorr_edsp_process1u_loop4 + MOV r8, r8, LSR #16 +celt_pitch_xcorr_edsp_process1u_loop4_done + ADDS r12, r12, #4 +celt_pitch_xcorr_edsp_process1u_loop1 + LDRHGE r6, [r4], #2 + ; Stall + SMLABBGE r14, r6, r8, r14 ; sum = MAC16_16(sum, *x, *y) + SUBSGE r12, r12, #1 + LDRHGT r8, [r5], #2 + BGT celt_pitch_xcorr_edsp_process1u_loop1 + ; Restore _x + SUB r4, r4, r3, LSL #1 + ; Restore and advance _y + SUB r5, r5, r3, LSL #1 + ; maxcorr = max(maxcorr, sum) + CMP r0, r14 + ADD r5, r5, #2 + MOVLT r0, r14 + SUBS r1, r1, #1 + ; xcorr[i] = sum + STR r14, [r2], #4 + BLE celt_pitch_xcorr_edsp_done +celt_pitch_xcorr_edsp_process1u_done + ; if (max_pitch < 4) goto celt_pitch_xcorr_edsp_process2 + SUBS r1, r1, #4 + BLT celt_pitch_xcorr_edsp_process2 +celt_pitch_xcorr_edsp_process4 + ; xcorr_kernel_edsp parameters: + ; r3 = len, r4 = _x, r5 = _y, r6...r9 = sum[4] = {0, 0, 0, 0} + MOV r6, #0 + MOV r7, #0 + MOV r8, #0 + MOV r9, #0 + BL xcorr_kernel_edsp_start ; xcorr_kernel_edsp(_x, _y+i, xcorr+i, len) + ; maxcorr = max(maxcorr, sum0, sum1, sum2, sum3) + CMP r0, r6 + ; _y+=4 + ADD r5, r5, #8 + MOVLT r0, r6 + CMP r0, r7 + MOVLT r0, r7 + CMP r0, r8 + MOVLT r0, r8 + CMP r0, r9 + MOVLT r0, r9 + STMIA r2!, {r6-r9} + SUBS r1, r1, #4 + BGE celt_pitch_xcorr_edsp_process4 +celt_pitch_xcorr_edsp_process2 + ADDS r1, r1, #2 + BLT celt_pitch_xcorr_edsp_process1a + SUBS r12, r3, #4 + ; {r10, r11} = {sum0, sum1} = {0, 0} + MOV r10, #0 + MOV r11, #0 + LDR r8, [r5], #4 + BLE celt_pitch_xcorr_edsp_process2_loop_done + LDR r6, [r4], #4 + LDR r9, [r5], #4 +celt_pitch_xcorr_edsp_process2_loop4 + SMLABB r10, r6, r8, r10 ; sum0 = MAC16_16(sum0, x_0, y_0) + LDR r7, [r4], #4 + SMLABT r11, r6, r8, r11 ; sum1 = MAC16_16(sum1, x_0, y_1) + SUBS r12, r12, #4 ; j-=4 + SMLATT r10, r6, r8, r10 ; sum0 = MAC16_16(sum0, x_1, y_1) + LDR r8, [r5], #4 + SMLATB r11, r6, r9, r11 ; sum1 = MAC16_16(sum1, x_1, y_2) + LDRGT r6, [r4], #4 + SMLABB r10, r7, r9, r10 ; sum0 = MAC16_16(sum0, x_2, y_2) + SMLABT r11, r7, r9, r11 ; sum1 = MAC16_16(sum1, x_2, y_3) + SMLATT r10, r7, r9, r10 ; sum0 = MAC16_16(sum0, x_3, y_3) + LDRGT r9, [r5], #4 + SMLATB r11, r7, r8, r11 ; sum1 = MAC16_16(sum1, x_3, y_4) + BGT celt_pitch_xcorr_edsp_process2_loop4 +celt_pitch_xcorr_edsp_process2_loop_done + ADDS r12, r12, #2 + BLE celt_pitch_xcorr_edsp_process2_1 + LDR r6, [r4], #4 + ; Stall + SMLABB r10, r6, r8, r10 ; sum0 = MAC16_16(sum0, x_0, y_0) + LDR r9, [r5], #4 + SMLABT r11, r6, r8, r11 ; sum1 = MAC16_16(sum1, x_0, y_1) + SUB r12, r12, #2 + SMLATT r10, r6, r8, r10 ; sum0 = MAC16_16(sum0, x_1, y_1) + MOV r8, r9 + SMLATB r11, r6, r9, r11 ; sum1 = MAC16_16(sum1, x_1, y_2) +celt_pitch_xcorr_edsp_process2_1 + LDRH r6, [r4], #2 + ADDS r12, r12, #1 + ; Stall + SMLABB r10, r6, r8, r10 ; sum0 = MAC16_16(sum0, x_0, y_0) + LDRHGT r7, [r4], #2 + SMLABT r11, r6, r8, r11 ; sum1 = MAC16_16(sum1, x_0, y_1) + BLE celt_pitch_xcorr_edsp_process2_done + LDRH r9, [r5], #2 + SMLABT r10, r7, r8, r10 ; sum0 = MAC16_16(sum0, x_0, y_1) + SMLABB r11, r7, r9, r11 ; sum1 = MAC16_16(sum1, x_0, y_2) +celt_pitch_xcorr_edsp_process2_done + ; Restore _x + SUB r4, r4, r3, LSL #1 + ; Restore and advance _y + SUB r5, r5, r3, LSL #1 + ; maxcorr = max(maxcorr, sum0) + CMP r0, r10 + ADD r5, r5, #2 + MOVLT r0, r10 + SUB r1, r1, #2 + ; maxcorr = max(maxcorr, sum1) + CMP r0, r11 + ; xcorr[i] = sum + STR r10, [r2], #4 + MOVLT r0, r11 + STR r11, [r2], #4 +celt_pitch_xcorr_edsp_process1a + ADDS r1, r1, #1 + BLT celt_pitch_xcorr_edsp_done + SUBS r12, r3, #4 + ; r14 = sum = 0 + MOV r14, #0 + BLT celt_pitch_xcorr_edsp_process1a_loop_done + LDR r6, [r4], #4 + LDR r8, [r5], #4 + LDR r7, [r4], #4 + LDR r9, [r5], #4 +celt_pitch_xcorr_edsp_process1a_loop4 + SMLABB r14, r6, r8, r14 ; sum = MAC16_16(sum, x_0, y_0) + SUBS r12, r12, #4 ; j-=4 + SMLATT r14, r6, r8, r14 ; sum = MAC16_16(sum, x_1, y_1) + LDRGE r6, [r4], #4 + SMLABB r14, r7, r9, r14 ; sum = MAC16_16(sum, x_2, y_2) + LDRGE r8, [r5], #4 + SMLATT r14, r7, r9, r14 ; sum = MAC16_16(sum, x_3, y_3) + LDRGE r7, [r4], #4 + LDRGE r9, [r5], #4 + BGE celt_pitch_xcorr_edsp_process1a_loop4 +celt_pitch_xcorr_edsp_process1a_loop_done + ADDS r12, r12, #2 + LDRGE r6, [r4], #4 + LDRGE r8, [r5], #4 + ; Stall + SMLABBGE r14, r6, r8, r14 ; sum = MAC16_16(sum, x_0, y_0) + SUBGE r12, r12, #2 + SMLATTGE r14, r6, r8, r14 ; sum = MAC16_16(sum, x_1, y_1) + ADDS r12, r12, #1 + LDRHGE r6, [r4], #2 + LDRHGE r8, [r5], #2 + ; Stall + SMLABBGE r14, r6, r8, r14 ; sum = MAC16_16(sum, *x, *y) + ; maxcorr = max(maxcorr, sum) + CMP r0, r14 + ; xcorr[i] = sum + STR r14, [r2], #4 + MOVLT r0, r14 +celt_pitch_xcorr_edsp_done + LDMFD sp!, {r4-r11, pc} + ENDP + +ENDIF + +END diff --git a/managed_components/78__esp-opus/celt/arm/fft_arm.h b/managed_components/78__esp-opus/celt/arm/fft_arm.h new file mode 100644 index 0000000..0b78175 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/fft_arm.h @@ -0,0 +1,71 @@ +/* Copyright (c) 2015 Xiph.Org Foundation + Written by Viswanath Puttagunta */ +/** + @file fft_arm.h + @brief ARM Neon Intrinsic optimizations for fft using NE10 library + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + + +#if !defined(FFT_ARM_H) +#define FFT_ARM_H + +#include "kiss_fft.h" + +#if defined(HAVE_ARM_NE10) + +int opus_fft_alloc_arm_neon(kiss_fft_state *st); +void opus_fft_free_arm_neon(kiss_fft_state *st); + +void opus_fft_neon(const kiss_fft_state *st, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout); + +void opus_ifft_neon(const kiss_fft_state *st, + const kiss_fft_cpx *fin, + kiss_fft_cpx *fout); + +#if !defined(OPUS_HAVE_RTCD) +#define OVERRIDE_OPUS_FFT (1) + +#define opus_fft_alloc_arch(_st, arch) \ + ((void)(arch), opus_fft_alloc_arm_neon(_st)) + +#define opus_fft_free_arch(_st, arch) \ + ((void)(arch), opus_fft_free_arm_neon(_st)) + +#define opus_fft(_st, _fin, _fout, arch) \ + ((void)(arch), opus_fft_neon(_st, _fin, _fout)) + +#define opus_ifft(_st, _fin, _fout, arch) \ + ((void)(arch), opus_ifft_neon(_st, _fin, _fout)) + +#endif /* OPUS_HAVE_RTCD */ + +#endif /* HAVE_ARM_NE10 */ + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/fixed_arm64.h b/managed_components/78__esp-opus/celt/arm/fixed_arm64.h new file mode 100644 index 0000000..c6fbd3d --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/fixed_arm64.h @@ -0,0 +1,35 @@ +/* Copyright (C) 2015 Vidyo */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_ARM64_H +#define FIXED_ARM64_H + +#include + +#undef SIG2WORD16 +#define SIG2WORD16(x) (vqmovns_s32(PSHR32((x), SIG_SHIFT))) + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/fixed_armv4.h b/managed_components/78__esp-opus/celt/arm/fixed_armv4.h new file mode 100644 index 0000000..d84888a --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/fixed_armv4.h @@ -0,0 +1,80 @@ +/* Copyright (C) 2013 Xiph.Org Foundation and contributors */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_ARMv4_H +#define FIXED_ARMv4_H + +/** 16x32 multiplication, followed by a 16-bit shift right. Results fits in 32 bits */ +#undef MULT16_32_Q16 +static OPUS_INLINE opus_val32 MULT16_32_Q16_armv4(opus_val16 a, opus_val32 b) +{ + unsigned rd_lo; + int rd_hi; + __asm__( + "#MULT16_32_Q16\n\t" + "smull %0, %1, %2, %3\n\t" + : "=&r"(rd_lo), "=&r"(rd_hi) + : "%r"(b),"r"(SHL32(a,16)) + ); + return rd_hi; +} +#define MULT16_32_Q16(a, b) (MULT16_32_Q16_armv4(a, b)) + + +/** 16x32 multiplication, followed by a 15-bit shift right. Results fits in 32 bits */ +#undef MULT16_32_Q15 +static OPUS_INLINE opus_val32 MULT16_32_Q15_armv4(opus_val16 a, opus_val32 b) +{ + unsigned rd_lo; + int rd_hi; + __asm__( + "#MULT16_32_Q15\n\t" + "smull %0, %1, %2, %3\n\t" + : "=&r"(rd_lo), "=&r"(rd_hi) + : "%r"(b), "r"(SHL32(a,16)) + ); + /*We intentionally don't OR in the high bit of rd_lo for speed.*/ + return SHL32(rd_hi,1); +} +#define MULT16_32_Q15(a, b) (MULT16_32_Q15_armv4(a, b)) + + +/** 16x32 multiply, followed by a 15-bit shift right and 32-bit add. + b must fit in 31 bits. + Result fits in 32 bits. */ +#undef MAC16_32_Q15 +#define MAC16_32_Q15(c, a, b) ADD32(c, MULT16_32_Q15(a, b)) + +/** 16x32 multiply, followed by a 16-bit shift right and 32-bit add. + Result fits in 32 bits. */ +#undef MAC16_32_Q16 +#define MAC16_32_Q16(c, a, b) ADD32(c, MULT16_32_Q16(a, b)) + +/** 32x32 multiplication, followed by a 31-bit shift right. Results fits in 32 bits */ +#undef MULT32_32_Q31 +#define MULT32_32_Q31(a,b) (opus_val32)((((opus_int64)(a)) * ((opus_int64)(b)))>>31) + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/fixed_armv5e.h b/managed_components/78__esp-opus/celt/arm/fixed_armv5e.h new file mode 100644 index 0000000..6bf73cb --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/fixed_armv5e.h @@ -0,0 +1,151 @@ +/* Copyright (C) 2007-2009 Xiph.Org Foundation + Copyright (C) 2003-2008 Jean-Marc Valin + Copyright (C) 2007-2008 CSIRO + Copyright (C) 2013 Parrot */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_ARMv5E_H +#define FIXED_ARMv5E_H + +#include "fixed_armv4.h" + +/** 16x32 multiplication, followed by a 16-bit shift right. Results fits in 32 bits */ +#undef MULT16_32_Q16 +static OPUS_INLINE opus_val32 MULT16_32_Q16_armv5e(opus_val16 a, opus_val32 b) +{ + int res; + __asm__( + "#MULT16_32_Q16\n\t" + "smulwb %0, %1, %2\n\t" + : "=r"(res) + : "r"(b),"r"(a) + ); + return res; +} +#define MULT16_32_Q16(a, b) (MULT16_32_Q16_armv5e(a, b)) + + +/** 16x32 multiplication, followed by a 15-bit shift right. Results fits in 32 bits */ +#undef MULT16_32_Q15 +static OPUS_INLINE opus_val32 MULT16_32_Q15_armv5e(opus_val16 a, opus_val32 b) +{ + int res; + __asm__( + "#MULT16_32_Q15\n\t" + "smulwb %0, %1, %2\n\t" + : "=r"(res) + : "r"(b), "r"(a) + ); + return SHL32(res,1); +} +#define MULT16_32_Q15(a, b) (MULT16_32_Q15_armv5e(a, b)) + + +/** 16x32 multiply, followed by a 15-bit shift right and 32-bit add. + b must fit in 31 bits. + Result fits in 32 bits. */ +#undef MAC16_32_Q15 +static OPUS_INLINE opus_val32 MAC16_32_Q15_armv5e(opus_val32 c, opus_val16 a, + opus_val32 b) +{ + int res; + __asm__( + "#MAC16_32_Q15\n\t" + "smlawb %0, %1, %2, %3;\n" + : "=r"(res) + : "r"(SHL32(b,1)), "r"(a), "r"(c) + ); + return res; +} +#define MAC16_32_Q15(c, a, b) (MAC16_32_Q15_armv5e(c, a, b)) + +/** 16x32 multiply, followed by a 16-bit shift right and 32-bit add. + Result fits in 32 bits. */ +#undef MAC16_32_Q16 +static OPUS_INLINE opus_val32 MAC16_32_Q16_armv5e(opus_val32 c, opus_val16 a, + opus_val32 b) +{ + int res; + __asm__( + "#MAC16_32_Q16\n\t" + "smlawb %0, %1, %2, %3;\n" + : "=r"(res) + : "r"(b), "r"(a), "r"(c) + ); + return res; +} +#define MAC16_32_Q16(c, a, b) (MAC16_32_Q16_armv5e(c, a, b)) + +/** 16x16 multiply-add where the result fits in 32 bits */ +#undef MAC16_16 +static OPUS_INLINE opus_val32 MAC16_16_armv5e(opus_val32 c, opus_val16 a, + opus_val16 b) +{ + int res; + __asm__( + "#MAC16_16\n\t" + "smlabb %0, %1, %2, %3;\n" + : "=r"(res) + : "r"(a), "r"(b), "r"(c) + ); + return res; +} +#define MAC16_16(c, a, b) (MAC16_16_armv5e(c, a, b)) + +/** 16x16 multiplication where the result fits in 32 bits */ +#undef MULT16_16 +static OPUS_INLINE opus_val32 MULT16_16_armv5e(opus_val16 a, opus_val16 b) +{ + int res; + __asm__( + "#MULT16_16\n\t" + "smulbb %0, %1, %2;\n" + : "=r"(res) + : "r"(a), "r"(b) + ); + return res; +} +#define MULT16_16(a, b) (MULT16_16_armv5e(a, b)) + +#ifdef OPUS_ARM_INLINE_MEDIA + +#undef SIG2WORD16 +static OPUS_INLINE opus_val16 SIG2WORD16_armv6(opus_val32 x) +{ + celt_sig res; + __asm__( + "#SIG2WORD16\n\t" + "ssat %0, #16, %1, ASR #12\n\t" + : "=r"(res) + : "r"(x+2048) + ); + return EXTRACT16(res); +} +#define SIG2WORD16(x) (SIG2WORD16_armv6(x)) + +#endif /* OPUS_ARM_INLINE_MEDIA */ + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/kiss_fft_armv4.h b/managed_components/78__esp-opus/celt/arm/kiss_fft_armv4.h new file mode 100644 index 0000000..e4faad6 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/kiss_fft_armv4.h @@ -0,0 +1,121 @@ +/*Copyright (c) 2013, Xiph.Org Foundation and contributors. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.*/ + +#ifndef KISS_FFT_ARMv4_H +#define KISS_FFT_ARMv4_H + +#if !defined(KISS_FFT_GUTS_H) +#error "This file should only be included from _kiss_fft_guts.h" +#endif + +#ifdef FIXED_POINT + +#undef C_MUL +#define C_MUL(m,a,b) \ + do{ \ + int br__; \ + int bi__; \ + int tt__; \ + __asm__ __volatile__( \ + "#C_MUL\n\t" \ + "ldrsh %[br], [%[bp], #0]\n\t" \ + "ldm %[ap], {r0,r1}\n\t" \ + "ldrsh %[bi], [%[bp], #2]\n\t" \ + "smull %[tt], %[mi], r1, %[br]\n\t" \ + "smlal %[tt], %[mi], r0, %[bi]\n\t" \ + "rsb %[bi], %[bi], #0\n\t" \ + "smull %[br], %[mr], r0, %[br]\n\t" \ + "mov %[tt], %[tt], lsr #15\n\t" \ + "smlal %[br], %[mr], r1, %[bi]\n\t" \ + "orr %[mi], %[tt], %[mi], lsl #17\n\t" \ + "mov %[br], %[br], lsr #15\n\t" \ + "orr %[mr], %[br], %[mr], lsl #17\n\t" \ + : [mr]"=r"((m).r), [mi]"=r"((m).i), \ + [br]"=&r"(br__), [bi]"=r"(bi__), [tt]"=r"(tt__) \ + : [ap]"r"(&(a)), [bp]"r"(&(b)) \ + : "r0", "r1" \ + ); \ + } \ + while(0) + +#undef C_MUL4 +#define C_MUL4(m,a,b) \ + do{ \ + int br__; \ + int bi__; \ + int tt__; \ + __asm__ __volatile__( \ + "#C_MUL4\n\t" \ + "ldrsh %[br], [%[bp], #0]\n\t" \ + "ldm %[ap], {r0,r1}\n\t" \ + "ldrsh %[bi], [%[bp], #2]\n\t" \ + "smull %[tt], %[mi], r1, %[br]\n\t" \ + "smlal %[tt], %[mi], r0, %[bi]\n\t" \ + "rsb %[bi], %[bi], #0\n\t" \ + "smull %[br], %[mr], r0, %[br]\n\t" \ + "mov %[tt], %[tt], lsr #17\n\t" \ + "smlal %[br], %[mr], r1, %[bi]\n\t" \ + "orr %[mi], %[tt], %[mi], lsl #15\n\t" \ + "mov %[br], %[br], lsr #17\n\t" \ + "orr %[mr], %[br], %[mr], lsl #15\n\t" \ + : [mr]"=r"((m).r), [mi]"=r"((m).i), \ + [br]"=&r"(br__), [bi]"=r"(bi__), [tt]"=r"(tt__) \ + : [ap]"r"(&(a)), [bp]"r"(&(b)) \ + : "r0", "r1" \ + ); \ + } \ + while(0) + +#undef C_MULC +#define C_MULC(m,a,b) \ + do{ \ + int br__; \ + int bi__; \ + int tt__; \ + __asm__ __volatile__( \ + "#C_MULC\n\t" \ + "ldrsh %[br], [%[bp], #0]\n\t" \ + "ldm %[ap], {r0,r1}\n\t" \ + "ldrsh %[bi], [%[bp], #2]\n\t" \ + "smull %[tt], %[mr], r0, %[br]\n\t" \ + "smlal %[tt], %[mr], r1, %[bi]\n\t" \ + "rsb %[bi], %[bi], #0\n\t" \ + "smull %[br], %[mi], r1, %[br]\n\t" \ + "mov %[tt], %[tt], lsr #15\n\t" \ + "smlal %[br], %[mi], r0, %[bi]\n\t" \ + "orr %[mr], %[tt], %[mr], lsl #17\n\t" \ + "mov %[br], %[br], lsr #15\n\t" \ + "orr %[mi], %[br], %[mi], lsl #17\n\t" \ + : [mr]"=r"((m).r), [mi]"=r"((m).i), \ + [br]"=&r"(br__), [bi]"=r"(bi__), [tt]"=r"(tt__) \ + : [ap]"r"(&(a)), [bp]"r"(&(b)) \ + : "r0", "r1" \ + ); \ + } \ + while(0) + +#endif /* FIXED_POINT */ + +#endif /* KISS_FFT_ARMv4_H */ diff --git a/managed_components/78__esp-opus/celt/arm/kiss_fft_armv5e.h b/managed_components/78__esp-opus/celt/arm/kiss_fft_armv5e.h new file mode 100644 index 0000000..9eca183 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/kiss_fft_armv5e.h @@ -0,0 +1,118 @@ +/*Copyright (c) 2013, Xiph.Org Foundation and contributors. + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.*/ + +#ifndef KISS_FFT_ARMv5E_H +#define KISS_FFT_ARMv5E_H + +#if !defined(KISS_FFT_GUTS_H) +#error "This file should only be included from _kiss_fft_guts.h" +#endif + +#ifdef FIXED_POINT + +#if defined(__thumb__)||defined(__thumb2__) +#define LDRD_CONS "Q" +#else +#define LDRD_CONS "Uq" +#endif + +#undef C_MUL +#define C_MUL(m,a,b) \ + do{ \ + int mr1__; \ + int mr2__; \ + int mi__; \ + long long aval__; \ + int bval__; \ + __asm__( \ + "#C_MUL\n\t" \ + "ldrd %[aval], %H[aval], %[ap]\n\t" \ + "ldr %[bval], %[bp]\n\t" \ + "smulwb %[mi], %H[aval], %[bval]\n\t" \ + "smulwb %[mr1], %[aval], %[bval]\n\t" \ + "smulwt %[mr2], %H[aval], %[bval]\n\t" \ + "smlawt %[mi], %[aval], %[bval], %[mi]\n\t" \ + : [mr1]"=r"(mr1__), [mr2]"=r"(mr2__), [mi]"=r"(mi__), \ + [aval]"=&r"(aval__), [bval]"=r"(bval__) \ + : [ap]LDRD_CONS(a), [bp]"m"(b) \ + ); \ + (m).r = SHL32(SUB32(mr1__, mr2__), 1); \ + (m).i = SHL32(mi__, 1); \ + } \ + while(0) + +#undef C_MUL4 +#define C_MUL4(m,a,b) \ + do{ \ + int mr1__; \ + int mr2__; \ + int mi__; \ + long long aval__; \ + int bval__; \ + __asm__( \ + "#C_MUL4\n\t" \ + "ldrd %[aval], %H[aval], %[ap]\n\t" \ + "ldr %[bval], %[bp]\n\t" \ + "smulwb %[mi], %H[aval], %[bval]\n\t" \ + "smulwb %[mr1], %[aval], %[bval]\n\t" \ + "smulwt %[mr2], %H[aval], %[bval]\n\t" \ + "smlawt %[mi], %[aval], %[bval], %[mi]\n\t" \ + : [mr1]"=r"(mr1__), [mr2]"=r"(mr2__), [mi]"=r"(mi__), \ + [aval]"=&r"(aval__), [bval]"=r"(bval__) \ + : [ap]LDRD_CONS(a), [bp]"m"(b) \ + ); \ + (m).r = SHR32(SUB32(mr1__, mr2__), 1); \ + (m).i = SHR32(mi__, 1); \ + } \ + while(0) + +#undef C_MULC +#define C_MULC(m,a,b) \ + do{ \ + int mr__; \ + int mi1__; \ + int mi2__; \ + long long aval__; \ + int bval__; \ + __asm__( \ + "#C_MULC\n\t" \ + "ldrd %[aval], %H[aval], %[ap]\n\t" \ + "ldr %[bval], %[bp]\n\t" \ + "smulwb %[mr], %[aval], %[bval]\n\t" \ + "smulwb %[mi1], %H[aval], %[bval]\n\t" \ + "smulwt %[mi2], %[aval], %[bval]\n\t" \ + "smlawt %[mr], %H[aval], %[bval], %[mr]\n\t" \ + : [mr]"=r"(mr__), [mi1]"=r"(mi1__), [mi2]"=r"(mi2__), \ + [aval]"=&r"(aval__), [bval]"=r"(bval__) \ + : [ap]LDRD_CONS(a), [bp]"m"(b) \ + ); \ + (m).r = SHL32(mr__, 1); \ + (m).i = SHL32(SUB32(mi1__, mi2__), 1); \ + } \ + while(0) + +#endif /* FIXED_POINT */ + +#endif /* KISS_FFT_GUTS_H */ diff --git a/managed_components/78__esp-opus/celt/arm/mathops_arm.h b/managed_components/78__esp-opus/celt/arm/mathops_arm.h new file mode 100644 index 0000000..b1f9169 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/mathops_arm.h @@ -0,0 +1,103 @@ +/* Copyright (c) 2024 Arm Limited */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(MATHOPS_ARM_H) +# define MATHOPS_ARM_H + +#include "armcpu.h" +#include "cpu_support.h" +#include "opus_defines.h" + +# if !defined(DISABLE_FLOAT_API) && defined(OPUS_ARM_MAY_HAVE_NEON_INTR) + +#include + +static inline int32x4_t vroundf(float32x4_t x) +{ +# if defined(__aarch64__) || (defined(__ARM_ARCH) && __ARM_ARCH >= 8) + return vcvtaq_s32_f32(x); +# else + uint32x4_t sign = vandq_u32(vreinterpretq_u32_f32(x), vdupq_n_u32(0x80000000)); + uint32x4_t bias = vdupq_n_u32(0x3F000000); + return vcvtq_s32_f32(vaddq_f32(x, vreinterpretq_f32_u32(vorrq_u32(bias, sign)))); +# endif +} + +static inline float vminvf(float32x4_t a) +{ +#if defined(__aarch64__) + return vminvq_f32(a); +#else + float32x2_t xy = vmin_f32(vget_low_f32(a), vget_high_f32(a)); + float x = vget_lane_f32(xy, 0); + float y = vget_lane_f32(xy, 1); + return x < y ? x : y; +#endif +} + +static inline float vmaxvf(float32x4_t a) +{ +#if defined(__aarch64__) + return vmaxvq_f32(a); +#else + float32x2_t xy = vmax_f32(vget_low_f32(a), vget_high_f32(a)); + float x = vget_lane_f32(xy, 0); + float y = vget_lane_f32(xy, 1); + return x > y ? x : y; +#endif +} + +void celt_float2int16_neon(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt); +# if defined(OPUS_HAVE_RTCD) && \ + (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) +extern void +(*const CELT_FLOAT2INT16_IMPL[OPUS_ARCHMASK+1])(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt); + +# define OVERRIDE_FLOAT2INT16 (1) +# define celt_float2int16(in, out, cnt, arch) \ + ((*CELT_FLOAT2INT16_IMPL[(arch)&OPUS_ARCHMASK])(in, out, cnt)) + +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) +# define OVERRIDE_FLOAT2INT16 (1) +# define celt_float2int16(in, out, cnt, arch) ((void)(arch), celt_float2int16_neon(in, out, cnt)) +# endif + +int opus_limit2_checkwithin1_neon(float * samples, int cnt); +# if defined(OPUS_HAVE_RTCD) && \ + (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) +extern int (*const OPUS_LIMIT2_CHECKWITHIN1_IMPL[OPUS_ARCHMASK+1])(float * samples, int cnt); + +# define OVERRIDE_LIMIT2_CHECKWITHIN1 (1) +# define opus_limit2_checkwithin1(samples, cnt, arch) \ + ((*OPUS_LIMIT2_CHECKWITHIN1_IMPL[(arch)&OPUS_ARCHMASK])(samples, cnt)) + +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) +# define OVERRIDE_LIMIT2_CHECKWITHIN1 (1) +# define opus_limit2_checkwithin1(samples, cnt, arch) ((void)(arch), opus_limit2_checkwithin1_neon(samples, cnt)) +# endif +# endif + +#endif /* MATHOPS_ARM_H */ diff --git a/managed_components/78__esp-opus/celt/arm/mdct_arm.h b/managed_components/78__esp-opus/celt/arm/mdct_arm.h new file mode 100644 index 0000000..14200ba --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/mdct_arm.h @@ -0,0 +1,59 @@ +/* Copyright (c) 2015 Xiph.Org Foundation + Written by Viswanath Puttagunta */ +/** + @file arm_mdct.h + @brief ARM Neon Intrinsic optimizations for mdct using NE10 library + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(MDCT_ARM_H) +#define MDCT_ARM_H + +#include "mdct.h" + +#if defined(HAVE_ARM_NE10) +/** Compute a forward MDCT and scale by 4/N, trashes the input array */ +void clt_mdct_forward_neon(const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, int overlap, + int shift, int stride, int arch); + +void clt_mdct_backward_neon(const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, int overlap, + int shift, int stride, int arch); + +#if !defined(OPUS_HAVE_RTCD) +#define OVERRIDE_OPUS_MDCT (1) +#define clt_mdct_forward(_l, _in, _out, _window, _int, _shift, _stride, _arch) \ + clt_mdct_forward_neon(_l, _in, _out, _window, _int, _shift, _stride, _arch) +#define clt_mdct_backward(_l, _in, _out, _window, _int, _shift, _stride, _arch) \ + clt_mdct_backward_neon(_l, _in, _out, _window, _int, _shift, _stride, _arch) +#endif /* OPUS_HAVE_RTCD */ +#endif /* HAVE_ARM_NE10 */ + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/meson.build b/managed_components/78__esp-opus/celt/arm/meson.build new file mode 100644 index 0000000..b4dec56 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/meson.build @@ -0,0 +1,12 @@ +arm2gnu = [find_program('arm2gnu.pl')] + arm2gnu_args +celt_sources_arm_asm = configure_file(input: 'celt_pitch_xcorr_arm.s', + output: '@BASENAME@-gnu.S', + command: arm2gnu + ['@INPUT@'], + capture: true) +celt_arm_armopts_s_in = configure_file(input: 'armopts.s.in', + output: 'armopts.s', + configuration: opus_conf) +celt_arm_armopts_s = configure_file(input: [celt_arm_armopts_s_in], + output: '@BASENAME@-gnu.S', + command: arm2gnu + ['@INPUT@'], + capture: true) diff --git a/managed_components/78__esp-opus/celt/arm/pitch_arm.h b/managed_components/78__esp-opus/celt/arm/pitch_arm.h new file mode 100644 index 0000000..bed8b04 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/pitch_arm.h @@ -0,0 +1,160 @@ +/* Copyright (c) 2010 Xiph.Org Foundation + * Copyright (c) 2013 Parrot */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(PITCH_ARM_H) +# define PITCH_ARM_H + +# include "armcpu.h" + +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +opus_val32 celt_inner_prod_neon(const opus_val16 *x, const opus_val16 *y, int N); +void dual_inner_prod_neon(const opus_val16 *x, const opus_val16 *y01, + const opus_val16 *y02, int N, opus_val32 *xy1, opus_val32 *xy2); + +# if !defined(OPUS_HAVE_RTCD) && defined(OPUS_ARM_PRESUME_NEON) +# define OVERRIDE_CELT_INNER_PROD (1) +# define OVERRIDE_DUAL_INNER_PROD (1) +# define celt_inner_prod(x, y, N, arch) ((void)(arch), PRESUME_NEON(celt_inner_prod)(x, y, N)) +# define dual_inner_prod(x, y01, y02, N, xy1, xy2, arch) ((void)(arch), PRESUME_NEON(dual_inner_prod)(x, y01, y02, N, xy1, xy2)) +# endif +# endif + +# if !defined(OVERRIDE_CELT_INNER_PROD) +# if defined(OPUS_HAVE_RTCD) && (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) +extern opus_val32 (*const CELT_INNER_PROD_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *x, const opus_val16 *y, int N); +# define OVERRIDE_CELT_INNER_PROD (1) +# define celt_inner_prod(x, y, N, arch) ((*CELT_INNER_PROD_IMPL[(arch)&OPUS_ARCHMASK])(x, y, N)) +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) +# define OVERRIDE_CELT_INNER_PROD (1) +# define celt_inner_prod(x, y, N, arch) ((void)(arch), celt_inner_prod_neon(x, y, N)) +# endif +# endif + +# if !defined(OVERRIDE_DUAL_INNER_PROD) +# if defined(OPUS_HAVE_RTCD) && (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) +extern void (*const DUAL_INNER_PROD_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *x, + const opus_val16 *y01, const opus_val16 *y02, int N, opus_val32 *xy1, opus_val32 *xy2); +# define OVERRIDE_DUAL_INNER_PROD (1) +# define dual_inner_prod(x, y01, y02, N, xy1, xy2, arch) ((*DUAL_INNER_PROD_IMPL[(arch)&OPUS_ARCHMASK])(x, y01, y02, N, xy1, xy2)) +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) +# define OVERRIDE_DUAL_INNER_PROD (1) +# define dual_inner_prod(x, y01, y02, N, xy1, xy2, arch) ((void)(arch), dual_inner_prod_neon(x, y01, y02, N, xy1, xy2)) +# endif +# endif + +# if defined(FIXED_POINT) + +# if defined(OPUS_ARM_MAY_HAVE_NEON) +opus_val32 celt_pitch_xcorr_neon(const opus_val16 *_x, const opus_val16 *_y, + opus_val32 *xcorr, int len, int max_pitch, int arch); +# endif + +# if defined(OPUS_ARM_MAY_HAVE_MEDIA) +# define celt_pitch_xcorr_media MAY_HAVE_EDSP(celt_pitch_xcorr) +# endif + +# if defined(OPUS_ARM_MAY_HAVE_EDSP) +opus_val32 celt_pitch_xcorr_edsp(const opus_val16 *_x, const opus_val16 *_y, + opus_val32 *xcorr, int len, int max_pitch, int arch); +# endif + +# if defined(OPUS_HAVE_RTCD) && \ + ((defined(OPUS_ARM_MAY_HAVE_NEON) && !defined(OPUS_ARM_PRESUME_NEON)) || \ + (defined(OPUS_ARM_MAY_HAVE_MEDIA) && !defined(OPUS_ARM_PRESUME_MEDIA)) || \ + (defined(OPUS_ARM_MAY_HAVE_EDSP) && !defined(OPUS_ARM_PRESUME_EDSP))) +extern opus_val32 +(*const CELT_PITCH_XCORR_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *, + const opus_val16 *, opus_val32 *, int, int, int); +# define OVERRIDE_PITCH_XCORR (1) +# define celt_pitch_xcorr(_x, _y, xcorr, len, max_pitch, arch) \ + ((*CELT_PITCH_XCORR_IMPL[(arch)&OPUS_ARCHMASK])(_x, _y, \ + xcorr, len, max_pitch, arch)) + +# elif defined(OPUS_ARM_PRESUME_EDSP) || \ + defined(OPUS_ARM_PRESUME_MEDIA) || \ + defined(OPUS_ARM_PRESUME_NEON) +# define OVERRIDE_PITCH_XCORR (1) +# define celt_pitch_xcorr (PRESUME_NEON(celt_pitch_xcorr)) + +# endif + +# if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +void xcorr_kernel_neon_fixed( + const opus_val16 *x, + const opus_val16 *y, + opus_val32 sum[4], + int len); +# endif + +# if defined(OPUS_HAVE_RTCD) && \ + (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) + +extern void (*const XCORR_KERNEL_IMPL[OPUS_ARCHMASK + 1])( + const opus_val16 *x, + const opus_val16 *y, + opus_val32 sum[4], + int len); + +# define OVERRIDE_XCORR_KERNEL (1) +# define xcorr_kernel(x, y, sum, len, arch) \ + ((*XCORR_KERNEL_IMPL[(arch) & OPUS_ARCHMASK])(x, y, sum, len)) + +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) +# define OVERRIDE_XCORR_KERNEL (1) +# define xcorr_kernel(x, y, sum, len, arch) \ + ((void)arch, xcorr_kernel_neon_fixed(x, y, sum, len)) + +# endif + +#else /* Start !FIXED_POINT */ +/* Float case */ +#if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +void celt_pitch_xcorr_float_neon(const opus_val16 *_x, const opus_val16 *_y, + opus_val32 *xcorr, int len, int max_pitch, int arch); +#endif + +# if defined(OPUS_HAVE_RTCD) && \ + (defined(OPUS_ARM_MAY_HAVE_NEON_INTR) && !defined(OPUS_ARM_PRESUME_NEON_INTR)) +extern void +(*const CELT_PITCH_XCORR_IMPL[OPUS_ARCHMASK+1])(const opus_val16 *, + const opus_val16 *, opus_val32 *, int, int, int); + +# define OVERRIDE_PITCH_XCORR (1) +# define celt_pitch_xcorr(_x, _y, xcorr, len, max_pitch, arch) \ + ((*CELT_PITCH_XCORR_IMPL[(arch)&OPUS_ARCHMASK])(_x, _y, \ + xcorr, len, max_pitch, arch)) + +# elif defined(OPUS_ARM_PRESUME_NEON_INTR) + +# define OVERRIDE_PITCH_XCORR (1) +# define celt_pitch_xcorr celt_pitch_xcorr_float_neon + +# endif + +#endif /* end !FIXED_POINT */ + +#endif diff --git a/managed_components/78__esp-opus/celt/arm/pitch_neon_intr.c b/managed_components/78__esp-opus/celt/arm/pitch_neon_intr.c new file mode 100644 index 0000000..43885f5 --- /dev/null +++ b/managed_components/78__esp-opus/celt/arm/pitch_neon_intr.c @@ -0,0 +1,288 @@ +/*********************************************************************** +Copyright (c) 2017 Google Inc. +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +- Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. +- Neither the name of Internet Society, IETF or IETF Trust, nor the +names of specific contributors, may be used to endorse or promote +products derived from this software without specific prior written +permission. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. +***********************************************************************/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include "pitch.h" + +#ifdef FIXED_POINT + +opus_val32 celt_inner_prod_neon(const opus_val16 *x, const opus_val16 *y, int N) +{ + int i; + opus_val32 xy; + int16x8_t x_s16x8, y_s16x8; + int32x4_t xy_s32x4 = vdupq_n_s32(0); + int64x2_t xy_s64x2; + int64x1_t xy_s64x1; + + for (i = 0; i < N - 7; i += 8) { + x_s16x8 = vld1q_s16(&x[i]); + y_s16x8 = vld1q_s16(&y[i]); + xy_s32x4 = vmlal_s16(xy_s32x4, vget_low_s16 (x_s16x8), vget_low_s16 (y_s16x8)); + xy_s32x4 = vmlal_s16(xy_s32x4, vget_high_s16(x_s16x8), vget_high_s16(y_s16x8)); + } + + if (N - i >= 4) { + const int16x4_t x_s16x4 = vld1_s16(&x[i]); + const int16x4_t y_s16x4 = vld1_s16(&y[i]); + xy_s32x4 = vmlal_s16(xy_s32x4, x_s16x4, y_s16x4); + i += 4; + } + + xy_s64x2 = vpaddlq_s32(xy_s32x4); + xy_s64x1 = vadd_s64(vget_low_s64(xy_s64x2), vget_high_s64(xy_s64x2)); + xy = vget_lane_s32(vreinterpret_s32_s64(xy_s64x1), 0); + + for (; i < N; i++) { + xy = MAC16_16(xy, x[i], y[i]); + } + +#ifdef OPUS_CHECK_ASM + celt_assert(celt_inner_prod_c(x, y, N) == xy); +#endif + + return xy; +} + +void dual_inner_prod_neon(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2) +{ + int i; + opus_val32 xy01, xy02; + int16x8_t x_s16x8, y01_s16x8, y02_s16x8; + int32x4_t xy01_s32x4 = vdupq_n_s32(0); + int32x4_t xy02_s32x4 = vdupq_n_s32(0); + int64x2_t xy01_s64x2, xy02_s64x2; + int64x1_t xy01_s64x1, xy02_s64x1; + + for (i = 0; i < N - 7; i += 8) { + x_s16x8 = vld1q_s16(&x[i]); + y01_s16x8 = vld1q_s16(&y01[i]); + y02_s16x8 = vld1q_s16(&y02[i]); + xy01_s32x4 = vmlal_s16(xy01_s32x4, vget_low_s16 (x_s16x8), vget_low_s16 (y01_s16x8)); + xy02_s32x4 = vmlal_s16(xy02_s32x4, vget_low_s16 (x_s16x8), vget_low_s16 (y02_s16x8)); + xy01_s32x4 = vmlal_s16(xy01_s32x4, vget_high_s16(x_s16x8), vget_high_s16(y01_s16x8)); + xy02_s32x4 = vmlal_s16(xy02_s32x4, vget_high_s16(x_s16x8), vget_high_s16(y02_s16x8)); + } + + if (N - i >= 4) { + const int16x4_t x_s16x4 = vld1_s16(&x[i]); + const int16x4_t y01_s16x4 = vld1_s16(&y01[i]); + const int16x4_t y02_s16x4 = vld1_s16(&y02[i]); + xy01_s32x4 = vmlal_s16(xy01_s32x4, x_s16x4, y01_s16x4); + xy02_s32x4 = vmlal_s16(xy02_s32x4, x_s16x4, y02_s16x4); + i += 4; + } + + xy01_s64x2 = vpaddlq_s32(xy01_s32x4); + xy02_s64x2 = vpaddlq_s32(xy02_s32x4); + xy01_s64x1 = vadd_s64(vget_low_s64(xy01_s64x2), vget_high_s64(xy01_s64x2)); + xy02_s64x1 = vadd_s64(vget_low_s64(xy02_s64x2), vget_high_s64(xy02_s64x2)); + xy01 = vget_lane_s32(vreinterpret_s32_s64(xy01_s64x1), 0); + xy02 = vget_lane_s32(vreinterpret_s32_s64(xy02_s64x1), 0); + + for (; i < N; i++) { + xy01 = MAC16_16(xy01, x[i], y01[i]); + xy02 = MAC16_16(xy02, x[i], y02[i]); + } + *xy1 = xy01; + *xy2 = xy02; + +#ifdef OPUS_CHECK_ASM + { + opus_val32 xy1_c, xy2_c; + dual_inner_prod_c(x, y01, y02, N, &xy1_c, &xy2_c); + celt_assert(xy1_c == *xy1); + celt_assert(xy2_c == *xy2); + } +#endif +} + +#else /* !FIXED_POINT */ + +/* ========================================================================== */ + +#ifdef __ARM_FEATURE_FMA +/* If we can, force the compiler to use an FMA instruction rather than break + vmlaq_f32() into fmul/fadd. */ +#define vmlaq_f32(a,b,c) vfmaq_f32(a,b,c) +#endif + + +#ifdef OPUS_CHECK_ASM + +/* This part of code simulates floating-point NEON operations. */ + +/* celt_inner_prod_neon_float_c_simulation() simulates the floating-point */ +/* operations of celt_inner_prod_neon(), and both functions should have bit */ +/* exact output. */ +static opus_val32 celt_inner_prod_neon_float_c_simulation(const opus_val16 *x, const opus_val16 *y, float *err, int N) +{ + int i; + *err = 0; + opus_val32 xy, xy0 = 0, xy1 = 0, xy2 = 0, xy3 = 0; + for (i = 0; i < N - 3; i += 4) { + xy0 = MAC16_16(xy0, x[i + 0], y[i + 0]); + xy1 = MAC16_16(xy1, x[i + 1], y[i + 1]); + xy2 = MAC16_16(xy2, x[i + 2], y[i + 2]); + xy3 = MAC16_16(xy3, x[i + 3], y[i + 3]); + *err += ABS32(xy0)+ABS32(xy1)+ABS32(xy2)+ABS32(xy3); + } + xy0 += xy2; + xy1 += xy3; + xy = xy0 + xy1; + *err += ABS32(xy1)+ABS32(xy0)+ABS32(xy); + for (; i < N; i++) { + xy = MAC16_16(xy, x[i], y[i]); + *err += ABS32(xy); + } + *err = *err*2e-7 + N*1e-37; + return xy; +} + +/* dual_inner_prod_neon_float_c_simulation() simulates the floating-point */ +/* operations of dual_inner_prod_neon(), and both functions should have bit */ +/* exact output. */ +static void dual_inner_prod_neon_float_c_simulation(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2, float *err) +{ + *xy1 = celt_inner_prod_neon_float_c_simulation(x, y01, &err[0], N); + *xy2 = celt_inner_prod_neon_float_c_simulation(x, y02, &err[1], N); +} + +#endif /* OPUS_CHECK_ASM */ + +/* ========================================================================== */ + +opus_val32 celt_inner_prod_neon(const opus_val16 *x, const opus_val16 *y, int N) +{ + int i; + opus_val32 xy; + float32x4_t xy_f32x4 = vdupq_n_f32(0); + float32x2_t xy_f32x2; + + for (i = 0; i < N - 7; i += 8) { + float32x4_t x_f32x4, y_f32x4; + x_f32x4 = vld1q_f32(&x[i]); + y_f32x4 = vld1q_f32(&y[i]); + xy_f32x4 = vmlaq_f32(xy_f32x4, x_f32x4, y_f32x4); + x_f32x4 = vld1q_f32(&x[i + 4]); + y_f32x4 = vld1q_f32(&y[i + 4]); + xy_f32x4 = vmlaq_f32(xy_f32x4, x_f32x4, y_f32x4); + } + + if (N - i >= 4) { + const float32x4_t x_f32x4 = vld1q_f32(&x[i]); + const float32x4_t y_f32x4 = vld1q_f32(&y[i]); + xy_f32x4 = vmlaq_f32(xy_f32x4, x_f32x4, y_f32x4); + i += 4; + } + + xy_f32x2 = vadd_f32(vget_low_f32(xy_f32x4), vget_high_f32(xy_f32x4)); + xy_f32x2 = vpadd_f32(xy_f32x2, xy_f32x2); + xy = vget_lane_f32(xy_f32x2, 0); + + for (; i < N; i++) { + xy = MAC16_16(xy, x[i], y[i]); + } + +#ifdef OPUS_CHECK_ASM + { + float err, res; + res = celt_inner_prod_neon_float_c_simulation(x, y, &err, N); + /*if (ABS32(res - xy) > err) fprintf(stderr, "%g %g %g\n", res, xy, err);*/ + celt_assert(ABS32(res - xy) <= err); + } +#endif + + return xy; +} + +void dual_inner_prod_neon(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2) +{ + int i; + opus_val32 xy01, xy02; + float32x4_t xy01_f32x4 = vdupq_n_f32(0); + float32x4_t xy02_f32x4 = vdupq_n_f32(0); + float32x2_t xy01_f32x2, xy02_f32x2; + + for (i = 0; i < N - 7; i += 8) { + float32x4_t x_f32x4, y01_f32x4, y02_f32x4; + x_f32x4 = vld1q_f32(&x[i]); + y01_f32x4 = vld1q_f32(&y01[i]); + y02_f32x4 = vld1q_f32(&y02[i]); + xy01_f32x4 = vmlaq_f32(xy01_f32x4, x_f32x4, y01_f32x4); + xy02_f32x4 = vmlaq_f32(xy02_f32x4, x_f32x4, y02_f32x4); + x_f32x4 = vld1q_f32(&x[i + 4]); + y01_f32x4 = vld1q_f32(&y01[i + 4]); + y02_f32x4 = vld1q_f32(&y02[i + 4]); + xy01_f32x4 = vmlaq_f32(xy01_f32x4, x_f32x4, y01_f32x4); + xy02_f32x4 = vmlaq_f32(xy02_f32x4, x_f32x4, y02_f32x4); + } + + if (N - i >= 4) { + const float32x4_t x_f32x4 = vld1q_f32(&x[i]); + const float32x4_t y01_f32x4 = vld1q_f32(&y01[i]); + const float32x4_t y02_f32x4 = vld1q_f32(&y02[i]); + xy01_f32x4 = vmlaq_f32(xy01_f32x4, x_f32x4, y01_f32x4); + xy02_f32x4 = vmlaq_f32(xy02_f32x4, x_f32x4, y02_f32x4); + i += 4; + } + + xy01_f32x2 = vadd_f32(vget_low_f32(xy01_f32x4), vget_high_f32(xy01_f32x4)); + xy02_f32x2 = vadd_f32(vget_low_f32(xy02_f32x4), vget_high_f32(xy02_f32x4)); + xy01_f32x2 = vpadd_f32(xy01_f32x2, xy01_f32x2); + xy02_f32x2 = vpadd_f32(xy02_f32x2, xy02_f32x2); + xy01 = vget_lane_f32(xy01_f32x2, 0); + xy02 = vget_lane_f32(xy02_f32x2, 0); + + for (; i < N; i++) { + xy01 = MAC16_16(xy01, x[i], y01[i]); + xy02 = MAC16_16(xy02, x[i], y02[i]); + } + *xy1 = xy01; + *xy2 = xy02; + +#ifdef OPUS_CHECK_ASM + { + opus_val32 xy1_c, xy2_c; + float err[2]; + dual_inner_prod_neon_float_c_simulation(x, y01, y02, N, &xy1_c, &xy2_c, err); + /*if (ABS32(xy1_c - *xy1) > err[0]) fprintf(stderr, "dual1 fail: %g %g %g\n", xy1_c, *xy1, err[0]); + if (ABS32(xy2_c - *xy2) > err[1]) fprintf(stderr, "dual2 fail: %g %g %g\n", xy2_c, *xy2, err[1]);*/ + celt_assert(ABS32(xy1_c - *xy1) <= err[0]); + celt_assert(ABS32(xy2_c - *xy2) <= err[1]); + } +#endif +} + +#endif /* FIXED_POINT */ diff --git a/managed_components/78__esp-opus/celt/bands.c b/managed_components/78__esp-opus/celt/bands.c new file mode 100644 index 0000000..8aeeac2 --- /dev/null +++ b/managed_components/78__esp-opus/celt/bands.c @@ -0,0 +1,1686 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2008-2009 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include "bands.h" +#include "modes.h" +#include "vq.h" +#include "cwrs.h" +#include "stack_alloc.h" +#include "os_support.h" +#include "mathops.h" +#include "rate.h" +#include "quant_bands.h" +#include "pitch.h" + +int hysteresis_decision(opus_val16 val, const opus_val16 *thresholds, const opus_val16 *hysteresis, int N, int prev) +{ + int i; + for (i=0;iprev && val < thresholds[prev]+hysteresis[prev]) + i=prev; + if (i thresholds[prev-1]-hysteresis[prev-1]) + i=prev; + return i; +} + +opus_uint32 celt_lcg_rand(opus_uint32 seed) +{ + return 1664525 * seed + 1013904223; +} + +/* This is a cos() approximation designed to be bit-exact on any platform. Bit exactness + with this approximation is important because it has an impact on the bit allocation */ +opus_int16 bitexact_cos(opus_int16 x) +{ + opus_int32 tmp; + opus_int16 x2; + tmp = (4096+((opus_int32)(x)*(x)))>>13; + celt_sig_assert(tmp<=32767); + x2 = tmp; + x2 = (32767-x2) + FRAC_MUL16(x2, (-7651 + FRAC_MUL16(x2, (8277 + FRAC_MUL16(-626, x2))))); + celt_sig_assert(x2<=32766); + return 1+x2; +} + +int bitexact_log2tan(int isin,int icos) +{ + int lc; + int ls; + lc=EC_ILOG(icos); + ls=EC_ILOG(isin); + icos<<=15-lc; + isin<<=15-ls; + return (ls-lc)*(1<<11) + +FRAC_MUL16(isin, FRAC_MUL16(isin, -2597) + 7932) + -FRAC_MUL16(icos, FRAC_MUL16(icos, -2597) + 7932); +} + +#ifdef FIXED_POINT +/* Compute the amplitude (sqrt energy) in each of the bands */ +void compute_band_energies(const CELTMode *m, const celt_sig *X, celt_ener *bandE, int end, int C, int LM, int arch) +{ + int i, c, N; + const opus_int16 *eBands = m->eBands; + (void)arch; + N = m->shortMdctSize< 0) + { + int shift, shift2; + shift = celt_ilog2(maxval) - 14; + shift2 = (((m->logN[i]>>BITRES)+LM+1)>>1); + j=eBands[i]<0) + { + do { + sum = ADD32(sum, SHR32(MULT16_16(EXTRACT16(SHR32(X[j+c*N],shift)), + EXTRACT16(SHR32(X[j+c*N],shift))), 2*shift2)); + } while (++jnbEBands] = EPSILON+VSHR32(celt_sqrt(sum),-shift); + } else { + bandE[i+c*m->nbEBands] = EPSILON; + } + /*printf ("%f ", bandE[i+c*m->nbEBands]);*/ + } + } while (++ceBands; + N = M*m->shortMdctSize; + c=0; do { + i=0; do { + opus_val16 g; + int j,shift; + opus_val32 E; + shift = celt_zlog2(bandE[i+c*m->nbEBands])-14; + E = VSHR32(bandE[i+c*m->nbEBands], shift-2); + g = EXTRACT16(celt_rcp(E)); + if (shift > 0) { + j=M*eBands[i]; do { + X[j+c*N] = PSHR32(MULT16_32_Q15(g, freq[j+c*N]),shift); + } while (++jeBands; + N = m->shortMdctSize<nbEBands] = celt_sqrt(sum); + /*printf ("%f ", bandE[i+c*m->nbEBands]);*/ + } + } while (++ceBands; + N = M*m->shortMdctSize; + c=0; do { + for (i=0;inbEBands]); + for (j=M*eBands[i];jeBands; + N = M*m->shortMdctSize; + bound = M*eBands[end]; + if (downsample!=1) + bound = IMIN(bound, N/downsample); + if (silence) + { + bound = 0; + start = end = 0; + } + f = freq; + x = X+M*eBands[start]; + for (i=0;i>DB_SHIFT); + if (shift>31) + { + shift=0; + g=0; + } else { + /* Handle the fractional part. */ + g = celt_exp2_db_frac((lg&((1< 16384 we'd be likely to overflow, so we're + capping the gain here, which is equivalent to a cap of 18 on lg. + This shouldn't trigger unless the bitstream is already corrupted. */ + if (shift <= -2) + { + g = 16384*32768; + shift = -2; + } + do { + *f++ = SHL32(MULT16_32_Q15(*x, g), -shift); + x++; + } while (++jeBands[i+1]-m->eBands[i]; + /* depth in 1/8 bits */ + celt_sig_assert(pulses[i]>=0); + depth = celt_udiv(1+pulses[i], (m->eBands[i+1]-m->eBands[i]))>>LM; + +#ifdef FIXED_POINT + thresh32 = SHR32(celt_exp2(-SHL16(depth, 10-BITRES)),1); + thresh = MULT16_32_Q15(QCONST16(0.5f, 15), MIN32(32767,thresh32)); + { + opus_val32 t; + t = N0<>1; + t = SHL32(t, (7-shift)<<1); + sqrt_1 = celt_rsqrt_norm(t); + } +#else + thresh = .5f*celt_exp2(-.125f*depth); + sqrt_1 = celt_rsqrt(N0<nbEBands+i]; + prev2 = prev2logE[c*m->nbEBands+i]; + if (!encode && C==1) + { + prev1 = MAXG(prev1,prev1logE[m->nbEBands+i]); + prev2 = MAXG(prev2,prev2logE[m->nbEBands+i]); + } + Ediff = logE[c*m->nbEBands+i]-MING(prev1,prev2); + Ediff = MAX32(0, Ediff); + +#ifdef FIXED_POINT + if (Ediff < GCONST(16.f)) + { + opus_val32 r32 = SHR32(celt_exp2_db(-Ediff),1); + r = 2*MIN16(16383,r32); + } else { + r = 0; + } + if (LM==3) + r = MULT16_16_Q14(23170, MIN32(23169, r)); + r = SHR16(MIN16(thresh, r),1); + r = SHR32(MULT16_16_Q15(sqrt_1, r),shift); +#else + /* r needs to be multiplied by 2 or 2*sqrt(2) depending on LM because + short blocks don't have the same energy as long */ + r = 2.f*celt_exp2_db(-Ediff); + if (LM==3) + r *= 1.41421356f; + r = MIN16(thresh, r); + r = r*sqrt_1; +#endif + X = X_+c*size+(m->eBands[i]<nbEBands]))-13; +#endif + left = VSHR32(bandE[i],shift); + right = VSHR32(bandE[i+m->nbEBands],shift); + norm = EPSILON + celt_sqrt(EPSILON+MULT16_16(left,left)+MULT16_16(right,right)); + a1 = DIV32_16(SHL32(EXTEND32(left),14),norm); + a2 = DIV32_16(SHL32(EXTEND32(right),14),norm); + for (j=0;j>1; + kr = celt_ilog2(Er)>>1; +#endif + t = VSHR32(El, (kl-7)<<1); + lgain = celt_rsqrt_norm(t); + t = VSHR32(Er, (kr-7)<<1); + rgain = celt_rsqrt_norm(t); + +#ifdef FIXED_POINT + if (kl < 7) + kl = 7; + if (kr < 7) + kr = 7; +#endif + + for (j=0;jeBands; + int decision; + int hf_sum=0; + + celt_assert(end>0); + + N0 = M*m->shortMdctSize; + + if (M*(eBands[end]-eBands[end-1]) <= 8) + return SPREAD_NONE; + c=0; do { + for (i=0;im->nbEBands-4) + hf_sum += celt_udiv(32*(tcount[1]+tcount[0]), N); + tmp = (2*tcount[2] >= N) + (2*tcount[1] >= N) + (2*tcount[0] >= N); + sum += tmp*spread_weight[i]; + nbBands+=spread_weight[i]; + } + } while (++cnbEBands+end)); + *hf_average = (*hf_average+hf_sum)>>1; + hf_sum = *hf_average; + if (*tapset_decision==2) + hf_sum += 4; + else if (*tapset_decision==0) + hf_sum -= 4; + if (hf_sum > 22) + *tapset_decision=2; + else if (hf_sum > 18) + *tapset_decision=1; + else + *tapset_decision=0; + } + /*printf("%d %d %d\n", hf_sum, *hf_average, *tapset_decision);*/ + celt_assert(nbBands>0); /* end has to be non-zero */ + celt_assert(sum>=0); + sum = celt_udiv((opus_int32)sum<<8, nbBands); + /* Recursive averaging */ + sum = (sum+*average)>>1; + *average = sum; + /* Hysteresis */ + sum = (3*sum + (((3-last_decision)<<7) + 64) + 2)>>2; + if (sum < 80) + { + decision = SPREAD_AGGRESSIVE; + } else if (sum < 256) + { + decision = SPREAD_NORMAL; + } else if (sum < 384) + { + decision = SPREAD_LIGHT; + } else { + decision = SPREAD_NONE; + } +#ifdef FUZZING + decision = rand()&0x3; + *tapset_decision=rand()%3; +#endif + return decision; +} + +/* Indexing table for converting from natural Hadamard to ordery Hadamard + This is essentially a bit-reversed Gray, on top of which we've added + an inversion of the order because we want the DC at the end rather than + the beginning. The lines are for N=2, 4, 8, 16 */ +static const int ordery_table[] = { + 1, 0, + 3, 0, 2, 1, + 7, 0, 4, 3, 6, 1, 5, 2, + 15, 0, 8, 7, 12, 3, 11, 4, 14, 1, 9, 6, 13, 2, 10, 5, +}; + +static void deinterleave_hadamard(celt_norm *X, int N0, int stride, int hadamard) +{ + int i,j; + VARDECL(celt_norm, tmp); + int N; + SAVE_STACK; + N = N0*stride; + ALLOC(tmp, N, celt_norm); + celt_assert(stride>0); + if (hadamard) + { + const int *ordery = ordery_table+stride-2; + for (i=0;i>= 1; + for (i=0;i>1)) { + qn = 1; + } else { + qn = exp2_table8[qb&0x7]>>(14-(qb>>BITRES)); + qn = (qn+1)>>1<<1; + } + celt_assert(qn <= 256); + return qn; +} + +struct band_ctx { + int encode; + int resynth; + const CELTMode *m; + int i; + int intensity; + int spread; + int tf_change; + ec_ctx *ec; + opus_int32 remaining_bits; + const celt_ener *bandE; + opus_uint32 seed; + int arch; + int theta_round; + int disable_inv; + int avoid_split_noise; +}; + +struct split_ctx { + int inv; + int imid; + int iside; + int delta; + int itheta; + int qalloc; +}; + +static void compute_theta(struct band_ctx *ctx, struct split_ctx *sctx, + celt_norm *X, celt_norm *Y, int N, int *b, int B, int B0, + int LM, + int stereo, int *fill) +{ + int qn; + int itheta=0; + int delta; + int imid, iside; + int qalloc; + int pulse_cap; + int offset; + opus_int32 tell; + int inv=0; + int encode; + const CELTMode *m; + int i; + int intensity; + ec_ctx *ec; + const celt_ener *bandE; + + encode = ctx->encode; + m = ctx->m; + i = ctx->i; + intensity = ctx->intensity; + ec = ctx->ec; + bandE = ctx->bandE; + + /* Decide on the resolution to give to the split parameter theta */ + pulse_cap = m->logN[i]+LM*(1<>1) - (stereo&&N==2 ? QTHETA_OFFSET_TWOPHASE : QTHETA_OFFSET); + qn = compute_qn(N, *b, offset, pulse_cap, stereo); + if (stereo && i>=intensity) + qn = 1; + if (encode) + { + /* theta is the atan() of the ratio between the (normalized) + side and mid. With just that parameter, we can re-scale both + mid and side because we know that 1) they have unit norm and + 2) they are orthogonal. */ + itheta = stereo_itheta(X, Y, stereo, N, ctx->arch); + } + tell = ec_tell_frac(ec); + if (qn!=1) + { + if (encode) + { + if (!stereo || ctx->theta_round == 0) + { + itheta = (itheta*(opus_int32)qn+8192)>>14; + if (!stereo && ctx->avoid_split_noise && itheta > 0 && itheta < qn) + { + /* Check if the selected value of theta will cause the bit allocation + to inject noise on one side. If so, make sure the energy of that side + is zero. */ + int unquantized = celt_udiv((opus_int32)itheta*16384, qn); + imid = bitexact_cos((opus_int16)unquantized); + iside = bitexact_cos((opus_int16)(16384-unquantized)); + delta = FRAC_MUL16((N-1)<<7,bitexact_log2tan(iside,imid)); + if (delta > *b) + itheta = qn; + else if (delta < -*b) + itheta = 0; + } + } else { + int down; + /* Bias quantization towards itheta=0 and itheta=16384. */ + int bias = itheta > 8192 ? 32767/qn : -32767/qn; + down = IMIN(qn-1, IMAX(0, (itheta*(opus_int32)qn + bias)>>14)); + if (ctx->theta_round < 0) + itheta = down; + else + itheta = down+1; + } + } + /* Entropy coding of the angle. We use a uniform pdf for the + time split, a step for stereo, and a triangular one for the rest. */ + if (stereo && N>2) + { + int p0 = 3; + int x = itheta; + int x0 = qn/2; + int ft = p0*(x0+1) + x0; + /* Use a probability of p0 up to itheta=8192 and then use 1 after */ + if (encode) + { + ec_encode(ec,x<=x0?p0*x:(x-1-x0)+(x0+1)*p0,x<=x0?p0*(x+1):(x-x0)+(x0+1)*p0,ft); + } else { + int fs; + fs=ec_decode(ec,ft); + if (fs<(x0+1)*p0) + x=fs/p0; + else + x=x0+1+(fs-(x0+1)*p0); + ec_dec_update(ec,x<=x0?p0*x:(x-1-x0)+(x0+1)*p0,x<=x0?p0*(x+1):(x-x0)+(x0+1)*p0,ft); + itheta = x; + } + } else if (B0>1 || stereo) { + /* Uniform pdf */ + if (encode) + ec_enc_uint(ec, itheta, qn+1); + else + itheta = ec_dec_uint(ec, qn+1); + } else { + int fs=1, ft; + ft = ((qn>>1)+1)*((qn>>1)+1); + if (encode) + { + int fl; + + fs = itheta <= (qn>>1) ? itheta + 1 : qn + 1 - itheta; + fl = itheta <= (qn>>1) ? itheta*(itheta + 1)>>1 : + ft - ((qn + 1 - itheta)*(qn + 2 - itheta)>>1); + + ec_encode(ec, fl, fl+fs, ft); + } else { + /* Triangular pdf */ + int fl=0; + int fm; + fm = ec_decode(ec, ft); + + if (fm < ((qn>>1)*((qn>>1) + 1)>>1)) + { + itheta = (isqrt32(8*(opus_uint32)fm + 1) - 1)>>1; + fs = itheta + 1; + fl = itheta*(itheta + 1)>>1; + } + else + { + itheta = (2*(qn + 1) + - isqrt32(8*(opus_uint32)(ft - fm - 1) + 1))>>1; + fs = qn + 1 - itheta; + fl = ft - ((qn + 1 - itheta)*(qn + 2 - itheta)>>1); + } + + ec_dec_update(ec, fl, fl+fs, ft); + } + } + celt_assert(itheta>=0); + itheta = celt_udiv((opus_int32)itheta*16384, qn); + if (encode && stereo) + { + if (itheta==0) + intensity_stereo(m, X, Y, bandE, i, N); + else + stereo_split(X, Y, N); + } + /* NOTE: Renormalising X and Y *may* help fixed-point a bit at very high rate. + Let's do that at higher complexity */ + } else if (stereo) { + if (encode) + { + inv = itheta > 8192 && !ctx->disable_inv; + if (inv) + { + int j; + for (j=0;j2<remaining_bits > 2<disable_inv) + inv = 0; + itheta = 0; + } + qalloc = ec_tell_frac(ec) - tell; + *b -= qalloc; + + if (itheta == 0) + { + imid = 32767; + iside = 0; + *fill &= (1<inv = inv; + sctx->imid = imid; + sctx->iside = iside; + sctx->delta = delta; + sctx->itheta = itheta; + sctx->qalloc = qalloc; +} +static unsigned quant_band_n1(struct band_ctx *ctx, celt_norm *X, celt_norm *Y, + celt_norm *lowband_out) +{ + int c; + int stereo; + celt_norm *x = X; + int encode; + ec_ctx *ec; + + encode = ctx->encode; + ec = ctx->ec; + + stereo = Y != NULL; + c=0; do { + int sign=0; + if (ctx->remaining_bits>=1<remaining_bits -= 1<resynth) + x[0] = sign ? -NORM_SCALING : NORM_SCALING; + x = Y; + } while (++c<1+stereo); + if (lowband_out) + lowband_out[0] = SHR16(X[0],4); + return 1; +} + +/* This function is responsible for encoding and decoding a mono partition. + It can split the band in two and transmit the energy difference with + the two half-bands. It can be called recursively so bands can end up being + split in 8 parts. */ +static unsigned quant_partition(struct band_ctx *ctx, celt_norm *X, + int N, int b, int B, celt_norm *lowband, + int LM, + opus_val32 gain, int fill) +{ + const unsigned char *cache; + int q; + int curr_bits; + int imid=0, iside=0; + int B0=B; + opus_val32 mid=0, side=0; + unsigned cm=0; + celt_norm *Y=NULL; + int encode; + const CELTMode *m; + int i; + int spread; + ec_ctx *ec; + + encode = ctx->encode; + m = ctx->m; + i = ctx->i; + spread = ctx->spread; + ec = ctx->ec; + + /* If we need 1.5 more bit than we can produce, split the band in two. */ + cache = m->cache.bits + m->cache.index[(LM+1)*m->nbEBands+i]; + if (LM != -1 && b > cache[cache[0]]+12 && N>2) + { + int mbits, sbits, delta; + int itheta; + int qalloc; + struct split_ctx sctx; + celt_norm *next_lowband2=NULL; + opus_int32 rebalance; + + N >>= 1; + Y = X+N; + LM -= 1; + if (B==1) + fill = (fill&1)|(fill<<1); + B = (B+1)>>1; + + compute_theta(ctx, &sctx, X, Y, N, &b, B, B0, LM, 0, &fill); + imid = sctx.imid; + iside = sctx.iside; + delta = sctx.delta; + itheta = sctx.itheta; + qalloc = sctx.qalloc; +#ifdef FIXED_POINT + mid = SHL32(EXTEND32(imid), 16); + side = SHL32(EXTEND32(iside), 16); +#else + mid = (1.f/32768)*imid; + side = (1.f/32768)*iside; +#endif + + /* Give more bits to low-energy MDCTs than they would otherwise deserve */ + if (B0>1 && (itheta&0x3fff)) + { + if (itheta > 8192) + /* Rough approximation for pre-echo masking */ + delta -= delta>>(4-LM); + else + /* Corresponds to a forward-masking slope of 1.5 dB per 10 ms */ + delta = IMIN(0, delta + (N<>(5-LM))); + } + mbits = IMAX(0, IMIN(b, (b-delta)/2)); + sbits = b-mbits; + ctx->remaining_bits -= qalloc; + + if (lowband) + next_lowband2 = lowband+N; /* >32-bit split case */ + + rebalance = ctx->remaining_bits; + if (mbits >= sbits) + { + cm = quant_partition(ctx, X, N, mbits, B, lowband, LM, + MULT32_32_Q31(gain,mid), fill); + rebalance = mbits - (rebalance-ctx->remaining_bits); + if (rebalance > 3<>B)<<(B0>>1); + } else { + cm = quant_partition(ctx, Y, N, sbits, B, next_lowband2, LM, + MULT32_32_Q31(gain,side), fill>>B)<<(B0>>1); + rebalance = sbits - (rebalance-ctx->remaining_bits); + if (rebalance > 3<remaining_bits -= curr_bits; + + /* Ensures we can never bust the budget */ + while (ctx->remaining_bits < 0 && q > 0) + { + ctx->remaining_bits += curr_bits; + q--; + curr_bits = pulses2bits(m, i, LM, q); + ctx->remaining_bits -= curr_bits; + } + + if (q!=0) + { + int K = get_pulses(q); + + /* Finally do the actual quantization */ + if (encode) + { + cm = alg_quant(X, N, K, spread, B, ec, gain, ctx->resynth, ctx->arch); + } else { + cm = alg_unquant(X, N, K, spread, B, ec, gain); + } + } else { + /* If there's no pulse, fill the band anyway */ + int j; + if (ctx->resynth) + { + unsigned cm_mask; + /* B can be as large as 16, so this shift might overflow an int on a + 16-bit platform; use a long to get defined behavior.*/ + cm_mask = (unsigned)(1UL<seed = celt_lcg_rand(ctx->seed); + X[j] = (celt_norm)((opus_int32)ctx->seed>>20); + } + cm = cm_mask; + } else { + /* Folded spectrum */ + for (j=0;jseed = celt_lcg_rand(ctx->seed); + /* About 48 dB below the "normal" folding level */ + tmp = QCONST16(1.0f/256, 10); + tmp = (ctx->seed)&0x8000 ? tmp : -tmp; + X[j] = lowband[j]+tmp; + } + cm = fill; + } + renormalise_vector(X, N, gain, ctx->arch); + } + } + } + } + + return cm; +} + + +/* This function is responsible for encoding and decoding a band for the mono case. */ +static unsigned quant_band(struct band_ctx *ctx, celt_norm *X, + int N, int b, int B, celt_norm *lowband, + int LM, celt_norm *lowband_out, + opus_val32 gain, celt_norm *lowband_scratch, int fill) +{ + int N0=N; + int N_B=N; + int N_B0; + int B0=B; + int time_divide=0; + int recombine=0; + int longBlocks; + unsigned cm=0; + int k; + int encode; + int tf_change; + + encode = ctx->encode; + tf_change = ctx->tf_change; + + longBlocks = B0==1; + + N_B = celt_udiv(N_B, B); + + /* Special case for one sample */ + if (N==1) + { + return quant_band_n1(ctx, X, NULL, lowband_out); + } + + if (tf_change>0) + recombine = tf_change; + /* Band recombining to increase frequency resolution */ + + if (lowband_scratch && lowband && (recombine || ((N_B&1) == 0 && tf_change<0) || B0>1)) + { + OPUS_COPY(lowband_scratch, lowband, N); + lowband = lowband_scratch; + } + + for (k=0;k>k, 1<>k, 1<>4]<<2; + } + B>>=recombine; + N_B<<=recombine; + + /* Increasing the time resolution */ + while ((N_B&1) == 0 && tf_change<0) + { + if (encode) + haar1(X, N_B, B); + if (lowband) + haar1(lowband, N_B, B); + fill |= fill<>= 1; + time_divide++; + tf_change++; + } + B0=B; + N_B0 = N_B; + + /* Reorganize the samples in time order instead of frequency order */ + if (B0>1) + { + if (encode) + deinterleave_hadamard(X, N_B>>recombine, B0<>recombine, B0<resynth) + { + /* Undo the sample reorganization going from time order to frequency order */ + if (B0>1) + interleave_hadamard(X, N_B>>recombine, B0<>= 1; + N_B <<= 1; + cm |= cm>>B; + haar1(X, N_B, B); + } + + for (k=0;k>k, 1<encode; + ec = ctx->ec; + + /* Special case for one sample */ + if (N==1) + { + return quant_band_n1(ctx, X, Y, lowband_out); + } + + orig_fill = fill; + + compute_theta(ctx, &sctx, X, Y, N, &b, B, B, LM, 1, &fill); + inv = sctx.inv; + imid = sctx.imid; + iside = sctx.iside; + delta = sctx.delta; + itheta = sctx.itheta; + qalloc = sctx.qalloc; +#ifdef FIXED_POINT + mid = SHL32(EXTEND32(imid), 16); + side = SHL32(EXTEND32(iside), 16); +#else + mid = (1.f/32768)*imid; + side = (1.f/32768)*iside; +#endif + + /* This is a special case for N=2 that only works for stereo and takes + advantage of the fact that mid and side are orthogonal to encode + the side with just one bit. */ + if (N==2) + { + int c; + int sign=0; + celt_norm *x2, *y2; + mbits = b; + sbits = 0; + /* Only need one bit for the side. */ + if (itheta != 0 && itheta != 16384) + sbits = 1< 8192; + ctx->remaining_bits -= qalloc+sbits; + + x2 = c ? Y : X; + y2 = c ? X : Y; + if (sbits) + { + if (encode) + { + /* Here we only need to encode a sign for the side. */ + sign = x2[0]*y2[1] - x2[1]*y2[0] < 0; + ec_enc_bits(ec, sign, 1); + } else { + sign = ec_dec_bits(ec, 1); + } + } + sign = 1-2*sign; + /* We use orig_fill here because we want to fold the side, but if + itheta==16384, we'll have cleared the low bits of fill. */ + cm = quant_band(ctx, x2, N, mbits, B, lowband, LM, lowband_out, Q31ONE, + lowband_scratch, orig_fill); + /* We don't split N=2 bands, so cm is either 1 or 0 (for a fold-collapse), + and there's no need to worry about mixing with the other channel. */ + y2[0] = -sign*x2[1]; + y2[1] = sign*x2[0]; + if (ctx->resynth) + { + celt_norm tmp; + X[0] = MULT32_32_Q31(mid, X[0]); + X[1] = MULT32_32_Q31(mid, X[1]); + Y[0] = MULT32_32_Q31(side, Y[0]); + Y[1] = MULT32_32_Q31(side, Y[1]); + tmp = X[0]; + X[0] = SUB16(tmp,Y[0]); + Y[0] = ADD16(tmp,Y[0]); + tmp = X[1]; + X[1] = SUB16(tmp,Y[1]); + Y[1] = ADD16(tmp,Y[1]); + } + } else { + /* "Normal" split code */ + opus_int32 rebalance; + + mbits = IMAX(0, IMIN(b, (b-delta)/2)); + sbits = b-mbits; + ctx->remaining_bits -= qalloc; + + rebalance = ctx->remaining_bits; + if (mbits >= sbits) + { + /* In stereo mode, we do not apply a scaling to the mid because we need the normalized + mid for folding later. */ + cm = quant_band(ctx, X, N, mbits, B, lowband, LM, lowband_out, Q31ONE, + lowband_scratch, fill); + rebalance = mbits - (rebalance-ctx->remaining_bits); + if (rebalance > 3<>B); + } else { + /* For a stereo split, the high bits of fill are always zero, so no + folding will be done to the side. */ + cm = quant_band(ctx, Y, N, sbits, B, NULL, LM, NULL, side, NULL, fill>>B); + rebalance = sbits - (rebalance-ctx->remaining_bits); + if (rebalance > 3<resynth) + { + if (N!=2) + stereo_merge(X, Y, mid, N, ctx->arch); + if (inv) + { + int j; + for (j=0;jeBands; + n1 = M*(eBands[start+1]-eBands[start]); + n2 = M*(eBands[start+2]-eBands[start+1]); + /* Duplicate enough of the first band folding data to be able to fold the second band. + Copies no data for CELT-only mode. */ + OPUS_COPY(&norm[n1], &norm[2*n1 - n2], n2-n1); + if (dual_stereo) + OPUS_COPY(&norm2[n1], &norm2[2*n1 - n2], n2-n1); +} +#endif + +void quant_all_bands(int encode, const CELTMode *m, int start, int end, + celt_norm *X_, celt_norm *Y_, unsigned char *collapse_masks, + const celt_ener *bandE, int *pulses, int shortBlocks, int spread, + int dual_stereo, int intensity, int *tf_res, opus_int32 total_bits, + opus_int32 balance, ec_ctx *ec, int LM, int codedBands, + opus_uint32 *seed, int complexity, int arch, int disable_inv) +{ + int i; + opus_int32 remaining_bits; + const opus_int16 * OPUS_RESTRICT eBands = m->eBands; + celt_norm * OPUS_RESTRICT norm, * OPUS_RESTRICT norm2; + VARDECL(celt_norm, _norm); + VARDECL(celt_norm, _lowband_scratch); + VARDECL(celt_norm, X_save); + VARDECL(celt_norm, Y_save); + VARDECL(celt_norm, X_save2); + VARDECL(celt_norm, Y_save2); + VARDECL(celt_norm, norm_save2); + int resynth_alloc; + celt_norm *lowband_scratch; + int B; + int M; + int lowband_offset; + int update_lowband = 1; + int C = Y_ != NULL ? 2 : 1; + int norm_offset; + int theta_rdo = encode && Y_!=NULL && !dual_stereo && complexity>=8; +#ifdef RESYNTH + int resynth = 1; +#else + int resynth = !encode || theta_rdo; +#endif + struct band_ctx ctx; + SAVE_STACK; + + M = 1<nbEBands-1]-norm_offset), celt_norm); + norm = _norm; + norm2 = norm + M*eBands[m->nbEBands-1]-norm_offset; + + /* For decoding, we can use the last band as scratch space because we don't need that + scratch space for the last band and we don't care about the data there until we're + decoding the last band. */ + if (encode && resynth) + resynth_alloc = M*(eBands[m->nbEBands]-eBands[m->nbEBands-1]); + else + resynth_alloc = ALLOC_NONE; + ALLOC(_lowband_scratch, resynth_alloc, celt_norm); + if (encode && resynth) + lowband_scratch = _lowband_scratch; + else + lowband_scratch = X_+M*eBands[m->effEBands-1]; + ALLOC(X_save, resynth_alloc, celt_norm); + ALLOC(Y_save, resynth_alloc, celt_norm); + ALLOC(X_save2, resynth_alloc, celt_norm); + ALLOC(Y_save2, resynth_alloc, celt_norm); + ALLOC(norm_save2, resynth_alloc, celt_norm); + + lowband_offset = 0; + ctx.bandE = bandE; + ctx.ec = ec; + ctx.encode = encode; + ctx.intensity = intensity; + ctx.m = m; + ctx.seed = *seed; + ctx.spread = spread; + ctx.arch = arch; + ctx.disable_inv = disable_inv; + ctx.resynth = resynth; + ctx.theta_round = 0; + /* Avoid injecting noise in the first band on transients. */ + ctx.avoid_split_noise = B > 1; + for (i=start;i 0); + tell = ec_tell_frac(ec); + + /* Compute how many bits we want to allocate to this band */ + if (i != start) + balance -= tell; + remaining_bits = total_bits-tell-1; + ctx.remaining_bits = remaining_bits; + if (i <= codedBands-1) + { + curr_balance = celt_sudiv(balance, IMIN(3, codedBands-i)); + b = IMAX(0, IMIN(16383, IMIN(remaining_bits+1,pulses[i]+curr_balance))); + } else { + b = 0; + } + +#ifndef DISABLE_UPDATE_DRAFT + if (resynth && (M*eBands[i]-N >= M*eBands[start] || i==start+1) && (update_lowband || lowband_offset==0)) + lowband_offset = i; + if (i == start+1) + special_hybrid_folding(m, norm, norm2, start, M, dual_stereo); +#else + if (resynth && M*eBands[i]-N >= M*eBands[start] && (update_lowband || lowband_offset==0)) + lowband_offset = i; +#endif + + tf_change = tf_res[i]; + ctx.tf_change = tf_change; + if (i>=m->effEBands) + { + X=norm; + if (Y_!=NULL) + Y = norm; + lowband_scratch = NULL; + } + if (last && !theta_rdo) + lowband_scratch = NULL; + + /* Get a conservative estimate of the collapse_mask's for the bands we're + going to be folding from. */ + if (lowband_offset != 0 && (spread!=SPREAD_AGGRESSIVE || B>1 || tf_change<0)) + { + int fold_start; + int fold_end; + int fold_i; + /* This ensures we never repeat spectral content within one band */ + effective_lowband = IMAX(0, M*eBands[lowband_offset]-norm_offset-N); + fold_start = lowband_offset; + while(M*eBands[--fold_start] > effective_lowband+norm_offset); + fold_end = lowband_offset-1; +#ifndef DISABLE_UPDATE_DRAFT + while(++fold_end < i && M*eBands[fold_end] < effective_lowband+norm_offset+N); +#else + while(M*eBands[++fold_end] < effective_lowband+norm_offset+N); +#endif + x_cm = y_cm = 0; + fold_i = fold_start; do { + x_cm |= collapse_masks[fold_i*C+0]; + y_cm |= collapse_masks[fold_i*C+C-1]; + } while (++fold_inbEBands], w); + /* Make a copy. */ + cm = x_cm|y_cm; + ec_save = *ec; + ctx_save = ctx; + OPUS_COPY(X_save, X, N); + OPUS_COPY(Y_save, Y, N); + /* Encode and round down. */ + ctx.theta_round = -1; + x_cm = quant_band_stereo(&ctx, X, Y, N, b, B, + effective_lowband != -1 ? norm+effective_lowband : NULL, LM, + last?NULL:norm+M*eBands[i]-norm_offset, lowband_scratch, cm); + dist0 = MULT16_32_Q15(w[0], celt_inner_prod(X_save, X, N, arch)) + MULT16_32_Q15(w[1], celt_inner_prod(Y_save, Y, N, arch)); + + /* Save first result. */ + cm2 = x_cm; + ec_save2 = *ec; + ctx_save2 = ctx; + OPUS_COPY(X_save2, X, N); + OPUS_COPY(Y_save2, Y, N); + if (!last) + OPUS_COPY(norm_save2, norm+M*eBands[i]-norm_offset, N); + nstart_bytes = ec_save.offs; + nend_bytes = ec_save.storage; + bytes_buf = ec_save.buf+nstart_bytes; + save_bytes = nend_bytes-nstart_bytes; + OPUS_COPY(bytes_save, bytes_buf, save_bytes); + + /* Restore */ + *ec = ec_save; + ctx = ctx_save; + OPUS_COPY(X, X_save, N); + OPUS_COPY(Y, Y_save, N); +#ifndef DISABLE_UPDATE_DRAFT + if (i == start+1) + special_hybrid_folding(m, norm, norm2, start, M, dual_stereo); +#endif + /* Encode and round up. */ + ctx.theta_round = 1; + x_cm = quant_band_stereo(&ctx, X, Y, N, b, B, + effective_lowband != -1 ? norm+effective_lowband : NULL, LM, + last?NULL:norm+M*eBands[i]-norm_offset, lowband_scratch, cm); + dist1 = MULT16_32_Q15(w[0], celt_inner_prod(X_save, X, N, arch)) + MULT16_32_Q15(w[1], celt_inner_prod(Y_save, Y, N, arch)); + if (dist0 >= dist1) { + x_cm = cm2; + *ec = ec_save2; + ctx = ctx_save2; + OPUS_COPY(X, X_save2, N); + OPUS_COPY(Y, Y_save2, N); + if (!last) + OPUS_COPY(norm+M*eBands[i]-norm_offset, norm_save2, N); + OPUS_COPY(bytes_buf, bytes_save, save_bytes); + } + } else { + ctx.theta_round = 0; + x_cm = quant_band_stereo(&ctx, X, Y, N, b, B, + effective_lowband != -1 ? norm+effective_lowband : NULL, LM, + last?NULL:norm+M*eBands[i]-norm_offset, lowband_scratch, x_cm|y_cm); + } + } else { + x_cm = quant_band(&ctx, X, N, b, B, + effective_lowband != -1 ? norm+effective_lowband : NULL, LM, + last?NULL:norm+M*eBands[i]-norm_offset, Q31ONE, lowband_scratch, x_cm|y_cm); + } + y_cm = x_cm; + } + collapse_masks[i*C+0] = (unsigned char)x_cm; + collapse_masks[i*C+C-1] = (unsigned char)y_cm; + balance += pulses[i] + tell; + + /* Update the folding position only as long as we have 1 bit/sample depth. */ + update_lowband = b>(N< +#include "celt.h" +#include "pitch.h" +#include "bands.h" +#include "modes.h" +#include "entcode.h" +#include "quant_bands.h" +#include "rate.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "float_cast.h" +#include +#include "celt_lpc.h" +#include "vq.h" + +#ifndef PACKAGE_VERSION +#define PACKAGE_VERSION "unknown" +#endif + +#if defined(MIPSr1_ASM) +#include "mips/celt_mipsr1.h" +#endif + + +int resampling_factor(opus_int32 rate) +{ + int ret; + switch (rate) + { + case 48000: + ret = 1; + break; + case 24000: + ret = 2; + break; + case 16000: + ret = 3; + break; + case 12000: + ret = 4; + break; + case 8000: + ret = 6; + break; + default: +#ifndef CUSTOM_MODES + celt_assert(0); +#endif + ret = 0; + break; + } + return ret; +} + + +#if !defined(OVERRIDE_COMB_FILTER_CONST) || defined(NON_STATIC_COMB_FILTER_CONST_C) +/* This version should be faster on ARM */ +#ifdef OPUS_ARM_ASM +#ifndef NON_STATIC_COMB_FILTER_CONST_C +static +#endif +void comb_filter_const_c(opus_val32 *y, opus_val32 *x, int T, int N, + celt_coef g10, celt_coef g11, celt_coef g12) +{ + opus_val32 x0, x1, x2, x3, x4; + int i; + x4 = SHL32(x[-T-2], 1); + x3 = SHL32(x[-T-1], 1); + x2 = SHL32(x[-T], 1); + x1 = SHL32(x[-T+1], 1); + for (i=0;inbEBands;i++) + { + int N; + N=(m->eBands[i+1]-m->eBands[i])<cache.caps[m->nbEBands*(2*LM+C-1)+i]+64)*C*N>>2; + } +} + + + +const char *opus_strerror(int error) +{ + static const char * const error_strings[8] = { + "success", + "invalid argument", + "buffer too small", + "internal error", + "corrupted stream", + "request not implemented", + "invalid state", + "memory allocation failed" + }; + if (error > 0 || error < -7) + return "unknown error"; + else + return error_strings[-error]; +} + +const char *opus_get_version_string(void) +{ + return "libopus " PACKAGE_VERSION + /* Applications may rely on the presence of this substring in the version + string to determine if they have a fixed-point or floating-point build + at runtime. */ +#ifdef FIXED_POINT + "-fixed" +#endif +#ifdef FUZZING + "-fuzzing" +#endif + ; +} diff --git a/managed_components/78__esp-opus/celt/celt.h b/managed_components/78__esp-opus/celt/celt.h new file mode 100644 index 0000000..8ee3e85 --- /dev/null +++ b/managed_components/78__esp-opus/celt/celt.h @@ -0,0 +1,255 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2008 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/** + @file celt.h + @brief Contains all the functions for encoding and decoding audio + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef CELT_H +#define CELT_H + +#include "opus_types.h" +#include "opus_defines.h" +#include "opus_custom.h" +#include "entenc.h" +#include "entdec.h" +#include "arch.h" +#include "kiss_fft.h" + +#ifdef ENABLE_DEEP_PLC +#include "lpcnet.h" +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +#define CELTEncoder OpusCustomEncoder +#define CELTDecoder OpusCustomDecoder +#define CELTMode OpusCustomMode + +#define LEAK_BANDS 19 + +typedef struct { + int valid; + float tonality; + float tonality_slope; + float noisiness; + float activity; + float music_prob; + float music_prob_min; + float music_prob_max; + int bandwidth; + float activity_probability; + float max_pitch_ratio; + /* Store as Q6 char to save space. */ + unsigned char leak_boost[LEAK_BANDS]; +} AnalysisInfo; + +typedef struct { + int signalType; + int offset; +} SILKInfo; + +#define __celt_check_mode_ptr_ptr(ptr) ((ptr) + ((ptr) - (const CELTMode**)(ptr))) + +#define __celt_check_analysis_ptr(ptr) ((ptr) + ((ptr) - (const AnalysisInfo*)(ptr))) + +#define __celt_check_silkinfo_ptr(ptr) ((ptr) + ((ptr) - (const SILKInfo*)(ptr))) + +#define __celt_check_glog_ptr(ptr) ((ptr) + ((ptr) - (celt_glog*)(ptr))) + +/* Encoder/decoder Requests */ + + +#define CELT_SET_PREDICTION_REQUEST 10002 +/** Controls the use of interframe prediction. + 0=Independent frames + 1=Short term interframe prediction allowed + 2=Long term prediction allowed + */ +#define CELT_SET_PREDICTION(x) CELT_SET_PREDICTION_REQUEST, __opus_check_int(x) + +#define CELT_SET_INPUT_CLIPPING_REQUEST 10004 +#define CELT_SET_INPUT_CLIPPING(x) CELT_SET_INPUT_CLIPPING_REQUEST, __opus_check_int(x) + +#define CELT_GET_AND_CLEAR_ERROR_REQUEST 10007 +#define CELT_GET_AND_CLEAR_ERROR(x) CELT_GET_AND_CLEAR_ERROR_REQUEST, __opus_check_int_ptr(x) + +#define CELT_SET_CHANNELS_REQUEST 10008 +#define CELT_SET_CHANNELS(x) CELT_SET_CHANNELS_REQUEST, __opus_check_int(x) + + +/* Internal */ +#define CELT_SET_START_BAND_REQUEST 10010 +#define CELT_SET_START_BAND(x) CELT_SET_START_BAND_REQUEST, __opus_check_int(x) + +#define CELT_SET_END_BAND_REQUEST 10012 +#define CELT_SET_END_BAND(x) CELT_SET_END_BAND_REQUEST, __opus_check_int(x) + +#define CELT_GET_MODE_REQUEST 10015 +/** Get the CELTMode used by an encoder or decoder */ +#define CELT_GET_MODE(x) CELT_GET_MODE_REQUEST, __celt_check_mode_ptr_ptr(x) + +#define CELT_SET_SIGNALLING_REQUEST 10016 +#define CELT_SET_SIGNALLING(x) CELT_SET_SIGNALLING_REQUEST, __opus_check_int(x) + +#define CELT_SET_TONALITY_REQUEST 10018 +#define CELT_SET_TONALITY(x) CELT_SET_TONALITY_REQUEST, __opus_check_int(x) +#define CELT_SET_TONALITY_SLOPE_REQUEST 10020 +#define CELT_SET_TONALITY_SLOPE(x) CELT_SET_TONALITY_SLOPE_REQUEST, __opus_check_int(x) + +#define CELT_SET_ANALYSIS_REQUEST 10022 +#define CELT_SET_ANALYSIS(x) CELT_SET_ANALYSIS_REQUEST, __celt_check_analysis_ptr(x) + +#define OPUS_SET_LFE_REQUEST 10024 +#define OPUS_SET_LFE(x) OPUS_SET_LFE_REQUEST, __opus_check_int(x) + +#define OPUS_SET_ENERGY_MASK_REQUEST 10026 +#define OPUS_SET_ENERGY_MASK(x) OPUS_SET_ENERGY_MASK_REQUEST, __celt_check_glog_ptr(x) + +#define CELT_SET_SILK_INFO_REQUEST 10028 +#define CELT_SET_SILK_INFO(x) CELT_SET_SILK_INFO_REQUEST, __celt_check_silkinfo_ptr(x) + +/* Encoder stuff */ + +int celt_encoder_get_size(int channels); + +int celt_encode_with_ec(OpusCustomEncoder * OPUS_RESTRICT st, const opus_res * pcm, int frame_size, unsigned char *compressed, int nbCompressedBytes, ec_enc *enc); + +int celt_encoder_init(CELTEncoder *st, opus_int32 sampling_rate, int channels, + int arch); + + + +/* Decoder stuff */ + +int celt_decoder_get_size(int channels); + + +int celt_decoder_init(CELTDecoder *st, opus_int32 sampling_rate, int channels); + +int celt_decode_with_ec_dred(CELTDecoder * OPUS_RESTRICT st, const unsigned char *data, + int len, opus_res * OPUS_RESTRICT pcm, int frame_size, ec_dec *dec, int accum +#ifdef ENABLE_DEEP_PLC + ,LPCNetPLCState *lpcnet +#endif + ); + +int celt_decode_with_ec(OpusCustomDecoder * OPUS_RESTRICT st, const unsigned char *data, + int len, opus_res * OPUS_RESTRICT pcm, int frame_size, ec_dec *dec, int accum); + +#define celt_encoder_ctl opus_custom_encoder_ctl +#define celt_decoder_ctl opus_custom_decoder_ctl + + +#ifdef CUSTOM_MODES +#define OPUS_CUSTOM_NOSTATIC +#else +#define OPUS_CUSTOM_NOSTATIC static OPUS_INLINE +#endif + +static const unsigned char trim_icdf[11] = {126, 124, 119, 109, 87, 41, 19, 9, 4, 2, 0}; +/* Probs: NONE: 21.875%, LIGHT: 6.25%, NORMAL: 65.625%, AGGRESSIVE: 6.25% */ +static const unsigned char spread_icdf[4] = {25, 23, 2, 0}; + +static const unsigned char tapset_icdf[3]={2,1,0}; + +#ifdef CUSTOM_MODES +static const unsigned char toOpusTable[20] = { + 0xE0, 0xE8, 0xF0, 0xF8, + 0xC0, 0xC8, 0xD0, 0xD8, + 0xA0, 0xA8, 0xB0, 0xB8, + 0x00, 0x00, 0x00, 0x00, + 0x80, 0x88, 0x90, 0x98, +}; + +static const unsigned char fromOpusTable[16] = { + 0x80, 0x88, 0x90, 0x98, + 0x40, 0x48, 0x50, 0x58, + 0x20, 0x28, 0x30, 0x38, + 0x00, 0x08, 0x10, 0x18 +}; + +static OPUS_INLINE int toOpus(unsigned char c) +{ + int ret=0; + if (c<0xA0) + ret = toOpusTable[c>>3]; + if (ret == 0) + return -1; + else + return ret|(c&0x7); +} + +static OPUS_INLINE int fromOpus(unsigned char c) +{ + if (c<0x80) + return -1; + else + return fromOpusTable[(c>>3)-16] | (c&0x7); +} +#endif /* CUSTOM_MODES */ + +#define COMBFILTER_MAXPERIOD 1024 +#define COMBFILTER_MINPERIOD 15 + +extern const signed char tf_select_table[4][8]; + +#if defined(ENABLE_HARDENING) || defined(ENABLE_ASSERTIONS) +void validate_celt_decoder(CELTDecoder *st); +#define VALIDATE_CELT_DECODER(st) validate_celt_decoder(st) +#else +#define VALIDATE_CELT_DECODER(st) +#endif + +int resampling_factor(opus_int32 rate); + +void celt_preemphasis(const opus_res * OPUS_RESTRICT pcmp, celt_sig * OPUS_RESTRICT inp, + int N, int CC, int upsample, const opus_val16 *coef, celt_sig *mem, int clip); + +void comb_filter(opus_val32 *y, opus_val32 *x, int T0, int T1, int N, + opus_val16 g0, opus_val16 g1, int tapset0, int tapset1, + const celt_coef *window, int overlap, int arch); + +void init_caps(const CELTMode *m,int *cap,int LM,int C); + +#ifdef RESYNTH +void deemphasis(celt_sig *in[], opus_res *pcm, int N, int C, int downsample, const opus_val16 *coef, celt_sig *mem, int accum); +void celt_synthesis(const CELTMode *mode, celt_norm *X, celt_sig * out_syn[], + celt_glog *oldBandE, int start, int effEnd, int C, int CC, int isTransient, + int LM, int downsample, int silence, int arch); +#endif + +#ifdef __cplusplus +} +#endif + +#endif /* CELT_H */ diff --git a/managed_components/78__esp-opus/celt/celt_decoder.c b/managed_components/78__esp-opus/celt/celt_decoder.c new file mode 100644 index 0000000..28c94a0 --- /dev/null +++ b/managed_components/78__esp-opus/celt/celt_decoder.c @@ -0,0 +1,1642 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2010 Xiph.Org Foundation + Copyright (c) 2008 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#define CELT_DECODER_C + +#include "cpu_support.h" +#include "os_support.h" +#include "mdct.h" +#include +#include "celt.h" +#include "pitch.h" +#include "bands.h" +#include "modes.h" +#include "entcode.h" +#include "quant_bands.h" +#include "rate.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "float_cast.h" +#include +#include "celt_lpc.h" +#include "vq.h" + +#ifdef ENABLE_DEEP_PLC +#include "lpcnet.h" +#include "lpcnet_private.h" +#endif + +/* The maximum pitch lag to allow in the pitch-based PLC. It's possible to save + CPU time in the PLC pitch search by making this smaller than MAX_PERIOD. The + current value corresponds to a pitch of 66.67 Hz. */ +#define PLC_PITCH_LAG_MAX (720) +/* The minimum pitch lag to allow in the pitch-based PLC. This corresponds to a + pitch of 480 Hz. */ +#define PLC_PITCH_LAG_MIN (100) + +/**********************************************************************/ +/* */ +/* DECODER */ +/* */ +/**********************************************************************/ +#define DECODE_BUFFER_SIZE 2048 + +#define PLC_UPDATE_FRAMES 4 +#define PLC_UPDATE_SAMPLES (PLC_UPDATE_FRAMES*FRAME_SIZE) + +/** Decoder state + @brief Decoder state + */ +struct OpusCustomDecoder { + const OpusCustomMode *mode; + int overlap; + int channels; + int stream_channels; + + int downsample; + int start, end; + int signalling; + int disable_inv; + int complexity; + int arch; + + /* Everything beyond this point gets cleared on a reset */ +#define DECODER_RESET_START rng + + opus_uint32 rng; + int error; + int last_pitch_index; + int loss_duration; + int skip_plc; + int postfilter_period; + int postfilter_period_old; + opus_val16 postfilter_gain; + opus_val16 postfilter_gain_old; + int postfilter_tapset; + int postfilter_tapset_old; + int prefilter_and_fold; + + celt_sig preemph_memD[2]; + +#ifdef ENABLE_DEEP_PLC + opus_int16 plc_pcm[PLC_UPDATE_SAMPLES]; + int plc_fill; + float plc_preemphasis_mem; +#endif + + celt_sig _decode_mem[1]; /* Size = channels*(DECODE_BUFFER_SIZE+mode->overlap) */ + /* opus_val16 lpc[], Size = channels*CELT_LPC_ORDER */ + /* celt_glog oldEBands[], Size = 2*mode->nbEBands */ + /* celt_glog oldLogE[], Size = 2*mode->nbEBands */ + /* celt_glog oldLogE2[], Size = 2*mode->nbEBands */ + /* celt_glog backgroundLogE[], Size = 2*mode->nbEBands */ +}; + +#if defined(ENABLE_HARDENING) || defined(ENABLE_ASSERTIONS) +/* Make basic checks on the CELT state to ensure we don't end + up writing all over memory. */ +void validate_celt_decoder(CELTDecoder *st) +{ +#ifndef CUSTOM_MODES + celt_assert(st->mode == opus_custom_mode_create(48000, 960, NULL)); + celt_assert(st->overlap == 120); + celt_assert(st->end <= 21); +#else +/* From Section 4.3 in the spec: "The normal CELT layer uses 21 of those bands, + though Opus Custom (see Section 6.2) may use a different number of bands" + + Check if it's within the maximum number of Bark frequency bands instead */ + celt_assert(st->end <= 25); +#endif + celt_assert(st->channels == 1 || st->channels == 2); + celt_assert(st->stream_channels == 1 || st->stream_channels == 2); + celt_assert(st->downsample > 0); + celt_assert(st->start == 0 || st->start == 17); + celt_assert(st->start < st->end); +#ifdef OPUS_ARCHMASK + celt_assert(st->arch >= 0); + celt_assert(st->arch <= OPUS_ARCHMASK); +#endif + celt_assert(st->last_pitch_index <= PLC_PITCH_LAG_MAX); + celt_assert(st->last_pitch_index >= PLC_PITCH_LAG_MIN || st->last_pitch_index == 0); + celt_assert(st->postfilter_period < MAX_PERIOD); + celt_assert(st->postfilter_period >= COMBFILTER_MINPERIOD || st->postfilter_period == 0); + celt_assert(st->postfilter_period_old < MAX_PERIOD); + celt_assert(st->postfilter_period_old >= COMBFILTER_MINPERIOD || st->postfilter_period_old == 0); + celt_assert(st->postfilter_tapset <= 2); + celt_assert(st->postfilter_tapset >= 0); + celt_assert(st->postfilter_tapset_old <= 2); + celt_assert(st->postfilter_tapset_old >= 0); +} +#endif + +int celt_decoder_get_size(int channels) +{ + const CELTMode *mode = opus_custom_mode_create(48000, 960, NULL); + return opus_custom_decoder_get_size(mode, channels); +} + +OPUS_CUSTOM_NOSTATIC int opus_custom_decoder_get_size(const CELTMode *mode, int channels) +{ + int size = sizeof(struct CELTDecoder) + + (channels*(DECODE_BUFFER_SIZE+mode->overlap)-1)*sizeof(celt_sig) + + channels*CELT_LPC_ORDER*sizeof(opus_val16) + + 4*2*mode->nbEBands*sizeof(celt_glog); + return size; +} + +#ifdef CUSTOM_MODES +CELTDecoder *opus_custom_decoder_create(const CELTMode *mode, int channels, int *error) +{ + int ret; + CELTDecoder *st = (CELTDecoder *)opus_alloc(opus_custom_decoder_get_size(mode, channels)); + ret = opus_custom_decoder_init(st, mode, channels); + if (ret != OPUS_OK) + { + opus_custom_decoder_destroy(st); + st = NULL; + } + if (error) + *error = ret; + return st; +} +#endif /* CUSTOM_MODES */ + +int celt_decoder_init(CELTDecoder *st, opus_int32 sampling_rate, int channels) +{ + int ret; + ret = opus_custom_decoder_init(st, opus_custom_mode_create(48000, 960, NULL), channels); + if (ret != OPUS_OK) + return ret; + st->downsample = resampling_factor(sampling_rate); + if (st->downsample==0) + return OPUS_BAD_ARG; + else + return OPUS_OK; +} + +OPUS_CUSTOM_NOSTATIC int opus_custom_decoder_init(CELTDecoder *st, const CELTMode *mode, int channels) +{ + if (channels < 0 || channels > 2) + return OPUS_BAD_ARG; + + if (st==NULL) + return OPUS_ALLOC_FAIL; + + OPUS_CLEAR((char*)st, opus_custom_decoder_get_size(mode, channels)); + + st->mode = mode; + st->overlap = mode->overlap; + st->stream_channels = st->channels = channels; + + st->downsample = 1; + st->start = 0; + st->end = st->mode->effEBands; + st->signalling = 1; +#ifndef DISABLE_UPDATE_DRAFT + st->disable_inv = channels == 1; +#else + st->disable_inv = 0; +#endif + st->arch = opus_select_arch(); + + opus_custom_decoder_ctl(st, OPUS_RESET_STATE); + + return OPUS_OK; +} + +#ifdef CUSTOM_MODES +void opus_custom_decoder_destroy(CELTDecoder *st) +{ + opus_free(st); +} +#endif /* CUSTOM_MODES */ + +#ifndef CUSTOM_MODES +/* Special case for stereo with no downsampling and no accumulation. This is + quite common and we can make it faster by processing both channels in the + same loop, reducing overhead due to the dependency loop in the IIR filter. */ +static void deemphasis_stereo_simple(celt_sig *in[], opus_res *pcm, int N, const opus_val16 coef0, + celt_sig *mem) +{ + celt_sig * OPUS_RESTRICT x0; + celt_sig * OPUS_RESTRICT x1; + celt_sig m0, m1; + int j; + x0=in[0]; + x1=in[1]; + m0 = mem[0]; + m1 = mem[1]; + for (j=0;j1) + { + /* Shortcut for the standard (non-custom modes) case */ + for (j=0;joverlap; + nbEBands = mode->nbEBands; + N = mode->shortMdctSize<shortMdctSize; + shift = mode->maxLM; + } else { + B = 1; + NB = mode->shortMdctSize<maxLM-LM; + } + + if (CC==2&&C==1) + { + /* Copying a mono streams to two channels */ + celt_sig *freq2; + denormalise_bands(mode, X, freq, oldBandE, start, effEnd, M, + downsample, silence); + /* Store a temporary copy in the output buffer because the IMDCT destroys its input. */ + freq2 = out_syn[1]+overlap/2; + OPUS_COPY(freq2, freq, N); + for (b=0;bmdct, &freq2[b], out_syn[0]+NB*b, mode->window, overlap, shift, B, arch); + for (b=0;bmdct, &freq[b], out_syn[1]+NB*b, mode->window, overlap, shift, B, arch); + } else if (CC==1&&C==2) + { + /* Downmixing a stereo stream to mono */ + celt_sig *freq2; + freq2 = out_syn[0]+overlap/2; + denormalise_bands(mode, X, freq, oldBandE, start, effEnd, M, + downsample, silence); + /* Use the output buffer as temp array before downmixing. */ + denormalise_bands(mode, X+N, freq2, oldBandE+nbEBands, start, effEnd, M, + downsample, silence); + for (i=0;imdct, &freq[b], out_syn[0]+NB*b, mode->window, overlap, shift, B, arch); + } else { + /* Normal case (mono or stereo) */ + c=0; do { + denormalise_bands(mode, X+c*N, freq, oldBandE+c*nbEBands, start, effEnd, M, + downsample, silence); + for (b=0;bmdct, &freq[b], out_syn[c]+NB*b, mode->window, overlap, shift, B, arch); + } while (++cstorage*8; + tell = ec_tell(dec); + logp = isTransient ? 2 : 4; + tf_select_rsv = LM>0 && tell+logp+1<=budget; + budget -= tf_select_rsv; + tf_changed = curr = 0; + for (i=start;i>1, opus_val16 ); + pitch_downsample(decode_mem, lp_pitch_buf, + DECODE_BUFFER_SIZE, C, arch); + pitch_search(lp_pitch_buf+(PLC_PITCH_LAG_MAX>>1), lp_pitch_buf, + DECODE_BUFFER_SIZE-PLC_PITCH_LAG_MAX, + PLC_PITCH_LAG_MAX-PLC_PITCH_LAG_MIN, &pitch_index, arch); + pitch_index = PLC_PITCH_LAG_MAX-pitch_index; + RESTORE_STACK; + return pitch_index; +} + +static void prefilter_and_fold(CELTDecoder * OPUS_RESTRICT st, int N) +{ + int c; + int CC; + int i; + int overlap; + celt_sig *decode_mem[2]; + const OpusCustomMode *mode; + VARDECL(opus_val32, etmp); + mode = st->mode; + overlap = st->overlap; + CC = st->channels; + ALLOC(etmp, overlap, opus_val32); + c=0; do { + decode_mem[c] = st->_decode_mem + c*(DECODE_BUFFER_SIZE+overlap); + } while (++cpostfilter_period_old, st->postfilter_period, overlap, + -st->postfilter_gain_old, -st->postfilter_gain, + st->postfilter_tapset_old, st->postfilter_tapset, NULL, 0, st->arch); + + /* Simulate TDAC on the concealed audio so that it blends with the + MDCT of the next frame. */ + for (i=0;iwindow[i]), etmp[overlap-1-i]) + + MULT16_32_Q15 (COEF2VAL16(mode->window[overlap-i-1]), etmp[i]); + } + } while (++cfec_read_pos; + tmp_fec_skip = lpcnet->fec_skip; + for (i=0;ifec_read_pos = tmp_read_post; + lpcnet->fec_skip = tmp_fec_skip; +} +#endif + +static void celt_decode_lost(CELTDecoder * OPUS_RESTRICT st, int N, int LM +#ifdef ENABLE_DEEP_PLC + ,LPCNetPLCState *lpcnet +#endif + ) +{ + int c; + int i; + const int C = st->channels; + celt_sig *decode_mem[2]; + celt_sig *out_syn[2]; + opus_val16 *lpc; + celt_glog *oldBandE, *oldLogE, *oldLogE2, *backgroundLogE; + const OpusCustomMode *mode; + int nbEBands; + int overlap; + int start; + int loss_duration; + int noise_based; + const opus_int16 *eBands; + SAVE_STACK; + + mode = st->mode; + nbEBands = mode->nbEBands; + overlap = mode->overlap; + eBands = mode->eBands; + + c=0; do { + decode_mem[c] = st->_decode_mem + c*(DECODE_BUFFER_SIZE+overlap); + out_syn[c] = decode_mem[c]+DECODE_BUFFER_SIZE-N; + } while (++c_decode_mem+(DECODE_BUFFER_SIZE+overlap)*C); + oldBandE = (celt_glog*)(lpc+C*CELT_LPC_ORDER); + oldLogE = oldBandE + 2*nbEBands; + oldLogE2 = oldLogE + 2*nbEBands; + backgroundLogE = oldLogE2 + 2*nbEBands; + + loss_duration = st->loss_duration; + start = st->start; +#ifdef ENABLE_DEEP_PLC + if (lpcnet != NULL) noise_based = start != 0 || (lpcnet->fec_fill_pos == 0 && (st->skip_plc || loss_duration >= 80)); + else +#endif + noise_based = loss_duration >= 40 || start != 0 || st->skip_plc; + if (noise_based) + { + /* Noise-based PLC/CNG */ + VARDECL(celt_norm, X); + opus_uint32 seed; + int end; + int effEnd; + celt_glog decay; + end = st->end; + effEnd = IMAX(start, IMIN(end, mode->effEBands)); + + ALLOC(X, C*N, celt_norm); /**< Interleaved normalised MDCTs */ + c=0; do { + OPUS_MOVE(decode_mem[c], decode_mem[c]+N, + DECODE_BUFFER_SIZE-N+overlap); + } while (++cprefilter_and_fold) { + prefilter_and_fold(st, N); + } + + /* Energy decay */ + decay = loss_duration==0 ? GCONST(1.5f) : GCONST(.5f); + c=0; do + { + for (i=start;irng; + for (c=0;c>20); + } + renormalise_vector(X+boffs, blen, Q31ONE, st->arch); + } + } + st->rng = seed; + + celt_synthesis(mode, X, out_syn, oldBandE, start, effEnd, C, C, 0, LM, st->downsample, 0, st->arch); + st->prefilter_and_fold = 0; + /* Skip regular PLC until we get two consecutive packets. */ + st->skip_plc = 1; + } else { + int exc_length; + /* Pitch-based PLC */ + const celt_coef *window; + opus_val16 *exc; + opus_val16 fade = Q15ONE; + int pitch_index; + VARDECL(opus_val16, _exc); + VARDECL(opus_val16, fir_tmp); + + if (loss_duration == 0) + { +#ifdef ENABLE_DEEP_PLC + if (lpcnet != NULL && lpcnet->loaded) update_plc_state(lpcnet, decode_mem, &st->plc_preemphasis_mem, C); +#endif + st->last_pitch_index = pitch_index = celt_plc_pitch_search(decode_mem, C, st->arch); + } else { + pitch_index = st->last_pitch_index; + fade = QCONST16(.8f,15); + } + + /* We want the excitation for 2 pitch periods in order to look for a + decaying signal, but we can't get more than MAX_PERIOD. */ + exc_length = IMIN(2*pitch_index, MAX_PERIOD); + + ALLOC(_exc, MAX_PERIOD+CELT_LPC_ORDER, opus_val16); + ALLOC(fir_tmp, exc_length, opus_val16); + exc = _exc+CELT_LPC_ORDER; + window = mode->window; + c=0; do { + opus_val16 decay; + opus_val16 attenuation; + opus_val32 S1=0; + celt_sig *buf; + int extrapolation_offset; + int extrapolation_len; + int j; + + buf = decode_mem[c]; + for (i=0;iarch); + /* Add a noise floor of -40 dB. */ +#ifdef FIXED_POINT + ac[0] += SHR32(ac[0],13); +#else + ac[0] *= 1.0001f; +#endif + /* Use lag windowing to stabilize the Levinson-Durbin recursion. */ + for (i=1;i<=CELT_LPC_ORDER;i++) + { + /*ac[i] *= exp(-.5*(2*M_PI*.002*i)*(2*M_PI*.002*i));*/ +#ifdef FIXED_POINT + ac[i] -= MULT16_32_Q15(2*i*i, ac[i]); +#else + ac[i] -= ac[i]*(0.008f*0.008f)*i*i; +#endif + } + _celt_lpc(lpc+c*CELT_LPC_ORDER, ac, CELT_LPC_ORDER); +#ifdef FIXED_POINT + /* For fixed-point, apply bandwidth expansion until we can guarantee that + no overflow can happen in the IIR filter. This means: + 32768*sum(abs(filter)) < 2^31 */ + while (1) { + opus_val16 tmp=Q15ONE; + opus_val32 sum=QCONST16(1., SIG_SHIFT); + for (i=0;iarch); + OPUS_COPY(exc+MAX_PERIOD-exc_length, fir_tmp, exc_length); + } + + /* Check if the waveform is decaying, and if so how fast. + We do this to avoid adding energy when concealing in a segment + with decaying energy. */ + { + opus_val32 E1=1, E2=1; + int decay_length; +#ifdef FIXED_POINT + int shift = IMAX(0,2*celt_zlog2(celt_maxabs16(&exc[MAX_PERIOD-exc_length], exc_length))-20); +#endif + decay_length = exc_length>>1; + for (i=0;i= pitch_index) { + j -= pitch_index; + attenuation = MULT16_16_Q15(attenuation, decay); + } + buf[DECODE_BUFFER_SIZE-N+i] = + SHL32(EXTEND32(MULT16_16_Q15(attenuation, + exc[extrapolation_offset+j])), SIG_SHIFT); + /* Compute the energy of the previously decoded signal whose + excitation we're copying. */ + tmp = SROUND16( + buf[DECODE_BUFFER_SIZE-MAX_PERIOD-N+extrapolation_offset+j], + SIG_SHIFT); + S1 += SHR32(MULT16_16(tmp, tmp), 10); + } + { + opus_val16 lpc_mem[CELT_LPC_ORDER]; + /* Copy the last decoded samples (prior to the overlap region) to + synthesis filter memory so we can have a continuous signal. */ + for (i=0;iarch); +#ifdef FIXED_POINT + for (i=0; i < extrapolation_len; i++) + buf[DECODE_BUFFER_SIZE-N+i] = SATURATE(buf[DECODE_BUFFER_SIZE-N+i], SIG_SAT); +#endif + } + + /* Check if the synthesis energy is higher than expected, which can + happen with the signal changes during our window. If so, + attenuate. */ + { + opus_val32 S2=0; + for (i=0;i SHR32(S2,2))) +#else + /* The float test is written this way to catch NaNs in the output + of the IIR filter at the same time. */ + if (!(S1 > 0.2f*S2)) +#endif + { + for (i=0;iloaded && (st->complexity >= 5 || lpcnet->fec_fill_pos > 0)) { + float overlap_mem; + int samples_needed16k; + celt_sig *buf; + VARDECL(float, buf_copy); + buf = decode_mem[0]; + ALLOC(buf_copy, C*overlap, float); + c=0; do { + OPUS_COPY(buf_copy+c*overlap, &decode_mem[c][DECODE_BUFFER_SIZE-N], overlap); + } while (++cplc_fill = 0; + } + while (st->plc_fill < samples_needed16k) { + lpcnet_plc_conceal(lpcnet, &st->plc_pcm[st->plc_fill]); + st->plc_fill += FRAME_SIZE; + } + /* Resample to 48 kHz. */ + for (i=0;i<(N+overlap)/3;i++) { + int j; + float sum; + for (sum=0, j=0;j<17;j++) sum += 3*st->plc_pcm[i+j]*sinc_filter[3*j]; + buf[DECODE_BUFFER_SIZE-N+3*i] = sum; + for (sum=0, j=0;j<16;j++) sum += 3*st->plc_pcm[i+j+1]*sinc_filter[3*j+2]; + buf[DECODE_BUFFER_SIZE-N+3*i+1] = sum; + for (sum=0, j=0;j<16;j++) sum += 3*st->plc_pcm[i+j+1]*sinc_filter[3*j+1]; + buf[DECODE_BUFFER_SIZE-N+3*i+2] = sum; + } + OPUS_MOVE(st->plc_pcm, &st->plc_pcm[N/3], st->plc_fill-N/3); + st->plc_fill -= N/3; + for (i=0;iplc_preemphasis_mem; + st->plc_preemphasis_mem = tmp; + } + overlap_mem = st->plc_preemphasis_mem; + for (i=0;iprefilter_and_fold = 1; + } + + /* Saturate to soemthing large to avoid wrap-around. */ + st->loss_duration = IMIN(10000, loss_duration+(1<channels; + int LM, M; + int start; + int end; + int effEnd; + int codedBands; + int alloc_trim; + int postfilter_pitch; + opus_val16 postfilter_gain; + int intensity=0; + int dual_stereo=0; + opus_int32 total_bits; + opus_int32 balance; + opus_int32 tell; + int dynalloc_logp; + int postfilter_tapset; + int anti_collapse_rsv; + int anti_collapse_on=0; + int silence; + int C = st->stream_channels; + const OpusCustomMode *mode; + int nbEBands; + int overlap; + const opus_int16 *eBands; + celt_glog max_background_increase; + ALLOC_STACK; + + VALIDATE_CELT_DECODER(st); + mode = st->mode; + nbEBands = mode->nbEBands; + overlap = mode->overlap; + eBands = mode->eBands; + start = st->start; + end = st->end; + frame_size *= st->downsample; + + lpc = (opus_val16*)(st->_decode_mem+(DECODE_BUFFER_SIZE+overlap)*CC); + oldBandE = (celt_glog*)(lpc+CC*CELT_LPC_ORDER); + oldLogE = oldBandE + 2*nbEBands; + oldLogE2 = oldLogE + 2*nbEBands; + backgroundLogE = oldLogE2 + 2*nbEBands; + +#ifdef CUSTOM_MODES + if (st->signalling && data!=NULL) + { + int data0=data[0]; + /* Convert "standard mode" to Opus header */ + if (mode->Fs==48000 && mode->shortMdctSize==120) + { + data0 = fromOpus(data0); + if (data0<0) + return OPUS_INVALID_PACKET; + } + st->end = end = IMAX(1, mode->effEBands-2*(data0>>5)); + LM = (data0>>3)&0x3; + C = 1 + ((data0>>2)&0x1); + if ((data[0] & 0x03) == 0x03) { + data++; + len--; + if (len<=0) + return OPUS_INVALID_PACKET; + if (data[0] & 0x40) { + int p; + int padding=0; + data++; + len--; + do { + int tmp; + if (len<=0) + return OPUS_INVALID_PACKET; + p = *data++; + len--; + tmp = p==255 ? 254: p; + len -= tmp; + padding += tmp; + } while (p==255); + padding--; + if (len <= 0 || padding<0) return OPUS_INVALID_PACKET; + } + } else + { + data++; + len--; + } + if (LM>mode->maxLM) + return OPUS_INVALID_PACKET; + if (frame_size < mode->shortMdctSize<shortMdctSize<maxLM;LM++) + if (mode->shortMdctSize<mode->maxLM) + return OPUS_BAD_ARG; + } + M=1<1275 || pcm==NULL) + return OPUS_BAD_ARG; + + N = M*mode->shortMdctSize; + c=0; do { + decode_mem[c] = st->_decode_mem + c*(DECODE_BUFFER_SIZE+overlap); + out_syn[c] = decode_mem[c]+DECODE_BUFFER_SIZE-N; + } while (++c mode->effEBands) + effEnd = mode->effEBands; + + if (data == NULL || len<=1) + { + celt_decode_lost(st, N, LM +#ifdef ENABLE_DEEP_PLC + , lpcnet +#endif + ); + deemphasis(out_syn, pcm, N, CC, st->downsample, mode->preemph, st->preemph_memD, accum); + RESTORE_STACK; + return frame_size/st->downsample; + } +#ifdef ENABLE_DEEP_PLC + else { + /* FIXME: This is a bit of a hack just to make sure opus_decode_native() knows we're no longer in PLC. */ + if (lpcnet) lpcnet->blend = 0; + } +#endif + + /* Check if there are at least two packets received consecutively before + * turning on the pitch-based PLC */ + if (st->loss_duration == 0) st->skip_plc = 0; + + if (dec == NULL) + { + ec_dec_init(&_dec,(unsigned char*)data,len); + dec = &_dec; + } + + if (C==1) + { + for (i=0;i= total_bits) + silence = 1; + else if (tell==1) + silence = ec_dec_bit_logp(dec, 15); + else + silence = 0; + if (silence) + { + /* Pretend we've read all the remaining bits */ + tell = len*8; + dec->nbits_total+=tell-ec_tell(dec); + } + + postfilter_gain = 0; + postfilter_pitch = 0; + postfilter_tapset = 0; + if (start==0 && tell+16 <= total_bits) + { + if(ec_dec_bit_logp(dec, 1)) + { + int qg, octave; + octave = ec_dec_uint(dec, 6); + postfilter_pitch = (16< 0 && tell+3 <= total_bits) + { + isTransient = ec_dec_bit_logp(dec, 3); + tell = ec_tell(dec); + } + else + isTransient = 0; + + if (isTransient) + shortBlocks = M; + else + shortBlocks = 0; + + /* Decode the global flags (first symbols in the stream) */ + intra_ener = tell+3<=total_bits ? ec_dec_bit_logp(dec, 3) : 0; + /* If recovering from packet loss, make sure we make the energy prediction safe to reduce the + risk of getting loud artifacts. */ + if (!intra_ener && st->loss_duration != 0) { + c=0; do + { + celt_glog safety = 0; + int missing = IMIN(10, st->loss_duration>>LM); + if (LM==0) safety = GCONST(1.5f); + else if (LM==1) safety = GCONST(.5f); + for (i=start;i0) + dynalloc_logp = IMAX(2, dynalloc_logp-1); + } + + ALLOC(fine_quant, nbEBands, int); + alloc_trim = tell+(6<=2&&bits>=((LM+2)<rng, 0, + st->arch, st->disable_inv); + + if (anti_collapse_rsv > 0) + { + anti_collapse_on = ec_dec_bits(dec, 1); + } + + unquant_energy_finalise(mode, start, end, oldBandE, + fine_quant, fine_priority, len*8-ec_tell(dec), dec, C); + + if (anti_collapse_on) + anti_collapse(mode, X, collapse_masks, LM, C, N, + start, end, oldBandE, oldLogE, oldLogE2, pulses, st->rng, 0, st->arch); + + if (silence) + { + for (i=0;iprefilter_and_fold) { + prefilter_and_fold(st, N); + } + celt_synthesis(mode, X, out_syn, oldBandE, start, effEnd, + C, CC, isTransient, LM, st->downsample, silence, st->arch); + + c=0; do { + st->postfilter_period=IMAX(st->postfilter_period, COMBFILTER_MINPERIOD); + st->postfilter_period_old=IMAX(st->postfilter_period_old, COMBFILTER_MINPERIOD); + comb_filter(out_syn[c], out_syn[c], st->postfilter_period_old, st->postfilter_period, mode->shortMdctSize, + st->postfilter_gain_old, st->postfilter_gain, st->postfilter_tapset_old, st->postfilter_tapset, + mode->window, overlap, st->arch); + if (LM!=0) + comb_filter(out_syn[c]+mode->shortMdctSize, out_syn[c]+mode->shortMdctSize, st->postfilter_period, postfilter_pitch, N-mode->shortMdctSize, + st->postfilter_gain, postfilter_gain, st->postfilter_tapset, postfilter_tapset, + mode->window, overlap, st->arch); + + } while (++cpostfilter_period_old = st->postfilter_period; + st->postfilter_gain_old = st->postfilter_gain; + st->postfilter_tapset_old = st->postfilter_tapset; + st->postfilter_period = postfilter_pitch; + st->postfilter_gain = postfilter_gain; + st->postfilter_tapset = postfilter_tapset; + if (LM!=0) + { + st->postfilter_period_old = st->postfilter_period; + st->postfilter_gain_old = st->postfilter_gain; + st->postfilter_tapset_old = st->postfilter_tapset; + } + + if (C==1) + OPUS_COPY(&oldBandE[nbEBands], oldBandE, nbEBands); + + if (!isTransient) + { + OPUS_COPY(oldLogE2, oldLogE, 2*nbEBands); + OPUS_COPY(oldLogE, oldBandE, 2*nbEBands); + } else { + for (i=0;i<2*nbEBands;i++) + oldLogE[i] = MING(oldLogE[i], oldBandE[i]); + } + /* In normal circumstances, we only allow the noise floor to increase by + up to 2.4 dB/second, but when we're in DTX we give the weight of + all missing packets to the update packet. */ + max_background_increase = IMIN(160, st->loss_duration+M)*GCONST(0.001f); + for (i=0;i<2*nbEBands;i++) + backgroundLogE[i] = MING(backgroundLogE[i] + max_background_increase, oldBandE[i]); + /* In case start or end were to change */ + c=0; do + { + for (i=0;irng = dec->rng; + + deemphasis(out_syn, pcm, N, CC, st->downsample, mode->preemph, st->preemph_memD, accum); + st->loss_duration = 0; + st->prefilter_and_fold = 0; + RESTORE_STACK; + if (ec_tell(dec) > 8*len) + return OPUS_INTERNAL_ERROR; + if(ec_get_error(dec)) + st->error = 1; + return frame_size/st->downsample; +} + +int celt_decode_with_ec(CELTDecoder * OPUS_RESTRICT st, const unsigned char *data, + int len, opus_res * OPUS_RESTRICT pcm, int frame_size, ec_dec *dec, int accum) +{ + return celt_decode_with_ec_dred(st, data, len, pcm, frame_size, dec, accum +#ifdef ENABLE_DEEP_PLC + , NULL +#endif + ); +} + +#ifdef CUSTOM_MODES + +#if defined(FIXED_POINT) && !defined(ENABLE_RES24) +int opus_custom_decode(CELTDecoder * OPUS_RESTRICT st, const unsigned char *data, int len, opus_int16 * OPUS_RESTRICT pcm, int frame_size) +{ + return celt_decode_with_ec(st, data, len, pcm, frame_size, NULL, 0); +} +#else +int opus_custom_decode(CELTDecoder * OPUS_RESTRICT st, const unsigned char *data, int len, opus_int16 * OPUS_RESTRICT pcm, int frame_size) +{ + int j, ret, C, N; + VARDECL(opus_res, out); + ALLOC_STACK; + + if (pcm==NULL) + return OPUS_BAD_ARG; + + C = st->channels; + N = frame_size; + + ALLOC(out, C*N, opus_res); + ret = celt_decode_with_ec(st, data, len, out, frame_size, NULL, 0); + if (ret>0) + for (j=0;jchannels; + N = frame_size; + + ALLOC(out, C*N, opus_res); + ret = celt_decode_with_ec(st, data, len, out, frame_size, NULL, 0); + if (ret>0) + for (j=0;jchannels; + N = frame_size; + + ALLOC(out, C*N, opus_res); + ret=celt_decode_with_ec(st, data, len, out, frame_size, NULL, 0); + if (ret>0) + for (j=0;j10) + { + goto bad_arg; + } + st->complexity = value; + } + break; + case OPUS_GET_COMPLEXITY_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (!value) + { + goto bad_arg; + } + *value = st->complexity; + } + break; + case CELT_SET_START_BAND_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<0 || value>=st->mode->nbEBands) + goto bad_arg; + st->start = value; + } + break; + case CELT_SET_END_BAND_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<1 || value>st->mode->nbEBands) + goto bad_arg; + st->end = value; + } + break; + case CELT_SET_CHANNELS_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<1 || value>2) + goto bad_arg; + st->stream_channels = value; + } + break; + case CELT_GET_AND_CLEAR_ERROR_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (value==NULL) + goto bad_arg; + *value=st->error; + st->error = 0; + } + break; + case OPUS_GET_LOOKAHEAD_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (value==NULL) + goto bad_arg; + *value = st->overlap/st->downsample; + } + break; + case OPUS_RESET_STATE: + { + int i; + opus_val16 *lpc; + celt_glog *oldBandE, *oldLogE, *oldLogE2; + lpc = (opus_val16*)(st->_decode_mem+(DECODE_BUFFER_SIZE+st->overlap)*st->channels); + oldBandE = (celt_glog*)(lpc+st->channels*CELT_LPC_ORDER); + oldLogE = oldBandE + 2*st->mode->nbEBands; + oldLogE2 = oldLogE + 2*st->mode->nbEBands; + OPUS_CLEAR((char*)&st->DECODER_RESET_START, + opus_custom_decoder_get_size(st->mode, st->channels)- + ((char*)&st->DECODER_RESET_START - (char*)st)); + for (i=0;i<2*st->mode->nbEBands;i++) + oldLogE[i]=oldLogE2[i]=-GCONST(28.f); + st->skip_plc = 1; + } + break; + case OPUS_GET_PITCH_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (value==NULL) + goto bad_arg; + *value = st->postfilter_period; + } + break; + case CELT_GET_MODE_REQUEST: + { + const CELTMode ** value = va_arg(ap, const CELTMode**); + if (value==0) + goto bad_arg; + *value=st->mode; + } + break; + case CELT_SET_SIGNALLING_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->signalling = value; + } + break; + case OPUS_GET_FINAL_RANGE_REQUEST: + { + opus_uint32 * value = va_arg(ap, opus_uint32 *); + if (value==0) + goto bad_arg; + *value=st->rng; + } + break; + case OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if(value<0 || value>1) + { + goto bad_arg; + } + st->disable_inv = value; + } + break; + case OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (!value) + { + goto bad_arg; + } + *value = st->disable_inv; + } + break; + default: + goto bad_request; + } + va_end(ap); + return OPUS_OK; +bad_arg: + va_end(ap); + return OPUS_BAD_ARG; +bad_request: + va_end(ap); + return OPUS_UNIMPLEMENTED; +} diff --git a/managed_components/78__esp-opus/celt/celt_encoder.c b/managed_components/78__esp-opus/celt/celt_encoder.c new file mode 100644 index 0000000..43b4357 --- /dev/null +++ b/managed_components/78__esp-opus/celt/celt_encoder.c @@ -0,0 +1,2854 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2010 Xiph.Org Foundation + Copyright (c) 2008 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#define CELT_ENCODER_C + +#include "cpu_support.h" +#include "os_support.h" +#include "mdct.h" +#include +#include "celt.h" +#include "pitch.h" +#include "bands.h" +#include "modes.h" +#include "entcode.h" +#include "quant_bands.h" +#include "rate.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "float_cast.h" +#include +#include "celt_lpc.h" +#include "vq.h" + + +#ifndef M_PI +#define M_PI 3.141592653 +#endif + + +/** Encoder state + @brief Encoder state + */ +struct OpusCustomEncoder { + const OpusCustomMode *mode; /**< Mode used by the encoder */ + int channels; + int stream_channels; + + int force_intra; + int clip; + int disable_pf; + int complexity; + int upsample; + int start, end; + + opus_int32 bitrate; + int vbr; + int signalling; + int constrained_vbr; /* If zero, VBR can do whatever it likes with the rate */ + int loss_rate; + int lsb_depth; + int lfe; + int disable_inv; + int arch; + + /* Everything beyond this point gets cleared on a reset */ +#define ENCODER_RESET_START rng + + opus_uint32 rng; + int spread_decision; + opus_val32 delayedIntra; + int tonal_average; + int lastCodedBands; + int hf_average; + int tapset_decision; + + int prefilter_period; + opus_val16 prefilter_gain; + int prefilter_tapset; +#ifdef RESYNTH + int prefilter_period_old; + opus_val16 prefilter_gain_old; + int prefilter_tapset_old; +#endif + int consec_transient; + AnalysisInfo analysis; + SILKInfo silk_info; + + opus_val32 preemph_memE[2]; + opus_val32 preemph_memD[2]; + + /* VBR-related parameters */ + opus_int32 vbr_reservoir; + opus_int32 vbr_drift; + opus_int32 vbr_offset; + opus_int32 vbr_count; + opus_val32 overlap_max; + opus_val16 stereo_saving; + int intensity; + celt_glog *energy_mask; + celt_glog spec_avg; + +#ifdef RESYNTH + /* +MAX_PERIOD/2 to make space for overlap */ + celt_sig syn_mem[2][2*MAX_PERIOD+MAX_PERIOD/2]; +#endif + + celt_sig in_mem[1]; /* Size = channels*mode->overlap */ + /* celt_sig prefilter_mem[], Size = channels*COMBFILTER_MAXPERIOD */ + /* celt_glog oldBandE[], Size = channels*mode->nbEBands */ + /* celt_glog oldLogE[], Size = channels*mode->nbEBands */ + /* celt_glog oldLogE2[], Size = channels*mode->nbEBands */ + /* celt_glog energyError[], Size = channels*mode->nbEBands */ +}; + +int celt_encoder_get_size(int channels) +{ + CELTMode *mode = opus_custom_mode_create(48000, 960, NULL); + return opus_custom_encoder_get_size(mode, channels); +} + +OPUS_CUSTOM_NOSTATIC int opus_custom_encoder_get_size(const CELTMode *mode, int channels) +{ + int size = sizeof(struct CELTEncoder) + + (channels*mode->overlap-1)*sizeof(celt_sig) /* celt_sig in_mem[channels*mode->overlap]; */ + + channels*COMBFILTER_MAXPERIOD*sizeof(celt_sig) /* celt_sig prefilter_mem[channels*COMBFILTER_MAXPERIOD]; */ + + 4*channels*mode->nbEBands*sizeof(celt_glog); /* celt_glog oldBandE[channels*mode->nbEBands]; */ + /* celt_glog oldLogE[channels*mode->nbEBands]; */ + /* celt_glog oldLogE2[channels*mode->nbEBands]; */ + /* celt_glog energyError[channels*mode->nbEBands]; */ + return size; +} + +#ifdef CUSTOM_MODES +CELTEncoder *opus_custom_encoder_create(const CELTMode *mode, int channels, int *error) +{ + int ret; + CELTEncoder *st = (CELTEncoder *)opus_alloc(opus_custom_encoder_get_size(mode, channels)); + /* init will handle the NULL case */ + ret = opus_custom_encoder_init(st, mode, channels); + if (ret != OPUS_OK) + { + opus_custom_encoder_destroy(st); + st = NULL; + } + if (error) + *error = ret; + return st; +} +#endif /* CUSTOM_MODES */ + +static int opus_custom_encoder_init_arch(CELTEncoder *st, const CELTMode *mode, + int channels, int arch) +{ + if (channels < 0 || channels > 2) + return OPUS_BAD_ARG; + + if (st==NULL || mode==NULL) + return OPUS_ALLOC_FAIL; + + OPUS_CLEAR((char*)st, opus_custom_encoder_get_size(mode, channels)); + + st->mode = mode; + st->stream_channels = st->channels = channels; + + st->upsample = 1; + st->start = 0; + st->end = st->mode->effEBands; + st->signalling = 1; + st->arch = arch; + + st->constrained_vbr = 1; + st->clip = 1; + + st->bitrate = OPUS_BITRATE_MAX; + st->vbr = 0; + st->force_intra = 0; + st->complexity = 5; + st->lsb_depth=24; + + opus_custom_encoder_ctl(st, OPUS_RESET_STATE); + + return OPUS_OK; +} + +#ifdef CUSTOM_MODES +int opus_custom_encoder_init(CELTEncoder *st, const CELTMode *mode, int channels) +{ + return opus_custom_encoder_init_arch(st, mode, channels, opus_select_arch()); +} +#endif + +int celt_encoder_init(CELTEncoder *st, opus_int32 sampling_rate, int channels, + int arch) +{ + int ret; + ret = opus_custom_encoder_init_arch(st, + opus_custom_mode_create(48000, 960, NULL), channels, arch); + if (ret != OPUS_OK) + return ret; + st->upsample = resampling_factor(sampling_rate); + return OPUS_OK; +} + +#ifdef CUSTOM_MODES +void opus_custom_encoder_destroy(CELTEncoder *st) +{ + opus_free(st); +} +#endif /* CUSTOM_MODES */ + + +static int transient_analysis(const opus_val32 * OPUS_RESTRICT in, int len, int C, + opus_val16 *tf_estimate, int *tf_chan, int allow_weak_transients, + int *weak_transient, opus_val16 tone_freq, opus_val32 toneishness) +{ + int i; + VARDECL(opus_val16, tmp); + opus_val32 mem0,mem1; + int is_transient = 0; + opus_int32 mask_metric = 0; + int c; + opus_val16 tf_max; + int len2; + /* Forward masking: 6.7 dB/ms. */ +#ifdef FIXED_POINT + int forward_shift = 4; +#else + opus_val16 forward_decay = QCONST16(.0625f,15); +#endif + /* Table of 6*64/x, trained on real data to minimize the average error */ + static const unsigned char inv_table[128] = { + 255,255,156,110, 86, 70, 59, 51, 45, 40, 37, 33, 31, 28, 26, 25, + 23, 22, 21, 20, 19, 18, 17, 16, 16, 15, 15, 14, 13, 13, 12, 12, + 12, 12, 11, 11, 11, 10, 10, 10, 9, 9, 9, 9, 9, 9, 8, 8, + 8, 8, 8, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, + 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, + }; + SAVE_STACK; + ALLOC(tmp, len, opus_val16); + + *weak_transient = 0; + /* For lower bitrates, let's be more conservative and have a forward masking + decay of 3.3 dB/ms. This avoids having to code transients at very low + bitrate (mostly for hybrid), which can result in unstable energy and/or + partial collapse. */ + if (allow_weak_transients) + { +#ifdef FIXED_POINT + forward_shift = 5; +#else + forward_decay = QCONST16(.03125f,15); +#endif + } + len2=len/2; + for (c=0;c=0;i--) + { + /* Backward masking: 13.9 dB/ms. */ +#ifdef FIXED_POINT + /* FIXME: Use PSHR16() instead */ + tmp[i] = mem0 + PSHR32(tmp[i]-mem0,3); + mem0 = tmp[i]; + maxE = MAX16(maxE, mem0); +#else + mem0 = tmp[i] + 0.875f*mem0; + tmp[i] = 0.125f*mem0; + maxE = MAX16(maxE, 0.125f*mem0); +#endif + } + /*for (i=0;i>1))); +#else + mean = celt_sqrt(mean * maxE*.5*len2); +#endif + /* Inverse of the mean energy in Q15+6 */ + norm = SHL32(EXTEND32(len2),6+14)/ADD32(EPSILON,SHR32(mean,1)); + /* Compute harmonic mean discarding the unreliable boundaries + The data is smooth, so we only take 1/4th of the samples */ + unmask=0; + /* We should never see NaNs here. If we find any, then something really bad happened and we better abort + before it does any damage later on. If these asserts are disabled (no hardening), then the table + lookup a few lines below (id = ...) is likely to crash dur to an out-of-bounds read. DO NOT FIX + that crash on NaN since it could result in a worse issue later on. */ + celt_assert(!celt_isnan(tmp[0])); + celt_assert(!celt_isnan(norm)); + for (i=12;imask_metric) + { + *tf_chan = c; + mask_metric = unmask; + } + } + is_transient = mask_metric>200; + /* Prevent the transient detector from confusing the partial cycle of a + very low frequency tone with a transient. */ + if (toneishness > QCONST32(.98f, 29) && tone_freq < QCONST16(0.026f, 13)) + is_transient = 0; + /* For low bitrates, define "weak transients" that need to be + handled differently to avoid partial collapse. */ + if (allow_weak_transients && is_transient && mask_metric<600) { + is_transient = 0; + *weak_transient = 1; + } + /* Arbitrary metric for VBR boost */ + tf_max = MAX16(0,celt_sqrt(27*mask_metric)-42); + /* *tf_estimate = 1 + MIN16(1, sqrt(MAX16(0, tf_max-30))/20); */ + *tf_estimate = celt_sqrt(MAX32(0, SHL32(MULT16_16(QCONST16(0.0069,14),MIN16(163,tf_max)),14)-QCONST32(0.139,28))); + /*printf("%d %f\n", tf_max, mask_metric);*/ + RESTORE_STACK; +#ifdef FUZZING + is_transient = rand()&0x1; +#endif + /*printf("%d %f %d\n", is_transient, (float)*tf_estimate, tf_max);*/ + return is_transient; +} + +/* Looks for sudden increases of energy to decide whether we need to patch + the transient decision */ +static int patch_transient_decision(celt_glog *newE, celt_glog *oldE, int nbEBands, + int start, int end, int C) +{ + int i, c; + opus_val32 mean_diff=0; + celt_glog spread_old[26]; + /* Apply an aggressive (-6 dB/Bark) spreading function to the old frame to + avoid false detection caused by irrelevant bands */ + if (C==1) + { + spread_old[start] = oldE[start]; + for (i=start+1;i=start;i--) + spread_old[i] = MAXG(spread_old[i], spread_old[i+1]-GCONST(1.0f)); + /* Compute mean increase */ + c=0; do { + for (i=IMAX(2,start);i GCONST(1.f); +} + +/** Apply window and compute the MDCT for all sub-frames and + all channels in a frame */ +static void compute_mdcts(const CELTMode *mode, int shortBlocks, celt_sig * OPUS_RESTRICT in, + celt_sig * OPUS_RESTRICT out, int C, int CC, int LM, int upsample, + int arch) +{ + const int overlap = mode->overlap; + int N; + int B; + int shift; + int i, b, c; + if (shortBlocks) + { + B = shortBlocks; + N = mode->shortMdctSize; + shift = mode->maxLM; + } else { + B = 1; + N = mode->shortMdctSize<maxLM-LM; + } + c=0; do { + for (b=0;bmdct, in+c*(B*N+overlap)+b*N, + &out[b+c*N*B], mode->window, overlap, shift, B, + arch); + } + } while (++ceBands[len]-m->eBands[len-1])<eBands[len]-m->eBands[len-1])<eBands[i+1]-m->eBands[i])<eBands[i+1]-m->eBands[i])==1; + OPUS_COPY(tmp, &X[tf_chan*N0 + (m->eBands[i]<eBands[i]<>LM, 1<>k, 1<=0;i--) + { + if (tf_res[i+1] == 1) + tf_res[i] = path1[i+1]; + else + tf_res[i] = path0[i+1]; + } + /*printf("%d %f\n", *tf_sum, tf_estimate);*/ + RESTORE_STACK; +#ifdef FUZZING + tf_select = rand()&0x1; + tf_res[0] = rand()&0x1; + for (i=1;istorage*8; + tell = ec_tell(enc); + logp = isTransient ? 2 : 4; + /* Reserve space to code the tf_select decision. */ + tf_select_rsv = LM>0 && tell+logp+1 <= budget; + budget -= tf_select_rsv; + curr = tf_changed = 0; + for (i=start;i> 10; + trim = QCONST16(4.f, 8) + QCONST16(1.f/16.f, 8)*frac; + } + if (C==2) + { + opus_val16 sum = 0; /* Q10 */ + opus_val16 minXC; /* Q10 */ + /* Compute inter-channel correlation for low frequencies */ + for (i=0;i<8;i++) + { + opus_val32 partial; + partial = celt_inner_prod(&X[m->eBands[i]<eBands[i]<eBands[i+1]-m->eBands[i])<eBands[i]<eBands[i]<eBands[i+1]-m->eBands[i])<nbEBands], 5)*(opus_int32)(2+2*i-end); + } + } while (++cvalid) + { + trim -= MAX16(-QCONST16(2.f, 8), MIN16(QCONST16(2.f, 8), + (opus_val16)(QCONST16(2.f, 8)*(analysis->tonality_slope+.05f)))); + } +#else + (void)analysis; +#endif + +#ifdef FIXED_POINT + trim_index = PSHR32(trim, 8); +#else + trim_index = (int)floor(.5f+trim); +#endif + trim_index = IMAX(0, IMIN(10, trim_index)); + /*printf("%d\n", trim_index);*/ +#ifdef FUZZING + trim_index = rand()%11; +#endif + return trim_index; +} + +static int stereo_analysis(const CELTMode *m, const celt_norm *X, + int LM, int N0) +{ + int i; + int thetas; + opus_val32 sumLR = EPSILON, sumMS = EPSILON; + + /* Use the L1 norm to model the entropy of the L/R signal vs the M/S signal */ + for (i=0;i<13;i++) + { + int j; + for (j=m->eBands[i]<eBands[i+1]<eBands[13]<<(LM+1))+thetas, sumMS) + > MULT16_32_Q15(m->eBands[13]<<(LM+1), sumLR); +} + +#define MSWAP(a,b) do {celt_glog tmp = a;a=b;b=tmp;} while(0) +static celt_glog median_of_5(const celt_glog *x) +{ + celt_glog t0, t1, t2, t3, t4; + t2 = x[2]; + if (x[0] > x[1]) + { + t0 = x[1]; + t1 = x[0]; + } else { + t0 = x[0]; + t1 = x[1]; + } + if (x[3] > x[4]) + { + t3 = x[4]; + t4 = x[3]; + } else { + t3 = x[3]; + t4 = x[4]; + } + if (t0 > t3) + { + MSWAP(t0, t3); + MSWAP(t1, t4); + } + if (t2 > t1) + { + if (t1 < t3) + return MING(t2, t3); + else + return MING(t4, t1); + } else { + if (t2 < t3) + return MING(t1, t3); + else + return MING(t2, t4); + } +} + +static celt_glog median_of_3(const celt_glog *x) +{ + celt_glog t0, t1, t2; + if (x[0] > x[1]) + { + t0 = x[1]; + t1 = x[0]; + } else { + t0 = x[0]; + t1 = x[1]; + } + t2 = x[2]; + if (t1 < t2) + return t1; + else if (t0 < t2) + return t2; + else + return t0; +} + +static celt_glog dynalloc_analysis(const celt_glog *bandLogE, const celt_glog *bandLogE2, const celt_glog *oldBandE, + int nbEBands, int start, int end, int C, int *offsets, int lsb_depth, const opus_int16 *logN, + int isTransient, int vbr, int constrained_vbr, const opus_int16 *eBands, int LM, + int effectiveBytes, opus_int32 *tot_boost_, int lfe, celt_glog *surround_dynalloc, + AnalysisInfo *analysis, int *importance, int *spread_weight, opus_val16 tone_freq, opus_val32 toneishness) +{ + int i, c; + opus_int32 tot_boost=0; + celt_glog maxDepth; + VARDECL(celt_glog, follower); + VARDECL(celt_glog, noise_floor); + VARDECL(celt_glog, bandLogE3); + SAVE_STACK; + ALLOC(follower, C*nbEBands, celt_glog); + ALLOC(noise_floor, C*nbEBands, celt_glog); + ALLOC(bandLogE3, nbEBands, celt_glog); + OPUS_CLEAR(offsets, nbEBands); + /* Dynamic allocation code */ + maxDepth=-GCONST(31.9f); + for (i=0;i=0;i--) + mask[i] = MAXG(mask[i], mask[i+1] - GCONST(3.f)); + for (i=0;i> shift; + } + /*for (i=0;i= (30 + 5*LM) && !lfe) + { + int last=0; + c=0;do + { + celt_glog offset; + celt_glog tmp; + celt_glog *f; + OPUS_COPY(bandLogE3, &bandLogE2[c*nbEBands], end); + if (LM==0) { + /* For 2.5 ms frames, the first 8 bands have just one bin, so the + energy is highly unreliable (high variance). For that reason, + we take the max with the previous energy so that at least 2 bins + are getting used. */ + for (i=0;i bandLogE3[i-1]+GCONST(.5f)) + last=i; + f[i] = MING(f[i-1]+GCONST(1.5f), bandLogE3[i]); + } + for (i=last-1;i>=0;i--) + f[i] = MING(f[i], MING(f[i+1]+GCONST(2.f), bandLogE3[i])); + + /* Combine with a median filter to avoid dynalloc triggering unnecessarily. + The "offset" value controls how conservative we are -- a higher offset + reduces the impact of the median filter and makes dynalloc use more bits. */ + offset = GCONST(1.f); + for (i=2;i=12) + follower[i] = HALF32(follower[i]); + } + /* Compensate for Opus' under-allocation on tones. */ + if (toneishness > QCONST32(.98f, 29)) { +#ifdef FIXED_POINT + int freq_bin = PSHR32(MULT16_16(tone_freq, QCONST16(120/M_PI, 9)), 13+9); +#else + int freq_bin = (int)floor(.5 + tone_freq*120/M_PI); +#endif + for (i=start;i= eBands[i] && freq_bin <= eBands[i+1]) follower[i] += GCONST(2.f); + if (freq_bin >= eBands[i]-1 && freq_bin <= eBands[i+1]+1) follower[i] += GCONST(1.f); + if (freq_bin >= eBands[i]-2 && freq_bin <= eBands[i+1]+2) follower[i] += GCONST(1.f); + if (freq_bin >= eBands[i]-3 && freq_bin <= eBands[i+1]+3) follower[i] += GCONST(.5f); + } + } +#ifdef DISABLE_FLOAT_API + (void)analysis; +#else + if (analysis->valid) + { + for (i=start;ileak_boost[i]; + } +#endif + for (i=start;i 48) { + boost = (int)SHR32(follower[i]*8,DB_SHIFT-8); + boost_bits = (boost*width<>BITRES>>3 > 2*effectiveBytes/3) + { + opus_int32 cap = ((2*effectiveBytes/3)< 0) { + for (i=0;i>15; + tmp = (762*x14>>14)-3308; + tmp = (tmp*x14>>14)+25726; + tmp = tmp*celt_sqrt(IMAX(0, (1<<30) - (x<<1)))>>16; + if (flip) tmp = 25736 - tmp; + return tmp; +} +#endif + +/* Compute the LPC coefficients using a least-squares fit for both forward and backward prediction. */ +static int tone_lpc(const opus_val16 *x, int len, int delay, opus_val32 *lpc) { + int i; + opus_val32 r00=0, r01=0, r11=0, r02=0, r12=0, r22=0; + opus_val32 edges; + opus_val32 num0, num1, den; + celt_assert(len > 2*delay); + /* Compute correlations as if using the forward prediction covariance method. */ + for (i=0;i= den) lpc[1] = QCONST32(1.f, 29); + else if (num1 <= -den) lpc[1] = -QCONST32(1.f, 29); + else lpc[1] = frac_div32_q29(num1, den); + num0 = MULT32_32_Q31(r00,r12) - MULT32_32_Q31(r02,r01); + if (HALF32(num0) >= den) lpc[0] = QCONST32(1.999999f, 29); + else if (HALF32(num0) <= -den) lpc[0] = -QCONST32(1.999999f, 29); + else lpc[0] = frac_div32_q29(num0, den); + /*printf("%f %f\n", lpc[0], lpc[1]);*/ + return 0; +} + +/* Detects pure of nearly pure tones so we can prevent them from causing problems with the encoder. */ +static opus_val16 tone_detect(const celt_sig *in, const celt_sig *prefilter_mem, int CC, int N, int overlap, opus_val32 *toneishness, opus_int32 Fs) { + int i; + int delay = 1; + int fail; + opus_val32 lpc[2]; + opus_val16 freq; + VARDECL(opus_val16, x); + ALLOC(x, N+overlap, opus_val16); + /* Shift by SIG_SHIFT+1 (+2 for stereo) to account for HF gain of the preemphasis filter. */ + if (CC==2) { + for (i=0;i QCONST32(1.f, 29) && lpc[1] < 0))) { + delay *= 2; + fail = tone_lpc(x, N+overlap, delay, lpc); + } + /* Check that our filter has complex roots. */ + if (!fail && MULT32_32_Q31(lpc[0],lpc[0]) + MULT32_32_Q31(QCONST32(3.999999, 29), lpc[1]) < 0) { + /* Squared radius of the poles. */ + *toneishness = -lpc[1]; +#ifdef FIXED_POINT + freq = acos_approx(lpc[0]>>1)/delay; +#else + freq = acos(.5f*lpc[0])/delay; +#endif + } else { + freq = -1; + *toneishness=0; + } + /*printf("%f %f %f %f\n", freq, lpc[0], lpc[1], *toneishness);*/ + return freq; +} + +static int run_prefilter(CELTEncoder *st, celt_sig *in, celt_sig *prefilter_mem, int CC, int N, + int prefilter_tapset, int *pitch, opus_val16 *gain, int *qgain, int enabled, int nbAvailableBytes, AnalysisInfo *analysis, opus_val16 tone_freq, opus_val32 toneishness) +{ + int c; + VARDECL(celt_sig, _pre); + celt_sig *pre[2]; + const CELTMode *mode; + int pitch_index; + opus_val16 gain1; + opus_val16 pf_threshold; + int pf_on; + int qg; + int overlap; + SAVE_STACK; + + mode = st->mode; + overlap = mode->overlap; + ALLOC(_pre, CC*(N+COMBFILTER_MAXPERIOD), celt_sig); + + pre[0] = _pre; + pre[1] = _pre + (N+COMBFILTER_MAXPERIOD); + + + c=0; do { + OPUS_COPY(pre[c], prefilter_mem+c*COMBFILTER_MAXPERIOD, COMBFILTER_MAXPERIOD); + OPUS_COPY(pre[c]+COMBFILTER_MAXPERIOD, in+c*(N+overlap)+overlap, N); + } while (++c>1, opus_val16); + + pitch_downsample(pre, pitch_buf, COMBFILTER_MAXPERIOD+N, CC, st->arch); + /* Don't search for the fir last 1.5 octave of the range because + there's too many false-positives due to short-term correlation */ + pitch_search(pitch_buf+(COMBFILTER_MAXPERIOD>>1), pitch_buf, N, + COMBFILTER_MAXPERIOD-3*COMBFILTER_MINPERIOD, &pitch_index, + st->arch); + pitch_index = COMBFILTER_MAXPERIOD-pitch_index; + + gain1 = remove_doubling(pitch_buf, COMBFILTER_MAXPERIOD, COMBFILTER_MINPERIOD, + N, &pitch_index, st->prefilter_period, st->prefilter_gain, st->arch); + if (pitch_index > COMBFILTER_MAXPERIOD-2) + pitch_index = COMBFILTER_MAXPERIOD-2; + gain1 = MULT16_16_Q15(QCONST16(.7f,15),gain1); + /* If we detect that the signal is dominated by a single tone, don't rely on the standard pitch + estimator, as it can become unreliable. */ + if (toneishness > QCONST32(.99f, 29)) { + /* If the pitch is too high for our post-filter, apply pitch doubling until + we can get something that fits (not ideal, but better than nothing). */ + while (tone_freq >= QCONST16(0.39f, 13)) tone_freq/=2; + if (tone_freq > QCONST16(0.006148f, 13)) { +#ifdef FIXED_POINT + pitch_index = IMIN(51472/tone_freq, COMBFILTER_MAXPERIOD-2); +#else + pitch_index = IMIN((int)floor(.5+2.f*M_PI/tone_freq), COMBFILTER_MAXPERIOD-2); +#endif + } else { + /* If the pitch is too low, using a very high pitch will actually give us an improvement + due to the DC component of the filter that will be close to our tone. Again, not ideal, + but if we only have a single tone, it's better than nothing. */ + pitch_index = COMBFILTER_MINPERIOD; + } + gain1 = QCONST16(.75f, 15); + } + /*printf("%d %d %f %f\n", pitch_change, pitch_index, gain1, st->analysis.tonality);*/ + if (st->loss_rate>2) + gain1 = HALF32(gain1); + if (st->loss_rate>4) + gain1 = HALF32(gain1); + if (st->loss_rate>8) + gain1 = 0; + } else { + gain1 = 0; + pitch_index = COMBFILTER_MINPERIOD; + } +#ifndef DISABLE_FLOAT_API + if (analysis->valid) + gain1 = (opus_val16)(gain1 * analysis->max_pitch_ratio); +#else + (void)analysis; +#endif + /* Gain threshold for enabling the prefilter/postfilter */ + pf_threshold = QCONST16(.2f,15); + + /* Adjusting the threshold based on rate and continuity */ + if (abs(pitch_index-st->prefilter_period)*10>pitch_index) + pf_threshold += QCONST16(.2f,15); + if (nbAvailableBytes<25) + pf_threshold += QCONST16(.1f,15); + if (nbAvailableBytes<35) + pf_threshold += QCONST16(.1f,15); + if (st->prefilter_gain > QCONST16(.4f,15)) + pf_threshold -= QCONST16(.1f,15); + if (st->prefilter_gain > QCONST16(.55f,15)) + pf_threshold -= QCONST16(.1f,15); + + /* Hard threshold at 0.2 */ + pf_threshold = MAX16(pf_threshold, QCONST16(.2f,15)); + if (gain1prefilter_gain)prefilter_gain; + +#ifdef FIXED_POINT + qg = ((gain1+1536)>>10)/3-1; +#else + qg = (int)floor(.5f+gain1*32/3)-1; +#endif + qg = IMAX(0, IMIN(7, qg)); + gain1 = QCONST16(0.09375f,15)*(qg+1); + pf_on = 1; + } + /*printf("%d %f\n", pitch_index, gain1);*/ + + c=0; do { + int offset = mode->shortMdctSize-overlap; + st->prefilter_period=IMAX(st->prefilter_period, COMBFILTER_MINPERIOD); + OPUS_COPY(in+c*(N+overlap), st->in_mem+c*(overlap), overlap); + if (offset) + comb_filter(in+c*(N+overlap)+overlap, pre[c]+COMBFILTER_MAXPERIOD, + st->prefilter_period, st->prefilter_period, offset, -st->prefilter_gain, -st->prefilter_gain, + st->prefilter_tapset, st->prefilter_tapset, NULL, 0, st->arch); + + comb_filter(in+c*(N+overlap)+overlap+offset, pre[c]+COMBFILTER_MAXPERIOD+offset, + st->prefilter_period, pitch_index, N-offset, -st->prefilter_gain, -gain1, + st->prefilter_tapset, prefilter_tapset, mode->window, overlap, st->arch); + OPUS_COPY(st->in_mem+c*(overlap), in+c*(N+overlap)+N, overlap); + + if (N>COMBFILTER_MAXPERIOD) + { + OPUS_COPY(prefilter_mem+c*COMBFILTER_MAXPERIOD, pre[c]+N, COMBFILTER_MAXPERIOD); + } else { + OPUS_MOVE(prefilter_mem+c*COMBFILTER_MAXPERIOD, prefilter_mem+c*COMBFILTER_MAXPERIOD+N, COMBFILTER_MAXPERIOD-N); + OPUS_COPY(prefilter_mem+c*COMBFILTER_MAXPERIOD+COMBFILTER_MAXPERIOD-N, pre[c]+COMBFILTER_MAXPERIOD, N); + } + } while (++cnbEBands; + eBands = mode->eBands; + + coded_bands = lastCodedBands ? lastCodedBands : nbEBands; + coded_bins = eBands[coded_bands]<analysis.activity, st->analysis.tonality, tf_estimate, st->stereo_saving, tot_boost, coded_bands);*/ +#ifndef DISABLE_FLOAT_API + if (analysis->valid && analysis->activity<.4) + target -= (opus_int32)((coded_bins<activity)); +#endif + /* Stereo savings */ + if (C==2) + { + int coded_stereo_bands; + int coded_stereo_dof; + opus_val16 max_frac; + coded_stereo_bands = IMIN(intensity, coded_bands); + coded_stereo_dof = (eBands[coded_stereo_bands]<valid && !lfe) + { + opus_int32 tonal_target; + float tonal; + + /* Tonality boost (compensating for the average). */ + tonal = MAX16(0.f,analysis->tonality-.15f)-0.12f; + tonal_target = target + (opus_int32)((coded_bins<tonality, tonal);*/ + target = tonal_target; + } +#else + (void)analysis; + (void)pitch_change; +#endif + + if (has_surround_mask&&!lfe) + { + opus_int32 surround_target = target + (opus_int32)SHR32(MULT16_16(SHR32(surround_masking,DB_SHIFT-10),coded_bins<end, st->intensity, surround_target, target, st->bitrate);*/ + target = IMAX(target/4, surround_target); + } + + { + opus_int32 floor_depth; + int bins; + bins = eBands[nbEBands-2]<>2); + target = IMIN(target, floor_depth); + /*printf("%f %d\n", maxDepth, floor_depth);*/ + } + + /* Make VBR less aggressive for constrained VBR because we can't keep a higher bitrate + for long. Needs tuning. */ + if ((!has_surround_mask||lfe) && constrained_vbr) + { + target = base_target + (opus_int32)MULT16_32_Q15(QCONST16(0.67f, 15), target-base_target); + } + + if (!has_surround_mask && tf_estimate < QCONST16(.2f, 14)) + { + opus_val16 amount; + opus_val16 tvbr_factor; + amount = MULT16_16_Q15(QCONST16(.0000031f, 30), IMAX(0, IMIN(32000, 96000-bitrate))); + tvbr_factor = SHR32(MULT16_16(SHR32(temporal_vbr, DB_SHIFT-10), amount), 10); + target += (opus_int32)MULT16_32_Q15(tvbr_factor, target); + } + + /* Don't allow more than doubling the rate */ + target = IMIN(2*base_target, target); + + return target; +} + +int celt_encode_with_ec(CELTEncoder * OPUS_RESTRICT st, const opus_res * pcm, int frame_size, unsigned char *compressed, int nbCompressedBytes, ec_enc *enc) +{ + int i, c, N; + opus_int32 bits; + ec_enc _enc; + VARDECL(celt_sig, in); + VARDECL(celt_sig, freq); + VARDECL(celt_norm, X); + VARDECL(celt_ener, bandE); + VARDECL(celt_glog, bandLogE); + VARDECL(celt_glog, bandLogE2); + VARDECL(int, fine_quant); + VARDECL(celt_glog, error); + VARDECL(int, pulses); + VARDECL(int, cap); + VARDECL(int, offsets); + VARDECL(int, importance); + VARDECL(int, spread_weight); + VARDECL(int, fine_priority); + VARDECL(int, tf_res); + VARDECL(unsigned char, collapse_masks); + celt_sig *prefilter_mem; + celt_glog *oldBandE, *oldLogE, *oldLogE2, *energyError; + int shortBlocks=0; + int isTransient=0; + const int CC = st->channels; + const int C = st->stream_channels; + int LM, M; + int tf_select; + int nbFilledBytes, nbAvailableBytes; + int start; + int end; + int effEnd; + int codedBands; + int alloc_trim; + int pitch_index=COMBFILTER_MINPERIOD; + opus_val16 gain1 = 0; + int dual_stereo=0; + int effectiveBytes; + int dynalloc_logp; + opus_int32 vbr_rate; + opus_int32 total_bits; + opus_int32 total_boost; + opus_int32 balance; + opus_int32 tell; + opus_int32 tell0_frac; + int prefilter_tapset=0; + int pf_on; + int anti_collapse_rsv; + int anti_collapse_on=0; + int silence=0; + int tf_chan = 0; + opus_val16 tf_estimate; + int pitch_change=0; + opus_int32 tot_boost; + opus_val32 sample_max; + celt_glog maxDepth; + const OpusCustomMode *mode; + int nbEBands; + int overlap; + const opus_int16 *eBands; + int secondMdct; + int signalBandwidth; + int transient_got_disabled=0; + celt_glog surround_masking=0; + celt_glog temporal_vbr=0; + celt_glog surround_trim = 0; + opus_int32 equiv_rate; + int hybrid; + int weak_transient = 0; + int enable_tf_analysis; + opus_val16 tone_freq=-1; + opus_val32 toneishness=0; + VARDECL(celt_glog, surround_dynalloc); + ALLOC_STACK; + + mode = st->mode; + nbEBands = mode->nbEBands; + overlap = mode->overlap; + eBands = mode->eBands; + start = st->start; + end = st->end; + hybrid = start != 0; + tf_estimate = 0; + if (nbCompressedBytes<2 || pcm==NULL) + { + RESTORE_STACK; + return OPUS_BAD_ARG; + } + + frame_size *= st->upsample; + for (LM=0;LM<=mode->maxLM;LM++) + if (mode->shortMdctSize<mode->maxLM) + { + RESTORE_STACK; + return OPUS_BAD_ARG; + } + M=1<shortMdctSize; + + prefilter_mem = st->in_mem+CC*(overlap); + oldBandE = (celt_glog*)(st->in_mem+CC*(overlap+COMBFILTER_MAXPERIOD)); + oldLogE = oldBandE + CC*nbEBands; + oldLogE2 = oldLogE + CC*nbEBands; + energyError = oldLogE2 + CC*nbEBands; + + if (enc==NULL) + { + tell0_frac=tell=1; + nbFilledBytes=0; + } else { + tell0_frac=ec_tell_frac(enc); + tell=ec_tell(enc); + nbFilledBytes=(tell+4)>>3; + } + +#ifdef CUSTOM_MODES + if (st->signalling && enc==NULL) + { + int tmp = (mode->effEBands-end)>>1; + end = st->end = IMAX(1, mode->effEBands-tmp); + compressed[0] = tmp<<5; + compressed[0] |= LM<<3; + compressed[0] |= (C==2)<<2; + /* Convert "standard mode" to Opus header */ + if (mode->Fs==48000 && mode->shortMdctSize==120) + { + int c0 = toOpus(compressed[0]); + if (c0<0) + { + RESTORE_STACK; + return OPUS_BAD_ARG; + } + compressed[0] = c0; + } + compressed++; + nbCompressedBytes--; + } +#else + celt_assert(st->signalling==0); +#endif + + /* Can't produce more than 1275 output bytes */ + nbCompressedBytes = IMIN(nbCompressedBytes,1275); + + if (st->vbr && st->bitrate!=OPUS_BITRATE_MAX) + { + opus_int32 den=mode->Fs>>BITRES; + vbr_rate=(st->bitrate*frame_size+(den>>1))/den; +#ifdef CUSTOM_MODES + if (st->signalling) + vbr_rate -= 8<>(3+BITRES); + } else { + opus_int32 tmp; + vbr_rate = 0; + tmp = st->bitrate*frame_size; + if (tell>1) + tmp += tell*mode->Fs; + if (st->bitrate!=OPUS_BITRATE_MAX) + { + nbCompressedBytes = IMAX(2, IMIN(nbCompressedBytes, + (tmp+4*mode->Fs)/(8*mode->Fs)-!!st->signalling)); + if (enc != NULL) + ec_enc_shrink(enc, nbCompressedBytes); + } + effectiveBytes = nbCompressedBytes - nbFilledBytes; + } + nbAvailableBytes = nbCompressedBytes - nbFilledBytes; + equiv_rate = ((opus_int32)nbCompressedBytes*8*50 << (3-LM)) - (40*C+20)*((400>>LM) - 50); + if (st->bitrate != OPUS_BITRATE_MAX) + equiv_rate = IMIN(equiv_rate, st->bitrate - (40*C+20)*((400>>LM) - 50)); + + if (enc==NULL) + { + ec_enc_init(&_enc, compressed, nbCompressedBytes); + enc = &_enc; + } + + if (vbr_rate>0) + { + /* Computes the max bit-rate allowed in VBR mode to avoid violating the + target rate and buffering. + We must do this up front so that bust-prevention logic triggers + correctly if we don't have enough bits. */ + if (st->constrained_vbr) + { + opus_int32 vbr_bound; + opus_int32 max_allowed; + /* We could use any multiple of vbr_rate as bound (depending on the + delay). + This is clamped to ensure we use at least two bytes if the encoder + was entirely empty, but to allow 0 in hybrid mode. */ + vbr_bound = vbr_rate; + max_allowed = IMIN(IMAX(tell==1?2:0, + (vbr_rate+vbr_bound-st->vbr_reservoir)>>(BITRES+3)), + nbAvailableBytes); + if(max_allowed < nbAvailableBytes) + { + nbCompressedBytes = nbFilledBytes+max_allowed; + nbAvailableBytes = max_allowed; + ec_enc_shrink(enc, nbCompressedBytes); + } + } + } + total_bits = nbCompressedBytes*8; + + effEnd = end; + if (effEnd > mode->effEBands) + effEnd = mode->effEBands; + + ALLOC(in, CC*(N+overlap), celt_sig); + + sample_max=MAX32(st->overlap_max, celt_maxabs_res(pcm, C*(N-overlap)/st->upsample)); + st->overlap_max=celt_maxabs_res(pcm+C*(N-overlap)/st->upsample, C*overlap/st->upsample); + sample_max=MAX32(sample_max, st->overlap_max); +#ifdef FIXED_POINT + silence = (sample_max==0); +#else + silence = (sample_max <= (opus_val16)1/(1<lsb_depth)); +#endif +#ifdef FUZZING + if ((rand()&0x3F)==0) + silence = 1; +#endif + if (tell==1) + ec_enc_bit_logp(enc, silence, 15); + else + silence=0; + if (silence) + { + /*In VBR mode there is no need to send more than the minimum. */ + if (vbr_rate>0) + { + effectiveBytes=nbCompressedBytes=IMIN(nbCompressedBytes, nbFilledBytes+2); + total_bits=nbCompressedBytes*8; + nbAvailableBytes=2; + ec_enc_shrink(enc, nbCompressedBytes); + } + /* Pretend we've filled all the remaining bits with zeros + (that's what the initialiser did anyway) */ + tell = nbCompressedBytes*8; + enc->nbits_total+=tell-ec_tell(enc); + } + c=0; do { + int need_clip=0; +#ifndef FIXED_POINT + need_clip = st->clip && sample_max>65536.f; +#endif + celt_preemphasis(pcm+c, in+c*(N+overlap)+overlap, N, CC, st->upsample, + mode->preemph, st->preemph_memE+c, need_clip); + } while (++cFs); + /* Find pitch period and gain */ + { + int enabled; + int qg; + enabled = ((st->lfe&&nbAvailableBytes>3) || nbAvailableBytes>12*C) && !hybrid && !silence && tell+16<=total_bits && !st->disable_pf + && st->complexity >= 5; + + prefilter_tapset = st->tapset_decision; + pf_on = run_prefilter(st, in, prefilter_mem, CC, N, prefilter_tapset, &pitch_index, &gain1, &qg, enabled, nbAvailableBytes, &st->analysis, tone_freq, toneishness); + if ((gain1 > QCONST16(.4f,15) || st->prefilter_gain > QCONST16(.4f,15)) && (!st->analysis.valid || st->analysis.tonality > .3) + && (pitch_index > 1.26*st->prefilter_period || pitch_index < .79*st->prefilter_period)) + pitch_change = 1; + if (pf_on==0) + { + if(!hybrid && tell+16<=total_bits) + ec_enc_bit_logp(enc, 0, 1); + } else { + /*This block is not gated by a total bits check only because + of the nbAvailableBytes check above.*/ + int octave; + ec_enc_bit_logp(enc, 1, 1); + pitch_index += 1; + octave = EC_ILOG(pitch_index)-5; + ec_enc_uint(enc, octave, 6); + ec_enc_bits(enc, pitch_index-(16<complexity >= 1 && !st->lfe) + { + /* Reduces the likelihood of energy instability on fricatives at low bitrate + in hybrid mode. It seems like we still want to have real transients on vowels + though (small SILK quantization offset value). */ + int allow_weak_transients = hybrid && effectiveBytes<15 && st->silk_info.signalType != 2; + isTransient = transient_analysis(in, N+overlap, CC, + &tf_estimate, &tf_chan, allow_weak_transients, &weak_transient, tone_freq, toneishness); + } + if (LM>0 && ec_tell(enc)+3<=total_bits) + { + if (isTransient) + shortBlocks = M; + } else { + isTransient = 0; + transient_got_disabled=1; + } + + ALLOC(freq, CC*N, celt_sig); /**< Interleaved signal MDCTs */ + ALLOC(bandE,nbEBands*CC, celt_ener); + ALLOC(bandLogE,nbEBands*CC, celt_glog); + + secondMdct = shortBlocks && st->complexity>=8; + ALLOC(bandLogE2, C*nbEBands, celt_glog); + if (secondMdct) + { + compute_mdcts(mode, 0, in, freq, C, CC, LM, st->upsample, st->arch); + compute_band_energies(mode, freq, bandE, effEnd, C, LM, st->arch); + amp2Log2(mode, effEnd, end, bandE, bandLogE2, C); + for (c=0;cupsample, st->arch); + /* This should catch any NaN in the CELT input. Since we're not supposed to see any (they're filtered + at the Opus layer), just abort. */ + celt_assert(!celt_isnan(freq[0]) && (C==1 || !celt_isnan(freq[N]))); + if (CC==2&&C==1) + tf_chan = 0; + compute_band_energies(mode, freq, bandE, effEnd, C, LM, st->arch); + + if (st->lfe) + { + for (i=2;ienergy_mask&&!st->lfe) + { + int mask_end; + int midband; + int count_dynalloc; + opus_val32 mask_avg=0; + opus_val32 diff=0; + int count=0; + mask_end = IMAX(2,st->lastCodedBands); + for (c=0;cenergy_mask[nbEBands*c+i], + GCONST(.25f)), -GCONST(2.0f)); + if (mask > 0) + mask = HALF32(mask); + mask16 = SHR32(mask, DB_SHIFT-10); + mask_avg += MULT16_16(mask16, eBands[i+1]-eBands[i]); + count += eBands[i+1]-eBands[i]; + diff += MULT16_16(mask16, 1+2*i-mask_end); + } + } + celt_assert(count>0); + mask_avg = SHL32(DIV32_16(mask_avg,count), DB_SHIFT-10); + mask_avg += GCONST(.2f); + diff = SHL32(diff*6/(C*(mask_end-1)*(mask_end+1)*mask_end), DB_SHIFT-10); + /* Again, being conservative */ + diff = HALF32(diff); + diff = MAX32(MIN32(diff, GCONST(.031f)), -GCONST(.031f)); + /* Find the band that's in the middle of the coded spectrum */ + for (midband=0;eBands[midband+1] < eBands[mask_end]/2;midband++); + count_dynalloc=0; + for(i=0;ienergy_mask[i], st->energy_mask[nbEBands+i]); + else + unmask = st->energy_mask[i]; + unmask = MING(unmask, GCONST(.0f)); + unmask -= lin; + if (unmask > GCONST(.25f)) + { + surround_dynalloc[i] = unmask - GCONST(.25f); + count_dynalloc++; + } + } + if (count_dynalloc>=3) + { + /* If we need dynalloc in many bands, it's probably because our + initial masking rate was too low. */ + mask_avg += GCONST(.25f); + if (mask_avg>0) + { + /* Something went really wrong in the original calculations, + disabling masking. */ + mask_avg = 0; + diff = 0; + OPUS_CLEAR(surround_dynalloc, mask_end); + } else { + for(i=0;ilfe) + { + celt_glog follow=-QCONST32(10.0f, DB_SHIFT-5); + opus_val32 frame_avg=0; + celt_glog offset = shortBlocks?HALF32(SHL32(LM, DB_SHIFT-5)):0; + for(i=start;ispec_avg); + temporal_vbr = MING(GCONST(3.f), MAXG(-GCONST(1.5f), temporal_vbr)); + st->spec_avg += MULT16_32_Q15(QCONST16(.02f, 15), temporal_vbr); + } + /*for (i=0;i<21;i++) + printf("%f ", bandLogE[i]); + printf("\n");*/ + + if (!secondMdct) + { + OPUS_COPY(bandLogE2, bandLogE, C*nbEBands); + } + + /* Last chance to catch any transient we might have missed in the + time-domain analysis */ + if (LM>0 && ec_tell(enc)+3<=total_bits && !isTransient && st->complexity>=5 && !st->lfe && !hybrid) + { + if (patch_transient_decision(bandLogE, oldBandE, nbEBands, start, end, C)) + { + isTransient = 1; + shortBlocks = M; + compute_mdcts(mode, shortBlocks, in, freq, C, CC, LM, st->upsample, st->arch); + compute_band_energies(mode, freq, bandE, effEnd, C, LM, st->arch); + amp2Log2(mode, effEnd, end, bandE, bandLogE, C); + /* Compensate for the scaling of short vs long mdcts */ + for (c=0;c0 && ec_tell(enc)+3<=total_bits) + ec_enc_bit_logp(enc, isTransient, 3); + + ALLOC(X, C*N, celt_norm); /**< Interleaved normalised MDCTs */ + + /* Band normalisation */ + normalise_bands(mode, freq, X, bandE, effEnd, C, M); + + enable_tf_analysis = effectiveBytes>=15*C && !hybrid && st->complexity>=2 && !st->lfe && toneishness < QCONST32(.98f, 29); + + ALLOC(offsets, nbEBands, int); + ALLOC(importance, nbEBands, int); + ALLOC(spread_weight, nbEBands, int); + + maxDepth = dynalloc_analysis(bandLogE, bandLogE2, oldBandE, nbEBands, start, end, C, offsets, + st->lsb_depth, mode->logN, isTransient, st->vbr, st->constrained_vbr, + eBands, LM, effectiveBytes, &tot_boost, st->lfe, surround_dynalloc, &st->analysis, importance, spread_weight, tone_freq, toneishness); + + ALLOC(tf_res, nbEBands, int); + /* Disable variable tf resolution for hybrid and at very low bitrate */ + if (enable_tf_analysis) + { + int lambda; + lambda = IMAX(80, 20480/effectiveBytes + 2); + tf_select = tf_analysis(mode, effEnd, isTransient, tf_res, lambda, X, N, LM, tf_estimate, tf_chan, importance); + for (i=effEnd;isilk_info.signalType != 2) + { + /* For low bitrate hybrid, we force temporal resolution to 5 ms rather than 2.5 ms. */ + for (i=0;iforce_intra, + &st->delayedIntra, st->complexity >= 4, st->loss_rate, st->lfe); + + tf_encode(start, end, isTransient, tf_res, LM, tf_select, enc); + + if (ec_tell(enc)+4<=total_bits) + { + if (st->lfe) + { + st->tapset_decision = 0; + st->spread_decision = SPREAD_NORMAL; + } else if (hybrid) + { + if (st->complexity == 0) + st->spread_decision = SPREAD_NONE; + else if (isTransient) + st->spread_decision = SPREAD_NORMAL; + else + st->spread_decision = SPREAD_AGGRESSIVE; + } else if (shortBlocks || st->complexity < 3 || nbAvailableBytes < 10*C) + { + if (st->complexity == 0) + st->spread_decision = SPREAD_NONE; + else + st->spread_decision = SPREAD_NORMAL; + } else { + /* Disable new spreading+tapset estimator until we can show it works + better than the old one. So far it seems like spreading_decision() + works best. */ +#if 0 + if (st->analysis.valid) + { + static const opus_val16 spread_thresholds[3] = {-QCONST16(.6f, 15), -QCONST16(.2f, 15), -QCONST16(.07f, 15)}; + static const opus_val16 spread_histeresis[3] = {QCONST16(.15f, 15), QCONST16(.07f, 15), QCONST16(.02f, 15)}; + static const opus_val16 tapset_thresholds[2] = {QCONST16(.0f, 15), QCONST16(.15f, 15)}; + static const opus_val16 tapset_histeresis[2] = {QCONST16(.1f, 15), QCONST16(.05f, 15)}; + st->spread_decision = hysteresis_decision(-st->analysis.tonality, spread_thresholds, spread_histeresis, 3, st->spread_decision); + st->tapset_decision = hysteresis_decision(st->analysis.tonality_slope, tapset_thresholds, tapset_histeresis, 2, st->tapset_decision); + } else +#endif + { + st->spread_decision = spreading_decision(mode, X, + &st->tonal_average, st->spread_decision, &st->hf_average, + &st->tapset_decision, pf_on&&!shortBlocks, effEnd, C, M, spread_weight); + } + /*printf("%d %d\n", st->tapset_decision, st->spread_decision);*/ + /*printf("%f %d %f %d\n\n", st->analysis.tonality, st->spread_decision, st->analysis.tonality_slope, st->tapset_decision);*/ + } + ec_enc_icdf(enc, st->spread_decision, spread_icdf, 5); + } else { + st->spread_decision = SPREAD_NORMAL; + } + + /* For LFE, everything interesting is in the first band */ + if (st->lfe) + offsets[0] = IMIN(8, effectiveBytes/3); + ALLOC(cap, nbEBands, int); + init_caps(mode,cap,LM,C); + + dynalloc_logp = 6; + total_bits<<=BITRES; + total_boost = 0; + tell = ec_tell_frac(enc); + for (i=start;iintensity = hysteresis_decision((opus_val16)(equiv_rate/1000), + intensity_thresholds, intensity_histeresis, 21, st->intensity); + st->intensity = IMIN(end,IMAX(start, st->intensity)); + } + + alloc_trim = 5; + if (tell+(6< 0 || st->lfe) + { + st->stereo_saving = 0; + alloc_trim = 5; + } else { + alloc_trim = alloc_trim_analysis(mode, X, bandLogE, + end, LM, C, N, &st->analysis, &st->stereo_saving, tf_estimate, + st->intensity, surround_trim, equiv_rate, st->arch); + } + ec_enc_icdf(enc, alloc_trim, trim_icdf, 7); + tell = ec_tell_frac(enc); + } + + /* Variable bitrate */ + if (vbr_rate>0) + { + opus_val16 alpha; + opus_int32 delta; + /* The target rate in 8th bits per frame */ + opus_int32 target, base_target; + opus_int32 min_allowed; + int lm_diff = mode->maxLM - LM; + + /* Don't attempt to use more than 510 kb/s, even for frames smaller than 20 ms. + The CELT allocator will just not be able to use more than that anyway. */ + nbCompressedBytes = IMIN(nbCompressedBytes,1275>>(3-LM)); + if (!hybrid) + { + base_target = vbr_rate - ((40*C+20)<constrained_vbr) + base_target += (st->vbr_offset>>lm_diff); + + if (!hybrid) + { + target = compute_vbr(mode, &st->analysis, base_target, LM, equiv_rate, + st->lastCodedBands, C, st->intensity, st->constrained_vbr, + st->stereo_saving, tot_boost, tf_estimate, pitch_change, maxDepth, + st->lfe, st->energy_mask!=NULL, surround_masking, + temporal_vbr); + } else { + target = base_target; + /* Tonal frames (offset<100) need more bits than noisy (offset>100) ones. */ + if (st->silk_info.offset < 100) target += 12 << BITRES >> (3-LM); + if (st->silk_info.offset > 100) target -= 18 << BITRES >> (3-LM); + /* Boosting bitrate on transients and vowels with significant temporal + spikes. */ + target += (opus_int32)MULT16_16_Q14(tf_estimate-QCONST16(.25f,14), (50< QCONST16(.7f,14)) + target = IMAX(target, 50<>(BITRES+3)) + 2; + /* Take into account the 37 bits we need to have left in the packet to + signal a redundant frame in hybrid mode. Creating a shorter packet would + create an entropy coder desync. */ + if (hybrid) + min_allowed = IMAX(min_allowed, (tell0_frac+(37<>(BITRES+3)); + + nbAvailableBytes = (target+(1<<(BITRES+2)))>>(BITRES+3); + nbAvailableBytes = IMAX(min_allowed,nbAvailableBytes); + nbAvailableBytes = IMIN(nbCompressedBytes,nbAvailableBytes); + + /* By how much did we "miss" the target on that frame */ + delta = target - vbr_rate; + + target=nbAvailableBytes<<(BITRES+3); + + /*If the frame is silent we don't adjust our drift, otherwise + the encoder will shoot to very high rates after hitting a + span of silence, but we do allow the bitres to refill. + This means that we'll undershoot our target in CVBR/VBR modes + on files with lots of silence. */ + if(silence) + { + nbAvailableBytes = 2; + target = 2*8<vbr_count < 970) + { + st->vbr_count++; + alpha = celt_rcp(SHL32(EXTEND32(st->vbr_count+20),16)); + } else + alpha = QCONST16(.001f,15); + /* How many bits have we used in excess of what we're allowed */ + if (st->constrained_vbr) + st->vbr_reservoir += target - vbr_rate; + /*printf ("%d\n", st->vbr_reservoir);*/ + + /* Compute the offset we need to apply in order to reach the target */ + if (st->constrained_vbr) + { + st->vbr_drift += (opus_int32)MULT16_32_Q15(alpha,(delta*(1<vbr_offset-st->vbr_drift); + st->vbr_offset = -st->vbr_drift; + } + /*printf ("%d\n", st->vbr_drift);*/ + + if (st->constrained_vbr && st->vbr_reservoir < 0) + { + /* We're under the min value -- increase rate */ + int adjust = (-st->vbr_reservoir)/(8<vbr_reservoir = 0; + /*printf ("+%d\n", adjust);*/ + } + nbCompressedBytes = IMIN(nbCompressedBytes,nbAvailableBytes); + /*printf("%d\n", nbCompressedBytes*50*8);*/ + /* This moves the raw bits to take into account the new compressed size */ + ec_enc_shrink(enc, nbCompressedBytes); + } + + /* Bit allocation */ + ALLOC(fine_quant, nbEBands, int); + ALLOC(pulses, nbEBands, int); + ALLOC(fine_priority, nbEBands, int); + + /* bits = packet size - where we are - safety*/ + bits = (((opus_int32)nbCompressedBytes*8)<=2&&bits>=((LM+2)<analysis.valid) + { + int min_bandwidth; + if (equiv_rate < (opus_int32)32000*C) + min_bandwidth = 13; + else if (equiv_rate < (opus_int32)48000*C) + min_bandwidth = 16; + else if (equiv_rate < (opus_int32)60000*C) + min_bandwidth = 18; + else if (equiv_rate < (opus_int32)80000*C) + min_bandwidth = 19; + else + min_bandwidth = 20; + signalBandwidth = IMAX(st->analysis.bandwidth, min_bandwidth); + } +#endif + if (st->lfe) + signalBandwidth = 1; + codedBands = clt_compute_allocation(mode, start, end, offsets, cap, + alloc_trim, &st->intensity, &dual_stereo, bits, &balance, pulses, + fine_quant, fine_priority, C, LM, enc, 1, st->lastCodedBands, signalBandwidth); + if (st->lastCodedBands) + st->lastCodedBands = IMIN(st->lastCodedBands+1,IMAX(st->lastCodedBands-1,codedBands)); + else + st->lastCodedBands = codedBands; + + quant_fine_energy(mode, start, end, oldBandE, error, fine_quant, enc, C); + + /* Residual quantisation */ + ALLOC(collapse_masks, C*nbEBands, unsigned char); + quant_all_bands(1, mode, start, end, X, C==2 ? X+N : NULL, collapse_masks, + bandE, pulses, shortBlocks, st->spread_decision, + dual_stereo, st->intensity, tf_res, nbCompressedBytes*(8<rng, st->complexity, st->arch, st->disable_inv); + + if (anti_collapse_rsv > 0) + { + anti_collapse_on = st->consec_transient<2; +#ifdef FUZZING + anti_collapse_on = rand()&0x1; +#endif + ec_enc_bits(enc, anti_collapse_on, 1); + } + quant_energy_finalise(mode, start, end, oldBandE, error, fine_quant, fine_priority, nbCompressedBytes*8-ec_tell(enc), enc, C); + OPUS_CLEAR(energyError, nbEBands*CC); + c=0; + do { + for (i=start;irng, 1, st->arch); + } + + c=0; do { + OPUS_MOVE(st->syn_mem[c], st->syn_mem[c]+N, 2*MAX_PERIOD-N+overlap/2); + } while (++csyn_mem[c]+2*MAX_PERIOD-N; + } while (++cupsample, silence, st->arch); + + c=0; do { + st->prefilter_period=IMAX(st->prefilter_period, COMBFILTER_MINPERIOD); + st->prefilter_period_old=IMAX(st->prefilter_period_old, COMBFILTER_MINPERIOD); + comb_filter(out_mem[c], out_mem[c], st->prefilter_period_old, st->prefilter_period, mode->shortMdctSize, + st->prefilter_gain_old, st->prefilter_gain, st->prefilter_tapset_old, st->prefilter_tapset, + mode->window, overlap, st->arch); + if (LM!=0) + comb_filter(out_mem[c]+mode->shortMdctSize, out_mem[c]+mode->shortMdctSize, st->prefilter_period, pitch_index, N-mode->shortMdctSize, + st->prefilter_gain, gain1, st->prefilter_tapset, prefilter_tapset, + mode->window, overlap, st->arch); + } while (++cupsample, mode->preemph, st->preemph_memD, 0); + st->prefilter_period_old = st->prefilter_period; + st->prefilter_gain_old = st->prefilter_gain; + st->prefilter_tapset_old = st->prefilter_tapset; + } +#endif + + st->prefilter_period = pitch_index; + st->prefilter_gain = gain1; + st->prefilter_tapset = prefilter_tapset; +#ifdef RESYNTH + if (LM!=0) + { + st->prefilter_period_old = st->prefilter_period; + st->prefilter_gain_old = st->prefilter_gain; + st->prefilter_tapset_old = st->prefilter_tapset; + } +#endif + + if (CC==2&&C==1) { + OPUS_COPY(&oldBandE[nbEBands], oldBandE, nbEBands); + } + + if (!isTransient) + { + OPUS_COPY(oldLogE2, oldLogE, CC*nbEBands); + OPUS_COPY(oldLogE, oldBandE, CC*nbEBands); + } else { + for (i=0;iconsec_transient++; + else + st->consec_transient=0; + st->rng = enc->rng; + + /* If there's any room left (can only happen for very high rates), + it's already filled with zeros */ + ec_enc_done(enc); + +#ifdef CUSTOM_MODES + if (st->signalling) + nbCompressedBytes++; +#endif + + RESTORE_STACK; + if (ec_get_error(enc)) + return OPUS_INTERNAL_ERROR; + else + return nbCompressedBytes; +} + + +#ifdef CUSTOM_MODES + +#if defined(FIXED_POINT) && !defined(ENABLE_RES24) +int opus_custom_encode(CELTEncoder * OPUS_RESTRICT st, const opus_int16 * pcm, int frame_size, unsigned char *compressed, int nbCompressedBytes) +{ + return celt_encode_with_ec(st, pcm, frame_size, compressed, nbCompressedBytes, NULL); +} +#else +int opus_custom_encode(CELTEncoder * OPUS_RESTRICT st, const opus_int16 * pcm, int frame_size, unsigned char *compressed, int nbCompressedBytes) +{ + int j, ret, C, N; + VARDECL(opus_res, in); + ALLOC_STACK; + + if (pcm==NULL) + return OPUS_BAD_ARG; + + C = st->channels; + N = frame_size; + ALLOC(in, C*N, opus_res); + + for (j=0;jchannels; + N = frame_size; + ALLOC(in, C*N, opus_res); + + for (j=0;jchannels; + N = frame_size; + ALLOC(in, C*N, opus_res); + + for (j=0;j10) + goto bad_arg; + st->complexity = value; + } + break; + case CELT_SET_START_BAND_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<0 || value>=st->mode->nbEBands) + goto bad_arg; + st->start = value; + } + break; + case CELT_SET_END_BAND_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<1 || value>st->mode->nbEBands) + goto bad_arg; + st->end = value; + } + break; + case CELT_SET_PREDICTION_REQUEST: + { + int value = va_arg(ap, opus_int32); + if (value<0 || value>2) + goto bad_arg; + st->disable_pf = value<=1; + st->force_intra = value==0; + } + break; + case OPUS_SET_PACKET_LOSS_PERC_REQUEST: + { + int value = va_arg(ap, opus_int32); + if (value<0 || value>100) + goto bad_arg; + st->loss_rate = value; + } + break; + case OPUS_SET_VBR_CONSTRAINT_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->constrained_vbr = value; + } + break; + case OPUS_SET_VBR_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->vbr = value; + } + break; + case OPUS_SET_BITRATE_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<=500 && value!=OPUS_BITRATE_MAX) + goto bad_arg; + value = IMIN(value, 260000*st->channels); + st->bitrate = value; + } + break; + case CELT_SET_CHANNELS_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<1 || value>2) + goto bad_arg; + st->stream_channels = value; + } + break; + case OPUS_SET_LSB_DEPTH_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if (value<8 || value>24) + goto bad_arg; + st->lsb_depth=value; + } + break; + case OPUS_GET_LSB_DEPTH_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + *value=st->lsb_depth; + } + break; + case OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + if(value<0 || value>1) + { + goto bad_arg; + } + st->disable_inv = value; + } + break; + case OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST: + { + opus_int32 *value = va_arg(ap, opus_int32*); + if (!value) + { + goto bad_arg; + } + *value = st->disable_inv; + } + break; + case OPUS_RESET_STATE: + { + int i; + celt_glog *oldBandE, *oldLogE, *oldLogE2; + oldBandE = (celt_glog*)(st->in_mem+st->channels*(st->mode->overlap+COMBFILTER_MAXPERIOD)); + oldLogE = oldBandE + st->channels*st->mode->nbEBands; + oldLogE2 = oldLogE + st->channels*st->mode->nbEBands; + OPUS_CLEAR((char*)&st->ENCODER_RESET_START, + opus_custom_encoder_get_size(st->mode, st->channels)- + ((char*)&st->ENCODER_RESET_START - (char*)st)); + for (i=0;ichannels*st->mode->nbEBands;i++) + oldLogE[i]=oldLogE2[i]=-GCONST(28.f); + st->vbr_offset = 0; + st->delayedIntra = 1; + st->spread_decision = SPREAD_NORMAL; + st->tonal_average = 256; + st->hf_average = 0; + st->tapset_decision = 0; + } + break; +#ifdef CUSTOM_MODES + case CELT_SET_INPUT_CLIPPING_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->clip = value; + } + break; +#endif + case CELT_SET_SIGNALLING_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->signalling = value; + } + break; + case CELT_SET_ANALYSIS_REQUEST: + { + AnalysisInfo *info = va_arg(ap, AnalysisInfo *); + if (info) + OPUS_COPY(&st->analysis, info, 1); + } + break; + case CELT_SET_SILK_INFO_REQUEST: + { + SILKInfo *info = va_arg(ap, SILKInfo *); + if (info) + OPUS_COPY(&st->silk_info, info, 1); + } + break; + case CELT_GET_MODE_REQUEST: + { + const CELTMode ** value = va_arg(ap, const CELTMode**); + if (value==0) + goto bad_arg; + *value=st->mode; + } + break; + case OPUS_GET_FINAL_RANGE_REQUEST: + { + opus_uint32 * value = va_arg(ap, opus_uint32 *); + if (value==0) + goto bad_arg; + *value=st->rng; + } + break; + case OPUS_SET_LFE_REQUEST: + { + opus_int32 value = va_arg(ap, opus_int32); + st->lfe = value; + } + break; + case OPUS_SET_ENERGY_MASK_REQUEST: + { + celt_glog *value = va_arg(ap, celt_glog*); + st->energy_mask = value; + } + break; + default: + goto bad_request; + } + va_end(ap); + return OPUS_OK; +bad_arg: + va_end(ap); + return OPUS_BAD_ARG; +bad_request: + va_end(ap); + return OPUS_UNIMPLEMENTED; +} diff --git a/managed_components/78__esp-opus/celt/celt_lpc.c b/managed_components/78__esp-opus/celt/celt_lpc.c new file mode 100644 index 0000000..2a98b7e --- /dev/null +++ b/managed_components/78__esp-opus/celt/celt_lpc.c @@ -0,0 +1,364 @@ +/* Copyright (c) 2009-2010 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "celt_lpc.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "pitch.h" + +void _celt_lpc( + opus_val16 *_lpc, /* out: [0...p-1] LPC coefficients */ +const opus_val32 *ac, /* in: [0...p] autocorrelation values */ +int p +) +{ + int i, j; + opus_val32 r; + opus_val32 error = ac[0]; +#ifdef FIXED_POINT + opus_val32 lpc[CELT_LPC_ORDER]; +#else + float *lpc = _lpc; +#endif + + OPUS_CLEAR(lpc, p); +#ifdef FIXED_POINT + if (ac[0] != 0) +#else + if (ac[0] > 1e-10f) +#endif + { + for (i = 0; i < p; i++) { + /* Sum up this iteration's reflection coefficient */ + opus_val32 rr = 0; + for (j = 0; j < i; j++) + rr += MULT32_32_Q31(lpc[j],ac[i - j]); + rr += SHR32(ac[i + 1],6); + r = -frac_div32(SHL32(rr,6), error); + /* Update LPC coefficients and total error */ + lpc[i] = SHR32(r,6); + for (j = 0; j < (i+1)>>1; j++) + { + opus_val32 tmp1, tmp2; + tmp1 = lpc[j]; + tmp2 = lpc[i-1-j]; + lpc[j] = tmp1 + MULT32_32_Q31(r,tmp2); + lpc[i-1-j] = tmp2 + MULT32_32_Q31(r,tmp1); + } + + error = error - MULT32_32_Q31(MULT32_32_Q31(r,r),error); + /* Bail out once we get 30 dB gain */ +#ifdef FIXED_POINT + if (error<=SHR32(ac[0],10)) + break; +#else + if (error<=.001f*ac[0]) + break; +#endif + } + } +#ifdef FIXED_POINT + { + /* Convert the int32 lpcs to int16 and ensure there are no wrap-arounds. + This reuses the logic in silk_LPC_fit() and silk_bwexpander_32(). Any bug + fixes should also be applied there. */ + int iter, idx = 0; + opus_val32 maxabs, absval, chirp_Q16, chirp_minus_one_Q16; + + for (iter = 0; iter < 10; iter++) { + maxabs = 0; + for (i = 0; i < p; i++) { + absval = ABS32(lpc[i]); + if (absval > maxabs) { + maxabs = absval; + idx = i; + } + } + maxabs = PSHR32(maxabs, 13); /* Q25->Q12 */ + + if (maxabs > 32767) { + maxabs = MIN32(maxabs, 163838); + chirp_Q16 = QCONST32(0.999, 16) - DIV32(SHL32(maxabs - 32767, 14), + SHR32(MULT32_32_32(maxabs, idx + 1), 2)); + chirp_minus_one_Q16 = chirp_Q16 - 65536; + + /* Apply bandwidth expansion. */ + for (i = 0; i < p - 1; i++) { + lpc[i] = MULT32_32_Q16(chirp_Q16, lpc[i]); + chirp_Q16 += PSHR32(MULT32_32_32(chirp_Q16, chirp_minus_one_Q16), 16); + } + lpc[p - 1] = MULT32_32_Q16(chirp_Q16, lpc[p - 1]); + } else { + break; + } + } + + if (iter == 10) { + /* If the coeffs still do not fit into the 16 bit range after 10 iterations, + fall back to the A(z)=1 filter. */ + OPUS_CLEAR(lpc, p); + _lpc[0] = 4096; /* Q12 */ + } else { + for (i = 0; i < p; i++) { + _lpc[i] = EXTRACT16(PSHR32(lpc[i], 13)); /* Q25->Q12 */ + } + } + } +#endif +} + + +void celt_fir_c( + const opus_val16 *x, + const opus_val16 *num, + opus_val16 *y, + int N, + int ord, + int arch) +{ + int i,j; + VARDECL(opus_val16, rnum); + SAVE_STACK; + celt_assert(x != y); + ALLOC(rnum, ord, opus_val16); + for(i=0;i=1;j--) + { + mem[j]=mem[j-1]; + } + mem[0] = SROUND16(sum, SIG_SHIFT); + _y[i] = sum; + } +#else + int i,j; + VARDECL(opus_val16, rden); + VARDECL(opus_val16, y); + SAVE_STACK; + + celt_assert((ord&3)==0); + ALLOC(rden, ord, opus_val16); + ALLOC(y, N+ord, opus_val16); + for(i=0;i0); + celt_assert(overlap>=0); + if (overlap == 0) + { + xptr = x; + } else { + for (i=0;i0) + { + for(i=0;i= 536870912) + { + int shift2=1; + if (ac[0] >= 1073741824) + shift2++; + for (i=0;i<=lag;i++) + ac[i] = SHR32(ac[i], shift2); + shift += shift2; + } +#endif + + RESTORE_STACK; + return shift; +} diff --git a/managed_components/78__esp-opus/celt/celt_lpc.h b/managed_components/78__esp-opus/celt/celt_lpc.h new file mode 100644 index 0000000..2040c18 --- /dev/null +++ b/managed_components/78__esp-opus/celt/celt_lpc.h @@ -0,0 +1,66 @@ +/* Copyright (c) 2009-2010 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef PLC_H +#define PLC_H + +#include "arch.h" +#include "cpu_support.h" + +#if defined(OPUS_X86_MAY_HAVE_SSE4_1) +#include "x86/celt_lpc_sse.h" +#endif + +#define CELT_LPC_ORDER 24 + +void _celt_lpc(opus_val16 *_lpc, const opus_val32 *ac, int p); + +void celt_fir_c( + const opus_val16 *x, + const opus_val16 *num, + opus_val16 *y, + int N, + int ord, + int arch); + +#if !defined(OVERRIDE_CELT_FIR) +#define celt_fir(x, num, y, N, ord, arch) \ + (celt_fir_c(x, num, y, N, ord, arch)) +#endif + +void celt_iir(const opus_val32 *x, + const opus_val16 *den, + opus_val32 *y, + int N, + int ord, + opus_val16 *mem, + int arch); + +int _celt_autocorr(const opus_val16 *x, opus_val32 *ac, + const celt_coef *window, int overlap, int lag, int n, int arch); + +#endif /* PLC_H */ diff --git a/managed_components/78__esp-opus/celt/cpu_support.h b/managed_components/78__esp-opus/celt/cpu_support.h new file mode 100644 index 0000000..9f13d8a --- /dev/null +++ b/managed_components/78__esp-opus/celt/cpu_support.h @@ -0,0 +1,72 @@ +/* Copyright (c) 2010 Xiph.Org Foundation + * Copyright (c) 2013 Parrot */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef CPU_SUPPORT_H +#define CPU_SUPPORT_H + +#include "opus_types.h" +#include "opus_defines.h" + +#if defined(OPUS_HAVE_RTCD) && \ + (defined(OPUS_ARM_ASM) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR)) +#include "arm/armcpu.h" + +/* We currently support 5 ARM variants: + * arch[0] -> ARMv4 + * arch[1] -> ARMv5E + * arch[2] -> ARMv6 + * arch[3] -> NEON + * arch[4] -> NEON+DOTPROD + */ +#define OPUS_ARCHMASK 7 + +#elif defined(OPUS_HAVE_RTCD) && \ + ((defined(OPUS_X86_MAY_HAVE_SSE) && !defined(OPUS_X86_PRESUME_SSE)) || \ + (defined(OPUS_X86_MAY_HAVE_SSE2) && !defined(OPUS_X86_PRESUME_SSE2)) || \ + (defined(OPUS_X86_MAY_HAVE_SSE4_1) && !defined(OPUS_X86_PRESUME_SSE4_1)) || \ + (defined(OPUS_X86_MAY_HAVE_AVX2) && !defined(OPUS_X86_PRESUME_AVX2))) + +#include "x86/x86cpu.h" +/* We currently support 5 x86 variants: + * arch[0] -> non-sse + * arch[1] -> sse + * arch[2] -> sse2 + * arch[3] -> sse4.1 + * arch[4] -> avx + */ +#define OPUS_ARCHMASK 7 +int opus_select_arch(void); + +#else +#define OPUS_ARCHMASK 0 + +static OPUS_INLINE int opus_select_arch(void) +{ + return 0; +} +#endif +#endif diff --git a/managed_components/78__esp-opus/celt/cwrs.c b/managed_components/78__esp-opus/celt/cwrs.c new file mode 100644 index 0000000..a552e4f --- /dev/null +++ b/managed_components/78__esp-opus/celt/cwrs.c @@ -0,0 +1,715 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2007-2009 Timothy B. Terriberry + Written by Timothy B. Terriberry and Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "os_support.h" +#include "cwrs.h" +#include "mathops.h" +#include "arch.h" + +#ifdef CUSTOM_MODES + +/*Guaranteed to return a conservatively large estimate of the binary logarithm + with frac bits of fractional precision. + Tested for all possible 32-bit inputs with frac=4, where the maximum + overestimation is 0.06254243 bits.*/ +int log2_frac(opus_uint32 val, int frac) +{ + int l; + l=EC_ILOG(val); + if(val&(val-1)){ + /*This is (val>>l-16), but guaranteed to round up, even if adding a bias + before the shift would cause overflow (e.g., for 0xFFFFxxxx). + Doesn't work for val=0, but that case fails the test above.*/ + if(l>16)val=((val-1)>>(l-16))+1; + else val<<=16-l; + l=(l-1)<>16); + l+=b<>b; + val=(val*val+0x7FFF)>>15; + } + while(frac-->0); + /*If val is not exactly 0x8000, then we have to round up the remainder.*/ + return l+(val>0x8000); + } + /*Exact powers of two require no rounding.*/ + else return (l-1)<0 ? sum(k=1...K,2**k*choose(N,k)*choose(K-1,k-1)) : 1, + where choose() is the binomial function. + A table of values for N<10 and K<10 looks like: + V[10][10] = { + {1, 0, 0, 0, 0, 0, 0, 0, 0, 0}, + {1, 2, 2, 2, 2, 2, 2, 2, 2, 2}, + {1, 4, 8, 12, 16, 20, 24, 28, 32, 36}, + {1, 6, 18, 38, 66, 102, 146, 198, 258, 326}, + {1, 8, 32, 88, 192, 360, 608, 952, 1408, 1992}, + {1, 10, 50, 170, 450, 1002, 1970, 3530, 5890, 9290}, + {1, 12, 72, 292, 912, 2364, 5336, 10836, 20256, 35436}, + {1, 14, 98, 462, 1666, 4942, 12642, 28814, 59906, 115598}, + {1, 16, 128, 688, 2816, 9424, 27008, 68464, 157184, 332688}, + {1, 18, 162, 978, 4482, 16722, 53154, 148626, 374274, 864146} + }; + + U(N,K) = the number of such combinations wherein N-1 objects are taken at + most K-1 at a time. + This is given by + U(N,K) = sum(k=0...K-1,V(N-1,k)) + = K>0 ? (V(N-1,K-1) + V(N,K-1))/2 : 0. + The latter expression also makes clear that U(N,K) is half the number of such + combinations wherein the first object is taken at least once. + Although it may not be clear from either of these definitions, U(N,K) is the + natural function to work with when enumerating the pulse vector codebooks, + not V(N,K). + U(N,K) is not well-defined for N=0, but with the extension + U(0,K) = K>0 ? 0 : 1, + the function becomes symmetric: U(N,K) = U(K,N), with a similar table: + U[10][10] = { + {1, 0, 0, 0, 0, 0, 0, 0, 0, 0}, + {0, 1, 1, 1, 1, 1, 1, 1, 1, 1}, + {0, 1, 3, 5, 7, 9, 11, 13, 15, 17}, + {0, 1, 5, 13, 25, 41, 61, 85, 113, 145}, + {0, 1, 7, 25, 63, 129, 231, 377, 575, 833}, + {0, 1, 9, 41, 129, 321, 681, 1289, 2241, 3649}, + {0, 1, 11, 61, 231, 681, 1683, 3653, 7183, 13073}, + {0, 1, 13, 85, 377, 1289, 3653, 8989, 19825, 40081}, + {0, 1, 15, 113, 575, 2241, 7183, 19825, 48639, 108545}, + {0, 1, 17, 145, 833, 3649, 13073, 40081, 108545, 265729} + }; + + With this extension, V(N,K) may be written in terms of U(N,K): + V(N,K) = U(N,K) + U(N,K+1) + for all N>=0, K>=0. + Thus U(N,K+1) represents the number of combinations where the first element + is positive or zero, and U(N,K) represents the number of combinations where + it is negative. + With a large enough table of U(N,K) values, we could write O(N) encoding + and O(min(N*log(K),N+K)) decoding routines, but such a table would be + prohibitively large for small embedded devices (K may be as large as 32767 + for small N, and N may be as large as 200). + + Both functions obey the same recurrence relation: + V(N,K) = V(N-1,K) + V(N,K-1) + V(N-1,K-1), + U(N,K) = U(N-1,K) + U(N,K-1) + U(N-1,K-1), + for all N>0, K>0, with different initial conditions at N=0 or K=0. + This allows us to construct a row of one of the tables above given the + previous row or the next row. + Thus we can derive O(NK) encoding and decoding routines with O(K) memory + using only addition and subtraction. + + When encoding, we build up from the U(2,K) row and work our way forwards. + When decoding, we need to start at the U(N,K) row and work our way backwards, + which requires a means of computing U(N,K). + U(N,K) may be computed from two previous values with the same N: + U(N,K) = ((2*N-1)*U(N,K-1) - U(N,K-2))/(K-1) + U(N,K-2) + for all N>1, and since U(N,K) is symmetric, a similar relation holds for two + previous values with the same K: + U(N,K>1) = ((2*K-1)*U(N-1,K) - U(N-2,K))/(N-1) + U(N-2,K) + for all K>1. + This allows us to construct an arbitrary row of the U(N,K) table by starting + with the first two values, which are constants. + This saves roughly 2/3 the work in our O(NK) decoding routine, but costs O(K) + multiplications. + Similar relations can be derived for V(N,K), but are not used here. + + For N>0 and K>0, U(N,K) and V(N,K) take on the form of an (N-1)-degree + polynomial for fixed N. + The first few are + U(1,K) = 1, + U(2,K) = 2*K-1, + U(3,K) = (2*K-2)*K+1, + U(4,K) = (((4*K-6)*K+8)*K-3)/3, + U(5,K) = ((((2*K-4)*K+10)*K-8)*K+3)/3, + and + V(1,K) = 2, + V(2,K) = 4*K, + V(3,K) = 4*K*K+2, + V(4,K) = 8*(K*K+2)*K/3, + V(5,K) = ((4*K*K+20)*K*K+6)/3, + for all K>0. + This allows us to derive O(N) encoding and O(N*log(K)) decoding routines for + small N (and indeed decoding is also O(N) for N<3). + + @ARTICLE{Fis86, + author="Thomas R. Fischer", + title="A Pyramid Vector Quantizer", + journal="IEEE Transactions on Information Theory", + volume="IT-32", + number=4, + pages="568--583", + month=Jul, + year=1986 + }*/ + +#if !defined(SMALL_FOOTPRINT) + +/*U(N,K) = U(K,N) := N>0?K>0?U(N-1,K)+U(N,K-1)+U(N-1,K-1):0:K>0?1:0*/ +# define CELT_PVQ_U(_n,_k) (CELT_PVQ_U_ROW[IMIN(_n,_k)][IMAX(_n,_k)]) +/*V(N,K) := U(N,K)+U(N,K+1) = the number of PVQ codewords for a band of size N + with K pulses allocated to it.*/ +# define CELT_PVQ_V(_n,_k) (CELT_PVQ_U(_n,_k)+CELT_PVQ_U(_n,(_k)+1)) + +/*For each V(N,K) supported, we will access element U(min(N,K+1),max(N,K+1)). + Thus, the number of entries in row I is the larger of the maximum number of + pulses we will ever allocate for a given N=I (K=128, or however many fit in + 32 bits, whichever is smaller), plus one, and the maximum N for which + K=I-1 pulses fit in 32 bits. + The largest band size in an Opus Custom mode is 208. + Otherwise, we can limit things to the set of N which can be achieved by + splitting a band from a standard Opus mode: 176, 144, 96, 88, 72, 64, 48, + 44, 36, 32, 24, 22, 18, 16, 8, 4, 2).*/ +#if defined(CUSTOM_MODES) +static const opus_uint32 CELT_PVQ_U_DATA[1488]={ +#else +static const opus_uint32 CELT_PVQ_U_DATA[1272]={ +#endif + /*N=0, K=0...176:*/ + 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +#if defined(CUSTOM_MODES) + /*...208:*/ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, +#endif + /*N=1, K=1...176:*/ + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, +#if defined(CUSTOM_MODES) + /*...208:*/ + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, +#endif + /*N=2, K=2...176:*/ + 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, + 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, + 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, + 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, + 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, + 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, + 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, + 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, + 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, + 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, + 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, +#if defined(CUSTOM_MODES) + /*...208:*/ + 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, + 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, + 413, 415, +#endif + /*N=3, K=3...176:*/ + 13, 25, 41, 61, 85, 113, 145, 181, 221, 265, 313, 365, 421, 481, 545, 613, + 685, 761, 841, 925, 1013, 1105, 1201, 1301, 1405, 1513, 1625, 1741, 1861, + 1985, 2113, 2245, 2381, 2521, 2665, 2813, 2965, 3121, 3281, 3445, 3613, 3785, + 3961, 4141, 4325, 4513, 4705, 4901, 5101, 5305, 5513, 5725, 5941, 6161, 6385, + 6613, 6845, 7081, 7321, 7565, 7813, 8065, 8321, 8581, 8845, 9113, 9385, 9661, + 9941, 10225, 10513, 10805, 11101, 11401, 11705, 12013, 12325, 12641, 12961, + 13285, 13613, 13945, 14281, 14621, 14965, 15313, 15665, 16021, 16381, 16745, + 17113, 17485, 17861, 18241, 18625, 19013, 19405, 19801, 20201, 20605, 21013, + 21425, 21841, 22261, 22685, 23113, 23545, 23981, 24421, 24865, 25313, 25765, + 26221, 26681, 27145, 27613, 28085, 28561, 29041, 29525, 30013, 30505, 31001, + 31501, 32005, 32513, 33025, 33541, 34061, 34585, 35113, 35645, 36181, 36721, + 37265, 37813, 38365, 38921, 39481, 40045, 40613, 41185, 41761, 42341, 42925, + 43513, 44105, 44701, 45301, 45905, 46513, 47125, 47741, 48361, 48985, 49613, + 50245, 50881, 51521, 52165, 52813, 53465, 54121, 54781, 55445, 56113, 56785, + 57461, 58141, 58825, 59513, 60205, 60901, 61601, +#if defined(CUSTOM_MODES) + /*...208:*/ + 62305, 63013, 63725, 64441, 65161, 65885, 66613, 67345, 68081, 68821, 69565, + 70313, 71065, 71821, 72581, 73345, 74113, 74885, 75661, 76441, 77225, 78013, + 78805, 79601, 80401, 81205, 82013, 82825, 83641, 84461, 85285, 86113, +#endif + /*N=4, K=4...176:*/ + 63, 129, 231, 377, 575, 833, 1159, 1561, 2047, 2625, 3303, 4089, 4991, 6017, + 7175, 8473, 9919, 11521, 13287, 15225, 17343, 19649, 22151, 24857, 27775, + 30913, 34279, 37881, 41727, 45825, 50183, 54809, 59711, 64897, 70375, 76153, + 82239, 88641, 95367, 102425, 109823, 117569, 125671, 134137, 142975, 152193, + 161799, 171801, 182207, 193025, 204263, 215929, 228031, 240577, 253575, + 267033, 280959, 295361, 310247, 325625, 341503, 357889, 374791, 392217, + 410175, 428673, 447719, 467321, 487487, 508225, 529543, 551449, 573951, + 597057, 620775, 645113, 670079, 695681, 721927, 748825, 776383, 804609, + 833511, 863097, 893375, 924353, 956039, 988441, 1021567, 1055425, 1090023, + 1125369, 1161471, 1198337, 1235975, 1274393, 1313599, 1353601, 1394407, + 1436025, 1478463, 1521729, 1565831, 1610777, 1656575, 1703233, 1750759, + 1799161, 1848447, 1898625, 1949703, 2001689, 2054591, 2108417, 2163175, + 2218873, 2275519, 2333121, 2391687, 2451225, 2511743, 2573249, 2635751, + 2699257, 2763775, 2829313, 2895879, 2963481, 3032127, 3101825, 3172583, + 3244409, 3317311, 3391297, 3466375, 3542553, 3619839, 3698241, 3777767, + 3858425, 3940223, 4023169, 4107271, 4192537, 4278975, 4366593, 4455399, + 4545401, 4636607, 4729025, 4822663, 4917529, 5013631, 5110977, 5209575, + 5309433, 5410559, 5512961, 5616647, 5721625, 5827903, 5935489, 6044391, + 6154617, 6266175, 6379073, 6493319, 6608921, 6725887, 6844225, 6963943, + 7085049, 7207551, +#if defined(CUSTOM_MODES) + /*...208:*/ + 7331457, 7456775, 7583513, 7711679, 7841281, 7972327, 8104825, 8238783, + 8374209, 8511111, 8649497, 8789375, 8930753, 9073639, 9218041, 9363967, + 9511425, 9660423, 9810969, 9963071, 10116737, 10271975, 10428793, 10587199, + 10747201, 10908807, 11072025, 11236863, 11403329, 11571431, 11741177, + 11912575, +#endif + /*N=5, K=5...176:*/ + 321, 681, 1289, 2241, 3649, 5641, 8361, 11969, 16641, 22569, 29961, 39041, + 50049, 63241, 78889, 97281, 118721, 143529, 172041, 204609, 241601, 283401, + 330409, 383041, 441729, 506921, 579081, 658689, 746241, 842249, 947241, + 1061761, 1186369, 1321641, 1468169, 1626561, 1797441, 1981449, 2179241, + 2391489, 2618881, 2862121, 3121929, 3399041, 3694209, 4008201, 4341801, + 4695809, 5071041, 5468329, 5888521, 6332481, 6801089, 7295241, 7815849, + 8363841, 8940161, 9545769, 10181641, 10848769, 11548161, 12280841, 13047849, + 13850241, 14689089, 15565481, 16480521, 17435329, 18431041, 19468809, + 20549801, 21675201, 22846209, 24064041, 25329929, 26645121, 28010881, + 29428489, 30899241, 32424449, 34005441, 35643561, 37340169, 39096641, + 40914369, 42794761, 44739241, 46749249, 48826241, 50971689, 53187081, + 55473921, 57833729, 60268041, 62778409, 65366401, 68033601, 70781609, + 73612041, 76526529, 79526721, 82614281, 85790889, 89058241, 92418049, + 95872041, 99421961, 103069569, 106816641, 110664969, 114616361, 118672641, + 122835649, 127107241, 131489289, 135983681, 140592321, 145317129, 150160041, + 155123009, 160208001, 165417001, 170752009, 176215041, 181808129, 187533321, + 193392681, 199388289, 205522241, 211796649, 218213641, 224775361, 231483969, + 238341641, 245350569, 252512961, 259831041, 267307049, 274943241, 282741889, + 290705281, 298835721, 307135529, 315607041, 324252609, 333074601, 342075401, + 351257409, 360623041, 370174729, 379914921, 389846081, 399970689, 410291241, + 420810249, 431530241, 442453761, 453583369, 464921641, 476471169, 488234561, + 500214441, 512413449, 524834241, 537479489, 550351881, 563454121, 576788929, + 590359041, 604167209, 618216201, 632508801, +#if defined(CUSTOM_MODES) + /*...208:*/ + 647047809, 661836041, 676876329, 692171521, 707724481, 723538089, 739615241, + 755958849, 772571841, 789457161, 806617769, 824056641, 841776769, 859781161, + 878072841, 896654849, 915530241, 934702089, 954173481, 973947521, 994027329, + 1014416041, 1035116809, 1056132801, 1077467201, 1099123209, 1121104041, + 1143412929, 1166053121, 1189027881, 1212340489, 1235994241, +#endif + /*N=6, K=6...96:*/ + 1683, 3653, 7183, 13073, 22363, 36365, 56695, 85305, 124515, 177045, 246047, + 335137, 448427, 590557, 766727, 982729, 1244979, 1560549, 1937199, 2383409, + 2908411, 3522221, 4235671, 5060441, 6009091, 7095093, 8332863, 9737793, + 11326283, 13115773, 15124775, 17372905, 19880915, 22670725, 25765455, + 29189457, 32968347, 37129037, 41699767, 46710137, 52191139, 58175189, + 64696159, 71789409, 79491819, 87841821, 96879431, 106646281, 117185651, + 128542501, 140763503, 153897073, 167993403, 183104493, 199284183, 216588185, + 235074115, 254801525, 275831935, 298228865, 322057867, 347386557, 374284647, + 402823977, 433078547, 465124549, 499040399, 534906769, 572806619, 612825229, + 655050231, 699571641, 746481891, 795875861, 847850911, 902506913, 959946283, + 1020274013, 1083597703, 1150027593, 1219676595, 1292660325, 1369097135, + 1449108145, 1532817275, 1620351277, 1711839767, 1807415257, 1907213187, + 2011371957, 2120032959, +#if defined(CUSTOM_MODES) + /*...109:*/ + 2233340609U, 2351442379U, 2474488829U, 2602633639U, 2736033641U, 2874848851U, + 3019242501U, 3169381071U, 3325434321U, 3487575323U, 3655980493U, 3830829623U, + 4012305913U, +#endif + /*N=7, K=7...54*/ + 8989, 19825, 40081, 75517, 134245, 227305, 369305, 579125, 880685, 1303777, + 1884961, 2668525, 3707509, 5064793, 6814249, 9041957, 11847485, 15345233, + 19665841, 24957661, 31388293, 39146185, 48442297, 59511829, 72616013, + 88043969, 106114625, 127178701, 151620757, 179861305, 212358985, 249612805, + 292164445, 340600625, 395555537, 457713341, 527810725, 606639529, 695049433, + 793950709, 904317037, 1027188385, 1163673953, 1314955181, 1482288821, + 1667010073, 1870535785, 2094367717, +#if defined(CUSTOM_MODES) + /*...60:*/ + 2340095869U, 2609401873U, 2904062449U, 3225952925U, 3577050821U, 3959439497U, +#endif + /*N=8, K=8...37*/ + 48639, 108545, 224143, 433905, 795455, 1392065, 2340495, 3800305, 5984767, + 9173505, 13726991, 20103025, 28875327, 40754369, 56610575, 77500017, + 104692735, 139703809, 184327311, 240673265, 311207743, 398796225, 506750351, + 638878193, 799538175, 993696769, 1226990095, 1505789553, 1837271615, + 2229491905U, +#if defined(CUSTOM_MODES) + /*...40:*/ + 2691463695U, 3233240945U, 3866006015U, +#endif + /*N=9, K=9...28:*/ + 265729, 598417, 1256465, 2485825, 4673345, 8405905, 14546705, 24331777, + 39490049, 62390545, 96220561, 145198913, 214828609, 312193553, 446304145, + 628496897, 872893441, 1196924561, 1621925137, 2173806145U, +#if defined(CUSTOM_MODES) + /*...29:*/ + 2883810113U, +#endif + /*N=10, K=10...24:*/ + 1462563, 3317445, 7059735, 14218905, 27298155, 50250765, 89129247, 152951073, + 254831667, 413442773, 654862247, 1014889769, 1541911931, 2300409629U, + 3375210671U, + /*N=11, K=11...19:*/ + 8097453, 18474633, 39753273, 81270333, 158819253, 298199265, 540279585, + 948062325, 1616336765, +#if defined(CUSTOM_MODES) + /*...20:*/ + 2684641785U, +#endif + /*N=12, K=12...18:*/ + 45046719, 103274625, 224298231, 464387817, 921406335, 1759885185, + 3248227095U, + /*N=13, K=13...16:*/ + 251595969, 579168825, 1267854873, 2653649025U, + /*N=14, K=14:*/ + 1409933619 +}; + +#if defined(CUSTOM_MODES) +static const opus_uint32 *const CELT_PVQ_U_ROW[15]={ + CELT_PVQ_U_DATA+ 0,CELT_PVQ_U_DATA+ 208,CELT_PVQ_U_DATA+ 415, + CELT_PVQ_U_DATA+ 621,CELT_PVQ_U_DATA+ 826,CELT_PVQ_U_DATA+1030, + CELT_PVQ_U_DATA+1233,CELT_PVQ_U_DATA+1336,CELT_PVQ_U_DATA+1389, + CELT_PVQ_U_DATA+1421,CELT_PVQ_U_DATA+1441,CELT_PVQ_U_DATA+1455, + CELT_PVQ_U_DATA+1464,CELT_PVQ_U_DATA+1470,CELT_PVQ_U_DATA+1473 +}; +#else +static const opus_uint32 *const CELT_PVQ_U_ROW[15]={ + CELT_PVQ_U_DATA+ 0,CELT_PVQ_U_DATA+ 176,CELT_PVQ_U_DATA+ 351, + CELT_PVQ_U_DATA+ 525,CELT_PVQ_U_DATA+ 698,CELT_PVQ_U_DATA+ 870, + CELT_PVQ_U_DATA+1041,CELT_PVQ_U_DATA+1131,CELT_PVQ_U_DATA+1178, + CELT_PVQ_U_DATA+1207,CELT_PVQ_U_DATA+1226,CELT_PVQ_U_DATA+1240, + CELT_PVQ_U_DATA+1248,CELT_PVQ_U_DATA+1254,CELT_PVQ_U_DATA+1257 +}; +#endif + +#if defined(CUSTOM_MODES) +void get_required_bits(opus_int16 *_bits,int _n,int _maxk,int _frac){ + int k; + /*_maxk==0 => there's nothing to do.*/ + celt_assert(_maxk>0); + _bits[0]=0; + for(k=1;k<=_maxk;k++)_bits[k]=log2_frac(CELT_PVQ_V(_n,k),_frac); +} +#endif + +static opus_uint32 icwrs(int _n,const int *_y){ + opus_uint32 i; + int j; + int k; + celt_assert(_n>=2); + j=_n-1; + i=_y[j]<0; + k=abs(_y[j]); + do{ + j--; + i+=CELT_PVQ_U(_n-j,k); + k+=abs(_y[j]); + if(_y[j]<0)i+=CELT_PVQ_U(_n-j,k+1); + } + while(j>0); + return i; +} + +void encode_pulses(const int *_y,int _n,int _k,ec_enc *_enc){ + celt_assert(_k>0); + ec_enc_uint(_enc,icwrs(_n,_y),CELT_PVQ_V(_n,_k)); +} + +static opus_val32 cwrsi(int _n,int _k,opus_uint32 _i,int *_y){ + opus_uint32 p; + int s; + int k0; + opus_int16 val; + opus_val32 yy=0; + celt_assert(_k>0); + celt_assert(_n>1); + while(_n>2){ + opus_uint32 q; + /*Lots of pulses case:*/ + if(_k>=_n){ + const opus_uint32 *row; + row=CELT_PVQ_U_ROW[_n]; + /*Are the pulses in this dimension negative?*/ + p=row[_k+1]; + s=-(_i>=p); + _i-=p&s; + /*Count how many pulses were placed in this dimension.*/ + k0=_k; + q=row[_n]; + if(q>_i){ + celt_sig_assert(p>q); + _k=_n; + do p=CELT_PVQ_U_ROW[--_k][_n]; + while(p>_i); + } + else for(p=row[_k];p>_i;p=row[_k])_k--; + _i-=p; + val=(k0-_k+s)^s; + *_y++=val; + yy=MAC16_16(yy,val,val); + } + /*Lots of dimensions case:*/ + else{ + /*Are there any pulses in this dimension at all?*/ + p=CELT_PVQ_U_ROW[_k][_n]; + q=CELT_PVQ_U_ROW[_k+1][_n]; + if(p<=_i&&_i=q); + _i-=q&s; + /*Count how many pulses were placed in this dimension.*/ + k0=_k; + do p=CELT_PVQ_U_ROW[--_k][_n]; + while(p>_i); + _i-=p; + val=(k0-_k+s)^s; + *_y++=val; + yy=MAC16_16(yy,val,val); + } + } + _n--; + } + /*_n==2*/ + p=2*_k+1; + s=-(_i>=p); + _i-=p&s; + k0=_k; + _k=(_i+1)>>1; + if(_k)_i-=2*_k-1; + val=(k0-_k+s)^s; + *_y++=val; + yy=MAC16_16(yy,val,val); + /*_n==1*/ + s=-(int)_i; + val=(_k+s)^s; + *_y=val; + yy=MAC16_16(yy,val,val); + return yy; +} + +opus_val32 decode_pulses(int *_y,int _n,int _k,ec_dec *_dec){ + return cwrsi(_n,_k,ec_dec_uint(_dec,CELT_PVQ_V(_n,_k)),_y); +} + +#else /* SMALL_FOOTPRINT */ + +/*Computes the next row/column of any recurrence that obeys the relation + u[i][j]=u[i-1][j]+u[i][j-1]+u[i-1][j-1]. + _ui0 is the base case for the new row/column.*/ +static OPUS_INLINE void unext(opus_uint32 *_ui,unsigned _len,opus_uint32 _ui0){ + opus_uint32 ui1; + unsigned j; + /*This do-while will overrun the array if we don't have storage for at least + 2 values.*/ + j=1; do { + ui1=UADD32(UADD32(_ui[j],_ui[j-1]),_ui0); + _ui[j-1]=_ui0; + _ui0=ui1; + } while (++j<_len); + _ui[j-1]=_ui0; +} + +/*Computes the previous row/column of any recurrence that obeys the relation + u[i-1][j]=u[i][j]-u[i][j-1]-u[i-1][j-1]. + _ui0 is the base case for the new row/column.*/ +static OPUS_INLINE void uprev(opus_uint32 *_ui,unsigned _n,opus_uint32 _ui0){ + opus_uint32 ui1; + unsigned j; + /*This do-while will overrun the array if we don't have storage for at least + 2 values.*/ + j=1; do { + ui1=USUB32(USUB32(_ui[j],_ui[j-1]),_ui0); + _ui[j-1]=_ui0; + _ui0=ui1; + } while (++j<_n); + _ui[j-1]=_ui0; +} + +/*Compute V(_n,_k), as well as U(_n,0..._k+1). + _u: On exit, _u[i] contains U(_n,i) for i in [0..._k+1].*/ +static opus_uint32 ncwrs_urow(unsigned _n,unsigned _k,opus_uint32 *_u){ + opus_uint32 um2; + unsigned len; + unsigned k; + len=_k+2; + /*We require storage at least 3 values (e.g., _k>0).*/ + celt_assert(len>=3); + _u[0]=0; + _u[1]=um2=1; + /*If _n==0, _u[0] should be 1 and the rest should be 0.*/ + /*If _n==1, _u[i] should be 1 for i>1.*/ + celt_assert(_n>=2); + /*If _k==0, the following do-while loop will overflow the buffer.*/ + celt_assert(_k>0); + k=2; + do _u[k]=(k<<1)-1; + while(++k0); + j=0; + do{ + opus_uint32 p; + int s; + int yj; + p=_u[_k+1]; + s=-(_i>=p); + _i-=p&s; + yj=_k; + p=_u[_k]; + while(p>_i)p=_u[--_k]; + _i-=p; + yj-=_k; + val=(yj+s)^s; + _y[j]=val; + yy=MAC16_16(yy,val,val); + uprev(_u,_k+2,0); + } + while(++j<_n); + return yy; +} + +/*Returns the index of the given combination of K elements chosen from a set + of size 1 with associated sign bits. + _y: The vector of pulses, whose sum of absolute values is K. + _k: Returns K.*/ +static OPUS_INLINE opus_uint32 icwrs1(const int *_y,int *_k){ + *_k=abs(_y[0]); + return _y[0]<0; +} + +/*Returns the index of the given combination of K elements chosen from a set + of size _n with associated sign bits. + _y: The vector of pulses, whose sum of absolute values must be _k. + _nc: Returns V(_n,_k).*/ +static OPUS_INLINE opus_uint32 icwrs(int _n,int _k,opus_uint32 *_nc,const int *_y, + opus_uint32 *_u){ + opus_uint32 i; + int j; + int k; + /*We can't unroll the first two iterations of the loop unless _n>=2.*/ + celt_assert(_n>=2); + _u[0]=0; + for(k=1;k<=_k+1;k++)_u[k]=(k<<1)-1; + i=icwrs1(_y+_n-1,&k); + j=_n-2; + i+=_u[k]; + k+=abs(_y[j]); + if(_y[j]<0)i+=_u[k+1]; + while(j-->0){ + unext(_u,_k+2,0); + i+=_u[k]; + k+=abs(_y[j]); + if(_y[j]<0)i+=_u[k+1]; + } + *_nc=_u[k]+_u[k+1]; + return i; +} + +#ifdef CUSTOM_MODES +void get_required_bits(opus_int16 *_bits,int _n,int _maxk,int _frac){ + int k; + /*_maxk==0 => there's nothing to do.*/ + celt_assert(_maxk>0); + _bits[0]=0; + if (_n==1) + { + for (k=1;k<=_maxk;k++) + _bits[k] = 1<<_frac; + } + else { + VARDECL(opus_uint32,u); + SAVE_STACK; + ALLOC(u,_maxk+2U,opus_uint32); + ncwrs_urow(_n,_maxk,u); + for(k=1;k<=_maxk;k++) + _bits[k]=log2_frac(u[k]+u[k+1],_frac); + RESTORE_STACK; + } +} +#endif /* CUSTOM_MODES */ + +void encode_pulses(const int *_y,int _n,int _k,ec_enc *_enc){ + opus_uint32 i; + VARDECL(opus_uint32,u); + opus_uint32 nc; + SAVE_STACK; + celt_assert(_k>0); + ALLOC(u,_k+2U,opus_uint32); + i=icwrs(_n,_k,&nc,_y,u); + ec_enc_uint(_enc,i,nc); + RESTORE_STACK; +} + +opus_val32 decode_pulses(int *_y,int _n,int _k,ec_dec *_dec){ + VARDECL(opus_uint32,u); + int ret; + SAVE_STACK; + celt_assert(_k>0); + ALLOC(u,_k+2U,opus_uint32); + ret = cwrsi(_n,_k,ec_dec_uint(_dec,ncwrs_urow(_n,_k,u)),_y,u); + RESTORE_STACK; + return ret; +} + +#endif /* SMALL_FOOTPRINT */ diff --git a/managed_components/78__esp-opus/celt/cwrs.h b/managed_components/78__esp-opus/celt/cwrs.h new file mode 100644 index 0000000..7cd4717 --- /dev/null +++ b/managed_components/78__esp-opus/celt/cwrs.h @@ -0,0 +1,48 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2007-2009 Timothy B. Terriberry + Written by Timothy B. Terriberry and Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef CWRS_H +#define CWRS_H + +#include "arch.h" +#include "stack_alloc.h" +#include "entenc.h" +#include "entdec.h" + +#ifdef CUSTOM_MODES +int log2_frac(opus_uint32 val, int frac); +#endif + +void get_required_bits(opus_int16 *bits, int N, int K, int frac); + +void encode_pulses(const int *_y, int N, int K, ec_enc *enc); + +opus_val32 decode_pulses(int *_y, int N, int K, ec_dec *dec); + +#endif /* CWRS_H */ diff --git a/managed_components/78__esp-opus/celt/dump_modes/dump_modes.c b/managed_components/78__esp-opus/celt/dump_modes/dump_modes.c new file mode 100644 index 0000000..644e21c --- /dev/null +++ b/managed_components/78__esp-opus/celt/dump_modes/dump_modes.c @@ -0,0 +1,400 @@ +/* Copyright (c) 2008 CSIRO + Copyright (c) 2008-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include "modes.h" +#include "celt.h" +#include "rate.h" +#include "dump_modes_arch.h" + +#define INT16 "%d" +#define INT32 "%d" +#define FLOAT "%#0.8gf" + +#ifdef FIXED_POINT +#define WORD16 INT16 +#define WORD32 INT32 +#else +#define WORD16 FLOAT +#define WORD32 FLOAT +#endif + +#define COEF16(x, a) ((opus_int16)SATURATE(((opus_int64)(x)+(1<<(a)>>1))>>(a), 32767)) +int opus_select_arch(void) { + return 0; +} + +void dump_modes(FILE *file, CELTMode **modes, int nb_modes) +{ + int i, j, k; + int mdct_twiddles_size; + fprintf(file, "/* The contents of this file was automatically generated by dump_modes.c\n"); + fprintf(file, " with arguments:"); + for (i=0;iFs,mode->shortMdctSize*mode->nbShortMdcts); + } + fprintf(file, "\n It contains static definitions for some pre-defined modes. */\n"); + fprintf(file, "#include \"modes.h\"\n"); + fprintf(file, "#include \"rate.h\"\n"); + fprintf(file, "\n#ifdef HAVE_ARM_NE10\n"); + fprintf(file, "#define OVERRIDE_FFT 1\n"); + fprintf(file, "#include \"%s\"\n", ARM_NE10_ARCH_FILE_NAME); + fprintf(file, "#endif\n"); + + fprintf(file, "\n"); + + for (i=0;ishortMdctSize*mode->nbShortMdcts; + standard = (mode->Fs == 400*(opus_int32)mode->shortMdctSize); + framerate = mode->Fs/mode->shortMdctSize; + + if (!standard) + { + fprintf(file, "#ifndef DEF_EBANDS%d_%d\n", mode->Fs, mdctSize); + fprintf(file, "#define DEF_EBANDS%d_%d\n", mode->Fs, mdctSize); + fprintf (file, "static const opus_int16 eBands%d_%d[%d] = {\n", mode->Fs, mdctSize, mode->nbEBands+2); + for (j=0;jnbEBands+2;j++) + fprintf (file, "%d, ", mode->eBands[j]); + fprintf (file, "};\n"); + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + } + + fprintf(file, "#ifndef DEF_WINDOW%d\n", mode->overlap); + fprintf(file, "#define DEF_WINDOW%d\n", mode->overlap); + fprintf (file, "static const celt_coef window%d[%d] = {\n", mode->overlap, mode->overlap); +#if defined(FIXED_POINT) && defined(ENABLE_QEXT) + fprintf(file, "#ifdef ENABLE_QEXT\n"); + for (j=0;joverlap;j++) + fprintf (file, WORD32 ",%c", mode->window[j],(j+6)%5==0?'\n':' '); + fprintf(file, "#else\n"); + for (j=0;joverlap;j++) + fprintf (file, WORD16 ",%c", COEF16(mode->window[j], 16),(j+6)%5==0?'\n':' '); + fprintf(file, "#endif\n"); +#else + for (j=0;joverlap;j++) + fprintf (file, WORD16 ",%c", mode->window[j],(j+6)%5==0?'\n':' '); +#endif + fprintf (file, "};\n"); + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + + if (!standard) + { + fprintf(file, "#ifndef DEF_ALLOC_VECTORS%d_%d\n", mode->Fs, mdctSize); + fprintf(file, "#define DEF_ALLOC_VECTORS%d_%d\n", mode->Fs, mdctSize); + fprintf (file, "static const unsigned char allocVectors%d_%d[%d] = {\n", mode->Fs, mdctSize, mode->nbEBands*mode->nbAllocVectors); + for (j=0;jnbAllocVectors;j++) + { + for (k=0;knbEBands;k++) + fprintf (file, "%2d, ", mode->allocVectors[j*mode->nbEBands+k]); + fprintf (file, "\n"); + } + fprintf (file, "};\n"); + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + } + + fprintf(file, "#ifndef DEF_LOGN%d\n", framerate); + fprintf(file, "#define DEF_LOGN%d\n", framerate); + fprintf (file, "static const opus_int16 logN%d[%d] = {\n", framerate, mode->nbEBands); + for (j=0;jnbEBands;j++) + fprintf (file, "%d, ", mode->logN[j]); + fprintf (file, "};\n"); + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + + /* Pulse cache */ + fprintf(file, "#ifndef DEF_PULSE_CACHE%d\n", mode->Fs/mdctSize); + fprintf(file, "#define DEF_PULSE_CACHE%d\n", mode->Fs/mdctSize); + fprintf (file, "static const opus_int16 cache_index%d[%d] = {\n", mode->Fs/mdctSize, (mode->maxLM+2)*mode->nbEBands); + for (j=0;jnbEBands*(mode->maxLM+2);j++) + fprintf (file, "%d,%c", mode->cache.index[j],(j+16)%15==0?'\n':' '); + fprintf (file, "};\n"); + fprintf (file, "static const unsigned char cache_bits%d[%d] = {\n", mode->Fs/mdctSize, mode->cache.size); + for (j=0;jcache.size;j++) + fprintf (file, "%d,%c", mode->cache.bits[j],(j+16)%15==0?'\n':' '); + fprintf (file, "};\n"); + fprintf (file, "static const unsigned char cache_caps%d[%d] = {\n", mode->Fs/mdctSize, (mode->maxLM+1)*2*mode->nbEBands); + for (j=0;j<(mode->maxLM+1)*2*mode->nbEBands;j++) + fprintf (file, "%d,%c", mode->cache.caps[j],(j+16)%15==0?'\n':' '); + fprintf (file, "};\n"); + + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + + /* FFT twiddles */ + fprintf(file, "#ifndef FFT_TWIDDLES%d_%d\n", mode->Fs, mdctSize); + fprintf(file, "#define FFT_TWIDDLES%d_%d\n", mode->Fs, mdctSize); + + fprintf (file, "static const kiss_twiddle_cpx fft_twiddles%d_%d[%d] = {\n", + mode->Fs, mdctSize, mode->mdct.kfft[0]->nfft); +#if defined(FIXED_POINT) && defined(ENABLE_QEXT) + fprintf(file, "#ifdef ENABLE_QEXT\n"); + for (j=0;jmdct.kfft[0]->nfft;j++) + fprintf (file, "{" WORD32 ", " WORD32 "},%c", mode->mdct.kfft[0]->twiddles[j].r, mode->mdct.kfft[0]->twiddles[j].i,(j+3)%2==0?'\n':' '); + fprintf(file, "#else\n"); + for (j=0;jmdct.kfft[0]->nfft;j++) + fprintf (file, "{" WORD16 ", " WORD16 "},%c", COEF16(mode->mdct.kfft[0]->twiddles[j].r,16), COEF16(mode->mdct.kfft[0]->twiddles[j].i,16),(j+3)%2==0?'\n':' '); + fprintf(file, "#endif\n"); +#else + for (j=0;jmdct.kfft[0]->nfft;j++) + fprintf (file, "{" WORD16 ", " WORD16 "},%c", mode->mdct.kfft[0]->twiddles[j].r, mode->mdct.kfft[0]->twiddles[j].i,(j+3)%2==0?'\n':' '); +#endif + fprintf (file, "};\n"); + +#ifdef OVERRIDE_FFT + dump_mode_arch(mode); +#endif + /* FFT Bitrev tables */ + for (k=0;k<=mode->mdct.maxshift;k++) + { + fprintf(file, "#ifndef FFT_BITREV%d\n", mode->mdct.kfft[k]->nfft); + fprintf(file, "#define FFT_BITREV%d\n", mode->mdct.kfft[k]->nfft); + fprintf (file, "static const opus_int16 fft_bitrev%d[%d] = {\n", + mode->mdct.kfft[k]->nfft, mode->mdct.kfft[k]->nfft); + for (j=0;jmdct.kfft[k]->nfft;j++) + fprintf (file, "%d,%c", mode->mdct.kfft[k]->bitrev[j],(j+16)%15==0?'\n':' '); + fprintf (file, "};\n"); + + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + } + + /* FFT States */ + for (k=0;k<=mode->mdct.maxshift;k++) + { + fprintf(file, "#ifndef FFT_STATE%d_%d_%d\n", mode->Fs, mdctSize, k); + fprintf(file, "#define FFT_STATE%d_%d_%d\n", mode->Fs, mdctSize, k); + fprintf (file, "static const kiss_fft_state fft_state%d_%d_%d = {\n", + mode->Fs, mdctSize, k); + fprintf (file, "%d, /* nfft */\n", mode->mdct.kfft[k]->nfft); + +#if defined(FIXED_POINT) && defined(ENABLE_QEXT) + fprintf(file, "#ifdef ENABLE_QEXT\n"); + fprintf (file, WORD32 ", /* scale */\n", mode->mdct.kfft[k]->scale); + fprintf(file, "#else\n"); + fprintf (file, WORD16 ", /* scale */\n", COEF16(mode->mdct.kfft[k]->scale, 15)); + fprintf(file, "#endif\n"); +#else + fprintf (file, WORD16 ", /* scale */\n", mode->mdct.kfft[k]->scale); +#endif +#ifdef FIXED_POINT + fprintf (file, "%d, /* scale_shift */\n", mode->mdct.kfft[k]->scale_shift); +#endif + fprintf (file, "%d, /* shift */\n", mode->mdct.kfft[k]->shift); + fprintf (file, "{"); + for (j=0;j<2*MAXFACTORS;j++) + fprintf (file, "%d, ", mode->mdct.kfft[k]->factors[j]); + fprintf (file, "}, /* factors */\n"); + fprintf (file, "fft_bitrev%d, /* bitrev */\n", mode->mdct.kfft[k]->nfft); + fprintf (file, "fft_twiddles%d_%d, /* bitrev */\n", mode->Fs, mdctSize); + + fprintf (file, "#ifdef OVERRIDE_FFT\n"); + fprintf (file, "(arch_fft_state *)&cfg_arch_%d,\n", mode->mdct.kfft[k]->nfft); + fprintf (file, "#else\n"); + fprintf (file, "NULL,\n"); + fprintf(file, "#endif\n"); + + fprintf (file, "};\n"); + + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + } + + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + + /* MDCT twiddles */ + mdct_twiddles_size = mode->mdct.n-(mode->mdct.n/2>>mode->mdct.maxshift); + fprintf(file, "#ifndef MDCT_TWIDDLES%d\n", mdctSize); + fprintf(file, "#define MDCT_TWIDDLES%d\n", mdctSize); + fprintf (file, "static const celt_coef mdct_twiddles%d[%d] = {\n", + mdctSize, mdct_twiddles_size); + +#if defined(FIXED_POINT) && defined(ENABLE_QEXT) + fprintf(file, "#ifdef ENABLE_QEXT\n"); + for (j=0;jmdct.trig[j],(j+6)%5==0?'\n':' '); + fprintf(file, "#else\n"); + for (j=0;jmdct.trig[j], 16),(j+6)%5==0?'\n':' '); + fprintf(file, "#endif\n"); +#else + for (j=0;jmdct.trig[j],(j+6)%5==0?'\n':' '); +#endif + + fprintf (file, "};\n"); + + fprintf(file, "#endif\n"); + fprintf(file, "\n"); + + + /* Print the actual mode data */ + fprintf(file, "static const CELTMode mode%d_%d_%d = {\n", mode->Fs, mdctSize, mode->overlap); + fprintf(file, INT32 ", /* Fs */\n", mode->Fs); + fprintf(file, "%d, /* overlap */\n", mode->overlap); + fprintf(file, "%d, /* nbEBands */\n", mode->nbEBands); + fprintf(file, "%d, /* effEBands */\n", mode->effEBands); + fprintf(file, "{"); + for (j=0;j<4;j++) + fprintf(file, WORD16 ", ", mode->preemph[j]); + fprintf(file, "}, /* preemph */\n"); + if (standard) + fprintf(file, "eband5ms, /* eBands */\n"); + else + fprintf(file, "eBands%d_%d, /* eBands */\n", mode->Fs, mdctSize); + + fprintf(file, "%d, /* maxLM */\n", mode->maxLM); + fprintf(file, "%d, /* nbShortMdcts */\n", mode->nbShortMdcts); + fprintf(file, "%d, /* shortMdctSize */\n", mode->shortMdctSize); + + fprintf(file, "%d, /* nbAllocVectors */\n", mode->nbAllocVectors); + if (standard) + fprintf(file, "band_allocation, /* allocVectors */\n"); + else + fprintf(file, "allocVectors%d_%d, /* allocVectors */\n", mode->Fs, mdctSize); + + fprintf(file, "logN%d, /* logN */\n", framerate); + fprintf(file, "window%d, /* window */\n", mode->overlap); + fprintf(file, "{%d, %d, {", mode->mdct.n, mode->mdct.maxshift); + for (k=0;k<=mode->mdct.maxshift;k++) + fprintf(file, "&fft_state%d_%d_%d, ", mode->Fs, mdctSize, k); + fprintf (file, "}, mdct_twiddles%d}, /* mdct */\n", mdctSize); + + fprintf(file, "{%d, cache_index%d, cache_bits%d, cache_caps%d}, /* cache */\n", + mode->cache.size, mode->Fs/mdctSize, mode->Fs/mdctSize, mode->Fs/mdctSize); + fprintf(file, "};\n"); + } + fprintf(file, "\n"); + fprintf(file, "/* List of all the available modes */\n"); + fprintf(file, "#define TOTAL_MODES %d\n", nb_modes); + fprintf(file, "static const CELTMode * const static_mode_list[TOTAL_MODES] = {\n"); + for (i=0;ishortMdctSize*mode->nbShortMdcts; + fprintf(file, "&mode%d_%d_%d,\n", mode->Fs, mdctSize, mode->overlap); + } + fprintf(file, "};\n"); +} + +void dump_header(FILE *file, CELTMode **modes, int nb_modes) +{ + int i; + int channels = 0; + int frame_size = 0; + int overlap = 0; + fprintf (file, "/* This header file is generated automatically*/\n"); + for (i=0;ishortMdctSize*mode->nbShortMdcts; + else if (frame_size != mode->shortMdctSize*mode->nbShortMdcts) + frame_size = -1; + if (overlap==0) + overlap = mode->overlap; + else if (overlap != mode->overlap) + overlap = -1; + } + if (channels>0) + { + fprintf (file, "#define CHANNELS(mode) %d\n", channels); + if (channels==1) + fprintf (file, "#define DISABLE_STEREO\n"); + } + if (frame_size>0) + { + fprintf (file, "#define FRAMESIZE(mode) %d\n", frame_size); + } + if (overlap>0) + { + fprintf (file, "#define OVERLAP(mode) %d\n", overlap); + } +} + +#ifdef FIXED_POINT +#define BASENAME "static_modes_fixed" +#else +#define BASENAME "static_modes_float" +#endif + +int main(int argc, char **argv) +{ + int i, nb; + FILE *file; + CELTMode **m; + if (argc%2 != 1 || argc<3) + { + fprintf (stderr, "Usage: %s rate frame_size [rate frame_size] [rate frame_size]...\n",argv[0]); + return 1; + } + nb = (argc-1)/2; + m = malloc(nb*sizeof(CELTMode*)); + for (i=0;i +#include +#include "modes.h" +#include "dump_modes_arch.h" +#include + +#if !defined(FIXED_POINT) +# define NE10_FFT_CFG_TYPE_T ne10_fft_cfg_float32_t +# define NE10_FFT_CPX_TYPE_T_STR "ne10_fft_cpx_float32_t" +# define NE10_FFT_STATE_TYPE_T_STR "ne10_fft_state_float32_t" +#else +# define NE10_FFT_CFG_TYPE_T ne10_fft_cfg_int32_t +# define NE10_FFT_CPX_TYPE_T_STR "ne10_fft_cpx_int32_t" +# define NE10_FFT_STATE_TYPE_T_STR "ne10_fft_state_int32_t" +#endif + +static FILE *file; + +void dump_modes_arch_init(CELTMode **modes, int nb_modes) +{ + int i; + + file = fopen(ARM_NE10_ARCH_FILE_NAME, "w"); + fprintf(file, "/* The contents of this file was automatically generated by\n"); + fprintf(file, " * dump_mode_arm_ne10.c with arguments:"); + for (i=0;iFs,mode->shortMdctSize*mode->nbShortMdcts); + } + fprintf(file, "\n * It contains static definitions for some pre-defined modes. */\n"); + fprintf(file, "#include \n\n"); +} + +void dump_modes_arch_finalize() +{ + fclose(file); +} + +void dump_mode_arch(CELTMode *mode) +{ + int k, j; + int mdctSize; + + mdctSize = mode->shortMdctSize*mode->nbShortMdcts; + + fprintf(file, "#ifndef NE10_FFT_PARAMS%d_%d\n", mode->Fs, mdctSize); + fprintf(file, "#define NE10_FFT_PARAMS%d_%d\n", mode->Fs, mdctSize); + /* cfg->factors */ + for(k=0;k<=mode->mdct.maxshift;k++) { + NE10_FFT_CFG_TYPE_T cfg; + cfg = (NE10_FFT_CFG_TYPE_T)mode->mdct.kfft[k]->arch_fft->priv; + if (!cfg) + continue; + fprintf(file, "static const ne10_int32_t ne10_factors_%d[%d] = {\n", + mode->mdct.kfft[k]->nfft, (NE10_MAXFACTORS * 2)); + for(j=0;j<(NE10_MAXFACTORS * 2);j++) { + fprintf(file, "%d,%c", cfg->factors[j],(j+16)%15==0?'\n':' '); + } + fprintf (file, "};\n"); + } + + /* cfg->twiddles */ + for(k=0;k<=mode->mdct.maxshift;k++) { + NE10_FFT_CFG_TYPE_T cfg; + cfg = (NE10_FFT_CFG_TYPE_T)mode->mdct.kfft[k]->arch_fft->priv; + if (!cfg) + continue; + fprintf(file, "static const %s ne10_twiddles_%d[%d] = {\n", + NE10_FFT_CPX_TYPE_T_STR, mode->mdct.kfft[k]->nfft, + mode->mdct.kfft[k]->nfft); + for(j=0;jmdct.kfft[k]->nfft;j++) { +#if !defined(FIXED_POINT) + fprintf(file, "{%#0.8gf,%#0.8gf},%c", + cfg->twiddles[j].r, cfg->twiddles[j].i,(j+4)%3==0?'\n':' '); +#else + fprintf(file, "{%d,%d},%c", + cfg->twiddles[j].r, cfg->twiddles[j].i,(j+4)%3==0?'\n':' '); +#endif + } + fprintf (file, "};\n"); + } + + for(k=0;k<=mode->mdct.maxshift;k++) { + NE10_FFT_CFG_TYPE_T cfg; + cfg = (NE10_FFT_CFG_TYPE_T)mode->mdct.kfft[k]->arch_fft->priv; + if (!cfg) { + fprintf(file, "/* Ne10 does not support scaled FFT for length = %d */\n", + mode->mdct.kfft[k]->nfft); + fprintf(file, "static const arch_fft_state cfg_arch_%d = {\n", mode->mdct.kfft[k]->nfft); + fprintf(file, "0,\n"); + fprintf(file, "NULL\n"); + fprintf(file, "};\n"); + continue; + } + fprintf(file, "static const %s %s_%d = {\n", NE10_FFT_STATE_TYPE_T_STR, + NE10_FFT_STATE_TYPE_T_STR, mode->mdct.kfft[k]->nfft); + fprintf(file, "%d,\n", cfg->nfft); + fprintf(file, "(ne10_int32_t *)ne10_factors_%d,\n", mode->mdct.kfft[k]->nfft); + fprintf(file, "(%s *)ne10_twiddles_%d,\n", + NE10_FFT_CPX_TYPE_T_STR, mode->mdct.kfft[k]->nfft); + fprintf(file, "NULL,\n"); /* buffer */ + fprintf(file, "(%s *)&ne10_twiddles_%d[%d],\n", + NE10_FFT_CPX_TYPE_T_STR, mode->mdct.kfft[k]->nfft, cfg->nfft); +#if !defined(FIXED_POINT) + fprintf(file, "/* is_forward_scaled = true */\n"); + fprintf(file, "(ne10_int32_t) 1,\n"); + fprintf(file, "/* is_backward_scaled = false */\n"); + fprintf(file, "(ne10_int32_t) 0,\n"); +#endif + fprintf(file, "};\n"); + + fprintf(file, "static const arch_fft_state cfg_arch_%d = {\n", + mode->mdct.kfft[k]->nfft); + fprintf(file, "1,\n"); + fprintf(file, "(void *)&%s_%d,\n", + NE10_FFT_STATE_TYPE_T_STR, mode->mdct.kfft[k]->nfft); + fprintf(file, "};\n\n"); + } + fprintf(file, "#endif /* end NE10_FFT_PARAMS%d_%d */\n", mode->Fs, mdctSize); +} diff --git a/managed_components/78__esp-opus/celt/ecintrin.h b/managed_components/78__esp-opus/celt/ecintrin.h new file mode 100644 index 0000000..66a4c36 --- /dev/null +++ b/managed_components/78__esp-opus/celt/ecintrin.h @@ -0,0 +1,91 @@ +/* Copyright (c) 2003-2008 Timothy B. Terriberry + Copyright (c) 2008 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/*Some common macros for potential platform-specific optimization.*/ +#include "opus_types.h" +#include +#include +#include "arch.h" +#if !defined(_ecintrin_H) +# define _ecintrin_H (1) + +/*Some specific platforms may have optimized intrinsic or OPUS_INLINE assembly + versions of these functions which can substantially improve performance. + We define macros for them to allow easy incorporation of these non-ANSI + features.*/ + +/*Modern gcc (4.x) can compile the naive versions of min and max with cmov if + given an appropriate architecture, but the branchless bit-twiddling versions + are just as fast, and do not require any special target architecture. + Earlier gcc versions (3.x) compiled both code to the same assembly + instructions, because of the way they represented ((_b)>(_a)) internally.*/ +# define EC_MINI(_a,_b) ((_a)+(((_b)-(_a))&-((_b)<(_a)))) + +/*Count leading zeros. + This macro should only be used for implementing ec_ilog(), if it is defined. + All other code should use EC_ILOG() instead.*/ +#if defined(_MSC_VER) && (_MSC_VER >= 1400) +#if defined(_MSC_VER) && (_MSC_VER >= 1910) +# include /* Improve compiler throughput. */ +#else +# include +#endif +/*In _DEBUG mode this is not an intrinsic by default.*/ +# pragma intrinsic(_BitScanReverse) + +static __inline int ec_bsr(unsigned long _x){ + unsigned long ret; + _BitScanReverse(&ret,_x); + return (int)ret; +} +# define EC_CLZ0 (1) +# define EC_CLZ(_x) (-ec_bsr(_x)) +#elif defined(ENABLE_TI_DSPLIB) +# include "dsplib.h" +# define EC_CLZ0 (31) +# define EC_CLZ(_x) (_lnorm(_x)) +#elif __GNUC_PREREQ(3,4) +# if INT_MAX>=2147483647 +# define EC_CLZ0 ((int)sizeof(unsigned)*CHAR_BIT) +# define EC_CLZ(_x) (__builtin_clz(_x)) +# elif LONG_MAX>=2147483647L +# define EC_CLZ0 ((int)sizeof(unsigned long)*CHAR_BIT) +# define EC_CLZ(_x) (__builtin_clzl(_x)) +# endif +#endif + +#if defined(EC_CLZ) +/*Note that __builtin_clz is not defined when _x==0, according to the gcc + documentation (and that of the BSR instruction that implements it on x86). + The majority of the time we can never pass it zero. + When we need to, it can be special cased.*/ +# define EC_ILOG(_x) (EC_CLZ0-EC_CLZ(_x)) +#else +int ec_ilog(opus_uint32 _v); +# define EC_ILOG(_x) (ec_ilog(_x)) +#endif +#endif diff --git a/managed_components/78__esp-opus/celt/entcode.c b/managed_components/78__esp-opus/celt/entcode.c new file mode 100644 index 0000000..70f3201 --- /dev/null +++ b/managed_components/78__esp-opus/celt/entcode.c @@ -0,0 +1,153 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "entcode.h" +#include "arch.h" + +#if !defined(EC_CLZ) +/*This is a fallback for systems where we don't know how to access + a BSR or CLZ instruction (see ecintrin.h). + If you are optimizing Opus on a new platform and it has a native CLZ or + BZR (e.g. cell, MIPS, x86, etc) then making it available to Opus will be + an easy performance win.*/ +int ec_ilog(opus_uint32 _v){ + /*On a Pentium M, this branchless version tested as the fastest on + 1,000,000,000 random 32-bit integers, edging out a similar version with + branches, and a 256-entry LUT version.*/ + int ret; + int m; + ret=!!_v; + m=!!(_v&0xFFFF0000)<<4; + _v>>=m; + ret|=m; + m=!!(_v&0xFF00)<<3; + _v>>=m; + ret|=m; + m=!!(_v&0xF0)<<2; + _v>>=m; + ret|=m; + m=!!(_v&0xC)<<1; + _v>>=m; + ret|=m; + ret+=!!(_v&0x2); + return ret; +} +#endif + +#if 1 +/* This is a faster version of ec_tell_frac() that takes advantage + of the low (1/8 bit) resolution to use just a linear function + followed by a lookup to determine the exact transition thresholds. */ +opus_uint32 ec_tell_frac(ec_ctx *_this){ + static const unsigned correction[8] = + {35733, 38967, 42495, 46340, + 50535, 55109, 60097, 65535}; + opus_uint32 nbits; + opus_uint32 r; + int l; + unsigned b; + nbits=_this->nbits_total<rng); + r=_this->rng>>(l-16); + b = (r>>12)-8; + b += r>correction[b]; + l = (l<<3)+b; + return nbits-l; +} +#else +opus_uint32 ec_tell_frac(ec_ctx *_this){ + opus_uint32 nbits; + opus_uint32 r; + int l; + int i; + /*To handle the non-integral number of bits still left in the encoder/decoder + state, we compute the worst-case number of bits of val that must be + encoded to ensure that the value is inside the range for any possible + subsequent bits. + The computation here is independent of val itself (the decoder does not + even track that value), even though the real number of bits used after + ec_enc_done() may be 1 smaller if rng is a power of two and the + corresponding trailing bits of val are all zeros. + If we did try to track that special case, then coding a value with a + probability of 1/(1<nbits_total<rng); + r=_this->rng>>(l-16); + for(i=BITRES;i-->0;){ + int b; + r=r*r>>15; + b=(int)(r>>16); + l=l<<1|b; + r>>=b; + } + return nbits-l; +} +#endif + +#ifdef USE_SMALL_DIV_TABLE +/* Result of 2^32/(2*i+1), except for i=0. */ +const opus_uint32 SMALL_DIV_TABLE[129] = { + 0xFFFFFFFF, 0x55555555, 0x33333333, 0x24924924, + 0x1C71C71C, 0x1745D174, 0x13B13B13, 0x11111111, + 0x0F0F0F0F, 0x0D79435E, 0x0C30C30C, 0x0B21642C, + 0x0A3D70A3, 0x097B425E, 0x08D3DCB0, 0x08421084, + 0x07C1F07C, 0x07507507, 0x06EB3E45, 0x06906906, + 0x063E7063, 0x05F417D0, 0x05B05B05, 0x0572620A, + 0x05397829, 0x05050505, 0x04D4873E, 0x04A7904A, + 0x047DC11F, 0x0456C797, 0x04325C53, 0x04104104, + 0x03F03F03, 0x03D22635, 0x03B5CC0E, 0x039B0AD1, + 0x0381C0E0, 0x0369D036, 0x03531DEC, 0x033D91D2, + 0x0329161F, 0x03159721, 0x03030303, 0x02F14990, + 0x02E05C0B, 0x02D02D02, 0x02C0B02C, 0x02B1DA46, + 0x02A3A0FD, 0x0295FAD4, 0x0288DF0C, 0x027C4597, + 0x02702702, 0x02647C69, 0x02593F69, 0x024E6A17, + 0x0243F6F0, 0x0239E0D5, 0x02302302, 0x0226B902, + 0x021D9EAD, 0x0214D021, 0x020C49BA, 0x02040810, + 0x01FC07F0, 0x01F44659, 0x01ECC07B, 0x01E573AC, + 0x01DE5D6E, 0x01D77B65, 0x01D0CB58, 0x01CA4B30, + 0x01C3F8F0, 0x01BDD2B8, 0x01B7D6C3, 0x01B20364, + 0x01AC5701, 0x01A6D01A, 0x01A16D3F, 0x019C2D14, + 0x01970E4F, 0x01920FB4, 0x018D3018, 0x01886E5F, + 0x0183C977, 0x017F405F, 0x017AD220, 0x01767DCE, + 0x01724287, 0x016E1F76, 0x016A13CD, 0x01661EC6, + 0x01623FA7, 0x015E75BB, 0x015AC056, 0x01571ED3, + 0x01539094, 0x01501501, 0x014CAB88, 0x0149539E, + 0x01460CBC, 0x0142D662, 0x013FB013, 0x013C995A, + 0x013991C2, 0x013698DF, 0x0133AE45, 0x0130D190, + 0x012E025C, 0x012B404A, 0x01288B01, 0x0125E227, + 0x01234567, 0x0120B470, 0x011E2EF3, 0x011BB4A4, + 0x01194538, 0x0116E068, 0x011485F0, 0x0112358E, + 0x010FEF01, 0x010DB20A, 0x010B7E6E, 0x010953F3, + 0x01073260, 0x0105197F, 0x0103091B, 0x01010101 +}; +#endif diff --git a/managed_components/78__esp-opus/celt/entcode.h b/managed_components/78__esp-opus/celt/entcode.h new file mode 100644 index 0000000..3763e3f --- /dev/null +++ b/managed_components/78__esp-opus/celt/entcode.h @@ -0,0 +1,152 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include "opus_types.h" +#include "opus_defines.h" + +#if !defined(_entcode_H) +# define _entcode_H (1) +# include +# include +# include "ecintrin.h" + +extern const opus_uint32 SMALL_DIV_TABLE[129]; + +#ifdef OPUS_ARM_ASM +#define USE_SMALL_DIV_TABLE +#endif + +/*OPT: ec_window must be at least 32 bits, but if you have fast arithmetic on a + larger type, you can speed up the decoder by using it here.*/ +typedef opus_uint32 ec_window; +typedef struct ec_ctx ec_ctx; +typedef struct ec_ctx ec_enc; +typedef struct ec_ctx ec_dec; + +# define EC_WINDOW_SIZE ((int)sizeof(ec_window)*CHAR_BIT) + +/*The number of bits to use for the range-coded part of unsigned integers.*/ +# define EC_UINT_BITS (8) + +/*The resolution of fractional-precision bit usage measurements, i.e., + 3 => 1/8th bits.*/ +# define BITRES 3 + +/*The entropy encoder/decoder context. + We use the same structure for both, so that common functions like ec_tell() + can be used on either one.*/ +struct ec_ctx{ + /*Buffered input/output.*/ + unsigned char *buf; + /*The size of the buffer.*/ + opus_uint32 storage; + /*The offset at which the last byte containing raw bits was read/written.*/ + opus_uint32 end_offs; + /*Bits that will be read from/written at the end.*/ + ec_window end_window; + /*Number of valid bits in end_window.*/ + int nend_bits; + /*The total number of whole bits read/written. + This does not include partial bits currently in the range coder.*/ + int nbits_total; + /*The offset at which the next range coder byte will be read/written.*/ + opus_uint32 offs; + /*The number of values in the current range.*/ + opus_uint32 rng; + /*In the decoder: the difference between the top of the current range and + the input value, minus one. + In the encoder: the low end of the current range.*/ + opus_uint32 val; + /*In the decoder: the saved normalization factor from ec_decode(). + In the encoder: the number of oustanding carry propagating symbols.*/ + opus_uint32 ext; + /*A buffered input/output symbol, awaiting carry propagation.*/ + int rem; + /*Nonzero if an error occurred.*/ + int error; +}; + +static OPUS_INLINE opus_uint32 ec_range_bytes(ec_ctx *_this){ + return _this->offs; +} + +static OPUS_INLINE unsigned char *ec_get_buffer(ec_ctx *_this){ + return _this->buf; +} + +static OPUS_INLINE int ec_get_error(ec_ctx *_this){ + return _this->error; +} + +/*Returns the number of bits "used" by the encoded or decoded symbols so far. + This same number can be computed in either the encoder or the decoder, and is + suitable for making coding decisions. + Return: The number of bits. + This will always be slightly larger than the exact value (e.g., all + rounding error is in the positive direction).*/ +static OPUS_INLINE int ec_tell(ec_ctx *_this){ + return _this->nbits_total-EC_ILOG(_this->rng); +} + +/*Returns the number of bits "used" by the encoded or decoded symbols so far. + This same number can be computed in either the encoder or the decoder, and is + suitable for making coding decisions. + Return: The number of bits scaled by 2**BITRES. + This will always be slightly larger than the exact value (e.g., all + rounding error is in the positive direction).*/ +opus_uint32 ec_tell_frac(ec_ctx *_this); + +/* Tested exhaustively for all n and for 1<=d<=256 */ +static OPUS_INLINE opus_uint32 celt_udiv(opus_uint32 n, opus_uint32 d) { + celt_sig_assert(d>0); +#ifdef USE_SMALL_DIV_TABLE + if (d>256) + return n/d; + else { + opus_uint32 t, q; + t = EC_ILOG(d&-d); + q = (opus_uint64)SMALL_DIV_TABLE[d>>t]*(n>>(t-1))>>32; + return q+(n-q*d >= d); + } +#else + return n/d; +#endif +} + +static OPUS_INLINE opus_int32 celt_sudiv(opus_int32 n, opus_int32 d) { + celt_sig_assert(d>0); +#ifdef USE_SMALL_DIV_TABLE + if (n<0) + return -(opus_int32)celt_udiv(-n, d); + else + return celt_udiv(n, d); +#else + return n/d; +#endif +} + +#endif diff --git a/managed_components/78__esp-opus/celt/entdec.c b/managed_components/78__esp-opus/celt/entdec.c new file mode 100644 index 0000000..027aa24 --- /dev/null +++ b/managed_components/78__esp-opus/celt/entdec.c @@ -0,0 +1,266 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include "os_support.h" +#include "arch.h" +#include "entdec.h" +#include "mfrngcod.h" + +/*A range decoder. + This is an entropy decoder based upon \cite{Mar79}, which is itself a + rediscovery of the FIFO arithmetic code introduced by \cite{Pas76}. + It is very similar to arithmetic encoding, except that encoding is done with + digits in any base, instead of with bits, and so it is faster when using + larger bases (i.e.: a byte). + The author claims an average waste of $\frac{1}{2}\log_b(2b)$ bits, where $b$ + is the base, longer than the theoretical optimum, but to my knowledge there + is no published justification for this claim. + This only seems true when using near-infinite precision arithmetic so that + the process is carried out with no rounding errors. + + An excellent description of implementation details is available at + http://www.arturocampos.com/ac_range.html + A recent work \cite{MNW98} which proposes several changes to arithmetic + encoding for efficiency actually re-discovers many of the principles + behind range encoding, and presents a good theoretical analysis of them. + + End of stream is handled by writing out the smallest number of bits that + ensures that the stream will be correctly decoded regardless of the value of + any subsequent bits. + ec_tell() can be used to determine how many bits were needed to decode + all the symbols thus far; other data can be packed in the remaining bits of + the input buffer. + @PHDTHESIS{Pas76, + author="Richard Clark Pasco", + title="Source coding algorithms for fast data compression", + school="Dept. of Electrical Engineering, Stanford University", + address="Stanford, CA", + month=May, + year=1976 + } + @INPROCEEDINGS{Mar79, + author="Martin, G.N.N.", + title="Range encoding: an algorithm for removing redundancy from a digitised + message", + booktitle="Video & Data Recording Conference", + year=1979, + address="Southampton", + month=Jul + } + @ARTICLE{MNW98, + author="Alistair Moffat and Radford Neal and Ian H. Witten", + title="Arithmetic Coding Revisited", + journal="{ACM} Transactions on Information Systems", + year=1998, + volume=16, + number=3, + pages="256--294", + month=Jul, + URL="http://www.stanford.edu/class/ee398a/handouts/papers/Moffat98ArithmCoding.pdf" + }*/ + +static int ec_read_byte(ec_dec *_this){ + return _this->offs<_this->storage?_this->buf[_this->offs++]:0; +} + +static int ec_read_byte_from_end(ec_dec *_this){ + return _this->end_offs<_this->storage? + _this->buf[_this->storage-++(_this->end_offs)]:0; +} + +/*Normalizes the contents of val and rng so that rng lies entirely in the + high-order symbol.*/ +static void ec_dec_normalize(ec_dec *_this){ + /*If the range is too small, rescale it and input some bits.*/ + while(_this->rng<=EC_CODE_BOT){ + int sym; + _this->nbits_total+=EC_SYM_BITS; + _this->rng<<=EC_SYM_BITS; + /*Use up the remaining bits from our last symbol.*/ + sym=_this->rem; + /*Read the next value from the input.*/ + _this->rem=ec_read_byte(_this); + /*Take the rest of the bits we need from this new symbol.*/ + sym=(sym<rem)>>(EC_SYM_BITS-EC_CODE_EXTRA); + /*And subtract them from val, capped to be less than EC_CODE_TOP.*/ + _this->val=((_this->val<buf=_buf; + _this->storage=_storage; + _this->end_offs=0; + _this->end_window=0; + _this->nend_bits=0; + /*This is the offset from which ec_tell() will subtract partial bits. + The final value after the ec_dec_normalize() call will be the same as in + the encoder, but we have to compensate for the bits that are added there.*/ + _this->nbits_total=EC_CODE_BITS+1 + -((EC_CODE_BITS-EC_CODE_EXTRA)/EC_SYM_BITS)*EC_SYM_BITS; + _this->offs=0; + _this->rng=1U<rem=ec_read_byte(_this); + _this->val=_this->rng-1-(_this->rem>>(EC_SYM_BITS-EC_CODE_EXTRA)); + _this->error=0; + /*Normalize the interval.*/ + ec_dec_normalize(_this); +} + +unsigned ec_decode(ec_dec *_this,unsigned _ft){ + unsigned s; + _this->ext=celt_udiv(_this->rng,_ft); + s=(unsigned)(_this->val/_this->ext); + return _ft-EC_MINI(s+1,_ft); +} + +unsigned ec_decode_bin(ec_dec *_this,unsigned _bits){ + unsigned s; + _this->ext=_this->rng>>_bits; + s=(unsigned)(_this->val/_this->ext); + return (1U<<_bits)-EC_MINI(s+1U,1U<<_bits); +} + +void ec_dec_update(ec_dec *_this,unsigned _fl,unsigned _fh,unsigned _ft){ + opus_uint32 s; + s=IMUL32(_this->ext,_ft-_fh); + _this->val-=s; + _this->rng=_fl>0?IMUL32(_this->ext,_fh-_fl):_this->rng-s; + ec_dec_normalize(_this); +} + +/*The probability of having a "one" is 1/(1<<_logp).*/ +int ec_dec_bit_logp(ec_dec *_this,unsigned _logp){ + opus_uint32 r; + opus_uint32 d; + opus_uint32 s; + int ret; + r=_this->rng; + d=_this->val; + s=r>>_logp; + ret=dval=d-s; + _this->rng=ret?s:r-s; + ec_dec_normalize(_this); + return ret; +} + +int ec_dec_icdf(ec_dec *_this,const unsigned char *_icdf,unsigned _ftb){ + opus_uint32 r; + opus_uint32 d; + opus_uint32 s; + opus_uint32 t; + int ret; + s=_this->rng; + d=_this->val; + r=s>>_ftb; + ret=-1; + do{ + t=s; + s=IMUL32(r,_icdf[++ret]); + } + while(dval=d-s; + _this->rng=t-s; + ec_dec_normalize(_this); + return ret; +} + +int ec_dec_icdf16(ec_dec *_this,const opus_uint16 *_icdf,unsigned _ftb){ + opus_uint32 r; + opus_uint32 d; + opus_uint32 s; + opus_uint32 t; + int ret; + s=_this->rng; + d=_this->val; + r=s>>_ftb; + ret=-1; + do{ + t=s; + s=IMUL32(r,_icdf[++ret]); + } + while(dval=d-s; + _this->rng=t-s; + ec_dec_normalize(_this); + return ret; +} + +opus_uint32 ec_dec_uint(ec_dec *_this,opus_uint32 _ft){ + unsigned ft; + unsigned s; + int ftb; + /*In order to optimize EC_ILOG(), it is undefined for the value 0.*/ + celt_assert(_ft>1); + _ft--; + ftb=EC_ILOG(_ft); + if(ftb>EC_UINT_BITS){ + opus_uint32 t; + ftb-=EC_UINT_BITS; + ft=(unsigned)(_ft>>ftb)+1; + s=ec_decode(_this,ft); + ec_dec_update(_this,s,s+1,ft); + t=(opus_uint32)s<error=1; + return _ft; + } + else{ + _ft++; + s=ec_decode(_this,(unsigned)_ft); + ec_dec_update(_this,s,s+1,(unsigned)_ft); + return s; + } +} + +opus_uint32 ec_dec_bits(ec_dec *_this,unsigned _bits){ + ec_window window; + int available; + opus_uint32 ret; + window=_this->end_window; + available=_this->nend_bits; + if((unsigned)available<_bits){ + do{ + window|=(ec_window)ec_read_byte_from_end(_this)<>=_bits; + available-=_bits; + _this->end_window=window; + _this->nend_bits=available; + _this->nbits_total+=_bits; + return ret; +} diff --git a/managed_components/78__esp-opus/celt/entdec.h b/managed_components/78__esp-opus/celt/entdec.h new file mode 100644 index 0000000..c81f26f --- /dev/null +++ b/managed_components/78__esp-opus/celt/entdec.h @@ -0,0 +1,110 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(_entdec_H) +# define _entdec_H (1) +# include +# include "entcode.h" + +/*Initializes the decoder. + _buf: The input buffer to use. + Return: 0 on success, or a negative value on error.*/ +void ec_dec_init(ec_dec *_this,unsigned char *_buf,opus_uint32 _storage); + +/*Calculates the cumulative frequency for the next symbol. + This can then be fed into the probability model to determine what that + symbol is, and the additional frequency information required to advance to + the next symbol. + This function cannot be called more than once without a corresponding call to + ec_dec_update(), or decoding will not proceed correctly. + _ft: The total frequency of the symbols in the alphabet the next symbol was + encoded with. + Return: A cumulative frequency representing the encoded symbol. + If the cumulative frequency of all the symbols before the one that + was encoded was fl, and the cumulative frequency of all the symbols + up to and including the one encoded is fh, then the returned value + will fall in the range [fl,fh).*/ +unsigned ec_decode(ec_dec *_this,unsigned _ft); + +/*Equivalent to ec_decode() with _ft==1<<_bits.*/ +unsigned ec_decode_bin(ec_dec *_this,unsigned _bits); + +/*Advance the decoder past the next symbol using the frequency information the + symbol was encoded with. + Exactly one call to ec_decode() must have been made so that all necessary + intermediate calculations are performed. + _fl: The cumulative frequency of all symbols that come before the symbol + decoded. + _fh: The cumulative frequency of all symbols up to and including the symbol + decoded. + Together with _fl, this defines the range [_fl,_fh) in which the value + returned above must fall. + _ft: The total frequency of the symbols in the alphabet the symbol decoded + was encoded in. + This must be the same as passed to the preceding call to ec_decode().*/ +void ec_dec_update(ec_dec *_this,unsigned _fl,unsigned _fh,unsigned _ft); + +/* Decode a bit that has a 1/(1<<_logp) probability of being a one */ +int ec_dec_bit_logp(ec_dec *_this,unsigned _logp); + +/*Decodes a symbol given an "inverse" CDF table. + No call to ec_dec_update() is necessary after this call. + _icdf: The "inverse" CDF, such that symbol s falls in the range + [s>0?ft-_icdf[s-1]:0,ft-_icdf[s]), where ft=1<<_ftb. + The values must be monotonically non-increasing, and the last value + must be 0. + _ftb: The number of bits of precision in the cumulative distribution. + Return: The decoded symbol s.*/ +int ec_dec_icdf(ec_dec *_this,const unsigned char *_icdf,unsigned _ftb); + +/*Decodes a symbol given an "inverse" CDF table. + No call to ec_dec_update() is necessary after this call. + _icdf: The "inverse" CDF, such that symbol s falls in the range + [s>0?ft-_icdf[s-1]:0,ft-_icdf[s]), where ft=1<<_ftb. + The values must be monotonically non-increasing, and the last value + must be 0. + _ftb: The number of bits of precision in the cumulative distribution. + Return: The decoded symbol s.*/ +int ec_dec_icdf16(ec_dec *_this,const opus_uint16 *_icdf,unsigned _ftb); + +/*Extracts a raw unsigned integer with a non-power-of-2 range from the stream. + The bits must have been encoded with ec_enc_uint(). + No call to ec_dec_update() is necessary after this call. + _ft: The number of integers that can be decoded (one more than the max). + This must be at least 2, and no more than 2**32-1. + Return: The decoded bits.*/ +opus_uint32 ec_dec_uint(ec_dec *_this,opus_uint32 _ft); + +/*Extracts a sequence of raw bits from the stream. + The bits must have been encoded with ec_enc_bits(). + No call to ec_dec_update() is necessary after this call. + _ftb: The number of bits to extract. + This must be between 0 and 25, inclusive. + Return: The decoded bits.*/ +opus_uint32 ec_dec_bits(ec_dec *_this,unsigned _ftb); + +#endif diff --git a/managed_components/78__esp-opus/celt/entenc.c b/managed_components/78__esp-opus/celt/entenc.c new file mode 100644 index 0000000..69c6f83 --- /dev/null +++ b/managed_components/78__esp-opus/celt/entenc.c @@ -0,0 +1,305 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if defined(HAVE_CONFIG_H) +# include "config.h" +#endif +#include "os_support.h" +#include "arch.h" +#include "entenc.h" +#include "mfrngcod.h" + +/*A range encoder. + See entdec.c and the references for implementation details \cite{Mar79,MNW98}. + + @INPROCEEDINGS{Mar79, + author="Martin, G.N.N.", + title="Range encoding: an algorithm for removing redundancy from a digitised + message", + booktitle="Video \& Data Recording Conference", + year=1979, + address="Southampton", + month=Jul + } + @ARTICLE{MNW98, + author="Alistair Moffat and Radford Neal and Ian H. Witten", + title="Arithmetic Coding Revisited", + journal="{ACM} Transactions on Information Systems", + year=1998, + volume=16, + number=3, + pages="256--294", + month=Jul, + URL="http://www.stanford.edu/class/ee398/handouts/papers/Moffat98ArithmCoding.pdf" + }*/ + +static int ec_write_byte(ec_enc *_this,unsigned _value){ + if(_this->offs+_this->end_offs>=_this->storage)return -1; + _this->buf[_this->offs++]=(unsigned char)_value; + return 0; +} + +static int ec_write_byte_at_end(ec_enc *_this,unsigned _value){ + if(_this->offs+_this->end_offs>=_this->storage)return -1; + _this->buf[_this->storage-++(_this->end_offs)]=(unsigned char)_value; + return 0; +} + +/*Outputs a symbol, with a carry bit. + If there is a potential to propagate a carry over several symbols, they are + buffered until it can be determined whether or not an actual carry will + occur. + If the counter for the buffered symbols overflows, then the stream becomes + undecodable. + This gives a theoretical limit of a few billion symbols in a single packet on + 32-bit systems. + The alternative is to truncate the range in order to force a carry, but + requires similar carry tracking in the decoder, needlessly slowing it down.*/ +static void ec_enc_carry_out(ec_enc *_this,int _c){ + if(_c!=EC_SYM_MAX){ + /*No further carry propagation possible, flush buffer.*/ + int carry; + carry=_c>>EC_SYM_BITS; + /*Don't output a byte on the first write. + This compare should be taken care of by branch-prediction thereafter.*/ + if(_this->rem>=0)_this->error|=ec_write_byte(_this,_this->rem+carry); + if(_this->ext>0){ + unsigned sym; + sym=(EC_SYM_MAX+carry)&EC_SYM_MAX; + do _this->error|=ec_write_byte(_this,sym); + while(--(_this->ext)>0); + } + _this->rem=_c&EC_SYM_MAX; + } + else _this->ext++; +} + +static OPUS_INLINE void ec_enc_normalize(ec_enc *_this){ + /*If the range is too small, output some bits and rescale it.*/ + while(_this->rng<=EC_CODE_BOT){ + ec_enc_carry_out(_this,(int)(_this->val>>EC_CODE_SHIFT)); + /*Move the next-to-high-order symbol into the high-order position.*/ + _this->val=(_this->val<rng<<=EC_SYM_BITS; + _this->nbits_total+=EC_SYM_BITS; + } +} + +void ec_enc_init(ec_enc *_this,unsigned char *_buf,opus_uint32 _size){ + _this->buf=_buf; + _this->end_offs=0; + _this->end_window=0; + _this->nend_bits=0; + /*This is the offset from which ec_tell() will subtract partial bits.*/ + _this->nbits_total=EC_CODE_BITS+1; + _this->offs=0; + _this->rng=EC_CODE_TOP; + _this->rem=-1; + _this->val=0; + _this->ext=0; + _this->storage=_size; + _this->error=0; +} + +void ec_encode(ec_enc *_this,unsigned _fl,unsigned _fh,unsigned _ft){ + opus_uint32 r; + r=celt_udiv(_this->rng,_ft); + if(_fl>0){ + _this->val+=_this->rng-IMUL32(r,(_ft-_fl)); + _this->rng=IMUL32(r,(_fh-_fl)); + } + else _this->rng-=IMUL32(r,(_ft-_fh)); + ec_enc_normalize(_this); +} + +void ec_encode_bin(ec_enc *_this,unsigned _fl,unsigned _fh,unsigned _bits){ + opus_uint32 r; + r=_this->rng>>_bits; + if(_fl>0){ + _this->val+=_this->rng-IMUL32(r,((1U<<_bits)-_fl)); + _this->rng=IMUL32(r,(_fh-_fl)); + } + else _this->rng-=IMUL32(r,((1U<<_bits)-_fh)); + ec_enc_normalize(_this); +} + +/*The probability of having a "one" is 1/(1<<_logp).*/ +void ec_enc_bit_logp(ec_enc *_this,int _val,unsigned _logp){ + opus_uint32 r; + opus_uint32 s; + opus_uint32 l; + r=_this->rng; + l=_this->val; + s=r>>_logp; + r-=s; + if(_val)_this->val=l+r; + _this->rng=_val?s:r; + ec_enc_normalize(_this); +} + +void ec_enc_icdf(ec_enc *_this,int _s,const unsigned char *_icdf,unsigned _ftb){ + opus_uint32 r; + r=_this->rng>>_ftb; + if(_s>0){ + _this->val+=_this->rng-IMUL32(r,_icdf[_s-1]); + _this->rng=IMUL32(r,_icdf[_s-1]-_icdf[_s]); + } + else _this->rng-=IMUL32(r,_icdf[_s]); + ec_enc_normalize(_this); +} + +void ec_enc_icdf16(ec_enc *_this,int _s,const opus_uint16 *_icdf,unsigned _ftb){ + opus_uint32 r; + r=_this->rng>>_ftb; + if(_s>0){ + _this->val+=_this->rng-IMUL32(r,_icdf[_s-1]); + _this->rng=IMUL32(r,_icdf[_s-1]-_icdf[_s]); + } + else _this->rng-=IMUL32(r,_icdf[_s]); + ec_enc_normalize(_this); +} + +void ec_enc_uint(ec_enc *_this,opus_uint32 _fl,opus_uint32 _ft){ + unsigned ft; + unsigned fl; + int ftb; + /*In order to optimize EC_ILOG(), it is undefined for the value 0.*/ + celt_assert(_ft>1); + _ft--; + ftb=EC_ILOG(_ft); + if(ftb>EC_UINT_BITS){ + ftb-=EC_UINT_BITS; + ft=(_ft>>ftb)+1; + fl=(unsigned)(_fl>>ftb); + ec_encode(_this,fl,fl+1,ft); + ec_enc_bits(_this,_fl&(((opus_uint32)1<end_window; + used=_this->nend_bits; + celt_assert(_bits>0); + if(used+_bits>EC_WINDOW_SIZE){ + do{ + _this->error|=ec_write_byte_at_end(_this,(unsigned)window&EC_SYM_MAX); + window>>=EC_SYM_BITS; + used-=EC_SYM_BITS; + } + while(used>=EC_SYM_BITS); + } + window|=(ec_window)_fl<end_window=window; + _this->nend_bits=used; + _this->nbits_total+=_bits; +} + +void ec_enc_patch_initial_bits(ec_enc *_this,unsigned _val,unsigned _nbits){ + int shift; + unsigned mask; + celt_assert(_nbits<=EC_SYM_BITS); + shift=EC_SYM_BITS-_nbits; + mask=((1<<_nbits)-1)<offs>0){ + /*The first byte has been finalized.*/ + _this->buf[0]=(unsigned char)((_this->buf[0]&~mask)|_val<rem>=0){ + /*The first byte is still awaiting carry propagation.*/ + _this->rem=(_this->rem&~mask)|_val<rng<=(EC_CODE_TOP>>_nbits)){ + /*The renormalization loop has never been run.*/ + _this->val=(_this->val&~((opus_uint32)mask<error=-1; +} + +void ec_enc_shrink(ec_enc *_this,opus_uint32 _size){ + celt_assert(_this->offs+_this->end_offs<=_size); + OPUS_MOVE(_this->buf+_size-_this->end_offs, + _this->buf+_this->storage-_this->end_offs,_this->end_offs); + _this->storage=_size; +} + +void ec_enc_done(ec_enc *_this){ + ec_window window; + int used; + opus_uint32 msk; + opus_uint32 end; + int l; + /*We output the minimum number of bits that ensures that the symbols encoded + thus far will be decoded correctly regardless of the bits that follow.*/ + l=EC_CODE_BITS-EC_ILOG(_this->rng); + msk=(EC_CODE_TOP-1)>>l; + end=(_this->val+msk)&~msk; + if((end|msk)>=_this->val+_this->rng){ + l++; + msk>>=1; + end=(_this->val+msk)&~msk; + } + while(l>0){ + ec_enc_carry_out(_this,(int)(end>>EC_CODE_SHIFT)); + end=(end<rem>=0||_this->ext>0)ec_enc_carry_out(_this,0); + /*If we have buffered extra bits, flush them as well.*/ + window=_this->end_window; + used=_this->nend_bits; + while(used>=EC_SYM_BITS){ + _this->error|=ec_write_byte_at_end(_this,(unsigned)window&EC_SYM_MAX); + window>>=EC_SYM_BITS; + used-=EC_SYM_BITS; + } + /*Clear any excess space and add any remaining extra bits to the last byte.*/ + if(!_this->error){ + OPUS_CLEAR(_this->buf+_this->offs, + _this->storage-_this->offs-_this->end_offs); + if(used>0){ + /*If there's no range coder data at all, give up.*/ + if(_this->end_offs>=_this->storage)_this->error=-1; + else{ + l=-l; + /*If we've busted, don't add too many extra bits to the last byte; it + would corrupt the range coder data, and that's more important.*/ + if(_this->offs+_this->end_offs>=_this->storage&&lerror=-1; + } + _this->buf[_this->storage-_this->end_offs-1]|=(unsigned char)window; + } + } + } +} diff --git a/managed_components/78__esp-opus/celt/entenc.h b/managed_components/78__esp-opus/celt/entenc.h new file mode 100644 index 0000000..010874b --- /dev/null +++ b/managed_components/78__esp-opus/celt/entenc.h @@ -0,0 +1,119 @@ +/* Copyright (c) 2001-2011 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(_entenc_H) +# define _entenc_H (1) +# include +# include "entcode.h" + +/*Initializes the encoder. + _buf: The buffer to store output bytes in. + _size: The size of the buffer, in chars.*/ +void ec_enc_init(ec_enc *_this,unsigned char *_buf,opus_uint32 _size); +/*Encodes a symbol given its frequency information. + The frequency information must be discernable by the decoder, assuming it + has read only the previous symbols from the stream. + It is allowable to change the frequency information, or even the entire + source alphabet, so long as the decoder can tell from the context of the + previously encoded information that it is supposed to do so as well. + _fl: The cumulative frequency of all symbols that come before the one to be + encoded. + _fh: The cumulative frequency of all symbols up to and including the one to + be encoded. + Together with _fl, this defines the range [_fl,_fh) in which the + decoded value will fall. + _ft: The sum of the frequencies of all the symbols*/ +void ec_encode(ec_enc *_this,unsigned _fl,unsigned _fh,unsigned _ft); + +/*Equivalent to ec_encode() with _ft==1<<_bits.*/ +void ec_encode_bin(ec_enc *_this,unsigned _fl,unsigned _fh,unsigned _bits); + +/* Encode a bit that has a 1/(1<<_logp) probability of being a one */ +void ec_enc_bit_logp(ec_enc *_this,int _val,unsigned _logp); + +/*Encodes a symbol given an "inverse" CDF table. + _s: The index of the symbol to encode. + _icdf: The "inverse" CDF, such that symbol _s falls in the range + [_s>0?ft-_icdf[_s-1]:0,ft-_icdf[_s]), where ft=1<<_ftb. + The values must be monotonically non-increasing, and the last value + must be 0. + _ftb: The number of bits of precision in the cumulative distribution.*/ +void ec_enc_icdf(ec_enc *_this,int _s,const unsigned char *_icdf,unsigned _ftb); + +/*Encodes a symbol given an "inverse" CDF table. + _s: The index of the symbol to encode. + _icdf: The "inverse" CDF, such that symbol _s falls in the range + [_s>0?ft-_icdf[_s-1]:0,ft-_icdf[_s]), where ft=1<<_ftb. + The values must be monotonically non-increasing, and the last value + must be 0. + _ftb: The number of bits of precision in the cumulative distribution.*/ +void ec_enc_icdf16(ec_enc *_this,int _s,const opus_uint16 *_icdf,unsigned _ftb); + +/*Encodes a raw unsigned integer in the stream. + _fl: The integer to encode. + _ft: The number of integers that can be encoded (one more than the max). + This must be at least 2, and no more than 2**32-1.*/ +void ec_enc_uint(ec_enc *_this,opus_uint32 _fl,opus_uint32 _ft); + +/*Encodes a sequence of raw bits in the stream. + _fl: The bits to encode. + _ftb: The number of bits to encode. + This must be between 1 and 25, inclusive.*/ +void ec_enc_bits(ec_enc *_this,opus_uint32 _fl,unsigned _ftb); + +/*Overwrites a few bits at the very start of an existing stream, after they + have already been encoded. + This makes it possible to have a few flags up front, where it is easy for + decoders to access them without parsing the whole stream, even if their + values are not determined until late in the encoding process, without having + to buffer all the intermediate symbols in the encoder. + In order for this to work, at least _nbits bits must have already been + encoded using probabilities that are an exact power of two. + The encoder can verify the number of encoded bits is sufficient, but cannot + check this latter condition. + _val: The bits to encode (in the least _nbits significant bits). + They will be decoded in order from most-significant to least. + _nbits: The number of bits to overwrite. + This must be no more than 8.*/ +void ec_enc_patch_initial_bits(ec_enc *_this,unsigned _val,unsigned _nbits); + +/*Compacts the data to fit in the target size. + This moves up the raw bits at the end of the current buffer so they are at + the end of the new buffer size. + The caller must ensure that the amount of data that's already been written + will fit in the new size. + _size: The number of bytes in the new buffer. + This must be large enough to contain the bits already written, and + must be no larger than the existing size.*/ +void ec_enc_shrink(ec_enc *_this,opus_uint32 _size); + +/*Indicates that there are no more symbols to encode. + All reamining output bytes are flushed to the output buffer. + ec_enc_init() must be called before the encoder can be used again.*/ +void ec_enc_done(ec_enc *_this); + +#endif diff --git a/managed_components/78__esp-opus/celt/fixed_c5x.h b/managed_components/78__esp-opus/celt/fixed_c5x.h new file mode 100644 index 0000000..ea95a99 --- /dev/null +++ b/managed_components/78__esp-opus/celt/fixed_c5x.h @@ -0,0 +1,79 @@ +/* Copyright (C) 2003 Jean-Marc Valin */ +/** + @file fixed_c5x.h + @brief Fixed-point operations for the TI C5x DSP family +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_C5X_H +#define FIXED_C5X_H + +#include "dsplib.h" + +#undef IMUL32 +static OPUS_INLINE long IMUL32(long i, long j) +{ + long ac0, ac1; + ac0 = _lmpy(i>>16,j); + ac1 = ac0 + _lmpy(i,j>>16); + return _lmpyu(i,j) + (ac1<<16); +} + +#undef MAX16 +#define MAX16(a,b) _max(a,b) + +#undef MIN16 +#define MIN16(a,b) _min(a,b) + +#undef MAX32 +#define MAX32(a,b) _lmax(a,b) + +#undef MIN32 +#define MIN32(a,b) _lmin(a,b) + +#undef VSHR32 +#define VSHR32(a, shift) _lshl(a,-(shift)) + +#undef MULT16_16_Q15 +#define MULT16_16_Q15(a,b) (_smpy(a,b)) + +#undef MULT16_16SU +#define MULT16_16SU(a,b) _lmpysu(a,b) + +#undef MULT_16_16 +#define MULT_16_16(a,b) _lmpy(a,b) + +/* FIXME: This is technically incorrect and is bound to cause problems. Is there any cleaner solution? */ +#undef MULT16_32_Q15 +#define MULT16_32_Q15(a,b) ADD32(SHL(MULT16_16((a),SHR((b),16)),1), SHR(MULT16_16SU((a),(b)),15)) + +#define celt_ilog2(x) (30 - _lnorm(x)) +#define OVERRIDE_CELT_ILOG2 + +#define celt_maxabs16(x, len) MAX32(EXTEND32(maxval((DATA *)x, len)),-EXTEND32(minval((DATA *)x, len))) +#define OVERRIDE_CELT_MAXABS16 + +#endif /* FIXED_C5X_H */ diff --git a/managed_components/78__esp-opus/celt/fixed_c6x.h b/managed_components/78__esp-opus/celt/fixed_c6x.h new file mode 100644 index 0000000..bb6ad92 --- /dev/null +++ b/managed_components/78__esp-opus/celt/fixed_c6x.h @@ -0,0 +1,70 @@ +/* Copyright (C) 2008 CSIRO */ +/** + @file fixed_c6x.h + @brief Fixed-point operations for the TI C6x DSP family +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_C6X_H +#define FIXED_C6X_H + +#undef MULT16_16SU +#define MULT16_16SU(a,b) _mpysu(a,b) + +#undef MULT_16_16 +#define MULT_16_16(a,b) _mpy(a,b) + +#define celt_ilog2(x) (30 - _norm(x)) +#define OVERRIDE_CELT_ILOG2 + +#undef MULT16_32_Q15 +#define MULT16_32_Q15(a,b) (_mpylill(a, b) >> 15) + +#if 0 +#include "dsplib.h" + +#undef MAX16 +#define MAX16(a,b) _max(a,b) + +#undef MIN16 +#define MIN16(a,b) _min(a,b) + +#undef MAX32 +#define MAX32(a,b) _lmax(a,b) + +#undef MIN32 +#define MIN32(a,b) _lmin(a,b) + +#undef VSHR32 +#define VSHR32(a, shift) _lshl(a,-(shift)) + +#undef MULT16_16_Q15 +#define MULT16_16_Q15(a,b) (_smpy(a,b)) + +#define celt_maxabs16(x, len) MAX32(EXTEND32(maxval((DATA *)x, len)),-EXTEND32(minval((DATA *)x, len))) +#define OVERRIDE_CELT_MAXABS16 + +#endif /* FIXED_C6X_H */ diff --git a/managed_components/78__esp-opus/celt/fixed_debug.h b/managed_components/78__esp-opus/celt/fixed_debug.h new file mode 100644 index 0000000..a879ec3 --- /dev/null +++ b/managed_components/78__esp-opus/celt/fixed_debug.h @@ -0,0 +1,843 @@ +/* Copyright (C) 2003-2008 Jean-Marc Valin + Copyright (C) 2007-2012 Xiph.Org Foundation */ +/** + @file fixed_debug.h + @brief Fixed-point operations with debugging +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_DEBUG_H +#define FIXED_DEBUG_H + +#include +#include "opus_defines.h" + +#ifdef CELT_C +OPUS_EXPORT opus_int64 celt_mips=0; +#else +extern opus_int64 celt_mips; +#endif + +#define MULT16_16SU(a,b) ((opus_val32)(opus_val16)(a)*(opus_val32)(opus_uint16)(b)) +#define MULT32_32_Q31(a,b) ADD32(ADD32(SHL32(MULT16_16(SHR32((a),16),SHR((b),16)),1), SHR32(MULT16_16SU(SHR32((a),16),((b)&0x0000ffff)),15)), SHR32(MULT16_16SU(SHR32((b),16),((a)&0x0000ffff)),15)) +#define MULT32_32_Q32(a,b) ADD32(ADD32(MULT16_16(SHR((a),16),SHR((b),16)), SHR(MULT16_16SU(SHR((a),16),((b)&0x0000ffff)),16)), SHR(MULT16_16SU(SHR((b),16),((a)&0x0000ffff)),16)) + +/** 16x32 multiplication, followed by a 16-bit shift right. Results fits in 32 bits */ +#define MULT16_32_Q16(a,b) ADD32(MULT16_16((a),SHR32((b),16)), SHR32(MULT16_16SU((a),((b)&0x0000ffff)),16)) + +#define MULT16_32_P16(a,b) MULT16_32_PX(a,b,16) + +#define QCONST16(x,bits) ((opus_val16)(.5+(x)*(((opus_val32)1)<<(bits)))) +#define QCONST32(x,bits) ((opus_val32)(.5+(x)*(((opus_val64)1)<<(bits)))) +#define GCONST2(x,bits) ((celt_glog)(.5+(x)*(((celt_glog)1)<<(bits)))) +#define GCONST(x) GCONST2((x),DB_SHIFT) + +#define VERIFY_SHORT(x) ((x)<=32767&&(x)>=-32768) +#define VERIFY_INT(x) ((x)<=2147483647LL&&(x)>=-2147483648LL) +#define VERIFY_UINT(x) ((x)<=(2147483647LLU<<1)) + +#define SHR(a,b) SHR32(a,b) +#define PSHR(a,b) PSHR32(a,b) + +/** Add two 32-bit values, ignore any overflows */ +#define ADD32_ovflw(a,b) (celt_mips+=2,(opus_val32)((opus_uint32)(a)+(opus_uint32)(b))) +/** Subtract two 32-bit values, ignore any overflows */ +#define SUB32_ovflw(a,b) (celt_mips+=2,(opus_val32)((opus_uint32)(a)-(opus_uint32)(b))) +/* Avoid MSVC warning C4146: unary minus operator applied to unsigned type */ +/** Negate 32-bit value, ignore any overflows */ +#define NEG32_ovflw(a) (celt_mips+=2,(opus_val32)(0-(opus_uint32)(a))) +/** 32-bit shift left, ignoring overflows */ +#define SHL32_ovflw(a,shift) ((opus_int32)((opus_uint32)(a)<<(shift))) +/** 32-bit arithmetic shift right with rounding-to-nearest, ignoring overflows */ +#define PSHR32_ovflw(a,shift) (SHR32(ADD32_ovflw(a, (EXTEND32(1)<<(shift)>>1)),shift)) + +static OPUS_INLINE short NEG16(int x) +{ + int res; + if (!VERIFY_SHORT(x)) + { + fprintf (stderr, "NEG16: input is not short: %d\n", (int)x); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = -x; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "NEG16: output is not short: %d\n", (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips++; + return res; +} +static OPUS_INLINE int NEG32(opus_int64 x) +{ + opus_int64 res; + if (!VERIFY_INT(x)) + { + fprintf (stderr, "NEG16: input is not int: %d\n", (int)x); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = -x; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "NEG16: output is not int: %d\n", (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} + +#define EXTRACT16(x) EXTRACT16_(x, __FILE__, __LINE__) +static OPUS_INLINE short EXTRACT16_(int x, char *file, int line) +{ + int res; + if (!VERIFY_SHORT(x)) + { + fprintf (stderr, "EXTRACT16: input is not short: %d in %s: line %d\n", x, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = x; + celt_mips++; + return res; +} + +#define EXTEND32(x) EXTEND32_(x, __FILE__, __LINE__) +static OPUS_INLINE int EXTEND32_(int x, char *file, int line) +{ + int res; + if (!VERIFY_SHORT(x)) + { + fprintf (stderr, "EXTEND32: input is not short: %d in %s: line %d\n", x, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = x; + celt_mips++; + return res; +} + +#define SHR16(a, shift) SHR16_(a, shift, __FILE__, __LINE__) +static OPUS_INLINE short SHR16_(int a, int shift, char *file, int line) +{ + int res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(shift)) + { + fprintf (stderr, "SHR16: inputs are not short: %d >> %d in %s: line %d\n", a, shift, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a>>shift; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "SHR16: output is not short: %d in %s: line %d\n", res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips++; + return res; +} +#define SHL16(a, shift) SHL16_(a, shift, __FILE__, __LINE__) +static OPUS_INLINE short SHL16_(int a, int shift, char *file, int line) +{ + opus_int32 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(shift)) + { + fprintf (stderr, "SHL16: inputs are not short: %d %d in %s: line %d\n", a, shift, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = (opus_int32)((opus_uint32)a<>shift; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "SHR32: output is not int: %d\n", (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} +#define SHL32(a, shift) SHL32_(a, shift, __FILE__, __LINE__) +static OPUS_INLINE int SHL32_(opus_int64 a, int shift, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_INT(a) || !VERIFY_SHORT(shift)) + { + fprintf (stderr, "SHL32: inputs are not int: %lld %d in %s: line %d\n", (long long)a, shift, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = (opus_int64)((opus_uint64)a<>1))),shift)) +#define VSHR32(a, shift) (((shift)>0) ? SHR32(a, shift) : SHL32(a, -(shift))) + +#define ROUND16(x,a) (celt_mips--,EXTRACT16(PSHR32((x),(a)))) +#define SROUND16(x,a) (celt_mips--,EXTRACT16(SATURATE(PSHR32(x,a), 32767))); + +#define HALF16(x) (SHR16(x,1)) +#define HALF32(x) (SHR32(x,1)) + +#define ADD16(a, b) ADD16_(a, b, __FILE__, __LINE__) +static OPUS_INLINE short ADD16_(int a, int b, char *file, int line) +{ + int res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "ADD16: inputs are not short: %d %d in %s: line %d\n", a, b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a+b; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "ADD16: output is not short: %d+%d=%d in %s: line %d\n", a,b,res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips++; + return res; +} + +#define SUB16(a, b) SUB16_(a, b, __FILE__, __LINE__) +static OPUS_INLINE short SUB16_(int a, int b, char *file, int line) +{ + int res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "SUB16: inputs are not short: %d %d in %s: line %d\n", a, b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a-b; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "SUB16: output is not short: %d in %s: line %d\n", res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips++; + return res; +} + +#define ADD32(a, b) ADD32_(a, b, __FILE__, __LINE__) +static OPUS_INLINE int ADD32_(opus_int64 a, opus_int64 b, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_INT(a) || !VERIFY_INT(b)) + { + fprintf (stderr, "ADD32: inputs are not int: %d %d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a+b; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "ADD32: output is not int: %d in %s: line %d\n", (int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} + +#define SUB32(a, b) SUB32_(a, b, __FILE__, __LINE__) +static OPUS_INLINE int SUB32_(opus_int64 a, opus_int64 b, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_INT(a) || !VERIFY_INT(b)) + { + fprintf (stderr, "SUB32: inputs are not int: %d %d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a-b; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "SUB32: output is not int: %d in %s: line %d\n", (int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} + +#undef UADD32 +#define UADD32(a, b) UADD32_(a, b, __FILE__, __LINE__) +static OPUS_INLINE unsigned int UADD32_(opus_uint64 a, opus_uint64 b, char *file, int line) +{ + opus_uint64 res; + if (!VERIFY_UINT(a) || !VERIFY_UINT(b)) + { + fprintf (stderr, "UADD32: inputs are not uint32: %llu %llu in %s: line %d\n", (unsigned long long)a, (unsigned long long)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a+b; + if (!VERIFY_UINT(res)) + { + fprintf (stderr, "UADD32: output is not uint32: %llu in %s: line %d\n", (unsigned long long)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} + +#undef USUB32 +#define USUB32(a, b) USUB32_(a, b, __FILE__, __LINE__) +static OPUS_INLINE unsigned int USUB32_(opus_uint64 a, opus_uint64 b, char *file, int line) +{ + opus_uint64 res; + if (!VERIFY_UINT(a) || !VERIFY_UINT(b)) + { + fprintf (stderr, "USUB32: inputs are not uint32: %llu %llu in %s: line %d\n", (unsigned long long)a, (unsigned long long)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + if (a> 16; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT32_32_Q16: output is not int: %lld*%lld=%lld\n", (long long)a, (long long)b, (long long)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=5; + return res; +} + +#define MULT16_16(a, b) MULT16_16_(a, b, __FILE__, __LINE__) +static OPUS_INLINE int MULT16_16_(int a, int b, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16: inputs are not short: %d %d in %s: line %d\n", a, b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_16: output is not int: %d in %s: line %d\n", (int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips++; + return res; +} + +#define MAC16_16(c,a,b) (celt_mips-=2,ADD32((c),MULT16_16((a),(b)))) + +#define MULT16_32_QX(a, b, Q) MULT16_32_QX_(a, b, Q, __FILE__, __LINE__) +static OPUS_INLINE int MULT16_32_QX_(int a, opus_int64 b, int Q, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_INT(b)) + { + fprintf (stderr, "MULT16_32_Q%d: inputs are not short+int: %d %d in %s: line %d\n", Q, (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + if (ABS32(b)>=((opus_int64)(1)<<(16+Q))) + { + fprintf (stderr, "MULT16_32_Q%d: second operand too large: %d %d in %s: line %d\n", Q, (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = (((opus_int64)a)*(opus_int64)b) >> Q; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_32_Q%d: output is not int: %d*%d=%d in %s: line %d\n", Q, (int)a, (int)b,(int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + if (Q==15) + celt_mips+=3; + else + celt_mips+=4; + return res; +} + +#define MULT16_32_PX(a, b, Q) MULT16_32_PX_(a, b, Q, __FILE__, __LINE__) +static OPUS_INLINE int MULT16_32_PX_(int a, opus_int64 b, int Q, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_INT(b)) + { + fprintf (stderr, "MULT16_32_P%d: inputs are not short+int: %d %d in %s: line %d\n\n", Q, (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + if (ABS32(b)>=((opus_int64)(1)<<(16+Q))) + { + fprintf (stderr, "MULT16_32_Q%d: second operand too large: %d %d in %s: line %d\n\n", Q, (int)a, (int)b,file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((((opus_int64)a)*(opus_int64)b) + (((opus_val32)(1)<>1))>> Q; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_32_P%d: output is not int: %d*%d=%d in %s: line %d\n\n", Q, (int)a, (int)b,(int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + if (Q==15) + celt_mips+=4; + else + celt_mips+=5; + return res; +} + +#define MULT16_32_Q15(a,b) MULT16_32_QX(a,b,15) +#define MAC16_32_Q15(c,a,b) (celt_mips-=2,ADD32((c),MULT16_32_Q15((a),(b)))) +#define MAC16_32_Q16(c,a,b) (celt_mips-=2,ADD32((c),MULT16_32_Q16((a),(b)))) + +static OPUS_INLINE int SATURATE(int a, int b) +{ + if (a>b) + a=b; + if (a<-b) + a = -b; + celt_mips+=3; + return a; +} + +static OPUS_INLINE opus_int16 SATURATE16(opus_int32 a) +{ + celt_mips+=3; + if (a>32767) + return 32767; + else if (a<-32768) + return -32768; + else return a; +} + +static OPUS_INLINE int MULT16_16_Q11_32(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_Q11: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res >>= 11; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_16_Q11: output is not short: %d*%d=%d\n", (int)a, (int)b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=3; + return res; +} +static OPUS_INLINE short MULT16_16_Q13(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_Q13: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res >>= 13; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_Q13: output is not short: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=3; + return res; +} +static OPUS_INLINE short MULT16_16_Q14(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_Q14: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res >>= 14; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_Q14: output is not short: %d\n", (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=3; + return res; +} + +#define MULT16_16_Q15(a, b) MULT16_16_Q15_(a, b, __FILE__, __LINE__) +static OPUS_INLINE short MULT16_16_Q15_(int a, int b, char *file, int line) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_Q15: inputs are not short: %d %d in %s: line %d\n", a, b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res >>= 15; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_Q15: output is not short: %d in %s: line %d\n", (int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=1; + return res; +} + +static OPUS_INLINE short MULT16_16_P13(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_P13: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res += 4096; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_16_P13: overflow: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res >>= 13; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_P13: output is not short: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=4; + return res; +} +static OPUS_INLINE short MULT16_16_P14(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_P14: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res += 8192; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_16_P14: overflow: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res >>= 14; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_P14: output is not short: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=4; + return res; +} +static OPUS_INLINE short MULT16_16_P15(int a, int b) +{ + opus_int64 res; + if (!VERIFY_SHORT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "MULT16_16_P15: inputs are not short: %d %d\n", a, b); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = ((opus_int64)a)*b; + res += 16384; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "MULT16_16_P15: overflow: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res >>= 15; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "MULT16_16_P15: output is not short: %d*%d=%d\n", a, b, (int)res); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=2; + return res; +} + +#define DIV32_16(a, b) DIV32_16_(a, b, __FILE__, __LINE__) + +static OPUS_INLINE int DIV32_16_(opus_int64 a, opus_int64 b, char *file, int line) +{ + opus_int64 res; + if (b==0) + { + fprintf(stderr, "DIV32_16: divide by zero: %d/%d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + return 0; + } + if (!VERIFY_INT(a) || !VERIFY_SHORT(b)) + { + fprintf (stderr, "DIV32_16: inputs are not int/short: %d %d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a/b; + if (!VERIFY_SHORT(res)) + { + fprintf (stderr, "DIV32_16: output is not short: %d / %d = %d in %s: line %d\n", (int)a,(int)b,(int)res, file, line); + if (res>32767) + res = 32767; + if (res<-32768) + res = -32768; +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=35; + return res; +} + +#define DIV32(a, b) DIV32_(a, b, __FILE__, __LINE__) +static OPUS_INLINE int DIV32_(opus_int64 a, opus_int64 b, char *file, int line) +{ + opus_int64 res; + if (b==0) + { + fprintf(stderr, "DIV32: divide by zero: %d/%d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + return 0; + } + + if (!VERIFY_INT(a) || !VERIFY_INT(b)) + { + fprintf (stderr, "DIV32: inputs are not int/short: %d %d in %s: line %d\n", (int)a, (int)b, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + res = a/b; + if (!VERIFY_INT(res)) + { + fprintf (stderr, "DIV32: output is not int: %d in %s: line %d\n", (int)res, file, line); +#ifdef FIXED_DEBUG_ASSERT + celt_assert(0); +#endif + } + celt_mips+=70; + return res; +} + +static OPUS_INLINE opus_val16 SIG2WORD16_generic(celt_sig x) +{ + x = PSHR32(x, SIG_SHIFT); + x = MAX32(x, -32768); + x = MIN32(x, 32767); + return EXTRACT16(x); +} +#define SIG2WORD16(x) (SIG2WORD16_generic(x)) + + +#undef PRINT_MIPS +#define PRINT_MIPS(file) do {fprintf (file, "total complexity = %llu MIPS\n", (unsigned long long)celt_mips);} while (0); + +#endif diff --git a/managed_components/78__esp-opus/celt/fixed_generic.h b/managed_components/78__esp-opus/celt/fixed_generic.h new file mode 100644 index 0000000..a4834df --- /dev/null +++ b/managed_components/78__esp-opus/celt/fixed_generic.h @@ -0,0 +1,205 @@ +/* Copyright (C) 2007-2009 Xiph.Org Foundation + Copyright (C) 2003-2008 Jean-Marc Valin + Copyright (C) 2007-2008 CSIRO */ +/** + @file fixed_generic.h + @brief Generic fixed-point operations +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FIXED_GENERIC_H +#define FIXED_GENERIC_H + +/** Multiply a 16-bit signed value by a 16-bit unsigned value. The result is a 32-bit signed value */ +#define MULT16_16SU(a,b) ((opus_val32)(opus_val16)(a)*(opus_val32)(opus_uint16)(b)) + +/** 16x32 multiplication, followed by a 16-bit shift right. Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT16_32_Q16(a,b) ((opus_val32)SHR((opus_int64)((opus_val16)(a))*(b),16)) +#else +#define MULT16_32_Q16(a,b) ADD32(MULT16_16((a),SHR((b),16)), SHR(MULT16_16SU((a),((b)&0x0000ffff)),16)) +#endif + +/** 16x32 multiplication, followed by a 16-bit shift right (round-to-nearest). Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT16_32_P16(a,b) ((opus_val32)PSHR((opus_int64)((opus_val16)(a))*(b),16)) +#else +#define MULT16_32_P16(a,b) ADD32(MULT16_16((a),SHR((b),16)), PSHR(MULT16_16SU((a),((b)&0x0000ffff)),16)) +#endif + +/** 16x32 multiplication, followed by a 15-bit shift right. Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT16_32_Q15(a,b) ((opus_val32)SHR((opus_int64)((opus_val16)(a))*(b),15)) +#else +#define MULT16_32_Q15(a,b) ADD32(SHL(MULT16_16((a),SHR((b),16)),1), SHR(MULT16_16SU((a),((b)&0x0000ffff)),15)) +#endif + +/** 32x32 multiplication, followed by a 16-bit shift right. Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT32_32_Q16(a,b) ((opus_val32)SHR((opus_int64)(a)*(opus_int64)(b),16)) +#else +#define MULT32_32_Q16(a,b) (ADD32(ADD32(ADD32((opus_val32)(SHR32(((opus_uint32)((a)&0x0000ffff)*(opus_uint32)((b)&0x0000ffff)),16)), MULT16_16SU(SHR32(a,16),((b)&0x0000ffff))), MULT16_16SU(SHR32(b,16),((a)&0x0000ffff))), SHL32(MULT16_16(SHR32(a,16),SHR32(b,16)),16))) +#endif + +/** 32x32 multiplication, followed by a 31-bit shift right. Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT32_32_Q31(a,b) ((opus_val32)SHR((opus_int64)(a)*(opus_int64)(b),31)) +#else +#define MULT32_32_Q31(a,b) ADD32(ADD32(SHL(MULT16_16(SHR((a),16),SHR((b),16)),1), SHR(MULT16_16SU(SHR((a),16),((b)&0x0000ffff)),15)), SHR(MULT16_16SU(SHR((b),16),((a)&0x0000ffff)),15)) +#endif + +/** 32x32 multiplication, followed by a 32-bit shift right. Results fits in 32 bits */ +#if OPUS_FAST_INT64 +#define MULT32_32_Q32(a,b) ((opus_val32)SHR((opus_int64)(a)*(opus_int64)(b),32)) +#else +#define MULT32_32_Q32(a,b) ADD32(ADD32(MULT16_16(SHR((a),16),SHR((b),16)), SHR(MULT16_16SU(SHR((a),16),((b)&0x0000ffff)),16)), SHR(MULT16_16SU(SHR((b),16),((a)&0x0000ffff)),16)) +#endif + +/** Compile-time conversion of float constant to 16-bit value */ +#define QCONST16(x,bits) ((opus_val16)(.5+(x)*(((opus_val32)1)<<(bits)))) + +/** Compile-time conversion of float constant to 32-bit value */ +#define QCONST32(x,bits) ((opus_val32)(.5+(x)*(((opus_int64)1)<<(bits)))) + +/** Compile-time conversion of float constant to log gain value */ +#define GCONST2(x,bits) ((celt_glog)(.5+(x)*(((celt_glog)1)<<(bits)))) + +/** Compile-time conversion of float constant to DB_SHFIT log gain value */ +#define GCONST(x) GCONST2((x),DB_SHIFT) + +/** Negate a 16-bit value */ +#define NEG16(x) (-(x)) +/** Negate a 32-bit value */ +#define NEG32(x) (-(x)) + +/** Change a 32-bit value into a 16-bit value. The value is assumed to fit in 16-bit, otherwise the result is undefined */ +#define EXTRACT16(x) ((opus_val16)(x)) +/** Change a 16-bit value into a 32-bit value */ +#define EXTEND32(x) ((opus_val32)(x)) + +/** Arithmetic shift-right of a 16-bit value */ +#define SHR16(a,shift) ((a) >> (shift)) +/** Arithmetic shift-left of a 16-bit value */ +#define SHL16(a,shift) ((opus_int16)((opus_uint16)(a)<<(shift))) +/** Arithmetic shift-right of a 32-bit value */ +#define SHR32(a,shift) ((a) >> (shift)) +/** Arithmetic shift-left of a 32-bit value */ +#define SHL32(a,shift) ((opus_int32)((opus_uint32)(a)<<(shift))) + +/** 32-bit arithmetic shift right with rounding-to-nearest instead of rounding down */ +#define PSHR32(a,shift) (SHR32((a)+((EXTEND32(1)<<((shift))>>1)),shift)) +/** 32-bit arithmetic shift right where the argument can be negative */ +#define VSHR32(a, shift) (((shift)>0) ? SHR32(a, shift) : SHL32(a, -(shift))) + +/** "RAW" macros, should not be used outside of this header file */ +#define SHR(a,shift) ((a) >> (shift)) +#define SHL(a,shift) SHL32(a,shift) +#define PSHR(a,shift) (SHR((a)+((EXTEND32(1)<<((shift))>>1)),shift)) +#define SATURATE(x,a) (((x)>(a) ? (a) : (x)<-(a) ? -(a) : (x))) + +#define SATURATE16(x) (EXTRACT16((x)>32767 ? 32767 : (x)<-32768 ? -32768 : (x))) + +/** Shift by a and round-to-nearest 32-bit value. Result is a 16-bit value */ +#define ROUND16(x,a) (EXTRACT16(PSHR32((x),(a)))) +/** Shift by a and round-to-nearest 32-bit value. Result is a saturated 16-bit value */ +#define SROUND16(x,a) EXTRACT16(SATURATE(PSHR32(x,a), 32767)); + +/** Divide by two */ +#define HALF16(x) (SHR16(x,1)) +#define HALF32(x) (SHR32(x,1)) + +/** Add two 16-bit values */ +#define ADD16(a,b) ((opus_val16)((opus_val16)(a)+(opus_val16)(b))) +/** Subtract two 16-bit values */ +#define SUB16(a,b) ((opus_val16)(a)-(opus_val16)(b)) +/** Add two 32-bit values */ +#define ADD32(a,b) ((opus_val32)(a)+(opus_val32)(b)) +/** Subtract two 32-bit values */ +#define SUB32(a,b) ((opus_val32)(a)-(opus_val32)(b)) + +/** Add two 32-bit values, ignore any overflows */ +#define ADD32_ovflw(a,b) ((opus_val32)((opus_uint32)(a)+(opus_uint32)(b))) +/** Subtract two 32-bit values, ignore any overflows */ +#define SUB32_ovflw(a,b) ((opus_val32)((opus_uint32)(a)-(opus_uint32)(b))) +/* Avoid MSVC warning C4146: unary minus operator applied to unsigned type */ +/** Negate 32-bit value, ignore any overflows */ +#define NEG32_ovflw(a) ((opus_val32)(0-(opus_uint32)(a))) +/** 32-bit shift left, ignoring overflows */ +#define SHL32_ovflw(a,shift) SHL32(a,shift) +/** 32-bit arithmetic shift right with rounding-to-nearest, ignoring overflows */ +#define PSHR32_ovflw(a,shift) (SHR32(ADD32_ovflw(a, (EXTEND32(1)<<(shift)>>1)),shift)) + +/** 16x16 multiplication where the result fits in 16 bits */ +#define MULT16_16_16(a,b) ((((opus_val16)(a))*((opus_val16)(b)))) + +/** 32x32 multiplication where the result fits in 32 bits */ +#define MULT32_32_32(a,b) ((((opus_val32)(a))*((opus_val32)(b)))) + +/* (opus_val32)(opus_val16) gives TI compiler a hint that it's 16x16->32 multiply */ +/** 16x16 multiplication where the result fits in 32 bits */ +#define MULT16_16(a,b) (((opus_val32)(opus_val16)(a))*((opus_val32)(opus_val16)(b))) + +/** 16x16 multiply-add where the result fits in 32 bits */ +#define MAC16_16(c,a,b) (ADD32((c),MULT16_16((a),(b)))) +/** 16x32 multiply, followed by a 15-bit shift right and 32-bit add. + b must fit in 31 bits. + Result fits in 32 bits. */ +#define MAC16_32_Q15(c,a,b) ADD32((c),ADD32(MULT16_16((a),SHR((b),15)), SHR(MULT16_16((a),((b)&0x00007fff)),15))) + +/** 16x32 multiplication, followed by a 16-bit shift right and 32-bit add. + Results fits in 32 bits */ +#define MAC16_32_Q16(c,a,b) ADD32((c),ADD32(MULT16_16((a),SHR((b),16)), SHR(MULT16_16SU((a),((b)&0x0000ffff)),16))) + +#define MULT16_16_Q11_32(a,b) (SHR(MULT16_16((a),(b)),11)) +#define MULT16_16_Q11(a,b) (SHR(MULT16_16((a),(b)),11)) +#define MULT16_16_Q13(a,b) (SHR(MULT16_16((a),(b)),13)) +#define MULT16_16_Q14(a,b) (SHR(MULT16_16((a),(b)),14)) +#define MULT16_16_Q15(a,b) (SHR(MULT16_16((a),(b)),15)) + +#define MULT16_16_P13(a,b) (SHR(ADD32(4096,MULT16_16((a),(b))),13)) +#define MULT16_16_P14(a,b) (SHR(ADD32(8192,MULT16_16((a),(b))),14)) +#define MULT16_16_P15(a,b) (SHR(ADD32(16384,MULT16_16((a),(b))),15)) + +/** Divide a 32-bit value by a 16-bit value. Result fits in 16 bits */ +#define DIV32_16(a,b) ((opus_val16)(((opus_val32)(a))/((opus_val16)(b)))) + +/** Divide a 32-bit value by a 32-bit value. Result fits in 32 bits */ +#define DIV32(a,b) (((opus_val32)(a))/((opus_val32)(b))) + +#if defined(MIPSr1_ASM) +#include "mips/fixed_generic_mipsr1.h" +#endif + +static OPUS_INLINE opus_val16 SIG2WORD16_generic(celt_sig x) +{ + x = PSHR32(x, SIG_SHIFT); + x = MAX32(x, -32768); + x = MIN32(x, 32767); + return EXTRACT16(x); +} +#define SIG2WORD16(x) (SIG2WORD16_generic(x)) + +#endif diff --git a/managed_components/78__esp-opus/celt/float_cast.h b/managed_components/78__esp-opus/celt/float_cast.h new file mode 100644 index 0000000..0645d54 --- /dev/null +++ b/managed_components/78__esp-opus/celt/float_cast.h @@ -0,0 +1,159 @@ +/* Copyright (C) 2001 Erik de Castro Lopo */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* Version 1.1 */ + +#ifndef FLOAT_CAST_H +#define FLOAT_CAST_H + + +#include "arch.h" + +/*============================================================================ +** On Intel Pentium processors (especially PIII and probably P4), converting +** from float to int is very slow. To meet the C specs, the code produced by +** most C compilers targeting Pentium needs to change the FPU rounding mode +** before the float to int conversion is performed. +** +** Changing the FPU rounding mode causes the FPU pipeline to be flushed. It +** is this flushing of the pipeline which is so slow. +** +** Fortunately the ISO C99 specifications define the functions lrint, lrintf, +** llrint and llrintf which fix this problem as a side effect. +** +** On Unix-like systems, the configure process should have detected the +** presence of these functions. If they weren't found we have to replace them +** here with a standard C cast. +*/ + +/* +** The C99 prototypes for lrint and lrintf are as follows: +** +** long int lrintf (float x) ; +** long int lrint (double x) ; +*/ + +/* The presence of the required functions are detected during the configure +** process and the values HAVE_LRINT and HAVE_LRINTF are set accordingly in +** the config.h file. +*/ + +/* With GCC, when SSE is available, the fastest conversion is cvtss2si. */ +#if defined(__GNUC__) && defined(__SSE__) + +#include +static OPUS_INLINE opus_int32 float2int(float x) {return _mm_cvt_ss2si(_mm_set_ss(x));} + +#elif (defined(_MSC_VER) && _MSC_VER >= 1400) && (defined(_M_X64) || (defined(_M_IX86_FP) && _M_IX86_FP >= 1)) + + #include + static OPUS_INLINE opus_int32 float2int(float value) + { + /* _mm_load_ss will generate same code as _mm_set_ss + ** in _MSC_VER >= 1914 /02 so keep __mm_load__ss + ** for backward compatibility. + */ + return _mm_cvtss_si32(_mm_load_ss(&value)); + } + +#elif (defined(_MSC_VER) && _MSC_VER >= 1400) && defined (_M_IX86) + + #include + + /* Win32 doesn't seem to have these functions. + ** Therefore implement OPUS_INLINE versions of these functions here. + */ + + static OPUS_INLINE opus_int32 + float2int (float flt) + { int intgr; + + _asm + { fld flt + fistp intgr + } ; + + return intgr ; + } +#elif defined(__aarch64__) + + #include + static OPUS_INLINE opus_int32 float2int(float flt) + { + return vcvtns_s32_f32(flt); + } + +#elif defined(HAVE_LRINTF) && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + +/* These defines enable functionality introduced with the 1999 ISO C +** standard. They must be defined before the inclusion of math.h to +** engage them. If optimisation is enabled, these functions will be +** inlined. With optimisation switched off, you have to link in the +** maths library using -lm. +*/ + +#define _ISOC9X_SOURCE 1 +#define _ISOC99_SOURCE 1 + +#define __USE_ISOC9X 1 +#define __USE_ISOC99 1 + +#include +#define float2int(x) lrintf(x) + +#elif defined(HAVE_LRINT) && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + +#define _ISOC9X_SOURCE 1 +#define _ISOC99_SOURCE 1 + +#define __USE_ISOC9X 1 +#define __USE_ISOC99 1 + +#include +#define float2int(x) lrint(x) + +#else + +#if (defined(__GNUC__) && defined(__STDC__) && __STDC__ && __STDC_VERSION__ >= 199901L) + /* supported by gcc in C99 mode, but not by all other compilers */ + #warning "Don't have the functions lrint() and lrintf ()." + #warning "Replacing these functions with a standard C cast." +#endif /* __STDC_VERSION__ >= 199901L */ + #include + #define float2int(flt) ((int)(floor(.5+flt))) +#endif + +#ifndef DISABLE_FLOAT_API +static OPUS_INLINE opus_int16 FLOAT2INT16(float x) +{ + x = x*CELT_SIG_SCALE; + x = MAX32(x, -32768); + x = MIN32(x, 32767); + return (opus_int16)float2int(x); +} +#endif /* DISABLE_FLOAT_API */ + +#endif /* FLOAT_CAST_H */ diff --git a/managed_components/78__esp-opus/celt/kiss_fft.c b/managed_components/78__esp-opus/celt/kiss_fft.c new file mode 100644 index 0000000..601d04b --- /dev/null +++ b/managed_components/78__esp-opus/celt/kiss_fft.c @@ -0,0 +1,648 @@ +/*Copyright (c) 2003-2004, Mark Borgerding + Lots of modifications by Jean-Marc Valin + Copyright (c) 2005-2007, Xiph.Org Foundation + Copyright (c) 2008, Xiph.Org Foundation, CSIRO + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.*/ + +/* This code is originally from Mark Borgerding's KISS-FFT but has been + heavily modified to better suit Opus */ + +#ifndef SKIP_CONFIG_H +# ifdef HAVE_CONFIG_H +# include "config.h" +# endif +#endif + +#include "_kiss_fft_guts.h" +#include "arch.h" +#include "os_support.h" +#include "mathops.h" +#include "stack_alloc.h" + +#ifndef M_PI +#define M_PI 3.141592653 +#endif + +/* The guts header contains all the multiplication and addition macros that are defined for + complex numbers. It also delares the kf_ internal functions. +*/ + +static void kf_bfly2( + kiss_fft_cpx * Fout, + int m, + int N + ) +{ + kiss_fft_cpx * Fout2; + int i; + (void)m; +#ifdef CUSTOM_MODES + if (m==1) + { + celt_assert(m==1); + for (i=0;itwiddles; + /* m is guaranteed to be a multiple of 4. */ + for (j=0;jtwiddles[fstride*m]; +#endif + for (i=0;itwiddles; + /* For non-custom modes, m is guaranteed to be a multiple of 4. */ + k=m; + do { + + C_MUL(scratch[1],Fout[m] , *tw1); + C_MUL(scratch[2],Fout[m2] , *tw2); + + C_ADD(scratch[3],scratch[1],scratch[2]); + C_SUB(scratch[0],scratch[1],scratch[2]); + tw1 += fstride; + tw2 += fstride*2; + + Fout[m].r = SUB32_ovflw(Fout->r, HALF_OF(scratch[3].r)); + Fout[m].i = SUB32_ovflw(Fout->i, HALF_OF(scratch[3].i)); + + C_MULBYSCALAR( scratch[0] , epi3.i ); + + C_ADDTO(*Fout,scratch[3]); + + Fout[m2].r = ADD32_ovflw(Fout[m].r, scratch[0].i); + Fout[m2].i = SUB32_ovflw(Fout[m].i, scratch[0].r); + + Fout[m].r = SUB32_ovflw(Fout[m].r, scratch[0].i); + Fout[m].i = ADD32_ovflw(Fout[m].i, scratch[0].r); + + ++Fout; + } while(--k); + } +} + + +#ifndef OVERRIDE_kf_bfly5 +static void kf_bfly5( + kiss_fft_cpx * Fout, + const size_t fstride, + const kiss_fft_state *st, + int m, + int N, + int mm + ) +{ + kiss_fft_cpx *Fout0,*Fout1,*Fout2,*Fout3,*Fout4; + int i, u; + kiss_fft_cpx scratch[13]; + const kiss_twiddle_cpx *tw; + kiss_twiddle_cpx ya,yb; + kiss_fft_cpx * Fout_beg = Fout; + +#ifdef FIXED_POINT + ya.r = QCONST32(0.30901699f, COEF_SHIFT-1); + ya.i = -QCONST32(0.95105652f, COEF_SHIFT-1); + yb.r = -QCONST32(0.80901699f, COEF_SHIFT-1); + yb.i = -QCONST32(0.58778525f, COEF_SHIFT-1); +#else + ya = st->twiddles[fstride*m]; + yb = st->twiddles[fstride*2*m]; +#endif + tw=st->twiddles; + + for (i=0;ir = ADD32_ovflw(Fout0->r, ADD32_ovflw(scratch[7].r, scratch[8].r)); + Fout0->i = ADD32_ovflw(Fout0->i, ADD32_ovflw(scratch[7].i, scratch[8].i)); + + scratch[5].r = ADD32_ovflw(scratch[0].r, ADD32_ovflw(S_MUL(scratch[7].r,ya.r), S_MUL(scratch[8].r,yb.r))); + scratch[5].i = ADD32_ovflw(scratch[0].i, ADD32_ovflw(S_MUL(scratch[7].i,ya.r), S_MUL(scratch[8].i,yb.r))); + + scratch[6].r = ADD32_ovflw(S_MUL(scratch[10].i,ya.i), S_MUL(scratch[9].i,yb.i)); + scratch[6].i = NEG32_ovflw(ADD32_ovflw(S_MUL(scratch[10].r,ya.i), S_MUL(scratch[9].r,yb.i))); + + C_SUB(*Fout1,scratch[5],scratch[6]); + C_ADD(*Fout4,scratch[5],scratch[6]); + + scratch[11].r = ADD32_ovflw(scratch[0].r, ADD32_ovflw(S_MUL(scratch[7].r,yb.r), S_MUL(scratch[8].r,ya.r))); + scratch[11].i = ADD32_ovflw(scratch[0].i, ADD32_ovflw(S_MUL(scratch[7].i,yb.r), S_MUL(scratch[8].i,ya.r))); + scratch[12].r = SUB32_ovflw(S_MUL(scratch[9].i,ya.i), S_MUL(scratch[10].i,yb.i)); + scratch[12].i = SUB32_ovflw(S_MUL(scratch[10].r,yb.i), S_MUL(scratch[9].r,ya.i)); + + C_ADD(*Fout2,scratch[11],scratch[12]); + C_SUB(*Fout3,scratch[11],scratch[12]); + + ++Fout0;++Fout1;++Fout2;++Fout3;++Fout4; + } + } +} +#endif /* OVERRIDE_kf_bfly5 */ + + +#endif + + +#ifdef CUSTOM_MODES + +static +void compute_bitrev_table( + int Fout, + opus_int16 *f, + const size_t fstride, + int in_stride, + opus_int16 * factors, + const kiss_fft_state *st + ) +{ + const int p=*factors++; /* the radix */ + const int m=*factors++; /* stage's fft length/p */ + + /*printf ("fft %d %d %d %d %d %d\n", p*m, m, p, s2, fstride*in_stride, N);*/ + if (m==1) + { + int j; + for (j=0;j32000 || (opus_int32)p*(opus_int32)p > n) + p = n; /* no more factors, skip to end */ + } + n /= p; +#ifdef RADIX_TWO_ONLY + if (p!=2 && p != 4) +#else + if (p>5) +#endif + { + return 0; + } + facbuf[2*stages] = p; + if (p==2 && stages > 1) + { + facbuf[2*stages] = 4; + facbuf[2] = 2; + } + stages++; + } while (n > 1); + n = nbak; + /* Reverse the order to get the radix 4 at the end, so we can use the + fast degenerate case. It turns out that reversing the order also + improves the noise behaviour. */ + for (i=0;i= memneeded) + st = (kiss_fft_state*)mem; + *lenmem = memneeded; + } + if (st) { + opus_int16 *bitrev; + kiss_twiddle_cpx *twiddles; + + st->nfft=nfft; +#ifdef FIXED_POINT + st->scale_shift = celt_ilog2(st->nfft); +# ifdef ENABLE_QEXT + if (st->nfft == 1<scale_shift) + st->scale = QCONST32(1.0f, 30); + else + st->scale = (((opus_int64)1073741824<scale_shift)+st->nfft/2)/st->nfft; +# else + if (st->nfft == 1<scale_shift) + st->scale = Q15ONE; + else + st->scale = (1073741824+st->nfft/2)/st->nfft>>(15-st->scale_shift); +# endif +#else + st->scale = 1.f/nfft; +#endif + if (base != NULL) + { + st->twiddles = base->twiddles; + st->shift = 0; + while (st->shift < 32 && nfft<shift != base->nfft) + st->shift++; + if (st->shift>=32) + goto fail; + } else { + st->twiddles = twiddles = (kiss_twiddle_cpx*)KISS_FFT_MALLOC(sizeof(kiss_twiddle_cpx)*nfft); + compute_twiddles(twiddles, nfft); + st->shift = -1; + } + if (!kf_factor(nfft,st->factors)) + { + goto fail; + } + + /* bitrev */ + st->bitrev = bitrev = (opus_int16*)KISS_FFT_MALLOC(sizeof(opus_int16)*nfft); + if (st->bitrev==NULL) + goto fail; + compute_bitrev_table(0, bitrev, 1,1, st->factors,st); + + /* Initialize architecture specific fft parameters */ + if (opus_fft_alloc_arch(st, arch)) + goto fail; + } + return st; +fail: + opus_fft_free(st, arch); + return NULL; +} + +kiss_fft_state *opus_fft_alloc(int nfft,void * mem,size_t * lenmem, int arch) +{ + return opus_fft_alloc_twiddles(nfft, mem, lenmem, NULL, arch); +} + +void opus_fft_free_arch_c(kiss_fft_state *st) { + (void)st; +} + +void opus_fft_free(const kiss_fft_state *cfg, int arch) +{ + if (cfg) + { + opus_fft_free_arch((kiss_fft_state *)cfg, arch); + opus_free((opus_int16*)cfg->bitrev); + if (cfg->shift < 0) + opus_free((kiss_twiddle_cpx*)cfg->twiddles); + opus_free((kiss_fft_state*)cfg); + } +} + +#endif /* CUSTOM_MODES */ + +#ifdef FIXED_POINT +static void fft_downshift(kiss_fft_cpx *x, int N, int *total, int step) { + int shift; + shift = IMIN(step, *total); + *total -= shift; + if (shift == 1) { + int i; + for (i=0;i0) { + int i; + for (i=0;ishift can be -1 */ + shift = st->shift>0 ? st->shift : 0; + + fstride[0] = 1; + L=0; + do { + p = st->factors[2*L]; + m = st->factors[2*L+1]; + fstride[L+1] = fstride[L]*p; + L++; + } while(m!=1); + m = st->factors[2*L-1]; + for (i=L-1;i>=0;i--) + { + if (i!=0) + m2 = st->factors[2*i-1]; + else + m2 = 1; + switch (st->factors[2*i]) + { + case 2: + fft_downshift(fout, st->nfft, &downshift, 1); + kf_bfly2(fout, m, fstride[i]); + break; + case 4: + fft_downshift(fout, st->nfft, &downshift, 2); + kf_bfly4(fout,fstride[i]<nfft, &downshift, 2); + kf_bfly3(fout,fstride[i]<nfft, &downshift, 3); + kf_bfly5(fout,fstride[i]<nfft, &downshift, downshift); +} + +void opus_fft_c(const kiss_fft_state *st,const kiss_fft_cpx *fin,kiss_fft_cpx *fout) +{ + int i; + celt_coef scale; +#ifdef FIXED_POINT + /* Allows us to scale with MULT16_32_Q16(), which is faster than + MULT16_32_Q15() on ARM. */ + int scale_shift = st->scale_shift-1; +#endif + scale = st->scale; + + celt_assert2 (fin != fout, "In-place FFT not supported"); + /* Bit-reverse the input */ + for (i=0;infft;i++) + { + kiss_fft_cpx x = fin[i]; + fout[st->bitrev[i]].r = S_MUL2(x.r, scale); + fout[st->bitrev[i]].i = S_MUL2(x.i, scale); + } + opus_fft_impl(st, fout ARG_FIXED(scale_shift)); +} + + +void opus_ifft_c(const kiss_fft_state *st,const kiss_fft_cpx *fin,kiss_fft_cpx *fout) +{ + int i; + celt_assert2 (fin != fout, "In-place FFT not supported"); + /* Bit-reverse the input */ + for (i=0;infft;i++) + fout[st->bitrev[i]] = fin[i]; + for (i=0;infft;i++) + fout[i].i = -fout[i].i; + opus_fft_impl(st, fout ARG_FIXED(0)); + for (i=0;infft;i++) + fout[i].i = -fout[i].i; +} diff --git a/managed_components/78__esp-opus/celt/kiss_fft.h b/managed_components/78__esp-opus/celt/kiss_fft.h new file mode 100644 index 0000000..10b8302 --- /dev/null +++ b/managed_components/78__esp-opus/celt/kiss_fft.h @@ -0,0 +1,217 @@ +/*Copyright (c) 2003-2004, Mark Borgerding + Lots of modifications by Jean-Marc Valin + Copyright (c) 2005-2007, Xiph.Org Foundation + Copyright (c) 2008, Xiph.Org Foundation, CSIRO + + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.*/ + +#ifndef KISS_FFT_H +#define KISS_FFT_H + +#include +#include +#include "arch.h" +#include "cpu_support.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef USE_SIMD +# include +# define kiss_fft_scalar __m128 +#define KISS_FFT_MALLOC(nbytes) memalign(16,nbytes) +#else +#define KISS_FFT_MALLOC opus_alloc +#endif + +#ifdef FIXED_POINT +#include "arch.h" + +# define kiss_fft_scalar opus_int32 +# ifdef ENABLE_QEXT +# define COEF_SHIFT 32 +# else +# define COEF_SHIFT 16 +# endif + +# define kiss_twiddle_scalar celt_coef + +/* Some 32-bit CPUs would load/store a kiss_twiddle_cpx with a single memory + * access, and could benefit from additional alignment. + */ +# define KISS_TWIDDLE_CPX_ALIGNMENT (sizeof(opus_int32)) + +#else + +# ifndef kiss_fft_scalar +/* default is float */ +# define kiss_fft_scalar float +# define kiss_twiddle_scalar float +# define KF_SUFFIX _celt_single +# endif +#endif + +#if defined(__GNUC__) && defined(KISS_TWIDDLE_CPX_ALIGNMENT) +#define KISS_TWIDDLE_CPX_ALIGNED __attribute__((aligned(KISS_TWIDDLE_CPX_ALIGNMENT))) +#else +#define KISS_TWIDDLE_CPX_ALIGNED +#endif + +typedef struct { + kiss_fft_scalar r; + kiss_fft_scalar i; +}kiss_fft_cpx; + +typedef struct { + kiss_twiddle_scalar r; + kiss_twiddle_scalar i; +} KISS_TWIDDLE_CPX_ALIGNED kiss_twiddle_cpx; + +#define MAXFACTORS 8 +/* e.g. an fft of length 128 has 4 factors + as far as kissfft is concerned + 4*4*4*2 + */ + +typedef struct arch_fft_state{ + int is_supported; + void *priv; +} arch_fft_state; + +typedef struct kiss_fft_state{ + int nfft; + celt_coef scale; +#ifdef FIXED_POINT + int scale_shift; +#endif + int shift; + opus_int16 factors[2*MAXFACTORS]; + const opus_int16 *bitrev; + const kiss_twiddle_cpx *twiddles; + arch_fft_state *arch_fft; +} kiss_fft_state; + +#if defined(HAVE_ARM_NE10) +#include "arm/fft_arm.h" +#endif + +/*typedef struct kiss_fft_state* kiss_fft_cfg;*/ + +/** + * opus_fft_alloc + * + * Initialize a FFT (or IFFT) algorithm's cfg/state buffer. + * + * typical usage: kiss_fft_cfg mycfg=opus_fft_alloc(1024,0,NULL,NULL); + * + * The return value from fft_alloc is a cfg buffer used internally + * by the fft routine or NULL. + * + * If lenmem is NULL, then opus_fft_alloc will allocate a cfg buffer using malloc. + * The returned value should be free()d when done to avoid memory leaks. + * + * The state can be placed in a user supplied buffer 'mem': + * If lenmem is not NULL and mem is not NULL and *lenmem is large enough, + * then the function places the cfg in mem and the size used in *lenmem + * and returns mem. + * + * If lenmem is not NULL and ( mem is NULL or *lenmem is not large enough), + * then the function returns NULL and places the minimum cfg + * buffer size in *lenmem. + * */ + +kiss_fft_state *opus_fft_alloc_twiddles(int nfft,void * mem,size_t * lenmem, const kiss_fft_state *base, int arch); + +kiss_fft_state *opus_fft_alloc(int nfft,void * mem,size_t * lenmem, int arch); + +/** + * opus_fft(cfg,in_out_buf) + * + * Perform an FFT on a complex input buffer. + * for a forward FFT, + * fin should be f[0] , f[1] , ... ,f[nfft-1] + * fout will be F[0] , F[1] , ... ,F[nfft-1] + * Note that each element is complex and can be accessed like + f[k].r and f[k].i + * */ +void opus_fft_c(const kiss_fft_state *cfg,const kiss_fft_cpx *fin,kiss_fft_cpx *fout); +void opus_ifft_c(const kiss_fft_state *cfg,const kiss_fft_cpx *fin,kiss_fft_cpx *fout); + +void opus_fft_impl(const kiss_fft_state *st,kiss_fft_cpx *fout ARG_FIXED(int downshift)); +void opus_ifft_impl(const kiss_fft_state *st,kiss_fft_cpx *fout); + +void opus_fft_free(const kiss_fft_state *cfg, int arch); + + +void opus_fft_free_arch_c(kiss_fft_state *st); +int opus_fft_alloc_arch_c(kiss_fft_state *st); + +#if !defined(OVERRIDE_OPUS_FFT) +/* Is run-time CPU detection enabled on this platform? */ +#if defined(OPUS_HAVE_RTCD) && (defined(HAVE_ARM_NE10)) + +extern int (*const OPUS_FFT_ALLOC_ARCH_IMPL[OPUS_ARCHMASK+1])( + kiss_fft_state *st); + +#define opus_fft_alloc_arch(_st, arch) \ + ((*OPUS_FFT_ALLOC_ARCH_IMPL[(arch)&OPUS_ARCHMASK])(_st)) + +extern void (*const OPUS_FFT_FREE_ARCH_IMPL[OPUS_ARCHMASK+1])( + kiss_fft_state *st); +#define opus_fft_free_arch(_st, arch) \ + ((*OPUS_FFT_FREE_ARCH_IMPL[(arch)&OPUS_ARCHMASK])(_st)) + +extern void (*const OPUS_FFT[OPUS_ARCHMASK+1])(const kiss_fft_state *cfg, + const kiss_fft_cpx *fin, kiss_fft_cpx *fout); +#define opus_fft(_cfg, _fin, _fout, arch) \ + ((*OPUS_FFT[(arch)&OPUS_ARCHMASK])(_cfg, _fin, _fout)) + +extern void (*const OPUS_IFFT[OPUS_ARCHMASK+1])(const kiss_fft_state *cfg, + const kiss_fft_cpx *fin, kiss_fft_cpx *fout); +#define opus_ifft(_cfg, _fin, _fout, arch) \ + ((*OPUS_IFFT[(arch)&OPUS_ARCHMASK])(_cfg, _fin, _fout)) + +#else /* else for if defined(OPUS_HAVE_RTCD) && (defined(HAVE_ARM_NE10)) */ + +#define opus_fft_alloc_arch(_st, arch) \ + ((void)(arch), opus_fft_alloc_arch_c(_st)) + +#define opus_fft_free_arch(_st, arch) \ + ((void)(arch), opus_fft_free_arch_c(_st)) + +#define opus_fft(_cfg, _fin, _fout, arch) \ + ((void)(arch), opus_fft_c(_cfg, _fin, _fout)) + +#define opus_ifft(_cfg, _fin, _fout, arch) \ + ((void)(arch), opus_ifft_c(_cfg, _fin, _fout)) + +#endif /* end if defined(OPUS_HAVE_RTCD) && (defined(HAVE_ARM_NE10)) */ +#endif /* end if !defined(OVERRIDE_OPUS_FFT) */ + +#ifdef __cplusplus +} +#endif + +#endif diff --git a/managed_components/78__esp-opus/celt/laplace.c b/managed_components/78__esp-opus/celt/laplace.c new file mode 100644 index 0000000..2180966 --- /dev/null +++ b/managed_components/78__esp-opus/celt/laplace.c @@ -0,0 +1,235 @@ +/* Copyright (c) 2007 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "laplace.h" +#include "mathops.h" + +/* The minimum probability of an energy delta (out of 32768). */ +#define LAPLACE_LOG_MINP (0) +#define LAPLACE_MINP (1<>15; +} + +void ec_laplace_encode(ec_enc *enc, int *value, unsigned fs, int decay) +{ + unsigned fl; + int val = *value; + fl = 0; + if (val) + { + int s; + int i; + s = -(val<0); + val = (val+s)^s; + fl = fs; + fs = ec_laplace_get_freq1(fs, decay); + /* Search the decaying part of the PDF.*/ + for (i=1; fs > 0 && i < val; i++) + { + fs *= 2; + fl += fs+2*LAPLACE_MINP; + fs = (fs*(opus_int32)decay)>>15; + } + /* Everything beyond that has probability LAPLACE_MINP. */ + if (!fs) + { + int di; + int ndi_max; + ndi_max = (32768-fl+LAPLACE_MINP-1)>>LAPLACE_LOG_MINP; + ndi_max = (ndi_max-s)>>1; + di = IMIN(val - i, ndi_max - 1); + fl += (2*di+1+s)*LAPLACE_MINP; + fs = IMIN(LAPLACE_MINP, 32768-fl); + *value = (i+di+s)^s; + } + else + { + fs += LAPLACE_MINP; + fl += fs&~s; + } + celt_assert(fl+fs<=32768); + celt_assert(fs>0); + } + ec_encode_bin(enc, fl, fl+fs, 15); +} + +int ec_laplace_decode(ec_dec *dec, unsigned fs, int decay) +{ + int val=0; + unsigned fl; + unsigned fm; + fm = ec_decode_bin(dec, 15); + fl = 0; + if (fm >= fs) + { + val++; + fl = fs; + fs = ec_laplace_get_freq1(fs, decay)+LAPLACE_MINP; + /* Search the decaying part of the PDF.*/ + while(fs > LAPLACE_MINP && fm >= fl+2*fs) + { + fs *= 2; + fl += fs; + fs = ((fs-2*LAPLACE_MINP)*(opus_int32)decay)>>15; + fs += LAPLACE_MINP; + val++; + } + /* Everything beyond that has probability LAPLACE_MINP. */ + if (fs <= LAPLACE_MINP) + { + int di; + di = (fm-fl)>>(LAPLACE_LOG_MINP+1); + val += di; + fl += 2*di*LAPLACE_MINP; + } + if (fm < fl+fs) + val = -val; + else + fl += fs; + } + celt_assert(fl<32768); + celt_assert(fs>0); + celt_assert(fl<=fm); + celt_assert(fm 0 ? 1 : 2); + ec_enc_icdf16(enc, s, sign_icdf, 15); + value = abs(value); + if (value) + { + int i; + opus_uint16 icdf[8]; + icdf[0] = IMAX(7, decay); + for (i=1;i<7;i++) + { + icdf[i] = IMAX(7-i, (icdf[i-1] * (opus_int32)decay) >> 15); + } + icdf[7] = 0; + value--; + do { + ec_enc_icdf16(enc, IMIN(value, 7), icdf, 15); + value -= 7; + } while (value >= 0); + } +} + +int ec_laplace_decode_p0(ec_dec *dec, opus_uint16 p0, opus_uint16 decay) +{ + int s; + int value; + opus_uint16 sign_icdf[3]; + sign_icdf[0] = 32768-p0; + sign_icdf[1] = sign_icdf[0]/2; + sign_icdf[2] = 0; + s = ec_dec_icdf16(dec, sign_icdf, 15); + if (s==2) s = -1; + if (s != 0) + { + int i; + int v; + opus_uint16 icdf[8]; + icdf[0] = IMAX(7, decay); + for (i=1;i<7;i++) + { + icdf[i] = IMAX(7-i, (icdf[i-1] * (opus_int32)decay) >> 15); + } + icdf[7] = 0; + value = 1; + do { + v = ec_dec_icdf16(dec, icdf, 15); + value += v; + } while (v == 7); + return s*value; + } else return 0; +} + +#if 0 + +#include +#define NB_VALS 10 +#define DATA_SIZE 10000 +int main() { + ec_enc enc; + ec_dec dec; + unsigned char *ptr; + int i; + int decay, p0; + int val[NB_VALS] = {6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; + /*for (i=0;i>1; + b=1U<>=1; + bshift--; + } + while(bshift>=0); + return g; +} + +#ifdef FIXED_POINT + +opus_val32 frac_div32_q29(opus_val32 a, opus_val32 b) +{ + opus_val16 rcp; + opus_val32 result, rem; + int shift = celt_ilog2(b)-29; + a = VSHR32(a,shift); + b = VSHR32(b,shift); + /* 16-bit reciprocal */ + rcp = ROUND16(celt_rcp(ROUND16(b,16)),3); + result = MULT16_32_Q15(rcp, a); + rem = PSHR32(a,2)-MULT32_32_Q31(result, b); + result = ADD32(result, SHL32(MULT16_32_Q15(rcp, rem),2)); + return result; +} + +opus_val32 frac_div32(opus_val32 a, opus_val32 b) { + opus_val32 result = frac_div32_q29(a,b); + if (result >= 536870912) /* 2^29 */ + return 2147483647; /* 2^31 - 1 */ + else if (result <= -536870912) /* -2^29 */ + return -2147483647; /* -2^31 */ + else + return SHL32(result, 2); +} + +/** Reciprocal sqrt approximation in the range [0.25,1) (Q16 in, Q14 out) */ +opus_val16 celt_rsqrt_norm(opus_val32 x) +{ + opus_val16 n; + opus_val16 r; + opus_val16 r2; + opus_val16 y; + /* Range of n is [-16384,32767] ([-0.5,1) in Q15). */ + n = x-32768; + /* Get a rough initial guess for the root. + The optimal minimax quadratic approximation (using relative error) is + r = 1.437799046117536+n*(-0.823394375837328+n*0.4096419668459485). + Coefficients here, and the final result r, are Q14.*/ + r = ADD16(23557, MULT16_16_Q15(n, ADD16(-13490, MULT16_16_Q15(n, 6713)))); + /* We want y = x*r*r-1 in Q15, but x is 32-bit Q16 and r is Q14. + We can compute the result from n and r using Q15 multiplies with some + adjustment, carefully done to avoid overflow. + Range of y is [-1564,1594]. */ + r2 = MULT16_16_Q15(r, r); + y = SHL16(SUB16(ADD16(MULT16_16_Q15(r2, n), r2), 16384), 1); + /* Apply a 2nd-order Householder iteration: r += r*y*(y*0.375-0.5). + This yields the Q14 reciprocal square root of the Q16 x, with a maximum + relative error of 1.04956E-4, a (relative) RMSE of 2.80979E-5, and a + peak absolute error of 2.26591/16384. */ + return ADD16(r, MULT16_16_Q15(r, MULT16_16_Q15(y, + SUB16(MULT16_16_Q15(y, 12288), 16384)))); +} + +/** Sqrt approximation (QX input, QX/2 output) */ +opus_val32 celt_sqrt(opus_val32 x) +{ + int k; + opus_val16 n; + opus_val32 rt; + /* These coeffs are optimized in fixed-point to minimize both RMS and max error + of sqrt(x) over .25=1073741824) + return 32767; + k = (celt_ilog2(x)>>1)-7; + x = VSHR32(x, 2*k); + n = x-32768; + rt = ADD32(C[0], MULT16_16_Q15(n, ADD16(C[1], MULT16_16_Q15(n, ADD16(C[2], + MULT16_16_Q15(n, ADD16(C[3], MULT16_16_Q15(n, ADD16(C[4], MULT16_16_Q15(n, (C[5]))))))))))); + rt = VSHR32(rt,7-k); + return rt; +} + +#define L1 32767 +#define L2 -7651 +#define L3 8277 +#define L4 -626 + +static OPUS_INLINE opus_val16 _celt_cos_pi_2(opus_val16 x) +{ + opus_val16 x2; + + x2 = MULT16_16_P15(x,x); + return ADD16(1,MIN16(32766,ADD32(SUB16(L1,x2), MULT16_16_P15(x2, ADD32(L2, MULT16_16_P15(x2, ADD32(L3, MULT16_16_P15(L4, x2 + )))))))); +} + +#undef L1 +#undef L2 +#undef L3 +#undef L4 + +opus_val16 celt_cos_norm(opus_val32 x) +{ + x = x&0x0001ffff; + if (x>SHL32(EXTEND32(1), 16)) + x = SUB32(SHL32(EXTEND32(1), 17),x); + if (x&0x00007fff) + { + if (x0); + i = celt_ilog2(x); + /* n is Q15 with range [0,1). */ + n = VSHR32(x,i-15)-32768; + /* Start with a linear approximation: + r = 1.8823529411764706-0.9411764705882353*n. + The coefficients and the result are Q14 in the range [15420,30840].*/ + r = ADD16(30840, MULT16_16_Q15(-15420, n)); + /* Perform two Newton iterations: + r -= r*((r*n)-1.Q15) + = r*((r*n)+(r-1.Q15)). */ + r = SUB16(r, MULT16_16_Q15(r, + ADD16(MULT16_16_Q15(r, n), ADD16(r, -32768)))); + /* We subtract an extra 1 in the second iteration to avoid overflow; it also + neatly compensates for truncation error in the rest of the process. */ + r = SUB16(r, ADD16(1, MULT16_16_Q15(r, + ADD16(MULT16_16_Q15(r, n), ADD16(r, -32768))))); + /* r is now the Q15 solution to 2/(n+1), with a maximum relative error + of 7.05346E-5, a (relative) RMSE of 2.14418E-5, and a peak absolute + error of 1.24665/32768. */ + return VSHR32(EXTEND32(r),i-16); +} + +#endif + +#ifndef DISABLE_FLOAT_API + +void celt_float2int16_c(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt) +{ + int i; + for (i = 0; i < cnt; i++) + { + out[i] = FLOAT2INT16(in[i]); + } +} + +int opus_limit2_checkwithin1_c(float * samples, int cnt) +{ + int i; + if (cnt <= 0) + { + return 1; + } + + for (i = 0; i < cnt; i++) + { + float clippedVal = samples[i]; + clippedVal = FMAX(-2.0f, clippedVal); + clippedVal = FMIN(2.0f, clippedVal); + samples[i] = clippedVal; + } + + /* C implementation can't provide quick hint. Assume it might exceed -1/+1. */ + return 0; +} + +#endif /* DISABLE_FLOAT_API */ diff --git a/managed_components/78__esp-opus/celt/mathops.h b/managed_components/78__esp-opus/celt/mathops.h new file mode 100644 index 0000000..6055c21 --- /dev/null +++ b/managed_components/78__esp-opus/celt/mathops.h @@ -0,0 +1,501 @@ +/* Copyright (c) 2002-2008 Jean-Marc Valin + Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2024 Arm Limited + Written by Jean-Marc Valin, and Yunho Huh */ +/** + @file mathops.h + @brief Various math functions +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef MATHOPS_H +#define MATHOPS_H + +#include "arch.h" +#include "entcode.h" +#include "os_support.h" + +#if defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +#include "arm/mathops_arm.h" +#endif + +#define PI 3.141592653f + +/* Multiplies two 16-bit fractional values. Bit-exactness of this macro is important */ +#define FRAC_MUL16(a,b) ((16384+((opus_int32)(opus_int16)(a)*(opus_int16)(b)))>>15) + +unsigned isqrt32(opus_uint32 _val); + +/* CELT doesn't need it for fixed-point, by analysis.c does. */ +#if !defined(FIXED_POINT) || defined(ANALYSIS_C) +#define cA 0.43157974f +#define cB 0.67848403f +#define cC 0.08595542f +#define cE ((float)PI/2) +static OPUS_INLINE float fast_atan2f(float y, float x) { + float x2, y2; + x2 = x*x; + y2 = y*y; + /* For very small values, we don't care about the answer, so + we can just return 0. */ + if (x2 + y2 < 1e-18f) + { + return 0; + } + if(x2>23)-127; + in.i = (opus_int32)in.i - (opus_int32)((opus_uint32)integer<<23); + + /* Normalize the mantissa range from [1, 2] to [1,1.125], and then shift x + * by 1.0625 to [-0.0625, 0.0625]. */ + range_idx = (in.i >> 20) & 0x7; + in.f = in.f * log2_x_norm_coeff[range_idx] - 1.0625f; + + /* Polynomial coefficients approximated in the [1, 1.125] range. + * Lolremez command: lolremez --degree 4 --range -0.0625:0.0625 + * "log(x+1.0625)/log(2)" + * Coefficients in Double Precision + * A0: 8.7462840624502679e-2 A1: 1.3578296070972002 + * A2: -6.3897703690210047e-1 A3: 4.0197125617419959e-1 + * A4: -2.8415445877832832e-1 */ + #define LOG2_COEFF_A0 8.74628424644470214843750000e-02f + #define LOG2_COEFF_A1 1.357829570770263671875000000000f + #define LOG2_COEFF_A2 -6.3897705078125000000000000e-01f + #define LOG2_COEFF_A3 4.01971250772476196289062500e-01f + #define LOG2_COEFF_A4 -2.8415444493293762207031250e-01f + in.f = LOG2_COEFF_A0 + in.f * (LOG2_COEFF_A1 + + in.f * (LOG2_COEFF_A2 + + in.f * (LOG2_COEFF_A3 + + in.f * (LOG2_COEFF_A4)))); + return integer + in.f + log2_y_norm_coeff[range_idx]; +} + +/* Calculates an approximation of 2^x. The approximation was achieved by + * employing a base-2 exponential function and utilizing a Remez approximation + * of order 5, ensuring a controlled relative error. + * exp2(x) = exp2(integer + fraction) + * = exp2(integer) * exp2(fraction) */ +static OPUS_INLINE float celt_exp2(float x) +{ + opus_int32 integer; + float frac; + union { + float f; + opus_uint32 i; + } res; + integer = (int)floor(x); + if (integer < -50) + return 0; + frac = x-integer; + + /* Polynomial coefficients approximated in the [0, 1] range. + * Lolremez command: lolremez --degree 5 --range 0:1 + * "exp(x*0.693147180559945)" "exp(x*0.693147180559945)" + * NOTE: log(2) ~ 0.693147180559945 */ + #define EXP2_COEFF_A0 9.999999403953552246093750000000e-01f + #define EXP2_COEFF_A1 6.931530833244323730468750000000e-01f + #define EXP2_COEFF_A2 2.401536107063293457031250000000e-01f + #define EXP2_COEFF_A3 5.582631751894950866699218750000e-02f + #define EXP2_COEFF_A4 8.989339694380760192871093750000e-03f + #define EXP2_COEFF_A5 1.877576694823801517486572265625e-03f + res.f = EXP2_COEFF_A0 + frac * (EXP2_COEFF_A1 + + frac * (EXP2_COEFF_A2 + + frac * (EXP2_COEFF_A3 + + frac * (EXP2_COEFF_A4 + + frac * (EXP2_COEFF_A5))))); + res.i = (opus_uint32)((opus_int32)res.i + (opus_int32)((opus_uint32)integer<<23)) & 0x7fffffff; + return res.f; +} + +#else +#define celt_log2(x) ((float)(1.442695040888963387*log(x))) +#define celt_exp2(x) ((float)exp(0.6931471805599453094*(x))) +#endif + +#define celt_exp2_db celt_exp2 +#define celt_log2_db celt_log2 + +#endif + +#ifdef FIXED_POINT + +#include "os_support.h" + +#ifndef OVERRIDE_CELT_ILOG2 +/** Integer log in base2. Undefined for zero and negative numbers */ +static OPUS_INLINE opus_int16 celt_ilog2(opus_int32 x) +{ + celt_sig_assert(x>0); + return EC_ILOG(x)-1; +} +#endif + + +/** Integer log in base2. Defined for zero, but not for negative numbers */ +static OPUS_INLINE opus_int16 celt_zlog2(opus_val32 x) +{ + return x <= 0 ? 0 : celt_ilog2(x); +} + +opus_val16 celt_rsqrt_norm(opus_val32 x); + +opus_val32 celt_sqrt(opus_val32 x); + +opus_val16 celt_cos_norm(opus_val32 x); + +/** Base-2 logarithm approximation (log2(x)). (Q14 input, Q10 output) */ +static OPUS_INLINE opus_val16 celt_log2(opus_val32 x) +{ + int i; + opus_val16 n, frac; + /* -0.41509302963303146, 0.9609890551383969, -0.31836011537636605, + 0.15530808010959576, -0.08556153059057618 */ + static const opus_val16 C[5] = {-6801+(1<<(13-10)), 15746, -5217, 2545, -1401}; + if (x==0) + return -32767; + i = celt_ilog2(x); + n = VSHR32(x,i-15)-32768-16384; + frac = ADD16(C[0], MULT16_16_Q15(n, ADD16(C[1], MULT16_16_Q15(n, ADD16(C[2], MULT16_16_Q15(n, ADD16(C[3], MULT16_16_Q15(n, C[4])))))))); + return SHL32(i-13,10)+SHR32(frac,14-10); +} + +/* + K0 = 1 + K1 = log(2) + K2 = 3-4*log(2) + K3 = 3*log(2) - 2 +*/ +#define D0 16383 +#define D1 22804 +#define D2 14819 +#define D3 10204 + +static OPUS_INLINE opus_val32 celt_exp2_frac(opus_val16 x) +{ + opus_val16 frac; + frac = SHL16(x, 4); + return ADD16(D0, MULT16_16_Q15(frac, ADD16(D1, MULT16_16_Q15(frac, ADD16(D2 , MULT16_16_Q15(D3,frac)))))); +} + +#undef D0 +#undef D1 +#undef D2 +#undef D3 + +/** Base-2 exponential approximation (2^x). (Q10 input, Q16 output) */ +static OPUS_INLINE opus_val32 celt_exp2(opus_val16 x) +{ + int integer; + opus_val16 frac; + integer = SHR16(x,10); + if (integer>14) + return 0x7f000000; + else if (integer < -15) + return 0; + frac = celt_exp2_frac(x-SHL16(integer,10)); + return VSHR32(EXTEND32(frac), -integer-2); +} + +#ifdef ENABLE_QEXT + +/* Calculates the base-2 logarithm of a Q14 input value. The result is returned + * in Q(DB_SHIFT). If the input value is 0, the function will output -32.0f. */ +static OPUS_INLINE opus_val32 celt_log2_db(opus_val32 x) { + /* Q30 */ + static const opus_val32 log2_x_norm_coeff[8] = { + 1073741824, 954437184, 858993472, 780903168, + 715827904, 660764224, 613566784, 572662336}; + /* Q24 */ + static const opus_val32 log2_y_norm_coeff[8] = { + 0, 2850868, 5401057, 7707983, + 9814042, 11751428, 13545168, 15215099}; + static const opus_val32 LOG2_COEFF_A0 = 1467383; /* Q24 */ + static const opus_val32 LOG2_COEFF_A1 = 182244800; /* Q27 */ + static const opus_val32 LOG2_COEFF_A2 = -21440512; /* Q25 */ + static const opus_val32 LOG2_COEFF_A3 = 107903336; /* Q28 */ + static const opus_val32 LOG2_COEFF_A4 = -610217024; /* Q31 */ + + opus_int32 integer, norm_coeff_idx, tmp; + opus_val32 mantissa; + if (x==0) { + return -536870912; /* -32.0f */ + } + integer = SUB32(celt_ilog2(x), 14); /* Q0 */ + mantissa = VSHR32(x, integer + 14 - 29); /* Q29 */ + norm_coeff_idx = SHR32(mantissa, 29 - 3) & 0x7; + /* mantissa is in Q28 (29 + Q_NORM_CONST - 31 where Q_NORM_CONST is Q30) + * 285212672 (Q28) is 1.0625f. */ + mantissa = SUB32(MULT32_32_Q31(mantissa, log2_x_norm_coeff[norm_coeff_idx]), + 285212672); + + /* q_a3(Q28): q_mantissa + q_a4 - 31 + * q_a2(Q25): q_mantissa + q_a3 - 31 + * q_a1(Q27): q_mantissa + q_a2 - 31 + 5 + * q_a0(Q24): q_mantissa + q_a1 - 31 + * where q_mantissa is Q28 */ + /* Split evaluation in steps to avoid exploding macro expansion. */ + tmp = MULT32_32_Q31(mantissa, LOG2_COEFF_A4); + tmp = MULT32_32_Q31(mantissa, ADD32(LOG2_COEFF_A3, tmp)); + tmp = SHL32(MULT32_32_Q31(mantissa, ADD32(LOG2_COEFF_A2, tmp)), 5 /* SHL32 for LOG2_COEFF_A1 */); + tmp = MULT32_32_Q31(mantissa, ADD32(LOG2_COEFF_A1, tmp)); + return ADD32(log2_y_norm_coeff[norm_coeff_idx], + ADD32(SHL32(integer, DB_SHIFT), + ADD32(LOG2_COEFF_A0, tmp))); +} + +/* Calculates exp2 for Q28 within a specific range (0 to 1.0) using fixed-point + * arithmetic. The input number must be adjusted for Q DB_SHIFT. */ +static OPUS_INLINE opus_val32 celt_exp2_db_frac(opus_val32 x) +{ + /* Approximation constants. */ + static const opus_int32 EXP2_COEFF_A0 = 268435440; /* Q28 */ + static const opus_int32 EXP2_COEFF_A1 = 744267456; /* Q30 */ + static const opus_int32 EXP2_COEFF_A2 = 1031451904; /* Q32 */ + static const opus_int32 EXP2_COEFF_A3 = 959088832; /* Q34 */ + static const opus_int32 EXP2_COEFF_A4 = 617742720; /* Q36 */ + static const opus_int32 EXP2_COEFF_A5 = 516104352; /* Q38 */ + opus_int32 tmp; + /* Converts input value from Q24 to Q29. */ + opus_val32 x_q29 = SHL32(x, 29 - 24); + /* Split evaluation in steps to avoid exploding macro expansion. */ + tmp = ADD32(EXP2_COEFF_A4, MULT32_32_Q31(x_q29, EXP2_COEFF_A5)); + tmp = ADD32(EXP2_COEFF_A3, MULT32_32_Q31(x_q29, tmp)); + tmp = ADD32(EXP2_COEFF_A2, MULT32_32_Q31(x_q29, tmp)); + tmp = ADD32(EXP2_COEFF_A1, MULT32_32_Q31(x_q29, tmp)); + return ADD32(EXP2_COEFF_A0, MULT32_32_Q31(x_q29, tmp)); +} + +/* Calculates exp2 for Q16 using fixed-point arithmetic. The input number must + * be adjusted for Q DB_SHIFT. */ +static OPUS_INLINE opus_val32 celt_exp2_db(opus_val32 x) +{ + int integer; + opus_val32 frac; + integer = SHR32(x,DB_SHIFT); + if (integer>14) + return 0x7f000000; + else if (integer <= -17) + return 0; + frac = celt_exp2_db_frac(x-SHL32(integer, DB_SHIFT)); /* Q28 */ + return VSHR32(frac, -integer + 28 - 16); /* Q16 */ +} +#else + +#define celt_log2_db(x) SHL32(EXTEND32(celt_log2(x)), DB_SHIFT-10) +#define celt_exp2_db_frac(x) SHL32(celt_exp2_frac(PSHR32(x, DB_SHIFT-10)), 14) +#define celt_exp2_db(x) celt_exp2(PSHR32(x, DB_SHIFT-10)) + +#endif + + +opus_val32 celt_rcp(opus_val32 x); + +#define celt_div(a,b) MULT32_32_Q31((opus_val32)(a),celt_rcp(b)) + +opus_val32 frac_div32_q29(opus_val32 a, opus_val32 b); +opus_val32 frac_div32(opus_val32 a, opus_val32 b); + +#define M1 32767 +#define M2 -21 +#define M3 -11943 +#define M4 4936 + +/* Atan approximation using a 4th order polynomial. Input is in Q15 format + and normalized by pi/4. Output is in Q15 format */ +static OPUS_INLINE opus_val16 celt_atan01(opus_val16 x) +{ + return MULT16_16_P15(x, ADD32(M1, MULT16_16_P15(x, ADD32(M2, MULT16_16_P15(x, ADD32(M3, MULT16_16_P15(M4, x))))))); +} + +#undef M1 +#undef M2 +#undef M3 +#undef M4 + +/* atan2() approximation valid for positive input values */ +static OPUS_INLINE opus_val16 celt_atan2p(opus_val16 y, opus_val16 x) +{ + if (y < x) + { + opus_val32 arg; + arg = celt_div(SHL32(EXTEND32(y),15),x); + if (arg >= 32767) + arg = 32767; + return SHR16(celt_atan01(EXTRACT16(arg)),1); + } else { + opus_val32 arg; + arg = celt_div(SHL32(EXTEND32(x),15),y); + if (arg >= 32767) + arg = 32767; + return 25736-SHR16(celt_atan01(EXTRACT16(arg)),1); + } +} + +#endif /* FIXED_POINT */ + +#ifndef DISABLE_FLOAT_API + +void celt_float2int16_c(const float * OPUS_RESTRICT in, short * OPUS_RESTRICT out, int cnt); + +#ifndef OVERRIDE_FLOAT2INT16 +#define celt_float2int16(in, out, cnt, arch) ((void)(arch), celt_float2int16_c(in, out, cnt)) +#endif + +int opus_limit2_checkwithin1_c(float *samples, int cnt); + +#ifndef OVERRIDE_LIMIT2_CHECKWITHIN1 +#define opus_limit2_checkwithin1(samples, cnt, arch) ((void)(arch), opus_limit2_checkwithin1_c(samples, cnt)) +#endif + +#endif /* DISABLE_FLOAT_API */ + +#endif /* MATHOPS_H */ diff --git a/managed_components/78__esp-opus/celt/mdct.c b/managed_components/78__esp-opus/celt/mdct.c new file mode 100644 index 0000000..b03e8a2 --- /dev/null +++ b/managed_components/78__esp-opus/celt/mdct.c @@ -0,0 +1,372 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2008 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* This is a simple MDCT implementation that uses a N/4 complex FFT + to do most of the work. It should be relatively straightforward to + plug in pretty much and FFT here. + + This replaces the Vorbis FFT (and uses the exact same API), which + was a bit too messy and that was ending up duplicating code + (might as well use the same FFT everywhere). + + The algorithm is similar to (and inspired from) Fabrice Bellard's + MDCT implementation in FFMPEG, but has differences in signs, ordering + and scaling in many places. +*/ + +#ifndef SKIP_CONFIG_H +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif +#endif + +#include "mdct.h" +#include "kiss_fft.h" +#include "_kiss_fft_guts.h" +#include +#include "os_support.h" +#include "mathops.h" +#include "stack_alloc.h" + +#if defined(MIPSr1_ASM) +#include "mips/mdct_mipsr1.h" +#endif + +#ifndef M_PI +#define M_PI 3.141592653 +#endif + +#ifdef CUSTOM_MODES + +int clt_mdct_init(mdct_lookup *l,int N, int maxshift, int arch) +{ + int i; + kiss_twiddle_scalar *trig; + int shift; + int N2=N>>1; + l->n = N; + l->maxshift = maxshift; + for (i=0;i<=maxshift;i++) + { + if (i==0) + l->kfft[i] = opus_fft_alloc(N>>2>>i, 0, 0, arch); + else + l->kfft[i] = opus_fft_alloc_twiddles(N>>2>>i, 0, 0, l->kfft[0], arch); +#ifndef ENABLE_TI_DSPLIB55 + if (l->kfft[i]==NULL) + return 0; +#endif + } + l->trig = trig = (kiss_twiddle_scalar*)opus_alloc((N-(N2>>maxshift))*sizeof(kiss_twiddle_scalar)); + if (l->trig==NULL) + return 0; + for (shift=0;shift<=maxshift;shift++) + { + /* We have enough points that sine isn't necessary */ +#if defined(FIXED_POINT) +#ifndef ENABLE_QEXT + for (i=0;i>= 1; + N >>= 1; + } + return 1; +} + +void clt_mdct_clear(mdct_lookup *l, int arch) +{ + int i; + for (i=0;i<=l->maxshift;i++) + opus_fft_free(l->kfft[i], arch); + opus_free((kiss_twiddle_scalar*)l->trig); +} + +#endif /* CUSTOM_MODES */ + +/* Forward MDCT trashes the input array */ +#ifndef OVERRIDE_clt_mdct_forward +void clt_mdct_forward_c(const mdct_lookup *l, kiss_fft_scalar *in, kiss_fft_scalar * OPUS_RESTRICT out, + const celt_coef *window, int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + VARDECL(kiss_fft_scalar, f); + VARDECL(kiss_fft_cpx, f2); + const kiss_fft_state *st = l->kfft[shift]; + const kiss_twiddle_scalar *trig; + celt_coef scale; +#ifdef FIXED_POINT + /* Allows us to scale with MULT16_32_Q16(), which is faster than + MULT16_32_Q15() on ARM. */ + int scale_shift = st->scale_shift-1; + int headroom; +#endif + SAVE_STACK; + (void)arch; + scale = st->scale; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + ALLOC(f, N2, kiss_fft_scalar); + ALLOC(f2, N4, kiss_fft_cpx); + + /* Consider the input to be composed of four blocks: [a, b, c, d] */ + /* Window, shuffle, fold */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in+(overlap>>1); + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+N2-1+(overlap>>1); + kiss_fft_scalar * OPUS_RESTRICT yp = f; + const celt_coef * OPUS_RESTRICT wp1 = window+(overlap>>1); + const celt_coef * OPUS_RESTRICT wp2 = window+(overlap>>1)-1; + for(i=0;i<((overlap+3)>>2);i++) + { + /* Real part arranged as -d-cR, Imag part arranged as -b+aR*/ + *yp++ = S_MUL(xp1[N2], *wp2) + S_MUL(*xp2, *wp1); + *yp++ = S_MUL(*xp1, *wp1) - S_MUL(xp2[-N2], *wp2); + xp1+=2; + xp2-=2; + wp1+=2; + wp2-=2; + } + wp1 = window; + wp2 = window+overlap-1; + for(;i>2);i++) + { + /* Real part arranged as a-bR, Imag part arranged as -c-dR */ + *yp++ = *xp2; + *yp++ = *xp1; + xp1+=2; + xp2-=2; + } + for(;ibitrev[i]] = yc; + } +#ifdef FIXED_POINT + headroom = IMAX(0, IMIN(scale_shift, 28-celt_ilog2(maxval))); +#endif + } + + /* N/4 complex FFT, does not downscale anymore */ + opus_fft_impl(st, f2 ARG_FIXED(scale_shift-headroom)); + + /* Post-rotate */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_cpx * OPUS_RESTRICT fp = f2; + kiss_fft_scalar * OPUS_RESTRICT yp1 = out; + kiss_fft_scalar * OPUS_RESTRICT yp2 = out+stride*(N2-1); + const kiss_twiddle_scalar *t = &trig[0]; + /* Temp pointers to make it really clear to the compiler what we're doing */ + for(i=0;ii,t1) - S_MUL(fp->r,t0), headroom); + yi = PSHR32(S_MUL(fp->r,t1) + S_MUL(fp->i,t0), headroom); + *yp1 = yr; + *yp2 = yi; + fp++; + yp1 += 2*stride; + yp2 -= 2*stride; + } + } + RESTORE_STACK; +} +#endif /* OVERRIDE_clt_mdct_forward */ + +#ifndef OVERRIDE_clt_mdct_backward +void clt_mdct_backward_c(const mdct_lookup *l, kiss_fft_scalar *in, kiss_fft_scalar * OPUS_RESTRICT out, + const celt_coef * OPUS_RESTRICT window, int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + const kiss_twiddle_scalar *trig; + (void) arch; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + /* Pre-rotate */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in; + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+stride*(N2-1); + kiss_fft_scalar * OPUS_RESTRICT yp = out+(overlap>>1); + const kiss_twiddle_scalar * OPUS_RESTRICT t = &trig[0]; + const opus_int16 * OPUS_RESTRICT bitrev = l->kfft[shift]->bitrev; + for(i=0;ikfft[shift], (kiss_fft_cpx*)(out+(overlap>>1)) ARG_FIXED(0)); + + /* Post-rotate and de-shuffle from both ends of the buffer at once to make + it in-place. */ + { + kiss_fft_scalar * yp0 = out+(overlap>>1); + kiss_fft_scalar * yp1 = out+(overlap>>1)+N2-2; + const kiss_twiddle_scalar *t = &trig[0]; + /* Loop to (N4+1)>>1 to handle odd N4. When N4 is odd, the + middle pair will be computed twice. */ + for(i=0;i<(N4+1)>>1;i++) + { + kiss_fft_scalar re, im, yr, yi; + kiss_twiddle_scalar t0, t1; + /* We swap real and imag because we're using an FFT instead of an IFFT. */ + re = yp0[1]; + im = yp0[0]; + t0 = t[i]; + t1 = t[N4+i]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = PSHR32_ovflw(ADD32_ovflw(S_MUL(re,t0), S_MUL(im,t1)), IMDCT_HEADROOM); + yi = PSHR32_ovflw(SUB32_ovflw(S_MUL(re,t1), S_MUL(im,t0)), IMDCT_HEADROOM); + /* We swap real and imag because we're using an FFT instead of an IFFT. */ + re = yp1[1]; + im = yp1[0]; + yp0[0] = yr; + yp1[1] = yi; + + t0 = t[(N4-i-1)]; + t1 = t[(N2-i-1)]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = PSHR32_ovflw(ADD32_ovflw(S_MUL(re,t0), S_MUL(im,t1)), IMDCT_HEADROOM); + yi = PSHR32_ovflw(SUB32_ovflw(S_MUL(re,t1), S_MUL(im,t0)), IMDCT_HEADROOM); + yp1[0] = yr; + yp0[1] = yi; + yp0 += 2; + yp1 -= 2; + } + } + + /* Mirror on both sides for TDAC */ + { + kiss_fft_scalar * OPUS_RESTRICT xp1 = out+overlap-1; + kiss_fft_scalar * OPUS_RESTRICT yp1 = out; + const celt_coef * OPUS_RESTRICT wp1 = window; + const celt_coef * OPUS_RESTRICT wp2 = window+overlap-1; + + for(i = 0; i < overlap/2; i++) + { + kiss_fft_scalar x1, x2; + x1 = *xp1; + x2 = *yp1; + *yp1++ = SUB32_ovflw(S_MUL(x2, *wp2), S_MUL(x1, *wp1)); + *xp1-- = ADD32_ovflw(S_MUL(x2, *wp1), S_MUL(x1, *wp2)); + wp1++; + wp2--; + } + } +} +#endif /* OVERRIDE_clt_mdct_backward */ diff --git a/managed_components/78__esp-opus/celt/mdct.h b/managed_components/78__esp-opus/celt/mdct.h new file mode 100644 index 0000000..1e43a2b --- /dev/null +++ b/managed_components/78__esp-opus/celt/mdct.h @@ -0,0 +1,115 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2008 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* This is a simple MDCT implementation that uses a N/4 complex FFT + to do most of the work. It should be relatively straightforward to + plug in pretty much and FFT here. + + This replaces the Vorbis FFT (and uses the exact same API), which + was a bit too messy and that was ending up duplicating code + (might as well use the same FFT everywhere). + + The algorithm is similar to (and inspired from) Fabrice Bellard's + MDCT implementation in FFMPEG, but has differences in signs, ordering + and scaling in many places. +*/ + +#ifndef MDCT_H +#define MDCT_H + +#include "opus_defines.h" +#include "kiss_fft.h" +#include "arch.h" + +typedef struct { + int n; + int maxshift; + const kiss_fft_state *kfft[4]; + const kiss_twiddle_scalar * OPUS_RESTRICT trig; +} mdct_lookup; + +#if defined(HAVE_ARM_NE10) +#include "arm/mdct_arm.h" +#endif + +/* There should be 2 bits of headroom in the IMDCT which we can take + advantage of to maximize accuracy. */ +#define IMDCT_HEADROOM 2 + +int clt_mdct_init(mdct_lookup *l,int N, int maxshift, int arch); +void clt_mdct_clear(mdct_lookup *l, int arch); + +/** Compute a forward MDCT and scale by 4/N, trashes the input array */ +void clt_mdct_forward_c(const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const celt_coef *window, int overlap, + int shift, int stride, int arch); + +/** Compute a backward MDCT (no scaling) and performs weighted overlap-add + (scales implicitly by 1/2) */ +void clt_mdct_backward_c(const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, + const celt_coef * OPUS_RESTRICT window, + int overlap, int shift, int stride, int arch); + +#if !defined(OVERRIDE_OPUS_MDCT) +/* Is run-time CPU detection enabled on this platform? */ +#if defined(OPUS_HAVE_RTCD) && defined(HAVE_ARM_NE10) + +extern void (*const CLT_MDCT_FORWARD_IMPL[OPUS_ARCHMASK+1])( + const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, const celt_coef *window, + int overlap, int shift, int stride, int arch); + +#define clt_mdct_forward(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) \ + ((*CLT_MDCT_FORWARD_IMPL[(arch)&OPUS_ARCHMASK])(_l, _in, _out, \ + _window, _overlap, _shift, \ + _stride, _arch)) + +extern void (*const CLT_MDCT_BACKWARD_IMPL[OPUS_ARCHMASK+1])( + const mdct_lookup *l, kiss_fft_scalar *in, + kiss_fft_scalar * OPUS_RESTRICT out, const celt_coef *window, + int overlap, int shift, int stride, int arch); + +#define clt_mdct_backward(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) \ + (*CLT_MDCT_BACKWARD_IMPL[(arch)&OPUS_ARCHMASK])(_l, _in, _out, \ + _window, _overlap, _shift, \ + _stride, _arch) + +#else /* if defined(OPUS_HAVE_RTCD) && defined(HAVE_ARM_NE10) */ + +#define clt_mdct_forward(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) \ + clt_mdct_forward_c(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) + +#define clt_mdct_backward(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) \ + clt_mdct_backward_c(_l, _in, _out, _window, _overlap, _shift, _stride, _arch) + +#endif /* end if defined(OPUS_HAVE_RTCD) && defined(HAVE_ARM_NE10) && !defined(FIXED_POINT) */ +#endif /* end if !defined(OVERRIDE_OPUS_MDCT) */ + +#endif diff --git a/managed_components/78__esp-opus/celt/meson.build b/managed_components/78__esp-opus/celt/meson.build new file mode 100644 index 0000000..46601ca --- /dev/null +++ b/managed_components/78__esp-opus/celt/meson.build @@ -0,0 +1,64 @@ +celt_sources = sources['CELT_SOURCES'] + +celt_sse_sources = sources['CELT_SOURCES_SSE'] + +celt_sse2_sources = sources['CELT_SOURCES_SSE2'] + +celt_sse4_1_sources = sources['CELT_SOURCES_SSE4_1'] + +celt_avx2_sources = sources['CELT_SOURCES_AVX2'] + +celt_neon_intr_sources = sources['CELT_SOURCES_ARM_NEON_INTR'] + +celt_static_libs = [] + +if host_cpu_family in ['x86', 'x86_64'] and opus_conf.has('OPUS_HAVE_RTCD') + celt_sources += sources['CELT_SOURCES_X86_RTCD'] +endif + +foreach intr_name : ['sse', 'sse2', 'sse4_1', 'avx2', 'neon_intr'] + have_intr = get_variable('have_' + intr_name) + if not have_intr + continue + endif + + intr_sources = get_variable('celt_@0@_sources'.format(intr_name)) + intr_args = get_variable('opus_@0@_args'.format(intr_name), []) + celt_static_libs += static_library('celt_' + intr_name, intr_sources, + c_args: intr_args, + include_directories: opus_includes, + install: false) +endforeach + +have_arm_intrinsics_or_asm = have_arm_ne10 +if (intrinsics_support.length() + asm_optimization.length() + inline_optimization.length()) > 0 + have_arm_intrinsics_or_asm = true +endif + +if host_cpu_family in ['arm', 'aarch64'] and have_arm_intrinsics_or_asm + if opus_conf.has('OPUS_HAVE_RTCD') + celt_sources += sources['CELT_SOURCES_ARM_RTCD'] + endif + if have_arm_ne10 + celt_sources += sources['CELT_SOURCES_ARM_NE10'] + endif + if opus_arm_external_asm + subdir('arm') + celt_static_libs += static_library('celt-armasm', + celt_arm_armopts_s, celt_sources_arm_asm, + install: false) + endif +endif + +celt_c_args = [] +if host_system == 'windows' + celt_c_args += ['-DDLL_EXPORT'] +endif + +celt_lib = static_library('opus-celt', + celt_sources, + c_args: celt_c_args, + include_directories: opus_includes, + link_whole: celt_static_libs, + dependencies: libm, + install: false) diff --git a/managed_components/78__esp-opus/celt/mfrngcod.h b/managed_components/78__esp-opus/celt/mfrngcod.h new file mode 100644 index 0000000..809152a --- /dev/null +++ b/managed_components/78__esp-opus/celt/mfrngcod.h @@ -0,0 +1,48 @@ +/* Copyright (c) 2001-2008 Timothy B. Terriberry + Copyright (c) 2008-2009 Xiph.Org Foundation */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(_mfrngcode_H) +# define _mfrngcode_H (1) +# include "entcode.h" + +/*Constants used by the entropy encoder/decoder.*/ + +/*The number of bits to output at a time.*/ +# define EC_SYM_BITS (8) +/*The total number of bits in each of the state registers.*/ +# define EC_CODE_BITS (32) +/*The maximum symbol value.*/ +# define EC_SYM_MAX ((1U<>EC_SYM_BITS) +/*The number of bits available for the last, partial symbol in the code field.*/ +# define EC_CODE_EXTRA ((EC_CODE_BITS-2)%EC_SYM_BITS+1) +#endif diff --git a/managed_components/78__esp-opus/celt/mips/celt_mipsr1.h b/managed_components/78__esp-opus/celt/mips/celt_mipsr1.h new file mode 100644 index 0000000..d1b25c2 --- /dev/null +++ b/managed_components/78__esp-opus/celt/mips/celt_mipsr1.h @@ -0,0 +1,152 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2010 Xiph.Org Foundation + Copyright (c) 2008 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef CELT_MIPSR1_H__ +#define CELT_MIPSR1_H__ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#define CELT_C + +#include "os_support.h" +#include "mdct.h" +#include +#include "celt.h" +#include "pitch.h" +#include "bands.h" +#include "modes.h" +#include "entcode.h" +#include "quant_bands.h" +#include "rate.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "float_cast.h" +#include +#include "celt_lpc.h" +#include "vq.h" + +#define OVERRIDE_COMB_FILTER_CONST +#define OVERRIDE_comb_filter +void comb_filter(opus_val32 *y, opus_val32 *x, int T0, int T1, int N, + opus_val16 g0, opus_val16 g1, int tapset0, int tapset1, + const opus_val16 *window, int overlap, int arch) +{ + int i; + opus_val32 x0, x1, x2, x3, x4; + + (void)arch; + + /* printf ("%d %d %f %f\n", T0, T1, g0, g1); */ + opus_val16 g00, g01, g02, g10, g11, g12; + static const opus_val16 gains[3][3] = { + {QCONST16(0.3066406250f, 15), QCONST16(0.2170410156f, 15), QCONST16(0.1296386719f, 15)}, + {QCONST16(0.4638671875f, 15), QCONST16(0.2680664062f, 15), QCONST16(0.f, 15)}, + {QCONST16(0.7998046875f, 15), QCONST16(0.1000976562f, 15), QCONST16(0.f, 15)}}; + + if (g0==0 && g1==0) + { + /* OPT: Happens to work without the OPUS_MOVE(), but only because the current encoder already copies x to y */ + if (x!=y) + OPUS_MOVE(y, x, N); + return; + } + + g00 = MULT16_16_P15(g0, gains[tapset0][0]); + g01 = MULT16_16_P15(g0, gains[tapset0][1]); + g02 = MULT16_16_P15(g0, gains[tapset0][2]); + g10 = MULT16_16_P15(g1, gains[tapset1][0]); + g11 = MULT16_16_P15(g1, gains[tapset1][1]); + g12 = MULT16_16_P15(g1, gains[tapset1][2]); + x1 = x[-T1+1]; + x2 = x[-T1 ]; + x3 = x[-T1-1]; + x4 = x[-T1-2]; + /* If the filter didn't change, we don't need the overlap */ + if (g0==g1 && T0==T1 && tapset0==tapset1) + overlap=0; + + for (i=0;itwiddles[fstride*m]; + yb = st->twiddles[fstride*2*m]; +#endif + + tw=st->twiddles; + + for (i=0;ir += scratch[7].r + scratch[8].r; + Fout0->i += scratch[7].i + scratch[8].i; + scratch[5].r = scratch[0].r + S_MUL_ADD(scratch[7].r,ya.r,scratch[8].r,yb.r); + scratch[5].i = scratch[0].i + S_MUL_ADD(scratch[7].i,ya.r,scratch[8].i,yb.r); + + scratch[6].r = S_MUL_ADD(scratch[10].i,ya.i,scratch[9].i,yb.i); + scratch[6].i = -S_MUL_ADD(scratch[10].r,ya.i,scratch[9].r,yb.i); + + C_SUB(*Fout1,scratch[5],scratch[6]); + C_ADD(*Fout4,scratch[5],scratch[6]); + + scratch[11].r = scratch[0].r + S_MUL_ADD(scratch[7].r,yb.r,scratch[8].r,ya.r); + scratch[11].i = scratch[0].i + S_MUL_ADD(scratch[7].i,yb.r,scratch[8].i,ya.r); + + scratch[12].r = S_MUL_SUB(scratch[9].i,ya.i,scratch[10].i,yb.i); + scratch[12].i = S_MUL_SUB(scratch[10].r,yb.i,scratch[9].r,ya.i); + + C_ADD(*Fout2,scratch[11],scratch[12]); + C_SUB(*Fout3,scratch[11],scratch[12]); + + ++Fout0;++Fout1;++Fout2;++Fout3;++Fout4; + } + } +} + + +#endif /* KISS_FFT_MIPSR1_H */ diff --git a/managed_components/78__esp-opus/celt/mips/mdct_mipsr1.h b/managed_components/78__esp-opus/celt/mips/mdct_mipsr1.h new file mode 100644 index 0000000..7456c18 --- /dev/null +++ b/managed_components/78__esp-opus/celt/mips/mdct_mipsr1.h @@ -0,0 +1,288 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2008 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* This is a simple MDCT implementation that uses a N/4 complex FFT + to do most of the work. It should be relatively straightforward to + plug in pretty much and FFT here. + + This replaces the Vorbis FFT (and uses the exact same API), which + was a bit too messy and that was ending up duplicating code + (might as well use the same FFT everywhere). + + The algorithm is similar to (and inspired from) Fabrice Bellard's + MDCT implementation in FFMPEG, but has differences in signs, ordering + and scaling in many places. +*/ +#ifndef MDCT_MIPSR1_H__ +#define MDCT_MIPSR1_H__ + +#ifndef SKIP_CONFIG_H +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif +#endif + +#include "mdct.h" +#include "kiss_fft.h" +#include "_kiss_fft_guts.h" +#include +#include "os_support.h" +#include "mathops.h" +#include "stack_alloc.h" + +/* Forward MDCT trashes the input array */ +#define OVERRIDE_clt_mdct_forward +void clt_mdct_forward(const mdct_lookup *l, kiss_fft_scalar *in, kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 *window, int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + VARDECL(kiss_fft_scalar, f); + VARDECL(kiss_fft_cpx, f2); + const kiss_fft_state *st = l->kfft[shift]; + const kiss_twiddle_scalar *trig; + opus_val16 scale; +#ifdef FIXED_POINT + /* Allows us to scale with MULT16_32_Q16(), which is faster than + MULT16_32_Q15() on ARM. */ + int scale_shift = st->scale_shift-1; +#endif + + (void)arch; + + SAVE_STACK; + scale = st->scale; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + ALLOC(f, N2, kiss_fft_scalar); + ALLOC(f2, N4, kiss_fft_cpx); + + /* Consider the input to be composed of four blocks: [a, b, c, d] */ + /* Window, shuffle, fold */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in+(overlap>>1); + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+N2-1+(overlap>>1); + kiss_fft_scalar * OPUS_RESTRICT yp = f; + const opus_val16 * OPUS_RESTRICT wp1 = window+(overlap>>1); + const opus_val16 * OPUS_RESTRICT wp2 = window+(overlap>>1)-1; + for(i=0;i<((overlap+3)>>2);i++) + { + /* Real part arranged as -d-cR, Imag part arranged as -b+aR*/ + *yp++ = S_MUL_ADD(*wp2, xp1[N2],*wp1,*xp2); + *yp++ = S_MUL_SUB(*wp1, *xp1,*wp2, xp2[-N2]); + xp1+=2; + xp2-=2; + wp1+=2; + wp2-=2; + } + wp1 = window; + wp2 = window+overlap-1; + for(;i>2);i++) + { + /* Real part arranged as a-bR, Imag part arranged as -c-dR */ + *yp++ = *xp2; + *yp++ = *xp1; + xp1+=2; + xp2-=2; + } + for(;ibitrev[i]] = yc; + } + } + + /* N/4 complex FFT, does not downscale anymore */ + opus_fft_impl(st, f2); + + /* Post-rotate */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_cpx * OPUS_RESTRICT fp = f2; + kiss_fft_scalar * OPUS_RESTRICT yp1 = out; + kiss_fft_scalar * OPUS_RESTRICT yp2 = out+stride*(N2-1); + const kiss_twiddle_scalar *t = &trig[0]; + /* Temp pointers to make it really clear to the compiler what we're doing */ + for(i=0;ii,t[N4+i] , fp->r,t[i]); + yi = S_MUL_ADD(fp->r,t[N4+i] ,fp->i,t[i]); + *yp1 = yr; + *yp2 = yi; + fp++; + yp1 += 2*stride; + yp2 -= 2*stride; + } + } + RESTORE_STACK; +} + +#define OVERRIDE_clt_mdct_backward +void clt_mdct_backward(const mdct_lookup *l, kiss_fft_scalar *in, kiss_fft_scalar * OPUS_RESTRICT out, + const opus_val16 * OPUS_RESTRICT window, int overlap, int shift, int stride, int arch) +{ + int i; + int N, N2, N4; + const kiss_twiddle_scalar *trig; + + (void)arch; + + N = l->n; + trig = l->trig; + for (i=0;i>= 1; + trig += N; + } + N2 = N>>1; + N4 = N>>2; + + /* Pre-rotate */ + { + /* Temp pointers to make it really clear to the compiler what we're doing */ + const kiss_fft_scalar * OPUS_RESTRICT xp1 = in; + const kiss_fft_scalar * OPUS_RESTRICT xp2 = in+stride*(N2-1); + kiss_fft_scalar * OPUS_RESTRICT yp = out+(overlap>>1); + const kiss_twiddle_scalar * OPUS_RESTRICT t = &trig[0]; + const opus_int16 * OPUS_RESTRICT bitrev = l->kfft[shift]->bitrev; + for(i=0;ikfft[shift], (kiss_fft_cpx*)(out+(overlap>>1))); + + /* Post-rotate and de-shuffle from both ends of the buffer at once to make + it in-place. */ + { + kiss_fft_scalar * OPUS_RESTRICT yp0 = out+(overlap>>1); + kiss_fft_scalar * OPUS_RESTRICT yp1 = out+(overlap>>1)+N2-2; + const kiss_twiddle_scalar *t = &trig[0]; + /* Loop to (N4+1)>>1 to handle odd N4. When N4 is odd, the + middle pair will be computed twice. */ + for(i=0;i<(N4+1)>>1;i++) + { + kiss_fft_scalar re, im, yr, yi; + kiss_twiddle_scalar t0, t1; + /* We swap real and imag because we're using an FFT instead of an IFFT. */ + re = yp0[1]; + im = yp0[0]; + t0 = t[i]; + t1 = t[N4+i]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = S_MUL_ADD(re,t0 , im,t1); + yi = S_MUL_SUB(re,t1 , im,t0); + /* We swap real and imag because we're using an FFT instead of an IFFT. */ + re = yp1[1]; + im = yp1[0]; + yp0[0] = yr; + yp1[1] = yi; + + t0 = t[(N4-i-1)]; + t1 = t[(N2-i-1)]; + /* We'd scale up by 2 here, but instead it's done when mixing the windows */ + yr = S_MUL_ADD(re,t0,im,t1); + yi = S_MUL_SUB(re,t1,im,t0); + yp1[0] = yr; + yp0[1] = yi; + yp0 += 2; + yp1 -= 2; + } + } + + /* Mirror on both sides for TDAC */ + { + kiss_fft_scalar * OPUS_RESTRICT xp1 = out+overlap-1; + kiss_fft_scalar * OPUS_RESTRICT yp1 = out; + const opus_val16 * OPUS_RESTRICT wp1 = window; + const opus_val16 * OPUS_RESTRICT wp2 = window+overlap-1; + + for(i = 0; i < overlap/2; i++) + { + kiss_fft_scalar x1, x2; + x1 = *xp1; + x2 = *yp1; + *yp1++ = MULT16_32_Q15(*wp2, x2) - MULT16_32_Q15(*wp1, x1); + *xp1-- = MULT16_32_Q15(*wp1, x2) + MULT16_32_Q15(*wp2, x1); + wp1++; + wp2--; + } + } +} +#endif /* MDCT_MIPSR1_H__ */ diff --git a/managed_components/78__esp-opus/celt/mips/pitch_mipsr1.h b/managed_components/78__esp-opus/celt/mips/pitch_mipsr1.h new file mode 100644 index 0000000..a9500af --- /dev/null +++ b/managed_components/78__esp-opus/celt/mips/pitch_mipsr1.h @@ -0,0 +1,161 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/** + @file pitch.h + @brief Pitch analysis + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef PITCH_MIPSR1_H +#define PITCH_MIPSR1_H + +#define OVERRIDE_DUAL_INNER_PROD +static inline void dual_inner_prod(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2, int arch) +{ + int j; + opus_val32 xy01=0; + opus_val32 xy02=0; + + (void)arch; + + asm volatile("MULT $ac1, $0, $0"); + asm volatile("MULT $ac2, $0, $0"); + /* Compute the norm of X+Y and X-Y as |X|^2 + |Y|^2 +/- sum(xy) */ + for (j=0;j=0;i--) + { + celt_norm x1, x2; + x1 = Xptr[0]; + x2 = Xptr[stride]; + Xptr[stride] = EXTRACT16(PSHR32(MAC16_16(MULT16_16(c, x2), s, x1), 15)); + *Xptr-- = EXTRACT16(PSHR32(MAC16_16(MULT16_16(c, x1), ms, x2), 15)); + } +} + +#define OVERRIDE_renormalise_vector +void renormalise_vector(celt_norm *X, int N, opus_val16 gain, int arch) +{ + int i; +#ifdef FIXED_POINT + int k; +#endif + opus_val32 E = EPSILON; + opus_val16 g; + opus_val32 t; + celt_norm *xptr = X; + int X0, X1; + + (void)arch; + + asm volatile("mult $ac1, $0, $0"); + asm volatile("MTLO %0, $ac1" : :"r" (E)); + /*if(N %4) + printf("error");*/ + for (i=0;i>1; +#endif + t = VSHR32(E, 2*(k-7)); + g = MULT16_16_P15(celt_rsqrt_norm(t),gain); + + xptr = X; + for (i=0;i= Fs) + break; + + /* Find where the linear part ends (i.e. where the spacing is more than min_width */ + for (lin=0;lin= res) + break; + + low = (bark_freq[lin]+res/2)/res; + high = nBark-lin; + *nbEBands = low+high; + eBands = opus_alloc(sizeof(opus_int16)*(*nbEBands+2)); + + if (eBands==NULL) + return NULL; + + /* Linear spacing (min_width) */ + for (i=0;i0) + offset = eBands[low-1]*res - bark_freq[lin-1]; + /* Spacing follows critical bands */ + for (i=0;i frame_size) + eBands[*nbEBands] = frame_size; + for (i=1;i<*nbEBands-1;i++) + { + if (eBands[i+1]-eBands[i] < eBands[i]-eBands[i-1]) + { + eBands[i] -= (2*eBands[i]-eBands[i-1]-eBands[i+1])/2; + } + } + /* Remove any empty bands. */ + for (i=j=0;i<*nbEBands;i++) + if(eBands[i+1]>eBands[j]) + eBands[++j]=eBands[i+1]; + *nbEBands=j; + + for (i=1;i<*nbEBands;i++) + { + /* Every band must be smaller than the last band. */ + celt_assert(eBands[i]-eBands[i-1]<=eBands[*nbEBands]-eBands[*nbEBands-1]); + /* Each band must be no larger than twice the size of the previous one. */ + celt_assert(eBands[i+1]-eBands[i]<=2*(eBands[i]-eBands[i-1])); + } + + return eBands; +} + +static void compute_allocation_table(CELTMode *mode) +{ + int i, j; + unsigned char *allocVectors; + int maxBands = sizeof(eband5ms)/sizeof(eband5ms[0])-1; + + mode->nbAllocVectors = BITALLOC_SIZE; + allocVectors = opus_alloc(sizeof(unsigned char)*(BITALLOC_SIZE*mode->nbEBands)); + if (allocVectors==NULL) + { + mode->allocVectors = NULL; + return; + } + + /* Check for standard mode */ + if (mode->Fs == 400*(opus_int32)mode->shortMdctSize) + { + for (i=0;inbEBands;i++) + allocVectors[i] = band_allocation[i]; + mode->allocVectors = allocVectors; + return; + } + /* If not the standard mode, interpolate */ + /* Compute per-codec-band allocation from per-critical-band matrix */ + for (i=0;inbEBands;j++) + { + int k; + for (k=0;k mode->eBands[j]*(opus_int32)mode->Fs/mode->shortMdctSize) + break; + } + if (k>maxBands-1) + allocVectors[i*mode->nbEBands+j] = band_allocation[i*maxBands + maxBands-1]; + else { + opus_int32 a0, a1; + a1 = mode->eBands[j]*(opus_int32)mode->Fs/mode->shortMdctSize - 400*(opus_int32)eband5ms[k-1]; + a0 = 400*(opus_int32)eband5ms[k] - mode->eBands[j]*(opus_int32)mode->Fs/mode->shortMdctSize; + allocVectors[i*mode->nbEBands+j] = (a0*band_allocation[i*maxBands+k-1] + + a1*band_allocation[i*maxBands+k])/(a0+a1); + } + } + } + + /*printf ("\n"); + for (i=0;inbEBands;j++) + printf ("%d ", allocVectors[i*mode->nbEBands+j]); + printf ("\n"); + } + exit(0);*/ + + mode->allocVectors = allocVectors; +} + +#endif /* CUSTOM_MODES */ + +CELTMode *opus_custom_mode_create(opus_int32 Fs, int frame_size, int *error) +{ + int i; +#ifdef CUSTOM_MODES + CELTMode *mode=NULL; + int res; + celt_coef *window; + opus_int16 *logN; + int LM; + int arch = opus_select_arch(); + ALLOC_STACK; +#if !defined(VAR_ARRAYS) && !defined(USE_ALLOCA) + if (global_stack==NULL) + goto failure; +#endif +#endif + +#ifndef CUSTOM_MODES_ONLY + for (i=0;iFs && + (frame_size<shortMdctSize*static_mode_list[i]->nbShortMdcts) + { + if (error) + *error = OPUS_OK; + return (CELTMode*)static_mode_list[i]; + } + } + } +#endif /* CUSTOM_MODES_ONLY */ + +#ifndef CUSTOM_MODES + if (error) + *error = OPUS_BAD_ARG; + return NULL; +#else + + /* The good thing here is that permutation of the arguments will automatically be invalid */ + + if (Fs < 8000 || Fs > 96000) + { + if (error) + *error = OPUS_BAD_ARG; + return NULL; + } + if (frame_size < 40 || frame_size > 1024 || frame_size%2!=0) + { + if (error) + *error = OPUS_BAD_ARG; + return NULL; + } + /* Frames of less than 1ms are not supported. */ + if ((opus_int32)frame_size*1000 < Fs) + { + if (error) + *error = OPUS_BAD_ARG; + return NULL; + } + + if ((opus_int32)frame_size*75 >= Fs && (frame_size%16)==0) + { + LM = 3; + } else if ((opus_int32)frame_size*150 >= Fs && (frame_size%8)==0) + { + LM = 2; + } else if ((opus_int32)frame_size*300 >= Fs && (frame_size%4)==0) + { + LM = 1; + } else + { + LM = 0; + } + + /* Shorts longer than 3.3ms are not supported. */ + if ((opus_int32)(frame_size>>LM)*300 > Fs) + { + if (error) + *error = OPUS_BAD_ARG; + return NULL; + } + + mode = opus_alloc(sizeof(CELTMode)); + if (mode==NULL) + goto failure; + mode->Fs = Fs; + + /* Pre/de-emphasis depends on sampling rate. The "standard" pre-emphasis + is defined as A(z) = 1 - 0.85*z^-1 at 48 kHz. Other rates should + approximate that. */ + if(Fs < 12000) /* 8 kHz */ + { + mode->preemph[0] = QCONST16(0.3500061035f, 15); + mode->preemph[1] = -QCONST16(0.1799926758f, 15); + mode->preemph[2] = QCONST16(0.2719968125f, SIG_SHIFT); /* exact 1/preemph[3] */ + mode->preemph[3] = QCONST16(3.6765136719f, 13); + } else if(Fs < 24000) /* 16 kHz */ + { + mode->preemph[0] = QCONST16(0.6000061035f, 15); + mode->preemph[1] = -QCONST16(0.1799926758f, 15); + mode->preemph[2] = QCONST16(0.4424998650f, SIG_SHIFT); /* exact 1/preemph[3] */ + mode->preemph[3] = QCONST16(2.2598876953f, 13); + } else if(Fs < 40000) /* 32 kHz */ + { + mode->preemph[0] = QCONST16(0.7799987793f, 15); + mode->preemph[1] = -QCONST16(0.1000061035f, 15); + mode->preemph[2] = QCONST16(0.7499771125f, SIG_SHIFT); /* exact 1/preemph[3] */ + mode->preemph[3] = QCONST16(1.3333740234f, 13); + } else /* 48 kHz */ + { + mode->preemph[0] = QCONST16(0.8500061035f, 15); + mode->preemph[1] = QCONST16(0.0f, 15); + mode->preemph[2] = QCONST16(1.f, SIG_SHIFT); + mode->preemph[3] = QCONST16(1.f, 13); + } + + mode->maxLM = LM; + mode->nbShortMdcts = 1<shortMdctSize = frame_size/mode->nbShortMdcts; + res = (mode->Fs+mode->shortMdctSize)/(2*mode->shortMdctSize); + + mode->eBands = compute_ebands(Fs, mode->shortMdctSize, res, &mode->nbEBands); + if (mode->eBands==NULL) + goto failure; +#if !defined(SMALL_FOOTPRINT) + /* Make sure we don't allocate a band larger than our PVQ table. + 208 should be enough, but let's be paranoid. */ + if ((mode->eBands[mode->nbEBands] - mode->eBands[mode->nbEBands-1])< + 208) { + goto failure; + } +#endif + + mode->effEBands = mode->nbEBands; + while (mode->eBands[mode->effEBands] > mode->shortMdctSize) + mode->effEBands--; + + /* Overlap must be divisible by 4 */ + mode->overlap = ((mode->shortMdctSize>>2)<<2); + + compute_allocation_table(mode); + if (mode->allocVectors==NULL) + goto failure; + + window = (celt_coef*)opus_alloc(mode->overlap*sizeof(*window)); + if (window==NULL) + goto failure; + +#ifndef FIXED_POINT + for (i=0;ioverlap;i++) + window[i] = Q15ONE*sin(.5*M_PI* sin(.5*M_PI*(i+.5)/mode->overlap) * sin(.5*M_PI*(i+.5)/mode->overlap)); +#else +# ifdef ENABLE_QEXT + for (i=0;ioverlap;i++) + window[i] = MIN32(2147483647, 2147483648*sin(.5*M_PI* sin(.5*M_PI*(i+.5)/mode->overlap) * sin(.5*M_PI*(i+.5)/mode->overlap))); +# else + for (i=0;ioverlap;i++) + window[i] = MIN32(32767,floor(.5+32768.*sin(.5*M_PI* sin(.5*M_PI*(i+.5)/mode->overlap) * sin(.5*M_PI*(i+.5)/mode->overlap)))); +# endif +#endif + mode->window = window; + + logN = (opus_int16*)opus_alloc(mode->nbEBands*sizeof(opus_int16)); + if (logN==NULL) + goto failure; + + for (i=0;inbEBands;i++) + logN[i] = log2_frac(mode->eBands[i+1]-mode->eBands[i], BITRES); + mode->logN = logN; + + compute_pulse_cache(mode, mode->maxLM); + + if (clt_mdct_init(&mode->mdct, 2*mode->shortMdctSize*mode->nbShortMdcts, + mode->maxLM, arch) == 0) + goto failure; + + if (error) + *error = OPUS_OK; + + return mode; +failure: + if (error) + *error = OPUS_ALLOC_FAIL; + if (mode!=NULL) + opus_custom_mode_destroy(mode); + return NULL; +#endif /* !CUSTOM_MODES */ +} + +#ifdef CUSTOM_MODES +void opus_custom_mode_destroy(CELTMode *mode) +{ + int arch = opus_select_arch(); + + if (mode == NULL) + return; +#ifndef CUSTOM_MODES_ONLY + { + int i; + for (i=0;ieBands); + opus_free((unsigned char*)mode->allocVectors); + + opus_free((opus_val16*)mode->window); + opus_free((opus_int16*)mode->logN); + + opus_free((opus_int16*)mode->cache.index); + opus_free((unsigned char*)mode->cache.bits); + opus_free((unsigned char*)mode->cache.caps); + clt_mdct_clear(&mode->mdct, arch); + + opus_free((CELTMode *)mode); +} +#endif diff --git a/managed_components/78__esp-opus/celt/modes.h b/managed_components/78__esp-opus/celt/modes.h new file mode 100644 index 0000000..997257d --- /dev/null +++ b/managed_components/78__esp-opus/celt/modes.h @@ -0,0 +1,75 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2008 Gregory Maxwell + Written by Jean-Marc Valin and Gregory Maxwell */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef MODES_H +#define MODES_H + +#include "opus_types.h" +#include "celt.h" +#include "arch.h" +#include "mdct.h" +#include "entenc.h" +#include "entdec.h" + +#define MAX_PERIOD 1024 + +typedef struct { + int size; + const opus_int16 *index; + const unsigned char *bits; + const unsigned char *caps; +} PulseCache; + +/** Mode definition (opaque) + @brief Mode definition + */ +struct OpusCustomMode { + opus_int32 Fs; + int overlap; + + int nbEBands; + int effEBands; + opus_val16 preemph[4]; + const opus_int16 *eBands; /**< Definition for each "pseudo-critical band" */ + + int maxLM; + int nbShortMdcts; + int shortMdctSize; + + int nbAllocVectors; /**< Number of lines in the matrix below */ + const unsigned char *allocVectors; /**< Number of bits in each band for several rates */ + const opus_int16 *logN; + + const celt_coef *window; + mdct_lookup mdct; + PulseCache cache; +}; + + +#endif diff --git a/managed_components/78__esp-opus/celt/opus_custom_demo.c b/managed_components/78__esp-opus/celt/opus_custom_demo.c new file mode 100644 index 0000000..ac3e9f1 --- /dev/null +++ b/managed_components/78__esp-opus/celt/opus_custom_demo.c @@ -0,0 +1,451 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "opus_custom.h" +#include "arch.h" +#include +#include +#include +#include + +#define MAX_PACKET 1275 + +static void print_usage(char **argv) { + fprintf (stderr, "Usage: %s [-e | -d] " + " [] [options] " + " \n", argv[0]); + fprintf (stderr, " -e encode only (default is encode and decode)\n"); + fprintf (stderr, " -d decode only (default is encode and decode)\n"); + fprintf (stderr, " : required only when encoding\n"); + fprintf (stderr, "options:\n"); + fprintf (stderr, " -16 format is 16-bit little-endian (default)\n"); + fprintf (stderr, " -24 format is 24-bit little-endian\n"); + fprintf (stderr, " -f32 format is 32-bit float little-endian\n"); + fprintf (stderr, " -complexity <0-10> optional only when encoding\n"); + fprintf (stderr, " -loss encoding (robsutness setting) and decoding (simulating loss)\n"); +} + +static void int_to_char(opus_uint32 i, unsigned char ch[4]) +{ + ch[0] = i>>24; + ch[1] = (i>>16)&0xFF; + ch[2] = (i>>8)&0xFF; + ch[3] = i&0xFF; +} + +static opus_uint32 char_to_int(unsigned char ch[4]) +{ + return ((opus_uint32)ch[0]<<24) | ((opus_uint32)ch[1]<<16) + | ((opus_uint32)ch[2]<< 8) | (opus_uint32)ch[3]; +} + +#define check_encoder_option(decode_only, opt) do {if (decode_only) {fprintf(stderr, "option %s is only for encoding\n", opt); goto failure;}} while(0) +#define check_decoder_option(encode_only, opt) do {if (encode_only) {fprintf(stderr, "option %s is only for decoding\n", opt); goto failure;}} while(0) + +#define FORMAT_S16_LE 0 +#define FORMAT_S24_LE 1 +#define FORMAT_F32_LE 2 + +static const int format_size[3] = {2, 3, 4}; + +typedef union { + opus_int32 i; + float f; +} float_bits; + + +int main(int argc, char *argv[]) +{ + int err; + int ret=1; + int args; + opus_uint32 enc_final_range; + opus_uint32 dec_final_range; + int encode_only=0, decode_only=0; + char *inFile, *outFile; + FILE *fin=NULL, *fout=NULL; + OpusCustomMode *mode=NULL; + OpusCustomEncoder *enc=NULL; + OpusCustomDecoder *dec=NULL; + int len; + opus_int32 frame_size, channels, rate; + int format=FORMAT_S16_LE; + int bytes_per_packet=0; + unsigned char data[MAX_PACKET]; + int complexity=-1; + float percent_loss = -1; + int i; +#if !(defined (FIXED_POINT) && !defined(CUSTOM_MODES)) && defined(RESYNTH) + double rmsd = 0; +#endif + int count = 0; + opus_int32 skip; + opus_int32 *in=NULL, *out=NULL; + unsigned char *fbytes=NULL; + args = 1; + if (argc < 7) + { + print_usage(argv); + goto failure; + } + if (strcmp(argv[args], "-e")==0) + { + encode_only = 1; + args++; + } else if (strcmp(argv[args], "-d")==0) + { + decode_only = 1; + args++; + } + + rate = (opus_int32)atol(argv[args]); + args++; + + if (rate != 8000 && rate != 12000 + && rate != 16000 && rate != 24000 + && rate != 48000) + { + fprintf(stderr, "Supported sampling rates are 8000, 12000, " + "16000, 24000 and 48000.\n"); + goto failure; + } + + channels = atoi(argv[args]); + args++; + + if (channels < 1 || channels > 2) + { + fprintf(stderr, "Opus_demo supports only 1 or 2 channels.\n"); + goto failure; + } + + frame_size = atoi(argv[args]); + args++; + + if (!decode_only) + { + bytes_per_packet = (opus_int32)atol(argv[args]); + args++; + if (bytes_per_packet < 0 || bytes_per_packet > MAX_PACKET) + { + fprintf (stderr, "bytes per packet must be between 0 and %d\n", + MAX_PACKET); + goto failure; + } + } + + mode = opus_custom_mode_create(rate, frame_size, NULL); + if (mode == NULL) + { + fprintf(stderr, "failed to create a mode\n"); + goto failure; + } + while( args < argc - 2 ) { + /* process command line options */ + if( strcmp( argv[ args ], "-complexity" ) == 0 ) { + check_encoder_option(decode_only, "-complexity"); + args++; + complexity=atoi(argv[args]); + args++; + } else if( strcmp( argv[ args ], "-loss" ) == 0 ) { + args++; + percent_loss = atof(argv[args]); + args++; + } else if( strcmp( argv[ args ], "-16" ) == 0 ) { + format = FORMAT_S16_LE; + args++; + } else if( strcmp( argv[ args ], "-24" ) == 0 ) { + format = FORMAT_S24_LE; + args++; + } else if( strcmp( argv[ args ], "-f32" ) == 0 ) { + format = FORMAT_F32_LE; + args++; + } else { + printf( "Error: unrecognized setting: %s\n\n", argv[ args ] ); + print_usage( argv ); + goto failure; + } + } + if (!decode_only) { + enc = opus_custom_encoder_create(mode, channels, &err); + if (err != 0) + { + fprintf(stderr, "Failed to create the encoder: %s\n", opus_strerror(err)); + goto failure; + } + if (complexity >= 0) + { + opus_custom_encoder_ctl(enc,OPUS_SET_COMPLEXITY(complexity)); + } + if (percent_loss >= 0) { + opus_custom_encoder_ctl(enc, OPUS_SET_PACKET_LOSS_PERC((int)percent_loss)); + } + } + if (!encode_only) { + dec = opus_custom_decoder_create(mode, channels, &err); + if (err != 0) + { + fprintf(stderr, "Failed to create the decoder: %s\n", opus_strerror(err)); + goto failure; + } + opus_custom_decoder_ctl(dec, OPUS_GET_LOOKAHEAD(&skip)); + } + if (argc-args != 2) + { + print_usage(argv); + goto failure; + } + inFile = argv[argc-2]; + fin = fopen(inFile, "rb"); + if (!fin) + { + fprintf (stderr, "Could not open input file %s\n", argv[argc-2]); + goto failure; + } + outFile = argv[argc-1]; + fout = fopen(outFile, "wb+"); + if (!fout) + { + fprintf (stderr, "Could not open output file %s\n", argv[argc-1]); + goto failure; + } + in = (opus_int32*)malloc(frame_size*channels*sizeof(opus_int32)); + out = (opus_int32*)malloc(frame_size*channels*sizeof(opus_int32)); + fbytes = (unsigned char*)malloc(frame_size*channels*4); + + while (!feof(fin)) + { + int lost = 0; + if (decode_only) + { + unsigned char ch[4]; + size_t num_read = fread(ch, 1, 4, fin); + if (num_read!=4) + break; + len = char_to_int(ch); + if (len>MAX_PACKET || len<0) + { + fprintf(stderr, "Invalid payload length: %d\n",len); + break; + } + num_read = fread(ch, 1, 4, fin); + if (num_read!=4) + break; + enc_final_range = char_to_int(ch); + num_read = fread(data, 1, len, fin); + if (num_read!=(size_t)len) + { + fprintf(stderr, "Ran out of input, " + "expecting %d bytes got %d\n", + len,(int)num_read); + break; + } + } else { + err = fread(fbytes, format_size[format], frame_size*channels, fin); + if (feof(fin)) + break; + if (format == FORMAT_S16_LE) { + for(i=0;i>8; + if (s > 32767) s = 32767; + if (s < -32767) s = -32767; + fbytes[2*i]=s&0xFF; + fbytes[2*i+1]=(s>>8)&0xFF; + } + } else if (format == FORMAT_S24_LE) { + for(i=0;i<(ret-skip)*channels;i++) + { + opus_int32 s; + s=out[i+(skip*channels)]; + if (s > 8388607) s = 8388607; + if (s < -8388607) s = -8388607; + fbytes[3*i]=s&0xFF; + fbytes[3*i+1]=(s>>8)&0xFF; + fbytes[3*i+2]=(s>>16)&0xFF; + } + } else if (format == FORMAT_F32_LE) { + for(i=0;i<(ret-skip)*channels;i++) + { + float_bits s; + s.f=out[i+(skip*channels)]*(1.f/8388608.f); + fbytes[4*i]=s.i&0xFF; + fbytes[4*i+1]=(s.i>>8)&0xFF; + fbytes[4*i+2]=(s.i>>16)&0xFF; + fbytes[4*i+3]=(s.i>>24)&0xFF; + } + } + fwrite(fbytes, format_size[format], (ret-skip)*channels, fout); + } + + /* compare final range encoder rng values of encoder and decoder */ + if( enc_final_range!=0 && !encode_only + && !lost + && dec_final_range != enc_final_range ) { + fprintf (stderr, "Error: Range coder state mismatch " + "between encoder and decoder " + "in frame %ld: 0x%8lx vs 0x%8lx\n", + (long)count, + (unsigned long)enc_final_range, + (unsigned long)dec_final_range); + goto failure; + } + + count++; + skip = 0; + } + PRINT_MIPS(stderr); + ret = EXIT_SUCCESS; +#if !(defined (FIXED_POINT) && !defined(CUSTOM_MODES)) && defined(RESYNTH) + if (!encode_only && !decode_only) + { + if (rmsd > 0) + { + rmsd = sqrt(rmsd/(1.0*frame_size*channels*count)); + fprintf (stderr, "Error: encoder doesn't match decoder\n"); + fprintf (stderr, "RMS mismatch is %f\n", rmsd); + ret = 1; + } else { + fprintf (stderr, "Encoder matches decoder!!\n"); + } + } +#endif +failure: + /* Cleanup after ourselves. */ + if (enc) opus_custom_encoder_destroy(enc); + if (dec) opus_custom_decoder_destroy(dec); + if (fin) fclose(fin); + if (fout) fclose(fout); + if (mode) opus_custom_mode_destroy(mode); + if (in) free(in); + if (out) free(out); + if (fbytes) free(fbytes); + return ret; +} + diff --git a/managed_components/78__esp-opus/celt/os_support.h b/managed_components/78__esp-opus/celt/os_support.h new file mode 100644 index 0000000..7d2d378 --- /dev/null +++ b/managed_components/78__esp-opus/celt/os_support.h @@ -0,0 +1,99 @@ +/* Copyright (C) 2007 Jean-Marc Valin + + File: os_support.h + This is the (tiny) OS abstraction layer. Aside from math.h, this is the + only place where system headers are allowed. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + 1. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR + IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES + OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, + INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, + STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef OS_SUPPORT_H +#define OS_SUPPORT_H + +#ifdef CUSTOM_SUPPORT +# include "custom_support.h" +#endif + +#include "opus_types.h" +#include "opus_defines.h" + +#include +#include + +/** Opus wrapper for malloc(). To do your own dynamic allocation replace this function, opus_realloc, and opus_free */ +#ifndef OVERRIDE_OPUS_ALLOC +static OPUS_INLINE void *opus_alloc (size_t size) +{ + return malloc(size); +} +#endif + +#ifndef OVERRIDE_OPUS_REALLOC +static OPUS_INLINE void *opus_realloc (void *ptr, size_t size) +{ + return realloc(ptr, size); +} +#endif + +/** Used only for non-threadsafe pseudostack. + If desired, this can always return the same area of memory rather than allocating a new one every time. */ +#ifndef OVERRIDE_OPUS_ALLOC_SCRATCH +static OPUS_INLINE void *opus_alloc_scratch (size_t size) +{ + /* Scratch space doesn't need to be cleared */ + return opus_alloc(size); +} +#endif + +/** Opus wrapper for free(). To do your own dynamic allocation replace this function, opus_realloc, and opus_free */ +#ifndef OVERRIDE_OPUS_FREE +static OPUS_INLINE void opus_free (void *ptr) +{ + free(ptr); +} +#endif + +/** Copy n elements from src to dst. The 0* term provides compile-time type checking */ +#ifndef OVERRIDE_OPUS_COPY +#define OPUS_COPY(dst, src, n) (memcpy((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) )) +#endif + +/** Copy n elements from src to dst, allowing overlapping regions. The 0* term + provides compile-time type checking */ +#ifndef OVERRIDE_OPUS_MOVE +#define OPUS_MOVE(dst, src, n) (memmove((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) )) +#endif + +/** Set n elements of dst to zero */ +#ifndef OVERRIDE_OPUS_CLEAR +#define OPUS_CLEAR(dst, n) (memset((dst), 0, (n)*sizeof(*(dst)))) +#endif + +/*#ifdef __GNUC__ +#pragma GCC poison printf sprintf +#pragma GCC poison malloc free realloc calloc +#endif*/ + +#endif /* OS_SUPPORT_H */ + diff --git a/managed_components/78__esp-opus/celt/pitch.c b/managed_components/78__esp-opus/celt/pitch.c new file mode 100644 index 0000000..a163327 --- /dev/null +++ b/managed_components/78__esp-opus/celt/pitch.c @@ -0,0 +1,555 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/** + @file pitch.c + @brief Pitch analysis + */ + +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "pitch.h" +#include "os_support.h" +#include "modes.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "celt_lpc.h" + +static void find_best_pitch(opus_val32 *xcorr, opus_val16 *y, int len, + int max_pitch, int *best_pitch +#ifdef FIXED_POINT + , int yshift, opus_val32 maxcorr +#endif + ) +{ + int i, j; + opus_val32 Syy=1; + opus_val16 best_num[2]; + opus_val32 best_den[2]; +#ifdef FIXED_POINT + int xshift; + + xshift = celt_ilog2(maxcorr)-14; +#endif + + best_num[0] = -1; + best_num[1] = -1; + best_den[0] = 0; + best_den[1] = 0; + best_pitch[0] = 0; + best_pitch[1] = 1; + for (j=0;j0) + { + opus_val16 num; + opus_val32 xcorr16; + xcorr16 = EXTRACT16(VSHR32(xcorr[i], xshift)); +#ifndef FIXED_POINT + /* Considering the range of xcorr16, this should avoid both underflows + and overflows (inf) when squaring xcorr16 */ + xcorr16 *= 1e-12f; +#endif + num = MULT16_16_Q15(xcorr16,xcorr16); + if (MULT16_32_Q15(num,best_den[1]) > MULT16_32_Q15(best_num[1],Syy)) + { + if (MULT16_32_Q15(num,best_den[0]) > MULT16_32_Q15(best_num[0],Syy)) + { + best_num[1] = best_num[0]; + best_den[1] = best_den[0]; + best_pitch[1] = best_pitch[0]; + best_num[0] = num; + best_den[0] = Syy; + best_pitch[0] = i; + } else { + best_num[1] = num; + best_den[1] = Syy; + best_pitch[1] = i; + } + } + } + Syy += SHR32(MULT16_16(y[i+len],y[i+len]),yshift) - SHR32(MULT16_16(y[i],y[i]),yshift); + Syy = MAX32(1, Syy); + } +} + +static void celt_fir5(opus_val16 *x, + const opus_val16 *num, + int N) +{ + int i; + opus_val16 num0, num1, num2, num3, num4; + opus_val32 mem0, mem1, mem2, mem3, mem4; + num0=num[0]; + num1=num[1]; + num2=num[2]; + num3=num[3]; + num4=num[4]; + mem0=0; + mem1=0; + mem2=0; + mem3=0; + mem4=0; + for (i=0;i>1;i++) + x_lp[i] = SHR32(x[0][(2*i-1)], shift+2) + SHR32(x[0][(2*i+1)], shift+2) + SHR32(x[0][2*i], shift+1); + x_lp[0] = SHR32(x[0][1], shift+2) + SHR32(x[0][0], shift+1); + if (C==2) + { + for (i=1;i>1;i++) + x_lp[i] += SHR32(x[1][(2*i-1)], shift+2) + SHR32(x[1][(2*i+1)], shift+2) + SHR32(x[1][2*i], shift+1); + x_lp[0] += SHR32(x[1][1], shift+2) + SHR32(x[1][0], shift+1); + } +#else + for (i=1;i>1;i++) + x_lp[i] = .25f*x[0][(2*i-1)] + .25f*x[0][(2*i+1)] + .5f*x[0][2*i]; + x_lp[0] = .25f*x[0][1] + .5f*x[0][0]; + if (C==2) + { + for (i=1;i>1;i++) + x_lp[i] += .25f*x[1][(2*i-1)] + .25f*x[1][(2*i+1)] + .5f*x[1][2*i]; + x_lp[0] += .25f*x[1][1] + .5f*x[1][0]; + } +#endif + _celt_autocorr(x_lp, ac, NULL, 0, + 4, len>>1, arch); + + /* Noise floor -40 dB */ +#ifdef FIXED_POINT + ac[0] += SHR32(ac[0],13); +#else + ac[0] *= 1.0001f; +#endif + /* Lag windowing */ + for (i=1;i<=4;i++) + { + /*ac[i] *= exp(-.5*(2*M_PI*.002*i)*(2*M_PI*.002*i));*/ +#ifdef FIXED_POINT + ac[i] -= MULT16_32_Q15(2*i*i, ac[i]); +#else + ac[i] -= ac[i]*(.008f*i)*(.008f*i); +#endif + } + + _celt_lpc(lpc, ac, 4); + for (i=0;i<4;i++) + { + tmp = MULT16_16_Q15(QCONST16(.9f,15), tmp); + lpc[i] = MULT16_16_Q15(lpc[i], tmp); + } + /* Add a zero */ + lpc2[0] = lpc[0] + QCONST16(.8f,SIG_SHIFT); + lpc2[1] = lpc[1] + MULT16_16_Q15(c1,lpc[0]); + lpc2[2] = lpc[2] + MULT16_16_Q15(c1,lpc[1]); + lpc2[3] = lpc[3] + MULT16_16_Q15(c1,lpc[2]); + lpc2[4] = MULT16_16_Q15(c1,lpc[3]); + celt_fir5(x_lp, lpc2, len>>1); +} + +/* Pure C implementation. */ +#ifdef FIXED_POINT +opus_val32 +#else +void +#endif +celt_pitch_xcorr_c(const opus_val16 *_x, const opus_val16 *_y, + opus_val32 *xcorr, int len, int max_pitch, int arch) +{ + +#if 0 /* This is a simple version of the pitch correlation that should work + well on DSPs like Blackfin and TI C5x/C6x */ + int i, j; +#ifdef FIXED_POINT + opus_val32 maxcorr=1; +#endif +#if !defined(OVERRIDE_PITCH_XCORR) + (void)arch; +#endif + for (i=0;i0); + celt_sig_assert(((size_t)_x&3)==0); + for (i=0;i0); + celt_assert(max_pitch>0); + lag = len+max_pitch; + + ALLOC(x_lp4, len>>2, opus_val16); + ALLOC(y_lp4, lag>>2, opus_val16); + ALLOC(xcorr, max_pitch>>1, opus_val32); + + /* Downsample by 2 again */ + for (j=0;j>2;j++) + x_lp4[j] = x_lp[2*j]; + for (j=0;j>2;j++) + y_lp4[j] = y[2*j]; + +#ifdef FIXED_POINT + xmax = celt_maxabs16(x_lp4, len>>2); + ymax = celt_maxabs16(y_lp4, lag>>2); + shift = celt_ilog2(MAX32(1, MAX32(xmax, ymax)))-11; + if (shift>0) + { + for (j=0;j>2;j++) + x_lp4[j] = SHR16(x_lp4[j], shift); + for (j=0;j>2;j++) + y_lp4[j] = SHR16(y_lp4[j], shift); + /* Use double the shift for a MAC */ + shift *= 2; + } else { + shift = 0; + } +#endif + + /* Coarse search with 4x decimation */ + +#ifdef FIXED_POINT + maxcorr = +#endif + celt_pitch_xcorr(x_lp4, y_lp4, xcorr, len>>2, max_pitch>>2, arch); + + find_best_pitch(xcorr, y_lp4, len>>2, max_pitch>>2, best_pitch +#ifdef FIXED_POINT + , 0, maxcorr +#endif + ); + + /* Finer search with 2x decimation */ +#ifdef FIXED_POINT + maxcorr=1; +#endif + for (i=0;i>1;i++) + { + opus_val32 sum; + xcorr[i] = 0; + if (abs(i-2*best_pitch[0])>2 && abs(i-2*best_pitch[1])>2) + continue; +#ifdef FIXED_POINT + sum = 0; + for (j=0;j>1;j++) + sum += SHR32(MULT16_16(x_lp[j],y[i+j]), shift); +#else + sum = celt_inner_prod(x_lp, y+i, len>>1, arch); +#endif + xcorr[i] = MAX32(-1, sum); +#ifdef FIXED_POINT + maxcorr = MAX32(maxcorr, sum); +#endif + } + find_best_pitch(xcorr, y, len>>1, max_pitch>>1, best_pitch +#ifdef FIXED_POINT + , shift+1, maxcorr +#endif + ); + + /* Refine by pseudo-interpolation */ + if (best_pitch[0]>0 && best_pitch[0]<(max_pitch>>1)-1) + { + opus_val32 a, b, c; + a = xcorr[best_pitch[0]-1]; + b = xcorr[best_pitch[0]]; + c = xcorr[best_pitch[0]+1]; + if ((c-a) > MULT16_32_Q15(QCONST16(.7f,15),b-a)) + offset = 1; + else if ((a-c) > MULT16_32_Q15(QCONST16(.7f,15),b-c)) + offset = -1; + else + offset = 0; + } else { + offset = 0; + } + *pitch = 2*best_pitch[0]-offset; + + RESTORE_STACK; +} + +#ifdef FIXED_POINT +static opus_val16 compute_pitch_gain(opus_val32 xy, opus_val32 xx, opus_val32 yy) +{ + opus_val32 x2y2; + int sx, sy, shift; + opus_val32 g; + opus_val16 den; + if (xy == 0 || xx == 0 || yy == 0) + return 0; + sx = celt_ilog2(xx)-14; + sy = celt_ilog2(yy)-14; + shift = sx + sy; + x2y2 = SHR32(MULT16_16(VSHR32(xx, sx), VSHR32(yy, sy)), 14); + if (shift & 1) { + if (x2y2 < 32768) + { + x2y2 <<= 1; + shift--; + } else { + x2y2 >>= 1; + shift++; + } + } + den = celt_rsqrt_norm(x2y2); + g = MULT16_32_Q15(den, xy); + g = VSHR32(g, (shift>>1)-1); + return EXTRACT16(MAX32(-Q15ONE, MIN32(g, Q15ONE))); +} +#else +static opus_val16 compute_pitch_gain(opus_val32 xy, opus_val32 xx, opus_val32 yy) +{ + return xy/celt_sqrt(1+xx*yy); +} +#endif + +static const int second_check[16] = {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2}; +opus_val16 remove_doubling(opus_val16 *x, int maxperiod, int minperiod, + int N, int *T0_, int prev_period, opus_val16 prev_gain, int arch) +{ + int k, i, T, T0; + opus_val16 g, g0; + opus_val16 pg; + opus_val32 xy,xx,yy,xy2; + opus_val32 xcorr[3]; + opus_val32 best_xy, best_yy; + int offset; + int minperiod0; + VARDECL(opus_val32, yy_lookup); + SAVE_STACK; + + minperiod0 = minperiod; + maxperiod /= 2; + minperiod /= 2; + *T0_ /= 2; + prev_period /= 2; + N /= 2; + x += maxperiod; + if (*T0_>=maxperiod) + *T0_=maxperiod-1; + + T = T0 = *T0_; + ALLOC(yy_lookup, maxperiod+1, opus_val32); + dual_inner_prod(x, x, x-T0, N, &xx, &xy, arch); + yy_lookup[0] = xx; + yy=xx; + for (i=1;i<=maxperiod;i++) + { + yy = yy+MULT16_16(x[-i],x[-i])-MULT16_16(x[N-i],x[N-i]); + yy_lookup[i] = MAX32(0, yy); + } + yy = yy_lookup[T0]; + best_xy = xy; + best_yy = yy; + g = g0 = compute_pitch_gain(xy, xx, yy); + /* Look for any pitch at T/k */ + for (k=2;k<=15;k++) + { + int T1, T1b; + opus_val16 g1; + opus_val16 cont=0; + opus_val16 thresh; + T1 = celt_udiv(2*T0+k, 2*k); + if (T1 < minperiod) + break; + /* Look for another strong correlation at T1b */ + if (k==2) + { + if (T1+T0>maxperiod) + T1b = T0; + else + T1b = T0+T1; + } else + { + T1b = celt_udiv(2*second_check[k]*T0+k, 2*k); + } + dual_inner_prod(x, &x[-T1], &x[-T1b], N, &xy, &xy2, arch); + xy = HALF32(xy + xy2); + yy = HALF32(yy_lookup[T1] + yy_lookup[T1b]); + g1 = compute_pitch_gain(xy, xx, yy); + if (abs(T1-prev_period)<=1) + cont = prev_gain; + else if (abs(T1-prev_period)<=2 && 5*k*k < T0) + cont = HALF16(prev_gain); + else + cont = 0; + thresh = MAX16(QCONST16(.3f,15), MULT16_16_Q15(QCONST16(.7f,15),g0)-cont); + /* Bias against very high pitch (very short period) to avoid false-positives + due to short-term correlation */ + if (T1<3*minperiod) + thresh = MAX16(QCONST16(.4f,15), MULT16_16_Q15(QCONST16(.85f,15),g0)-cont); + else if (T1<2*minperiod) + thresh = MAX16(QCONST16(.5f,15), MULT16_16_Q15(QCONST16(.9f,15),g0)-cont); + if (g1 > thresh) + { + best_xy = xy; + best_yy = yy; + T = T1; + g = g1; + } + } + best_xy = MAX32(0, best_xy); + if (best_yy <= best_xy) + pg = Q15ONE; + else + pg = SHR32(frac_div32(best_xy,best_yy+1),16); + + for (k=0;k<3;k++) + xcorr[k] = celt_inner_prod(x, x-(T+k-1), N, arch); + if ((xcorr[2]-xcorr[0]) > MULT16_32_Q15(QCONST16(.7f,15),xcorr[1]-xcorr[0])) + offset = 1; + else if ((xcorr[0]-xcorr[2]) > MULT16_32_Q15(QCONST16(.7f,15),xcorr[1]-xcorr[2])) + offset = -1; + else + offset = 0; + if (pg > g) + pg = g; + *T0_ = 2*T+offset; + + if (*T0_=3); + y_3=0; /* gcc doesn't realize that y_3 can't be used uninitialized */ + y_0=*y++; + y_1=*y++; + y_2=*y++; + for (j=0;j +#include "os_support.h" +#include "arch.h" +#include "mathops.h" +#include "stack_alloc.h" +#include "rate.h" + +#ifdef FIXED_POINT +/* Mean energy in each band quantized in Q4 */ +const signed char eMeans[25] = { + 103,100, 92, 85, 81, + 77, 72, 70, 78, 75, + 73, 71, 78, 74, 69, + 72, 70, 74, 76, 71, + 60, 60, 60, 60, 60 +}; +#else +/* Mean energy in each band quantized in Q4 and converted back to float */ +const opus_val16 eMeans[25] = { + 6.437500f, 6.250000f, 5.750000f, 5.312500f, 5.062500f, + 4.812500f, 4.500000f, 4.375000f, 4.875000f, 4.687500f, + 4.562500f, 4.437500f, 4.875000f, 4.625000f, 4.312500f, + 4.500000f, 4.375000f, 4.625000f, 4.750000f, 4.437500f, + 3.750000f, 3.750000f, 3.750000f, 3.750000f, 3.750000f +}; +#endif +/* prediction coefficients: 0.9, 0.8, 0.65, 0.5 */ +#ifdef FIXED_POINT +static const opus_val16 pred_coef[4] = {29440, 26112, 21248, 16384}; +static const opus_val16 beta_coef[4] = {30147, 22282, 12124, 6554}; +static const opus_val16 beta_intra = 4915; +#else +static const opus_val16 pred_coef[4] = {29440/32768., 26112/32768., 21248/32768., 16384/32768.}; +static const opus_val16 beta_coef[4] = {30147/32768., 22282/32768., 12124/32768., 6554/32768.}; +static const opus_val16 beta_intra = 4915/32768.; +#endif + +/*Parameters of the Laplace-like probability models used for the coarse energy. + There is one pair of parameters for each frame size, prediction type + (inter/intra), and band number. + The first number of each pair is the probability of 0, and the second is the + decay rate, both in Q8 precision.*/ +static const unsigned char e_prob_model[4][2][42] = { + /*120 sample frames.*/ + { + /*Inter*/ + { + 72, 127, 65, 129, 66, 128, 65, 128, 64, 128, 62, 128, 64, 128, + 64, 128, 92, 78, 92, 79, 92, 78, 90, 79, 116, 41, 115, 40, + 114, 40, 132, 26, 132, 26, 145, 17, 161, 12, 176, 10, 177, 11 + }, + /*Intra*/ + { + 24, 179, 48, 138, 54, 135, 54, 132, 53, 134, 56, 133, 55, 132, + 55, 132, 61, 114, 70, 96, 74, 88, 75, 88, 87, 74, 89, 66, + 91, 67, 100, 59, 108, 50, 120, 40, 122, 37, 97, 43, 78, 50 + } + }, + /*240 sample frames.*/ + { + /*Inter*/ + { + 83, 78, 84, 81, 88, 75, 86, 74, 87, 71, 90, 73, 93, 74, + 93, 74, 109, 40, 114, 36, 117, 34, 117, 34, 143, 17, 145, 18, + 146, 19, 162, 12, 165, 10, 178, 7, 189, 6, 190, 8, 177, 9 + }, + /*Intra*/ + { + 23, 178, 54, 115, 63, 102, 66, 98, 69, 99, 74, 89, 71, 91, + 73, 91, 78, 89, 86, 80, 92, 66, 93, 64, 102, 59, 103, 60, + 104, 60, 117, 52, 123, 44, 138, 35, 133, 31, 97, 38, 77, 45 + } + }, + /*480 sample frames.*/ + { + /*Inter*/ + { + 61, 90, 93, 60, 105, 42, 107, 41, 110, 45, 116, 38, 113, 38, + 112, 38, 124, 26, 132, 27, 136, 19, 140, 20, 155, 14, 159, 16, + 158, 18, 170, 13, 177, 10, 187, 8, 192, 6, 175, 9, 159, 10 + }, + /*Intra*/ + { + 21, 178, 59, 110, 71, 86, 75, 85, 84, 83, 91, 66, 88, 73, + 87, 72, 92, 75, 98, 72, 105, 58, 107, 54, 115, 52, 114, 55, + 112, 56, 129, 51, 132, 40, 150, 33, 140, 29, 98, 35, 77, 42 + } + }, + /*960 sample frames.*/ + { + /*Inter*/ + { + 42, 121, 96, 66, 108, 43, 111, 40, 117, 44, 123, 32, 120, 36, + 119, 33, 127, 33, 134, 34, 139, 21, 147, 23, 152, 20, 158, 25, + 154, 26, 166, 21, 173, 16, 184, 13, 184, 10, 150, 13, 139, 15 + }, + /*Intra*/ + { + 22, 178, 63, 114, 74, 82, 84, 83, 92, 82, 103, 62, 96, 72, + 96, 67, 101, 73, 107, 72, 113, 55, 118, 52, 125, 52, 118, 52, + 117, 55, 135, 49, 137, 39, 157, 32, 145, 29, 97, 33, 77, 40 + } + } +}; + +static const unsigned char small_energy_icdf[3]={2,1,0}; + +static opus_val32 loss_distortion(const celt_glog *eBands, celt_glog *oldEBands, int start, int end, int len, int C) +{ + int c, i; + opus_val32 dist = 0; + c=0; do { + for (i=start;inbEBands]; + oldE = MAXG(-GCONST(9.f), oldEBands[i+c*m->nbEBands]); +#ifdef FIXED_POINT + f = x - MULT16_32_Q15(coef,oldE) - prev[c]; + /* Rounding to nearest integer here is really important! */ + qi = (f+QCONST32(.5f,DB_SHIFT))>>DB_SHIFT; + decay_bound = MAXG(-GCONST(28.f), SUB32((opus_val32)oldEBands[i+c*m->nbEBands],max_decay)); +#else + f = x-coef*oldE-prev[c]; + /* Rounding to nearest integer here is really important! */ + qi = (int)floor(.5f+f); + decay_bound = MAXG(-GCONST(28.f), oldEBands[i+c*m->nbEBands]) - max_decay; +#endif + /* Prevent the energy from going down too quickly (e.g. for bands + that have just one bin) */ + if (qi < 0 && x < decay_bound) + { + qi += (int)SHR32(SUB32(decay_bound,x), DB_SHIFT); + if (qi > 0) + qi = 0; + } + qi0 = qi; + /* If we don't have enough bits to encode all the energy, just assume + something safe. */ + tell = ec_tell(enc); + bits_left = budget-tell-3*C*(end-i); + if (i!=start && bits_left < 30) + { + if (bits_left < 24) + qi = IMIN(1, qi); + if (bits_left < 16) + qi = IMAX(-1, qi); + } + if (lfe && i>=2) + qi = IMIN(qi, 0); + if (budget-tell >= 15) + { + int pi; + pi = 2*IMIN(i,20); + ec_laplace_encode(enc, &qi, + prob_model[pi]<<7, prob_model[pi+1]<<6); + } + else if(budget-tell >= 2) + { + qi = IMAX(-1, IMIN(qi, 1)); + ec_enc_icdf(enc, 2*qi^-(qi<0), small_energy_icdf, 2); + } + else if(budget-tell >= 1) + { + qi = IMIN(0, qi); + ec_enc_bit_logp(enc, -qi, 1); + } + else + qi = -1; + error[i+c*m->nbEBands] = f - SHL32(qi,DB_SHIFT); + badness += abs(qi0-qi); + q = (opus_val32)SHL32(EXTEND32(qi),DB_SHIFT); + + tmp = MULT16_32_Q15(coef,oldE) + prev[c] + q; +#ifdef FIXED_POINT + tmp = MAX32(-GCONST(28.f), tmp); +#endif + oldEBands[i+c*m->nbEBands] = tmp; + prev[c] = prev[c] + q - MULT16_32_Q15(beta,q); + } while (++c < C); + } + return lfe ? 0 : badness; +} + +void quant_coarse_energy(const CELTMode *m, int start, int end, int effEnd, + const celt_glog *eBands, celt_glog *oldEBands, opus_uint32 budget, + celt_glog *error, ec_enc *enc, int C, int LM, int nbAvailableBytes, + int force_intra, opus_val32 *delayedIntra, int two_pass, int loss_rate, int lfe) +{ + int intra; + celt_glog max_decay; + VARDECL(celt_glog, oldEBands_intra); + VARDECL(celt_glog, error_intra); + ec_enc enc_start_state; + opus_uint32 tell; + int badness1=0; + opus_int32 intra_bias; + opus_val32 new_distortion; + SAVE_STACK; + + intra = force_intra || (!two_pass && *delayedIntra>2*C*(end-start) && nbAvailableBytes > (end-start)*C); + intra_bias = (opus_int32)((budget**delayedIntra*loss_rate)/(C*512)); + new_distortion = loss_distortion(eBands, oldEBands, start, effEnd, m->nbEBands, C); + + tell = ec_tell(enc); + if (tell+3 > budget) + two_pass = intra = 0; + + max_decay = GCONST(16.f); + if (end-start>10) + { +#ifdef FIXED_POINT + max_decay = SHL32(MIN32(SHR32(max_decay,DB_SHIFT-3), EXTEND32(nbAvailableBytes)),DB_SHIFT-3); +#else + max_decay = MIN32(max_decay, .125f*nbAvailableBytes); +#endif + } + if (lfe) + max_decay = GCONST(3.f); + enc_start_state = *enc; + + ALLOC(oldEBands_intra, C*m->nbEBands, celt_glog); + ALLOC(error_intra, C*m->nbEBands, celt_glog); + OPUS_COPY(oldEBands_intra, oldEBands, C*m->nbEBands); + + if (two_pass || intra) + { + badness1 = quant_coarse_energy_impl(m, start, end, eBands, oldEBands_intra, budget, + tell, e_prob_model[LM][1], error_intra, enc, C, LM, 1, max_decay, lfe); + } + + if (!intra) + { + unsigned char *intra_buf; + ec_enc enc_intra_state; + opus_int32 tell_intra; + opus_uint32 nstart_bytes; + opus_uint32 nintra_bytes; + opus_uint32 save_bytes; + int badness2; + VARDECL(unsigned char, intra_bits); + + tell_intra = ec_tell_frac(enc); + + enc_intra_state = *enc; + + nstart_bytes = ec_range_bytes(&enc_start_state); + nintra_bytes = ec_range_bytes(&enc_intra_state); + intra_buf = ec_get_buffer(&enc_intra_state) + nstart_bytes; + save_bytes = nintra_bytes-nstart_bytes; + if (save_bytes == 0) + save_bytes = ALLOC_NONE; + ALLOC(intra_bits, save_bytes, unsigned char); + /* Copy bits from intra bit-stream */ + OPUS_COPY(intra_bits, intra_buf, nintra_bytes - nstart_bytes); + + *enc = enc_start_state; + + badness2 = quant_coarse_energy_impl(m, start, end, eBands, oldEBands, budget, + tell, e_prob_model[LM][intra], error, enc, C, LM, 0, max_decay, lfe); + + if (two_pass && (badness1 < badness2 || (badness1 == badness2 && ((opus_int32)ec_tell_frac(enc))+intra_bias > tell_intra))) + { + *enc = enc_intra_state; + /* Copy intra bits to bit-stream */ + OPUS_COPY(intra_buf, intra_bits, nintra_bytes - nstart_bytes); + OPUS_COPY(oldEBands, oldEBands_intra, C*m->nbEBands); + OPUS_COPY(error, error_intra, C*m->nbEBands); + intra = 1; + } + } else { + OPUS_COPY(oldEBands, oldEBands_intra, C*m->nbEBands); + OPUS_COPY(error, error_intra, C*m->nbEBands); + } + + if (intra) + *delayedIntra = new_distortion; + else + *delayedIntra = ADD32(MULT16_32_Q15(MULT16_16_Q15(pred_coef[LM], pred_coef[LM]),*delayedIntra), + new_distortion); + + RESTORE_STACK; +} + +void quant_fine_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, celt_glog *error, int *fine_quant, ec_enc *enc, int C) +{ + int i, c; + + /* Encode finer resolution */ + for (i=start;inbEBands]+GCONST(.5f))>>(DB_SHIFT-fine_quant[i]); +#else + q2 = (int)floor((error[i+c*m->nbEBands]+.5f)*frac); +#endif + if (q2 > frac-1) + q2 = frac-1; + if (q2<0) + q2 = 0; + ec_enc_bits(enc, q2, fine_quant[i]); +#ifdef FIXED_POINT + offset = SUB32(VSHR32(2*q2+1, fine_quant[i]-DB_SHIFT+1), GCONST(.5f)); +#else + offset = (q2+.5f)*(1<<(14-fine_quant[i]))*(1.f/16384) - .5f; +#endif + oldEBands[i+c*m->nbEBands] += offset; + error[i+c*m->nbEBands] -= offset; + /*printf ("%f ", error[i] - offset);*/ + } while (++c < C); + } +} + +void quant_energy_finalise(const CELTMode *m, int start, int end, celt_glog *oldEBands, celt_glog *error, int *fine_quant, int *fine_priority, int bits_left, ec_enc *enc, int C) +{ + int i, prio, c; + + /* Use up the remaining bits */ + for (prio=0;prio<2;prio++) + { + for (i=start;i=C ;i++) + { + if (fine_quant[i] >= MAX_FINE_BITS || fine_priority[i]!=prio) + continue; + c=0; + do { + int q2; + celt_glog offset; + q2 = error[i+c*m->nbEBands]<0 ? 0 : 1; + ec_enc_bits(enc, q2, 1); +#ifdef FIXED_POINT + offset = SHR32(SHL32(q2,DB_SHIFT)-GCONST(.5f),fine_quant[i]+1); +#else + offset = (q2-.5f)*(1<<(14-fine_quant[i]-1))*(1.f/16384); +#endif + oldEBands[i+c*m->nbEBands] += offset; + error[i+c*m->nbEBands] -= offset; + bits_left--; + } while (++c < C); + } + } +} + +void unquant_coarse_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, int intra, ec_dec *dec, int C, int LM) +{ + const unsigned char *prob_model = e_prob_model[LM][intra]; + int i, c; + opus_val64 prev[2] = {0, 0}; + opus_val16 coef; + opus_val16 beta; + opus_int32 budget; + opus_int32 tell; + + if (intra) + { + coef = 0; + beta = beta_intra; + } else { + beta = beta_coef[LM]; + coef = pred_coef[LM]; + } + + budget = dec->storage*8; + + /* Decode at a fixed coarse resolution */ + for (i=start;i=15) + { + int pi; + pi = 2*IMIN(i,20); + qi = ec_laplace_decode(dec, + prob_model[pi]<<7, prob_model[pi+1]<<6); + } + else if(budget-tell>=2) + { + qi = ec_dec_icdf(dec, small_energy_icdf, 2); + qi = (qi>>1)^-(qi&1); + } + else if(budget-tell>=1) + { + qi = -ec_dec_bit_logp(dec, 1); + } + else + qi = -1; + q = (opus_val32)SHL32(EXTEND32(qi),DB_SHIFT); + + oldEBands[i+c*m->nbEBands] = MAXG(-GCONST(9.f), oldEBands[i+c*m->nbEBands]); + tmp = MULT16_32_Q15(coef,oldEBands[i+c*m->nbEBands]) + prev[c] + q; +#ifdef FIXED_POINT + tmp = MIN32(GCONST(28.f), MAX32(-GCONST(28.f), tmp)); +#endif + oldEBands[i+c*m->nbEBands] = tmp; + prev[c] = prev[c] + q - MULT16_32_Q15(beta,q); + } while (++c < C); + } +} + +void unquant_fine_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, int *fine_quant, ec_dec *dec, int C) +{ + int i, c; + /* Decode finer resolution */ + for (i=start;inbEBands] += offset; + } while (++c < C); + } +} + +void unquant_energy_finalise(const CELTMode *m, int start, int end, celt_glog *oldEBands, int *fine_quant, int *fine_priority, int bits_left, ec_dec *dec, int C) +{ + int i, prio, c; + + /* Use up the remaining bits */ + for (prio=0;prio<2;prio++) + { + for (i=start;i=C ;i++) + { + if (fine_quant[i] >= MAX_FINE_BITS || fine_priority[i]!=prio) + continue; + c=0; + do { + int q2; + celt_glog offset; + q2 = ec_dec_bits(dec, 1); +#ifdef FIXED_POINT + offset = SHR32(SHL32(q2,DB_SHIFT)-GCONST(.5f),fine_quant[i]+1); +#else + offset = (q2-.5f)*(1<<(14-fine_quant[i]-1))*(1.f/16384); +#endif + oldEBands[i+c*m->nbEBands] += offset; + bits_left--; + } while (++c < C); + } + } +} + +void amp2Log2(const CELTMode *m, int effEnd, int end, + celt_ener *bandE, celt_glog *bandLogE, int C) +{ + int c, i; + c=0; + do { + for (i=0;inbEBands] = + celt_log2_db(bandE[i+c*m->nbEBands]) + - SHL32((celt_glog)eMeans[i],DB_SHIFT-4); +#ifdef FIXED_POINT + /* Compensate for bandE[] being Q12 but celt_log2() taking a Q14 input. */ + bandLogE[i+c*m->nbEBands] += GCONST(2.f); +#endif + } + for (i=effEnd;inbEBands+i] = -GCONST(14.f); + } while (++c < C); +} diff --git a/managed_components/78__esp-opus/celt/quant_bands.h b/managed_components/78__esp-opus/celt/quant_bands.h new file mode 100644 index 0000000..09b8109 --- /dev/null +++ b/managed_components/78__esp-opus/celt/quant_bands.h @@ -0,0 +1,66 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef QUANT_BANDS +#define QUANT_BANDS + +#include "arch.h" +#include "modes.h" +#include "entenc.h" +#include "entdec.h" +#include "mathops.h" + +#ifdef FIXED_POINT +extern const signed char eMeans[25]; +#else +extern const opus_val16 eMeans[25]; +#endif + +void amp2Log2(const CELTMode *m, int effEnd, int end, + celt_ener *bandE, celt_glog *bandLogE, int C); + +void log2Amp(const CELTMode *m, int start, int end, + celt_ener *eBands, const celt_glog *oldEBands, int C); + +void quant_coarse_energy(const CELTMode *m, int start, int end, int effEnd, + const celt_glog *eBands, celt_glog *oldEBands, opus_uint32 budget, + celt_glog *error, ec_enc *enc, int C, int LM, + int nbAvailableBytes, int force_intra, opus_val32 *delayedIntra, + int two_pass, int loss_rate, int lfe); + +void quant_fine_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, celt_glog *error, int *fine_quant, ec_enc *enc, int C); + +void quant_energy_finalise(const CELTMode *m, int start, int end, celt_glog *oldEBands, celt_glog *error, int *fine_quant, int *fine_priority, int bits_left, ec_enc *enc, int C); + +void unquant_coarse_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, int intra, ec_dec *dec, int C, int LM); + +void unquant_fine_energy(const CELTMode *m, int start, int end, celt_glog *oldEBands, int *fine_quant, ec_dec *dec, int C); + +void unquant_energy_finalise(const CELTMode *m, int start, int end, celt_glog *oldEBands, int *fine_quant, int *fine_priority, int bits_left, ec_dec *dec, int C); + +#endif /* QUANT_BANDS */ diff --git a/managed_components/78__esp-opus/celt/rate.c b/managed_components/78__esp-opus/celt/rate.c new file mode 100644 index 0000000..58a61f7 --- /dev/null +++ b/managed_components/78__esp-opus/celt/rate.c @@ -0,0 +1,646 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include "modes.h" +#include "cwrs.h" +#include "arch.h" +#include "os_support.h" + +#include "entcode.h" +#include "rate.h" + +static const unsigned char LOG2_FRAC_TABLE[24]={ + 0, + 8,13, + 16,19,21,23, + 24,26,27,28,29,30,31,32, + 32,33,34,34,35,36,36,37,37 +}; + +#ifdef CUSTOM_MODES + +/*Determines if V(N,K) fits in a 32-bit unsigned integer. + N and K are themselves limited to 15 bits.*/ +static int fits_in32(int _n, int _k) +{ + static const opus_int16 maxN[15] = { + 32767, 32767, 32767, 1476, 283, 109, 60, 40, + 29, 24, 20, 18, 16, 14, 13}; + static const opus_int16 maxK[15] = { + 32767, 32767, 32767, 32767, 1172, 238, 95, 53, + 36, 27, 22, 18, 16, 15, 13}; + if (_n>=14) + { + if (_k>=14) + return 0; + else + return _n <= maxN[_k]; + } else { + return _k <= maxK[_n]; + } +} + +void compute_pulse_cache(CELTMode *m, int LM) +{ + int C; + int i; + int j; + int curr=0; + int nbEntries=0; + int entryN[100], entryK[100], entryI[100]; + const opus_int16 *eBands = m->eBands; + PulseCache *cache = &m->cache; + opus_int16 *cindex; + unsigned char *bits; + unsigned char *cap; + + cindex = (opus_int16 *)opus_alloc(sizeof(cache->index[0])*m->nbEBands*(LM+2)); + cache->index = cindex; + + /* Scan for all unique band sizes */ + for (i=0;i<=LM+1;i++) + { + for (j=0;jnbEBands;j++) + { + int k; + int N = (eBands[j+1]-eBands[j])<>1; + cindex[i*m->nbEBands+j] = -1; + /* Find other bands that have the same size */ + for (k=0;k<=i;k++) + { + int n; + for (n=0;nnbEBands && (k!=i || n>1) + { + cindex[i*m->nbEBands+j] = cindex[k*m->nbEBands+n]; + break; + } + } + } + if (cache->index[i*m->nbEBands+j] == -1 && N!=0) + { + int K; + entryN[nbEntries] = N; + K = 0; + while (fits_in32(N,get_pulses(K+1)) && KnbEBands+j] = curr; + entryI[nbEntries] = curr; + + curr += K+1; + nbEntries++; + } + } + } + bits = (unsigned char *)opus_alloc(sizeof(unsigned char)*curr); + cache->bits = bits; + cache->size = curr; + /* Compute the cache for all unique sizes */ + for (i=0;icaps = cap = (unsigned char *)opus_alloc(sizeof(cache->caps[0])*(LM+1)*2*m->nbEBands); + for (i=0;i<=LM;i++) + { + for (C=1;C<=2;C++) + { + for (j=0;jnbEBands;j++) + { + int N0; + int max_bits; + N0 = m->eBands[j+1]-m->eBands[j]; + /* N=1 bands only have a sign bit and fine bits. */ + if (N0<1 are even, including custom modes.*/ + if (N0 > 2) + { + N0>>=1; + LM0--; + } + /* N0=1 bands can't be split down to N<2. */ + else if (N0 <= 1) + { + LM0=IMIN(i,1); + N0<<=LM0; + } + /* Compute the cost for the lowest-level PVQ of a fully split + band. */ + pcache = bits + cindex[(LM0+1)*m->nbEBands+j]; + max_bits = pcache[pcache[0]]+1; + /* Add in the cost of coding regular splits. */ + N = N0; + for(k=0;klogN[j]+(opus_int32)((opus_uint32)(LM0+k)<>1)-QTHETA_OFFSET; + /* The number of qtheta bits we'll allocate if the remainder + is to be max_bits. + The average measured cost for theta is 0.89701 times qb, + approximated here as 459/512. */ + num=459*(opus_int32)((2*N-1)*offset+max_bits); + den=((opus_int32)(2*N-1)<<9)-459; + qb = IMIN((num+(den>>1))/den, 57); + celt_assert(qb >= 0); + max_bits += qb; + N <<= 1; + } + /* Add in the cost of a stereo split, if necessary. */ + if (C==2) + { + max_bits <<= 1; + offset = ((m->logN[j]+(i<>1)-(N==2?QTHETA_OFFSET_TWOPHASE:QTHETA_OFFSET); + ndof = 2*N-1-(N==2); + /* The average measured cost for theta with the step PDF is + 0.95164 times qb, approximated here as 487/512. */ + num = (N==2?512:487)*(opus_int32)(max_bits+ndof*offset); + den = ((opus_int32)ndof<<9)-(N==2?512:487); + qb = IMIN((num+(den>>1))/den, (N==2?64:61)); + celt_assert(qb >= 0); + max_bits += qb; + } + /* Add the fine bits we'll use. */ + /* Compensate for the extra DoF in stereo */ + ndof = C*N + ((C==2 && N>2) ? 1 : 0); + /* Offset the number of fine bits by log2(N)/2 + FINE_OFFSET + compared to their "fair share" of total/N */ + offset = ((m->logN[j] + (i<>1)-FINE_OFFSET; + /* N=2 is the only point that doesn't match the curve */ + if (N==2) + offset += 1<>2; + /* The number of fine bits we'll allocate if the remainder is + to be max_bits. */ + num = max_bits+ndof*offset; + den = (ndof-1)<>1))/den, MAX_FINE_BITS); + celt_assert(qb >= 0); + max_bits += C*qb<eBands[j+1]-m->eBands[j])<= 0); + celt_assert(max_bits < 256); + *cap++ = (unsigned char)max_bits; + } + } + } +} + +#endif /* CUSTOM_MODES */ + +#define ALLOC_STEPS 6 + +static OPUS_INLINE int interp_bits2pulses(const CELTMode *m, int start, int end, int skip_start, + const int *bits1, const int *bits2, const int *thresh, const int *cap, opus_int32 total, opus_int32 *_balance, + int skip_rsv, int *intensity, int intensity_rsv, int *dual_stereo, int dual_stereo_rsv, int *bits, + int *ebits, int *fine_priority, int C, int LM, ec_ctx *ec, int encode, int prev, int signalBandwidth) +{ + opus_int32 psum; + int lo, hi; + int i, j; + int logM; + int stereo; + int codedBands=-1; + int alloc_floor; + opus_int32 left, percoeff; + int done; + opus_int32 balance; + SAVE_STACK; + + alloc_floor = C<1; + + logM = LM<>1; + psum = 0; + done = 0; + for (j=end;j-->start;) + { + int tmp = bits1[j] + (mid*(opus_int32)bits2[j]>>ALLOC_STEPS); + if (tmp >= thresh[j] || done) + { + done = 1; + /* Don't allocate more than we can actually use */ + psum += IMIN(tmp, cap[j]); + } else { + if (tmp >= alloc_floor) + psum += alloc_floor; + } + } + if (psum > total) + hi = mid; + else + lo = mid; + } + psum = 0; + /*printf ("interp bisection gave %d\n", lo);*/ + done = 0; + for (j=end;j-->start;) + { + int tmp = bits1[j] + ((opus_int32)lo*bits2[j]>>ALLOC_STEPS); + if (tmp < thresh[j] && !done) + { + if (tmp >= alloc_floor) + tmp = alloc_floor; + else + tmp = 0; + } else + done = 1; + /* Don't allocate more than we can actually use */ + tmp = IMIN(tmp, cap[j]); + bits[j] = tmp; + psum += tmp; + } + + /* Decide which bands to skip, working backwards from the end. */ + for (codedBands=end;;codedBands--) + { + int band_width; + int band_bits; + int rem; + j = codedBands-1; + /* Never skip the first band, nor a band that has been boosted by + dynalloc. + In the first case, we'd be coding a bit to signal we're going to waste + all the other bits. + In the second case, we'd be coding a bit to redistribute all the bits + we just signaled should be cocentrated in this band. */ + if (j<=skip_start) + { + /* Give the bit we reserved to end skipping back. */ + total += skip_rsv; + break; + } + /*Figure out how many left-over bits we would be adding to this band. + This can include bits we've stolen back from higher, skipped bands.*/ + left = total-psum; + percoeff = celt_udiv(left, m->eBands[codedBands]-m->eBands[start]); + left -= (m->eBands[codedBands]-m->eBands[start])*percoeff; + rem = IMAX(left-(m->eBands[j]-m->eBands[start]),0); + band_width = m->eBands[codedBands]-m->eBands[j]; + band_bits = (int)(bits[j] + percoeff*band_width + rem); + /*Only code a skip decision if we're above the threshold for this band. + Otherwise it is force-skipped. + This ensures that we have enough bits to code the skip flag.*/ + if (band_bits >= IMAX(thresh[j], alloc_floor+(1< 17) + depth_threshold = j (depth_threshold*band_width<>4 && j<=signalBandwidth)) +#endif + { + ec_enc_bit_logp(ec, 1, 1); + break; + } + ec_enc_bit_logp(ec, 0, 1); + } else if (ec_dec_bit_logp(ec, 1)) { + break; + } + /*We used a bit to skip this band.*/ + psum += 1< 0) + intensity_rsv = LOG2_FRAC_TABLE[j-start]; + psum += intensity_rsv; + if (band_bits >= alloc_floor) + { + /*If we have enough for a fine energy bit per channel, use it.*/ + psum += alloc_floor; + bits[j] = alloc_floor; + } else { + /*Otherwise this band gets nothing at all.*/ + bits[j] = 0; + } + } + + celt_assert(codedBands > start); + /* Code the intensity and dual stereo parameters. */ + if (intensity_rsv > 0) + { + if (encode) + { + *intensity = IMIN(*intensity, codedBands); + ec_enc_uint(ec, *intensity-start, codedBands+1-start); + } + else + *intensity = start+ec_dec_uint(ec, codedBands+1-start); + } + else + *intensity = 0; + if (*intensity <= start) + { + total += dual_stereo_rsv; + dual_stereo_rsv = 0; + } + if (dual_stereo_rsv > 0) + { + if (encode) + ec_enc_bit_logp(ec, *dual_stereo, 1); + else + *dual_stereo = ec_dec_bit_logp(ec, 1); + } + else + *dual_stereo = 0; + + /* Allocate the remaining bits */ + left = total-psum; + percoeff = celt_udiv(left, m->eBands[codedBands]-m->eBands[start]); + left -= (m->eBands[codedBands]-m->eBands[start])*percoeff; + for (j=start;jeBands[j+1]-m->eBands[j])); + for (j=start;jeBands[j+1]-m->eBands[j]); + bits[j] += tmp; + left -= tmp; + } + /*for (j=0;j= 0); + N0 = m->eBands[j+1]-m->eBands[j]; + N=N0<1) + { + excess = MAX32(bit-cap[j],0); + bits[j] = bit-excess; + + /* Compensate for the extra DoF in stereo */ + den=(C*N+ ((C==2 && N>2 && !*dual_stereo && j<*intensity) ? 1 : 0)); + + NClogN = den*(m->logN[j] + logM); + + /* Offset for the number of fine bits by log2(N)/2 + FINE_OFFSET + compared to their "fair share" of total/N */ + offset = (NClogN>>1)-den*FINE_OFFSET; + + /* N=2 is the only point that doesn't match the curve */ + if (N==2) + offset += den<>2; + + /* Changing the offset for allocating the second and third + fine energy bit */ + if (bits[j] + offset < den*2<>2; + else if (bits[j] + offset < den*3<>3; + + /* Divide with rounding */ + ebits[j] = IMAX(0, (bits[j] + offset + (den<<(BITRES-1)))); + ebits[j] = celt_udiv(ebits[j], den)>>BITRES; + + /* Make sure not to bust */ + if (C*ebits[j] > (bits[j]>>BITRES)) + ebits[j] = bits[j] >> stereo >> BITRES; + + /* More than that is useless because that's about as far as PVQ can go */ + ebits[j] = IMIN(ebits[j], MAX_FINE_BITS); + + /* If we rounded down or capped this band, make it a candidate for the + final fine energy pass */ + fine_priority[j] = ebits[j]*(den<= bits[j]+offset; + + /* Remove the allocated fine bits; the rest are assigned to PVQ */ + bits[j] -= C*ebits[j]< 0) + { + int extra_fine; + int extra_bits; + extra_fine = IMIN(excess>>(stereo+BITRES),MAX_FINE_BITS-ebits[j]); + ebits[j] += extra_fine; + extra_bits = extra_fine*C<= excess-balance; + excess -= extra_bits; + } + balance = excess; + + celt_assert(bits[j] >= 0); + celt_assert(ebits[j] >= 0); + } + /* Save any remaining bits over the cap for the rebalancing in + quant_all_bands(). */ + *_balance = balance; + + /* The skipped bands use all their bits for fine energy. */ + for (;j> stereo >> BITRES; + celt_assert(C*ebits[j]<nbEBands; + skip_start = start; + /* Reserve a bit to signal the end of manually skipped bands. */ + skip_rsv = total >= 1<total) + intensity_rsv = 0; + else + { + total -= intensity_rsv; + dual_stereo_rsv = total>=1<eBands[j+1]-m->eBands[j])<>4); + /* Tilt of the allocation curve */ + trim_offset[j] = C*(m->eBands[j+1]-m->eBands[j])*(alloc_trim-5-LM)*(end-j-1) + *(1<<(LM+BITRES))>>6; + /* Giving less resolution to single-coefficient bands because they get + more benefit from having one coarse value per coefficient*/ + if ((m->eBands[j+1]-m->eBands[j])<nbAllocVectors - 1; + do + { + int done = 0; + int psum = 0; + int mid = (lo+hi) >> 1; + for (j=end;j-->start;) + { + int bitsj; + int N = m->eBands[j+1]-m->eBands[j]; + bitsj = C*N*m->allocVectors[mid*len+j]<>2; + if (bitsj > 0) + bitsj = IMAX(0, bitsj + trim_offset[j]); + bitsj += offsets[j]; + if (bitsj >= thresh[j] || done) + { + done = 1; + /* Don't allocate more than we can actually use */ + psum += IMIN(bitsj, cap[j]); + } else { + if (bitsj >= C< total) + hi = mid - 1; + else + lo = mid + 1; + /*printf ("lo = %d, hi = %d\n", lo, hi);*/ + } + while (lo <= hi); + hi = lo--; + /*printf ("interp between %d and %d\n", lo, hi);*/ + for (j=start;jeBands[j+1]-m->eBands[j]; + bits1j = C*N*m->allocVectors[lo*len+j]<>2; + bits2j = hi>=m->nbAllocVectors ? + cap[j] : C*N*m->allocVectors[hi*len+j]<>2; + if (bits1j > 0) + bits1j = IMAX(0, bits1j + trim_offset[j]); + if (bits2j > 0) + bits2j = IMAX(0, bits2j + trim_offset[j]); + if (lo > 0) + bits1j += offsets[j]; + bits2j += offsets[j]; + if (offsets[j]>0) + skip_start = j; + bits2j = IMAX(0,bits2j-bits1j); + bits1[j] = bits1j; + bits2[j] = bits2j; + } + codedBands = interp_bits2pulses(m, start, end, skip_start, bits1, bits2, thresh, cap, + total, balance, skip_rsv, intensity, intensity_rsv, dual_stereo, dual_stereo_rsv, + pulses, ebits, fine_priority, C, LM, ec, encode, prev, signalBandwidth); + RESTORE_STACK; + return codedBands; +} + diff --git a/managed_components/78__esp-opus/celt/rate.h b/managed_components/78__esp-opus/celt/rate.h new file mode 100644 index 0000000..fad5e41 --- /dev/null +++ b/managed_components/78__esp-opus/celt/rate.h @@ -0,0 +1,101 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef RATE_H +#define RATE_H + +#define MAX_PSEUDO 40 +#define LOG_MAX_PSEUDO 6 + +#define CELT_MAX_PULSES 128 + +#define MAX_FINE_BITS 8 + +#define FINE_OFFSET 21 +#define QTHETA_OFFSET 4 +#define QTHETA_OFFSET_TWOPHASE 16 + +#include "cwrs.h" +#include "modes.h" + +void compute_pulse_cache(CELTMode *m, int LM); + +static OPUS_INLINE int get_pulses(int i) +{ + return i<8 ? i : (8 + (i&7)) << ((i>>3)-1); +} + +static OPUS_INLINE int bits2pulses(const CELTMode *m, int band, int LM, int bits) +{ + int i; + int lo, hi; + const unsigned char *cache; + + LM++; + cache = m->cache.bits + m->cache.index[LM*m->nbEBands+band]; + + lo = 0; + hi = cache[0]; + bits--; + for (i=0;i>1; + /* OPT: Make sure this is implemented with a conditional move */ + if ((int)cache[mid] >= bits) + hi = mid; + else + lo = mid; + } + if (bits- (lo == 0 ? -1 : (int)cache[lo]) <= (int)cache[hi]-bits) + return lo; + else + return hi; +} + +static OPUS_INLINE int pulses2bits(const CELTMode *m, int band, int LM, int pulses) +{ + const unsigned char *cache; + + LM++; + cache = m->cache.bits + m->cache.index[LM*m->nbEBands+band]; + return pulses == 0 ? 0 : cache[pulses]+1; +} + +/** Compute the pulse allocation, i.e. how many pulses will go in each + * band. + @param m mode + @param offsets Requested increase or decrease in the number of bits for + each band + @param total Number of bands + @param pulses Number of pulses per band (returned) + @return Total number of bits allocated +*/ +int clt_compute_allocation(const CELTMode *m, int start, int end, const int *offsets, const int *cap, int alloc_trim, int *intensity, int *dual_stereo, + opus_int32 total, opus_int32 *balance, int *pulses, int *ebits, int *fine_priority, int C, int LM, ec_ctx *ec, int encode, int prev, int signalBandwidth); + +#endif diff --git a/managed_components/78__esp-opus/celt/stack_alloc.h b/managed_components/78__esp-opus/celt/stack_alloc.h new file mode 100644 index 0000000..550711c --- /dev/null +++ b/managed_components/78__esp-opus/celt/stack_alloc.h @@ -0,0 +1,185 @@ +/* Copyright (C) 2002-2003 Jean-Marc Valin + Copyright (C) 2007-2009 Xiph.Org Foundation */ +/** + @file stack_alloc.h + @brief Temporary memory allocation on stack +*/ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef STACK_ALLOC_H +#define STACK_ALLOC_H + +#include "opus_types.h" +#include "opus_defines.h" + +#if (!defined (VAR_ARRAYS) && !defined (USE_ALLOCA) && !defined (NONTHREADSAFE_PSEUDOSTACK)) +#error "Opus requires one of VAR_ARRAYS, USE_ALLOCA, or NONTHREADSAFE_PSEUDOSTACK be defined to select the temporary allocation mode." +#endif + +#ifdef USE_ALLOCA +# ifdef _WIN32 +# include +# else +# ifdef HAVE_ALLOCA_H +# include +# else +# include +# endif +# endif +#endif + +/** + * @def ALIGN(stack, size) + * + * Aligns the stack to a 'size' boundary + * + * @param stack Stack + * @param size New size boundary + */ + +/** + * @def PUSH(stack, size, type) + * + * Allocates 'size' elements of type 'type' on the stack + * + * @param stack Stack + * @param size Number of elements + * @param type Type of element + */ + +/** + * @def VARDECL(var) + * + * Declare variable on stack + * + * @param var Variable to declare + */ + +/** + * @def ALLOC(var, size, type) + * + * Allocate 'size' elements of 'type' on stack + * + * @param var Name of variable to allocate + * @param size Number of elements + * @param type Type of element + */ + +#if defined(VAR_ARRAYS) + +#define VARDECL(type, var) +#define ALLOC(var, size, type) type var[size] +#define SAVE_STACK +#define RESTORE_STACK +#define ALLOC_STACK +/* C99 does not allow VLAs of size zero */ +#define ALLOC_NONE 1 + +#elif defined(USE_ALLOCA) + +#define VARDECL(type, var) type *var + +# ifdef _WIN32 +# define ALLOC(var, size, type) var = ((type*)_alloca(sizeof(type)*(size))) +# else +# define ALLOC(var, size, type) var = ((type*)alloca(sizeof(type)*(size))) +# endif + +#define SAVE_STACK +#define RESTORE_STACK +#define ALLOC_STACK +#define ALLOC_NONE 0 + +#else + +#ifdef CELT_C +char *scratch_ptr=0; +char *global_stack=0; +#else +extern char *global_stack; +extern char *scratch_ptr; +#endif /* CELT_C */ + +#ifdef ENABLE_VALGRIND + +#include + +#ifdef CELT_C +char *global_stack_top=0; +#else +extern char *global_stack_top; +#endif /* CELT_C */ + +#define ALIGN(stack, size) ((stack) += ((size) - (long)(stack)) & ((size) - 1)) +#define PUSH(stack, size, type) (VALGRIND_MAKE_MEM_NOACCESS(stack, global_stack_top-stack),ALIGN((stack),sizeof(type)/sizeof(char)),VALGRIND_MAKE_MEM_UNDEFINED(stack, ((size)*sizeof(type)/sizeof(char))),(stack)+=(2*(size)*sizeof(type)/sizeof(char)),(type*)((stack)-(2*(size)*sizeof(type)/sizeof(char)))) +#define RESTORE_STACK ((global_stack = _saved_stack),VALGRIND_MAKE_MEM_NOACCESS(global_stack, global_stack_top-global_stack)) +#define ALLOC_STACK char *_saved_stack; ((global_stack = (global_stack==0) ? ((global_stack_top=opus_alloc_scratch(GLOBAL_STACK_SIZE*2)+(GLOBAL_STACK_SIZE*2))-(GLOBAL_STACK_SIZE*2)) : global_stack),VALGRIND_MAKE_MEM_NOACCESS(global_stack, global_stack_top-global_stack)); _saved_stack = global_stack; + +#else + +#include "arch.h" +#define ALIGN(stack, size) ((stack) += ((size) - (long)(stack)) & ((size) - 1)) +#define PUSH(stack, size, type) (ALIGN((stack),sizeof(type)/(sizeof(char))),(void)(((int)((size)*(sizeof(type)/(sizeof(char)))) <= (scratch_ptr)+GLOBAL_STACK_SIZE-(stack))?0:CELT_FATAL("pseudostack overflow")),(stack)+=(size)*(sizeof(type)/(sizeof(char))),(type*)((stack)-(size)*(sizeof(type)/(sizeof(char))))) +#if 0 /* Set this to 1 to instrument pseudostack usage */ +#define RESTORE_STACK (printf("%ld %s:%d\n", global_stack-scratch_ptr, __FILE__, __LINE__),global_stack = _saved_stack) +#else +#define RESTORE_STACK (global_stack = _saved_stack) +#endif +#define ALLOC_STACK char *_saved_stack; (global_stack = (global_stack==0) ? (scratch_ptr=opus_alloc_scratch(GLOBAL_STACK_SIZE)) : global_stack); _saved_stack = global_stack; + +#endif /* ENABLE_VALGRIND */ + +#include "os_support.h" +#define VARDECL(type, var) type *var +#define ALLOC(var, size, type) var = PUSH(global_stack, size, type) +#define SAVE_STACK char *_saved_stack = global_stack; +#define ALLOC_NONE 0 + +#endif /* VAR_ARRAYS */ + + +#ifdef ENABLE_VALGRIND + +#include +#define OPUS_CHECK_ARRAY(ptr, len) VALGRIND_CHECK_MEM_IS_DEFINED(ptr, len*sizeof(*ptr)) +#define OPUS_CHECK_VALUE(value) VALGRIND_CHECK_VALUE_IS_DEFINED(value) +#define OPUS_CHECK_ARRAY_COND(ptr, len) VALGRIND_CHECK_MEM_IS_DEFINED(ptr, len*sizeof(*ptr)) +#define OPUS_CHECK_VALUE_COND(value) VALGRIND_CHECK_VALUE_IS_DEFINED(value) +#define OPUS_PRINT_INT(value) do {fprintf(stderr, #value " = %d at %s:%d\n", value, __FILE__, __LINE__);}while(0) +#define OPUS_FPRINTF fprintf + +#else + +static OPUS_INLINE int _opus_false(void) {return 0;} +#define OPUS_CHECK_ARRAY(ptr, len) _opus_false() +#define OPUS_CHECK_VALUE(value) _opus_false() +#define OPUS_PRINT_INT(value) do{}while(0) +#define OPUS_FPRINTF (void) + +#endif + + +#endif /* STACK_ALLOC_H */ diff --git a/managed_components/78__esp-opus/celt/static_modes_fixed.h b/managed_components/78__esp-opus/celt/static_modes_fixed.h new file mode 100644 index 0000000..49f94b5 --- /dev/null +++ b/managed_components/78__esp-opus/celt/static_modes_fixed.h @@ -0,0 +1,1541 @@ +/* The contents of this file was automatically generated by dump_modes.c + with arguments: 48000 960 + It contains static definitions for some pre-defined modes. */ +#include "modes.h" +#include "rate.h" + +#ifdef HAVE_ARM_NE10 +#define OVERRIDE_FFT 1 +#include "static_modes_fixed_arm_ne10.h" +#endif + +#ifndef DEF_WINDOW120 +#define DEF_WINDOW120 +static const celt_coef window120[120] = { +#ifdef ENABLE_QEXT +144497, 1300330, 3611201, 7075520, 11690888, +17454086, 24361057, 32406886, 41585775, 51891010, +63314937, 75848919, 89483305, 104207389, 120009370, +136876310, 154794092, 173747378, 193719571, 214692768, +236647730, 259563841, 283419076, 308189974, 333851610, +360377579, 387739975, 415909390, 444854905, 474544098, +504943052, 536016380, 567727246, 600037405, 632907246, +666295841, 700161014, 734459402, 769146541, 804176949, +839504226, 875081151, 910859801, 946791664, 982827766, +1018918806, 1055015289, 1091067669, 1127026498, 1162842572, +1198467087, 1233851789, 1268949131, 1303712427, 1338096005, +1372055357, 1405547287, 1438530057, 1470963523, 1502809271, +1534030739, 1564593342, 1594464576, 1623614127, 1652013955, +1679638381, 1706464157, 1732470523, 1757639262, 1781954728, +1805403878, 1827976281, 1849664119, 1870462176, 1890367815, +1909380945, 1927503971, 1944741740, 1961101474, 1976592691, +1991227121, 2005018606, 2017983003, 2030138066, 2041503334, +2052100005, 2061950805, 2071079860, 2079512552, 2087275383, +2094395834, 2100902217, 2106823531, 2112189320, 2117029526, +2121374346, 2125254091, 2128699048, 2131739342, 2134404803, +2136724837, 2138728300, 2140443379, 2141897477, 2143117096, +2144127739, 2144953806, 2145618501, 2146143740, 2146550076, +2146856617, 2147080957, 2147239112, 2147345466, 2147412715, +2147451824, 2147471990, 2147480610, 2147483253, 2147483642, +#else +2, 20, 55, 108, 178, +266, 372, 494, 635, 792, +966, 1157, 1365, 1590, 1831, +2089, 2362, 2651, 2956, 3276, +3611, 3961, 4325, 4703, 5094, +5499, 5916, 6346, 6788, 7241, +7705, 8179, 8663, 9156, 9657, +10167, 10684, 11207, 11736, 12271, +12810, 13353, 13899, 14447, 14997, +15547, 16098, 16648, 17197, 17744, +18287, 18827, 19363, 19893, 20418, +20936, 21447, 21950, 22445, 22931, +23407, 23874, 24330, 24774, 25208, +25629, 26039, 26435, 26819, 27190, +27548, 27893, 28224, 28541, 28845, +29135, 29411, 29674, 29924, 30160, +30384, 30594, 30792, 30977, 31151, +31313, 31463, 31602, 31731, 31849, +31958, 32057, 32148, 32229, 32303, +32370, 32429, 32481, 32528, 32568, +32604, 32634, 32661, 32683, 32701, +32717, 32729, 32740, 32748, 32754, +32758, 32762, 32764, 32766, 32767, +32767, 32767, 32767, 32767, 32767, +#endif +}; +#endif + +#ifndef DEF_LOGN400 +#define DEF_LOGN400 +static const opus_int16 logN400[21] = { +0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 16, 16, 16, 21, 21, 24, 29, 34, 36, }; +#endif + +#ifndef DEF_PULSE_CACHE50 +#define DEF_PULSE_CACHE50 +static const opus_int16 cache_index50[105] = { +-1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 41, 41, 41, +82, 82, 123, 164, 200, 222, 0, 0, 0, 0, 0, 0, 0, 0, 41, +41, 41, 41, 123, 123, 123, 164, 164, 240, 266, 283, 295, 41, 41, 41, +41, 41, 41, 41, 41, 123, 123, 123, 123, 240, 240, 240, 266, 266, 305, +318, 328, 336, 123, 123, 123, 123, 123, 123, 123, 123, 240, 240, 240, 240, +305, 305, 305, 318, 318, 343, 351, 358, 364, 240, 240, 240, 240, 240, 240, +240, 240, 305, 305, 305, 305, 343, 343, 343, 351, 351, 370, 376, 382, 387, +}; +static const unsigned char cache_bits50[392] = { +40, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, +7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, +7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 40, 15, 23, 28, +31, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 47, 49, 50, +51, 52, 53, 54, 55, 55, 57, 58, 59, 60, 61, 62, 63, 63, 65, +66, 67, 68, 69, 70, 71, 71, 40, 20, 33, 41, 48, 53, 57, 61, +64, 66, 69, 71, 73, 75, 76, 78, 80, 82, 85, 87, 89, 91, 92, +94, 96, 98, 101, 103, 105, 107, 108, 110, 112, 114, 117, 119, 121, 123, +124, 126, 128, 40, 23, 39, 51, 60, 67, 73, 79, 83, 87, 91, 94, +97, 100, 102, 105, 107, 111, 115, 118, 121, 124, 126, 129, 131, 135, 139, +142, 145, 148, 150, 153, 155, 159, 163, 166, 169, 172, 174, 177, 179, 35, +28, 49, 65, 78, 89, 99, 107, 114, 120, 126, 132, 136, 141, 145, 149, +153, 159, 165, 171, 176, 180, 185, 189, 192, 199, 205, 211, 216, 220, 225, +229, 232, 239, 245, 251, 21, 33, 58, 79, 97, 112, 125, 137, 148, 157, +166, 174, 182, 189, 195, 201, 207, 217, 227, 235, 243, 251, 17, 35, 63, +86, 106, 123, 139, 152, 165, 177, 187, 197, 206, 214, 222, 230, 237, 250, +25, 31, 55, 75, 91, 105, 117, 128, 138, 146, 154, 161, 168, 174, 180, +185, 190, 200, 208, 215, 222, 229, 235, 240, 245, 255, 16, 36, 65, 89, +110, 128, 144, 159, 173, 185, 196, 207, 217, 226, 234, 242, 250, 11, 41, +74, 103, 128, 151, 172, 191, 209, 225, 241, 255, 9, 43, 79, 110, 138, +163, 186, 207, 227, 246, 12, 39, 71, 99, 123, 144, 164, 182, 198, 214, +228, 241, 253, 9, 44, 81, 113, 142, 168, 192, 214, 235, 255, 7, 49, +90, 127, 160, 191, 220, 247, 6, 51, 95, 134, 170, 203, 234, 7, 47, +87, 123, 155, 184, 212, 237, 6, 52, 97, 137, 174, 208, 240, 5, 57, +106, 151, 192, 231, 5, 59, 111, 158, 202, 243, 5, 55, 103, 147, 187, +224, 5, 60, 113, 161, 206, 248, 4, 65, 122, 175, 224, 4, 67, 127, +182, 234, }; +static const unsigned char cache_caps50[168] = { +224, 224, 224, 224, 224, 224, 224, 224, 160, 160, 160, 160, 185, 185, 185, +178, 178, 168, 134, 61, 37, 224, 224, 224, 224, 224, 224, 224, 224, 240, +240, 240, 240, 207, 207, 207, 198, 198, 183, 144, 66, 40, 160, 160, 160, +160, 160, 160, 160, 160, 185, 185, 185, 185, 193, 193, 193, 183, 183, 172, +138, 64, 38, 240, 240, 240, 240, 240, 240, 240, 240, 207, 207, 207, 207, +204, 204, 204, 193, 193, 180, 143, 66, 40, 185, 185, 185, 185, 185, 185, +185, 185, 193, 193, 193, 193, 193, 193, 193, 183, 183, 172, 138, 65, 39, +207, 207, 207, 207, 207, 207, 207, 207, 204, 204, 204, 204, 201, 201, 201, +188, 188, 176, 141, 66, 40, 193, 193, 193, 193, 193, 193, 193, 193, 193, +193, 193, 193, 194, 194, 194, 184, 184, 173, 139, 65, 39, 204, 204, 204, +204, 204, 204, 204, 204, 201, 201, 201, 201, 198, 198, 198, 187, 187, 175, +140, 66, 40, }; +#endif + +#ifndef FFT_TWIDDLES48000_960 +#define FFT_TWIDDLES48000_960 +static const kiss_twiddle_cpx fft_twiddles48000_960[480] = { +#ifdef ENABLE_QEXT +{2147483647, 0}, {2147299667, -28109692}, +{2146747758, -56214568}, {2145828015, -84309812}, +{2144540595, -112390610}, {2142885720, -140452150}, +{2140863672, -168489625}, {2138474798, -196498230}, +{2135719507, -224473166}, {2132598272, -252409639}, +{2129111627, -280302863}, {2125260169, -308148059}, +{2121044560, -335940456}, {2116465520, -363675290}, +{2111523835, -391347811}, {2106220351, -418953276}, +{2100555977, -446486956}, {2094531684, -473944133}, +{2088148503, -501320101}, {2081407529, -528610171}, +{2074309916, -555809667}, {2066856881, -582913927}, +{2059049701, -609918308}, {2050889713, -636818183}, +{2042378316, -663608942}, {2033516968, -690285995}, +{2024307187, -716844771}, {2014750552, -743280720}, +{2004848699, -769589311}, {1994603326, -795766037}, +{1984016188, -821806413}, {1973089099, -847705976}, +{1961823931, -873460290}, {1950222615, -899064940}, +{1938287138, -924515540}, {1926019546, -949807729}, +{1913421940, -974937174}, {1900496479, -999899568}, +{1887245378, -1024690635}, {1873670907, -1049306126}, +{1859775393, -1073741823}, {1845561215, -1097993541}, +{1831030810, -1122057123}, {1816186667, -1145928447}, +{1801031330, -1169603421}, {1785567395, -1193077990}, +{1769797513, -1216348131}, {1753724385, -1239409858}, +{1737350766, -1262259217}, {1720679460, -1284892295}, +{1703713325, -1307305214}, {1686455267, -1329494132}, +{1668908244, -1351455249}, {1651075262, -1373184800}, +{1632959376, -1394679063}, {1614563692, -1415934356}, +{1595891360, -1436947035}, {1576945581, -1457713501}, +{1557729600, -1478230195}, {1538246710, -1498493602}, +{1518500249, -1518500249}, {1498493602, -1538246710}, +{1478230195, -1557729600}, {1457713501, -1576945581}, +{1436947035, -1595891360}, {1415934356, -1614563692}, +{1394679063, -1632959376}, {1373184800, -1651075262}, +{1351455249, -1668908244}, {1329494132, -1686455267}, +{1307305214, -1703713325}, {1284892295, -1720679460}, +{1262259217, -1737350766}, {1239409858, -1753724385}, +{1216348131, -1769797513}, {1193077990, -1785567395}, +{1169603421, -1801031330}, {1145928447, -1816186667}, +{1122057123, -1831030810}, {1097993541, -1845561215}, +{1073741824, -1859775393}, {1049306126, -1873670907}, +{1024690635, -1887245378}, {999899568, -1900496479}, +{974937174, -1913421940}, {949807729, -1926019546}, +{924515540, -1938287138}, {899064940, -1950222615}, +{873460290, -1961823931}, {847705976, -1973089099}, +{821806413, -1984016188}, {795766037, -1994603326}, +{769589311, -2004848699}, {743280720, -2014750552}, +{716844771, -2024307187}, {690285995, -2033516968}, +{663608942, -2042378316}, {636818183, -2050889713}, +{609918308, -2059049701}, {582913927, -2066856881}, +{555809667, -2074309916}, {528610171, -2081407529}, +{501320101, -2088148503}, {473944133, -2094531684}, +{446486956, -2100555977}, {418953276, -2106220351}, +{391347811, -2111523835}, {363675290, -2116465520}, +{335940456, -2121044560}, {308148059, -2125260169}, +{280302863, -2129111627}, {252409639, -2132598272}, +{224473166, -2135719507}, {196498230, -2138474798}, +{168489625, -2140863672}, {140452150, -2142885720}, +{112390610, -2144540595}, {84309812, -2145828015}, +{56214568, -2146747758}, {28109692, -2147299667}, +{0, -2147483647}, {-28109692, -2147299667}, +{-56214568, 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3425}, {-32541, 3851}, +{-32488, 4277}, {-32429, 4702}, +{-32365, 5126}, {-32295, 5549}, +{-32219, 5971}, {-32138, 6393}, +{-32052, 6813}, {-31960, 7232}, +{-31863, 7650}, {-31760, 8066}, +{-31651, 8481}, {-31538, 8895}, +{-31419, 9307}, {-31294, 9717}, +{-31164, 10126}, {-31029, 10533}, +{-30888, 10938}, {-30743, 11342}, +{-30592, 11743}, {-30435, 12142}, +{-30274, 12540}, {-30107, 12935}, +{-29935, 13328}, {-29758, 13719}, +{-29576, 14107}, {-29389, 14493}, +{-29197, 14876}, {-28999, 15257}, +{-28797, 15636}, {-28590, 16011}, +{-28378, 16384}, {-28161, 16754}, +{-27939, 17121}, {-27713, 17485}, +{-27482, 17847}, {-27246, 18205}, +{-27005, 18560}, {-26760, 18912}, +{-26510, 19261}, {-26255, 19606}, +{-25997, 19948}, {-25733, 20286}, +{-25466, 20622}, {-25193, 20953}, +{-24917, 21281}, {-24636, 21605}, +{-24351, 21926}, {-24062, 22243}, +{-23769, 22556}, {-23472, 22865}, +{-23170, 23170}, {-22865, 23472}, +{-22556, 23769}, {-22243, 24062}, +{-21926, 24351}, {-21605, 24636}, +{-21281, 24917}, {-20953, 25193}, +{-20622, 25466}, {-20286, 25733}, +{-19948, 25997}, {-19606, 26255}, +{-19261, 26510}, {-18912, 26760}, +{-18560, 27005}, {-18205, 27246}, +{-17847, 27482}, {-17485, 27713}, +{-17121, 27939}, {-16754, 28161}, +{-16384, 28378}, {-16011, 28590}, +{-15636, 28797}, {-15257, 28999}, +{-14876, 29197}, {-14493, 29389}, +{-14107, 29576}, {-13719, 29758}, +{-13328, 29935}, {-12935, 30107}, +{-12540, 30274}, {-12142, 30435}, +{-11743, 30592}, {-11342, 30743}, +{-10938, 30888}, {-10533, 31029}, +{-10126, 31164}, {-9717, 31294}, +{-9307, 31419}, {-8895, 31538}, +{-8481, 31651}, {-8066, 31760}, +{-7650, 31863}, {-7232, 31960}, +{-6813, 32052}, {-6393, 32138}, +{-5971, 32219}, {-5549, 32295}, +{-5126, 32365}, {-4702, 32429}, +{-4277, 32488}, {-3851, 32541}, +{-3425, 32588}, {-2998, 32631}, +{-2571, 32667}, {-2143, 32698}, +{-1715, 32723}, {-1286, 32743}, +{-858, 32757}, {-429, 32765}, +{0, 32767}, {429, 32765}, +{858, 32757}, {1286, 32743}, +{1715, 32723}, {2143, 32698}, +{2571, 32667}, {2998, 32631}, +{3425, 32588}, {3851, 32541}, +{4277, 32488}, {4702, 32429}, +{5126, 32365}, {5549, 32295}, +{5971, 32219}, {6393, 32138}, +{6813, 32052}, {7232, 31960}, +{7650, 31863}, {8066, 31760}, +{8481, 31651}, {8895, 31538}, +{9307, 31419}, {9717, 31294}, +{10126, 31164}, {10533, 31029}, +{10938, 30888}, {11342, 30743}, +{11743, 30592}, {12142, 30435}, +{12540, 30274}, {12935, 30107}, +{13328, 29935}, {13719, 29758}, +{14107, 29576}, {14493, 29389}, +{14876, 29197}, {15257, 28999}, +{15636, 28797}, {16011, 28590}, +{16384, 28378}, {16754, 28161}, +{17121, 27939}, {17485, 27713}, +{17847, 27482}, {18205, 27246}, +{18560, 27005}, {18912, 26760}, +{19261, 26510}, {19606, 26255}, +{19948, 25997}, {20286, 25733}, +{20622, 25466}, {20953, 25193}, +{21281, 24917}, {21605, 24636}, +{21926, 24351}, {22243, 24062}, +{22556, 23769}, {22865, 23472}, +{23170, 23170}, {23472, 22865}, +{23769, 22556}, {24062, 22243}, +{24351, 21926}, {24636, 21605}, +{24917, 21281}, {25193, 20953}, +{25466, 20622}, {25733, 20286}, +{25997, 19948}, {26255, 19606}, +{26510, 19261}, {26760, 18912}, +{27005, 18560}, {27246, 18205}, +{27482, 17847}, {27713, 17485}, +{27939, 17121}, {28161, 16754}, +{28378, 16384}, {28590, 16011}, +{28797, 15636}, {28999, 15257}, +{29197, 14876}, {29389, 14493}, +{29576, 14107}, {29758, 13719}, +{29935, 13328}, {30107, 12935}, +{30274, 12540}, {30435, 12142}, +{30592, 11743}, {30743, 11342}, +{30888, 10938}, {31029, 10533}, +{31164, 10126}, {31294, 9717}, +{31419, 9307}, {31538, 8895}, +{31651, 8481}, {31760, 8066}, +{31863, 7650}, {31960, 7232}, +{32052, 6813}, {32138, 6393}, +{32219, 5971}, {32295, 5549}, +{32365, 5126}, {32429, 4702}, +{32488, 4277}, {32541, 3851}, +{32588, 3425}, {32631, 2998}, +{32667, 2571}, {32698, 2143}, +{32723, 1715}, {32743, 1286}, +{32757, 858}, {32765, 429}, +#endif +}; +#ifndef FFT_BITREV480 +#define FFT_BITREV480 +static const opus_int16 fft_bitrev480[480] = { +0, 96, 192, 288, 384, 32, 128, 224, 320, 416, 64, 160, 256, 352, 448, +8, 104, 200, 296, 392, 40, 136, 232, 328, 424, 72, 168, 264, 360, 456, +16, 112, 208, 304, 400, 48, 144, 240, 336, 432, 80, 176, 272, 368, 464, +24, 120, 216, 312, 408, 56, 152, 248, 344, 440, 88, 184, 280, 376, 472, +4, 100, 196, 292, 388, 36, 132, 228, 324, 420, 68, 164, 260, 356, 452, +12, 108, 204, 300, 396, 44, 140, 236, 332, 428, 76, 172, 268, 364, 460, +20, 116, 212, 308, 404, 52, 148, 244, 340, 436, 84, 180, 276, 372, 468, +28, 124, 220, 316, 412, 60, 156, 252, 348, 444, 92, 188, 284, 380, 476, +1, 97, 193, 289, 385, 33, 129, 225, 321, 417, 65, 161, 257, 353, 449, +9, 105, 201, 297, 393, 41, 137, 233, 329, 425, 73, 169, 265, 361, 457, +17, 113, 209, 305, 401, 49, 145, 241, 337, 433, 81, 177, 273, 369, 465, +25, 121, 217, 313, 409, 57, 153, 249, 345, 441, 89, 185, 281, 377, 473, +5, 101, 197, 293, 389, 37, 133, 229, 325, 421, 69, 165, 261, 357, 453, +13, 109, 205, 301, 397, 45, 141, 237, 333, 429, 77, 173, 269, 365, 461, +21, 117, 213, 309, 405, 53, 149, 245, 341, 437, 85, 181, 277, 373, 469, +29, 125, 221, 317, 413, 61, 157, 253, 349, 445, 93, 189, 285, 381, 477, +2, 98, 194, 290, 386, 34, 130, 226, 322, 418, 66, 162, 258, 354, 450, +10, 106, 202, 298, 394, 42, 138, 234, 330, 426, 74, 170, 266, 362, 458, +18, 114, 210, 306, 402, 50, 146, 242, 338, 434, 82, 178, 274, 370, 466, +26, 122, 218, 314, 410, 58, 154, 250, 346, 442, 90, 186, 282, 378, 474, +6, 102, 198, 294, 390, 38, 134, 230, 326, 422, 70, 166, 262, 358, 454, +14, 110, 206, 302, 398, 46, 142, 238, 334, 430, 78, 174, 270, 366, 462, +22, 118, 214, 310, 406, 54, 150, 246, 342, 438, 86, 182, 278, 374, 470, +30, 126, 222, 318, 414, 62, 158, 254, 350, 446, 94, 190, 286, 382, 478, +3, 99, 195, 291, 387, 35, 131, 227, 323, 419, 67, 163, 259, 355, 451, +11, 107, 203, 299, 395, 43, 139, 235, 331, 427, 75, 171, 267, 363, 459, +19, 115, 211, 307, 403, 51, 147, 243, 339, 435, 83, 179, 275, 371, 467, +27, 123, 219, 315, 411, 59, 155, 251, 347, 443, 91, 187, 283, 379, 475, +7, 103, 199, 295, 391, 39, 135, 231, 327, 423, 71, 167, 263, 359, 455, +15, 111, 207, 303, 399, 47, 143, 239, 335, 431, 79, 175, 271, 367, 463, +23, 119, 215, 311, 407, 55, 151, 247, 343, 439, 87, 183, 279, 375, 471, +31, 127, 223, 319, 415, 63, 159, 255, 351, 447, 95, 191, 287, 383, 479, +}; +#endif + +#ifndef FFT_BITREV240 +#define FFT_BITREV240 +static const opus_int16 fft_bitrev240[240] = { +0, 48, 96, 144, 192, 16, 64, 112, 160, 208, 32, 80, 128, 176, 224, +4, 52, 100, 148, 196, 20, 68, 116, 164, 212, 36, 84, 132, 180, 228, +8, 56, 104, 152, 200, 24, 72, 120, 168, 216, 40, 88, 136, 184, 232, +12, 60, 108, 156, 204, 28, 76, 124, 172, 220, 44, 92, 140, 188, 236, +1, 49, 97, 145, 193, 17, 65, 113, 161, 209, 33, 81, 129, 177, 225, +5, 53, 101, 149, 197, 21, 69, 117, 165, 213, 37, 85, 133, 181, 229, +9, 57, 105, 153, 201, 25, 73, 121, 169, 217, 41, 89, 137, 185, 233, +13, 61, 109, 157, 205, 29, 77, 125, 173, 221, 45, 93, 141, 189, 237, +2, 50, 98, 146, 194, 18, 66, 114, 162, 210, 34, 82, 130, 178, 226, +6, 54, 102, 150, 198, 22, 70, 118, 166, 214, 38, 86, 134, 182, 230, +10, 58, 106, 154, 202, 26, 74, 122, 170, 218, 42, 90, 138, 186, 234, +14, 62, 110, 158, 206, 30, 78, 126, 174, 222, 46, 94, 142, 190, 238, +3, 51, 99, 147, 195, 19, 67, 115, 163, 211, 35, 83, 131, 179, 227, +7, 55, 103, 151, 199, 23, 71, 119, 167, 215, 39, 87, 135, 183, 231, +11, 59, 107, 155, 203, 27, 75, 123, 171, 219, 43, 91, 139, 187, 235, +15, 63, 111, 159, 207, 31, 79, 127, 175, 223, 47, 95, 143, 191, 239, +}; +#endif + +#ifndef FFT_BITREV120 +#define FFT_BITREV120 +static const opus_int16 fft_bitrev120[120] = { +0, 24, 48, 72, 96, 8, 32, 56, 80, 104, 16, 40, 64, 88, 112, +4, 28, 52, 76, 100, 12, 36, 60, 84, 108, 20, 44, 68, 92, 116, +1, 25, 49, 73, 97, 9, 33, 57, 81, 105, 17, 41, 65, 89, 113, +5, 29, 53, 77, 101, 13, 37, 61, 85, 109, 21, 45, 69, 93, 117, +2, 26, 50, 74, 98, 10, 34, 58, 82, 106, 18, 42, 66, 90, 114, +6, 30, 54, 78, 102, 14, 38, 62, 86, 110, 22, 46, 70, 94, 118, +3, 27, 51, 75, 99, 11, 35, 59, 83, 107, 19, 43, 67, 91, 115, +7, 31, 55, 79, 103, 15, 39, 63, 87, 111, 23, 47, 71, 95, 119, +}; +#endif + +#ifndef FFT_BITREV60 +#define FFT_BITREV60 +static const opus_int16 fft_bitrev60[60] = { +0, 12, 24, 36, 48, 4, 16, 28, 40, 52, 8, 20, 32, 44, 56, +1, 13, 25, 37, 49, 5, 17, 29, 41, 53, 9, 21, 33, 45, 57, +2, 14, 26, 38, 50, 6, 18, 30, 42, 54, 10, 22, 34, 46, 58, +3, 15, 27, 39, 51, 7, 19, 31, 43, 55, 11, 23, 35, 47, 59, +}; +#endif + +#ifndef FFT_STATE48000_960_0 +#define FFT_STATE48000_960_0 +static const kiss_fft_state fft_state48000_960_0 = { +480, /* nfft */ +#ifdef ENABLE_QEXT +572662306, /* scale */ +#else +17476, /* scale */ +#endif +8, /* scale_shift */ +-1, /* shift */ +{5, 96, 3, 32, 4, 8, 2, 4, 4, 1, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev480, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_480, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_1 +#define FFT_STATE48000_960_1 +static const kiss_fft_state fft_state48000_960_1 = { +240, /* nfft */ +#ifdef ENABLE_QEXT +572662306, /* scale */ +#else +17476, /* scale */ +#endif +7, /* scale_shift */ +1, /* shift */ +{5, 48, 3, 16, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev240, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_240, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_2 +#define FFT_STATE48000_960_2 +static const kiss_fft_state fft_state48000_960_2 = { +120, /* nfft */ +#ifdef ENABLE_QEXT +572662306, /* scale */ +#else +17476, /* scale */ +#endif +6, /* scale_shift */ +2, /* shift */ +{5, 24, 3, 8, 2, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev120, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_120, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_3 +#define FFT_STATE48000_960_3 +static const kiss_fft_state fft_state48000_960_3 = { +60, /* nfft */ +#ifdef ENABLE_QEXT +572662306, /* scale */ +#else +17476, /* scale */ +#endif +5, /* scale_shift */ +3, /* shift */ +{5, 12, 3, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev60, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_60, +#else +NULL, +#endif +}; +#endif + +#endif + +#ifndef MDCT_TWIDDLES960 +#define MDCT_TWIDDLES960 +static const celt_coef mdct_twiddles960[1800] = { +#ifdef ENABLE_QEXT +2147483468, 2147469095, 2147431723, 2147371355, 2147287990, +2147181629, 2147052273, 2146899924, 2146724584, 2146526254, +2146304937, 2146060634, 2145793349, 2145503083, 2145189842, +2144853627, 2144494442, 2144112292, 2143707180, 2143279110, +2142828088, 2142354118, 2141857204, 2141337354, 2140794571, +2140228862, 2139640233, 2139028690, 2138394240, 2137736889, +2137056645, 2136353515, 2135627506, 2134878626, 2134106884, +2133312286, 2132494843, 2131654563, 2130791454, 2129905526, +2128996788, 2128065251, 2127110924, 2126133817, 2125133941, +2124111307, 2123065925, 2121997806, 2120906963, 2119793407, +2118657149, 2117498202, 2116316578, 2115112291, 2113885352, +2112635775, 2111363574, 2110068761, 2108751352, 2107411359, +2106048798, 2104663683, 2103256028, 2101825849, 2100373162, +2098897981, 2097400322, 2095880202, 2094337637, 2092772643, +2091185237, 2089575437, 2087943258, 2086288720, 2084611839, +2082912633, 2081191122, 2079447322, 2077681253, 2075892934, +2074082383, 2072249621, 2070394667, 2068517540, 2066618262, +2064696851, 2062753329, 2060787717, 2058800036, 2056790306, +2054758550, 2052704789, 2050629045, 2048531340, 2046411698, +2044270140, 2042106689, 2039921369, 2037714204, 2035485216, +2033234429, 2030961869, 2028667558, 2026351522, 2024013785, +2021654373, 2019273310, 2016870623, 2014446336, 2012000477, +2009533071, 2007044144, 2004533723, 2002001835, 1999448508, +1996873768, 1994277643, 1991660161, 1989021350, 1986361238, +1983679853, 1980977226, 1978253383, 1975508355, 1972742171, 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-17213, -17937, -18648, -19347, +-20033, -20705, -21363, -22006, -22634, +-23246, -23843, -24423, -24986, -25533, +-26062, -26573, -27066, -27540, -27995, +-28431, -28848, -29245, -29622, -29979, +-30315, -30630, -30924, -31197, -31449, +-31679, -31887, -32074, -32239, -32381, +-32501, -32600, -32675, -32729, -32759, +#endif +}; +#endif + +static const CELTMode mode48000_960_120 = { +48000, /* Fs */ +120, /* overlap */ +21, /* nbEBands */ +21, /* effEBands */ +{27853, 0, 4096, 8192, }, /* preemph */ +eband5ms, /* eBands */ +3, /* maxLM */ +8, /* nbShortMdcts */ +120, /* shortMdctSize */ +11, /* nbAllocVectors */ +band_allocation, /* allocVectors */ +logN400, /* logN */ +window120, /* window */ +{1920, 3, {&fft_state48000_960_0, &fft_state48000_960_1, &fft_state48000_960_2, &fft_state48000_960_3, }, mdct_twiddles960}, /* mdct */ +{392, cache_index50, cache_bits50, cache_caps50}, /* cache */ +}; + +/* List of all the available modes */ +#define TOTAL_MODES 1 +static const CELTMode * const static_mode_list[TOTAL_MODES] = { +&mode48000_960_120, +}; diff --git a/managed_components/78__esp-opus/celt/static_modes_fixed_arm_ne10.h b/managed_components/78__esp-opus/celt/static_modes_fixed_arm_ne10.h new file mode 100644 index 0000000..7623092 --- /dev/null +++ b/managed_components/78__esp-opus/celt/static_modes_fixed_arm_ne10.h @@ -0,0 +1,388 @@ +/* The contents of this file was automatically generated by + * dump_mode_arm_ne10.c with arguments: 48000 960 + * It contains static definitions for some pre-defined modes. */ +#include + +#ifndef NE10_FFT_PARAMS48000_960 +#define NE10_FFT_PARAMS48000_960 +static const ne10_int32_t ne10_factors_480[64] = { +4, 40, 4, 30, 2, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_240[64] = { +3, 20, 4, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_120[64] = { +3, 10, 2, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_60[64] = { +2, 5, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_fft_cpx_int32_t ne10_twiddles_480[480] = { +{0,0}, {2147483647,0}, {2147483647,0}, +{2147483647,0}, {1961823921,-873460313}, {1436946998,-1595891394}, +{2147483647,0}, {1436946998,-1595891394}, {-224473265,-2135719496}, +{2147483647,0}, {663608871,-2042378339}, {-1737350854,-1262259096}, +{2147483647,0}, {-224473265,-2135719496}, {-2100555935,446487152}, +{2147483647,0}, {2100555974,-446486968}, {1961823921,-873460313}, +{1737350743,-1262259248}, {1436946998,-1595891394}, {1073741769,-1859775424}, +{663608871,-2042378339}, {224473078,-2135719516}, {-224473265,-2135719496}, +{-663609049,-2042378281}, {-1073741932,-1859775330}, {-1436947137,-1595891268}, +{-1737350854,-1262259096}, {-1961823997,-873460141}, {-2100556013,-446486785}, +{2147483647,0}, {2144540595,-112390613}, {2135719506,-224473172}, +{2121044558,-335940465}, {2100555974,-446486968}, {2074309912,-555809682}, +{2042378310,-663608960}, {2004848691,-769589332}, {1961823921,-873460313}, +{1913421927,-974937199}, {1859775377,-1073741851}, {1801031311,-1169603450}, +{1737350743,-1262259248}, {1668908218,-1351455280}, {1595891331,-1436947067}, +{1518500216,-1518500282}, {1436946998,-1595891394}, {1351455207,-1668908277}, +{1262259172,-1737350799}, {1169603371,-1801031362}, {1073741769,-1859775424}, +{974937230,-1913421912}, {873460227,-1961823959}, {769589125,-2004848771}, +{663608871,-2042378339}, {555809715,-2074309903}, {446486876,-2100555994}, +{335940246,-2121044593}, {224473078,-2135719516}, {112390647,-2144540593}, +{2147483647,0}, {2135719506,-224473172}, {2100555974,-446486968}, +{2042378310,-663608960}, {1961823921,-873460313}, {1859775377,-1073741851}, +{1737350743,-1262259248}, {1595891331,-1436947067}, {1436946998,-1595891394}, +{1262259172,-1737350799}, {1073741769,-1859775424}, {873460227,-1961823959}, +{663608871,-2042378339}, {446486876,-2100555994}, {224473078,-2135719516}, +{-94,-2147483647}, {-224473265,-2135719496}, {-446487060,-2100555955}, +{-663609049,-2042378281}, {-873460398,-1961823883}, {-1073741932,-1859775330}, +{-1262259116,-1737350839}, {-1436947137,-1595891268}, {-1595891628,-1436946738}, +{-1737350854,-1262259096}, {-1859775343,-1073741910}, {-1961823997,-873460141}, +{-2042378447,-663608538}, {-2100556013,-446486785}, {-2135719499,-224473240}, +{2147483647,0}, {2121044558,-335940465}, {2042378310,-663608960}, +{1913421927,-974937199}, {1737350743,-1262259248}, {1518500216,-1518500282}, +{1262259172,-1737350799}, {974937230,-1913421912}, {663608871,-2042378339}, +{335940246,-2121044593}, {-94,-2147483647}, {-335940431,-2121044564}, +{-663609049,-2042378281}, {-974937397,-1913421827}, {-1262259116,-1737350839}, +{-1518500258,-1518500240}, {-1737350854,-1262259096}, {-1913422071,-974936918}, +{-2042378447,-663608538}, {-2121044568,-335940406}, {-2147483647,188}, +{-2121044509,335940777}, {-2042378331,663608895}, {-1913421900,974937252}, +{-1737350633,1262259400}, {-1518499993,1518500506}, {-1262258813,1737351059}, +{-974936606,1913422229}, {-663609179,2042378239}, {-335940566,2121044542}, +{2147483647,0}, {2147299667,-28109693}, {2146747758,-56214570}, +{2145828015,-84309815}, {2144540595,-112390613}, {2142885719,-140452154}, +{2140863671,-168489630}, {2138474797,-196498235}, {2135719506,-224473172}, +{2132598271,-252409646}, {2129111626,-280302871}, {2125260168,-308148068}, +{2121044558,-335940465}, {2116465518,-363675300}, {2111523833,-391347822}, +{2106220349,-418953288}, {2100555974,-446486968}, {2094531681,-473944146}, +{2088148500,-501320115}, {2081407525,-528610186}, {2074309912,-555809682}, +{2066856885,-582913912}, {2059049696,-609918325}, {2050889698,-636818231}, +{2042378310,-663608960}, {2033516972,-690285983}, {2024307180,-716844791}, +{2014750533,-743280770}, {2004848691,-769589332}, {1994603329,-795766029}, +{1984016179,-821806435}, {1973089077,-847706028}, {1961823921,-873460313}, +{1950222618,-899064934}, {1938287127,-924515564}, {1926019520,-949807783}, +{1913421927,-974937199}, {1900496481,-999899565}, {1887245364,-1024690661}, +{1873670877,-1049306180}, {1859775377,-1073741851}, {1845561215,-1097993541}, +{1831030826,-1122057097}, {1816186632,-1145928502}, {1801031311,-1169603450}, +{1785567394,-1193077993}, {1769797456,-1216348214}, {1753724345,-1239409914}, +{1737350743,-1262259248}, {1720679456,-1284892300}, {1703713340,-1307305194}, +{1686455222,-1329494189}, {1668908218,-1351455280}, {1651075255,-1373184807}, +{1632959307,-1394679144}, {1614563642,-1415934412}, {1595891331,-1436947067}, +{1576945572,-1457713510}, {1557729613,-1478230181}, {1538246655,-1498493658}, +{1518500216,-1518500282}, {1498493590,-1538246721}, {1478230113,-1557729677}, +{1457713441,-1576945636}, {1436946998,-1595891394}, {1415934341,-1614563704}, +{1394679073,-1632959368}, {1373184735,-1651075315}, {1351455207,-1668908277}, +{1329494115,-1686455280}, {1307305120,-1703713397}, {1284892225,-1720679512}, +{1262259172,-1737350799}, {1239409837,-1753724400}, {1216348136,-1769797510}, +{1193077915,-1785567446}, {1169603371,-1801031362}, {1145928423,-1816186682}, +{1122057017,-1831030875}, {1097993571,-1845561197}, {1073741769,-1859775424}, +{1049305987,-1873670985}, {1024690635,-1887245378}, {999899482,-1900496524}, +{974937230,-1913421912}, {949807699,-1926019561}, {924515422,-1938287195}, +{899064965,-1950222603}, {873460227,-1961823959}, {847705824,-1973089164}, +{821806407,-1984016190}, {795765941,-1994603364}, {769589125,-2004848771}, +{743280682,-2014750566}, {716844642,-2024307233}, {690286016,-2033516961}, +{663608871,-2042378339}, {636818019,-2050889764}, {609918296,-2059049705}, +{582913822,-2066856911}, {555809715,-2074309903}, {528610126,-2081407540}, +{501319962,-2088148536}, {473944148,-2094531680}, {446486876,-2100555994}, +{418953102,-2106220386}, {391347792,-2111523838}, {363675176,-2116465540}, +{335940246,-2121044593}, {308148006,-2125260177}, {280302715,-2129111646}, +{252409648,-2132598271}, {224473078,-2135719516}, {196498046,-2138474814}, +{168489600,-2140863674}, {140452029,-2142885728}, {112390647,-2144540593}, +{84309753,-2145828017}, {56214412,-2146747762}, {28109695,-2147299667}, +{2147483647,0}, {2146747758,-56214570}, {2144540595,-112390613}, +{2140863671,-168489630}, {2135719506,-224473172}, {2129111626,-280302871}, +{2121044558,-335940465}, {2111523833,-391347822}, {2100555974,-446486968}, +{2088148500,-501320115}, {2074309912,-555809682}, {2059049696,-609918325}, +{2042378310,-663608960}, {2024307180,-716844791}, {2004848691,-769589332}, +{1984016179,-821806435}, {1961823921,-873460313}, {1938287127,-924515564}, +{1913421927,-974937199}, {1887245364,-1024690661}, {1859775377,-1073741851}, +{1831030826,-1122057097}, {1801031311,-1169603450}, {1769797456,-1216348214}, +{1737350743,-1262259248}, {1703713340,-1307305194}, {1668908218,-1351455280}, +{1632959307,-1394679144}, {1595891331,-1436947067}, {1557729613,-1478230181}, +{1518500216,-1518500282}, {1478230113,-1557729677}, {1436946998,-1595891394}, +{1394679073,-1632959368}, {1351455207,-1668908277}, {1307305120,-1703713397}, +{1262259172,-1737350799}, {1216348136,-1769797510}, {1169603371,-1801031362}, +{1122057017,-1831030875}, {1073741769,-1859775424}, {1024690635,-1887245378}, +{974937230,-1913421912}, {924515422,-1938287195}, {873460227,-1961823959}, +{821806407,-1984016190}, {769589125,-2004848771}, {716844642,-2024307233}, +{663608871,-2042378339}, {609918296,-2059049705}, {555809715,-2074309903}, +{501319962,-2088148536}, {446486876,-2100555994}, {391347792,-2111523838}, +{335940246,-2121044593}, {280302715,-2129111646}, {224473078,-2135719516}, +{168489600,-2140863674}, {112390647,-2144540593}, {56214412,-2146747762}, +{-94,-2147483647}, {-56214600,-2146747757}, {-112390835,-2144540584}, +{-168489787,-2140863659}, {-224473265,-2135719496}, {-280302901,-2129111622}, +{-335940431,-2121044564}, {-391347977,-2111523804}, {-446487060,-2100555955}, +{-501320144,-2088148493}, {-555809896,-2074309855}, {-609918476,-2059049651}, +{-663609049,-2042378281}, {-716844819,-2024307170}, {-769589300,-2004848703}, +{-821806581,-1984016118}, {-873460398,-1961823883}, {-924515591,-1938287114}, +{-974937397,-1913421827}, {-1024690575,-1887245411}, {-1073741932,-1859775330}, +{-1122057395,-1831030643}, {-1169603421,-1801031330}, {-1216348291,-1769797403}, +{-1262259116,-1737350839}, {-1307305268,-1703713283}, {-1351455453,-1668908078}, +{-1394679021,-1632959413}, {-1436947137,-1595891268}, {-1478230435,-1557729372}, +{-1518500258,-1518500240}, {-1557729742,-1478230045}, {-1595891628,-1436946738}, +{-1632959429,-1394679001}, {-1668908417,-1351455035}, {-1703713298,-1307305248}, +{-1737350854,-1262259096}, {-1769797708,-1216347848}, {-1801031344,-1169603400}, +{-1831030924,-1122056937}, {-1859775343,-1073741910}, {-1887245423,-1024690552}, +{-1913422071,-974936918}, {-1938287125,-924515568}, {-1961823997,-873460141}, +{-1984016324,-821806084}, {-2004848713,-769589276}, {-2024307264,-716844553}, +{-2042378447,-663608538}, {-2059049731,-609918206}, {-2074309994,-555809377}, +{-2088148499,-501320119}, {-2100556013,-446486785}, {-2111523902,-391347448}, +{-2121044568,-335940406}, {-2129111659,-280302621}, {-2135719499,-224473240}, +{-2140863681,-168489506}, {-2144540612,-112390298}, {-2146747758,-56214574}, +{2147483647,0}, {2145828015,-84309815}, {2140863671,-168489630}, +{2132598271,-252409646}, {2121044558,-335940465}, {2106220349,-418953288}, +{2088148500,-501320115}, {2066856885,-582913912}, {2042378310,-663608960}, +{2014750533,-743280770}, {1984016179,-821806435}, {1950222618,-899064934}, +{1913421927,-974937199}, {1873670877,-1049306180}, {1831030826,-1122057097}, +{1785567394,-1193077993}, {1737350743,-1262259248}, {1686455222,-1329494189}, +{1632959307,-1394679144}, {1576945572,-1457713510}, {1518500216,-1518500282}, +{1457713441,-1576945636}, {1394679073,-1632959368}, {1329494115,-1686455280}, +{1262259172,-1737350799}, {1193077915,-1785567446}, {1122057017,-1831030875}, +{1049305987,-1873670985}, {974937230,-1913421912}, {899064965,-1950222603}, +{821806407,-1984016190}, {743280682,-2014750566}, {663608871,-2042378339}, +{582913822,-2066856911}, {501319962,-2088148536}, {418953102,-2106220386}, +{335940246,-2121044593}, {252409648,-2132598271}, {168489600,-2140863674}, +{84309753,-2145828017}, {-94,-2147483647}, {-84309940,-2145828010}, +{-168489787,-2140863659}, {-252409834,-2132598249}, {-335940431,-2121044564}, +{-418953286,-2106220349}, {-501320144,-2088148493}, {-582914003,-2066856860}, +{-663609049,-2042378281}, {-743280858,-2014750501}, {-821806581,-1984016118}, +{-899065136,-1950222525}, {-974937397,-1913421827}, {-1049306374,-1873670768}, +{-1122057395,-1831030643}, {-1193078284,-1785567199}, {-1262259116,-1737350839}, +{-1329494061,-1686455323}, {-1394679021,-1632959413}, {-1457713485,-1576945595}, +{-1518500258,-1518500240}, {-1576945613,-1457713466}, {-1632959429,-1394679001}, +{-1686455338,-1329494041}, {-1737350854,-1262259096}, {-1785567498,-1193077837}, +{-1831030924,-1122056937}, {-1873671031,-1049305905}, {-1913422071,-974936918}, +{-1950222750,-899064648}, {-1984016324,-821806084}, {-2014750687,-743280354}, +{-2042378447,-663608538}, {-2066856867,-582913978}, {-2088148499,-501320119}, +{-2106220354,-418953261}, {-2121044568,-335940406}, {-2132598282,-252409555}, +{-2140863681,-168489506}, {-2145828021,-84309659}, {-2147483647,188}, +{-2145828006,84310034}, {-2140863651,168489881}, {-2132598237,252409928}, +{-2121044509,335940777}, {-2106220281,418953629}, {-2088148411,501320484}, +{-2066856765,582914339}, {-2042378331,663608895}, {-2014750557,743280706}, +{-1984016181,821806431}, {-1950222593,899064989}, {-1913421900,974937252}, +{-1873670848,1049306232}, {-1831030728,1122057257}, {-1785567289,1193078149}, +{-1737350633,1262259400}, {-1686455106,1329494336}, {-1632959185,1394679287}, +{-1576945358,1457713742}, {-1518499993,1518500506}, {-1457713209,1576945850}, +{-1394678735,1632959656}, {-1329493766,1686455555}, {-1262258813,1737351059}, +{-1193077546,1785567692}, {-1122056638,1831031107}, {-1049305599,1873671202}, +{-974936606,1913422229}, {-899064330,1950222896}, {-821805761,1984016458}, +{-743280025,2014750808}, {-663609179,2042378239}, {-582914134,2066856823}, +{-501320277,2088148461}, {-418953420,2106220322}, {-335940566,2121044542}, +{-252409716,2132598263}, {-168489668,2140863668}, {-84309821,2145828015}, +}; +static const ne10_fft_cpx_int32_t ne10_twiddles_240[240] = { +{0,0}, {2147483647,0}, {2147483647,0}, +{2147483647,0}, {1961823921,-873460313}, {1436946998,-1595891394}, +{2147483647,0}, {1436946998,-1595891394}, {-224473265,-2135719496}, +{2147483647,0}, {663608871,-2042378339}, {-1737350854,-1262259096}, +{2147483647,0}, {-224473265,-2135719496}, {-2100555935,446487152}, +{2147483647,0}, {2135719506,-224473172}, {2100555974,-446486968}, +{2042378310,-663608960}, {1961823921,-873460313}, {1859775377,-1073741851}, +{1737350743,-1262259248}, {1595891331,-1436947067}, {1436946998,-1595891394}, +{1262259172,-1737350799}, {1073741769,-1859775424}, {873460227,-1961823959}, +{663608871,-2042378339}, {446486876,-2100555994}, {224473078,-2135719516}, +{2147483647,0}, {2100555974,-446486968}, {1961823921,-873460313}, +{1737350743,-1262259248}, {1436946998,-1595891394}, {1073741769,-1859775424}, +{663608871,-2042378339}, {224473078,-2135719516}, {-224473265,-2135719496}, +{-663609049,-2042378281}, {-1073741932,-1859775330}, {-1436947137,-1595891268}, +{-1737350854,-1262259096}, {-1961823997,-873460141}, {-2100556013,-446486785}, +{2147483647,0}, {2042378310,-663608960}, {1737350743,-1262259248}, +{1262259172,-1737350799}, {663608871,-2042378339}, {-94,-2147483647}, +{-663609049,-2042378281}, {-1262259116,-1737350839}, {-1737350854,-1262259096}, +{-2042378447,-663608538}, {-2147483647,188}, {-2042378331,663608895}, +{-1737350633,1262259400}, {-1262258813,1737351059}, {-663609179,2042378239}, +{2147483647,0}, {2146747758,-56214570}, {2144540595,-112390613}, +{2140863671,-168489630}, {2135719506,-224473172}, {2129111626,-280302871}, +{2121044558,-335940465}, {2111523833,-391347822}, {2100555974,-446486968}, +{2088148500,-501320115}, {2074309912,-555809682}, {2059049696,-609918325}, +{2042378310,-663608960}, {2024307180,-716844791}, {2004848691,-769589332}, +{1984016179,-821806435}, {1961823921,-873460313}, {1938287127,-924515564}, +{1913421927,-974937199}, {1887245364,-1024690661}, {1859775377,-1073741851}, +{1831030826,-1122057097}, {1801031311,-1169603450}, {1769797456,-1216348214}, +{1737350743,-1262259248}, {1703713340,-1307305194}, {1668908218,-1351455280}, +{1632959307,-1394679144}, {1595891331,-1436947067}, {1557729613,-1478230181}, +{1518500216,-1518500282}, {1478230113,-1557729677}, {1436946998,-1595891394}, +{1394679073,-1632959368}, {1351455207,-1668908277}, {1307305120,-1703713397}, +{1262259172,-1737350799}, {1216348136,-1769797510}, {1169603371,-1801031362}, +{1122057017,-1831030875}, {1073741769,-1859775424}, {1024690635,-1887245378}, +{974937230,-1913421912}, {924515422,-1938287195}, {873460227,-1961823959}, +{821806407,-1984016190}, {769589125,-2004848771}, {716844642,-2024307233}, +{663608871,-2042378339}, {609918296,-2059049705}, {555809715,-2074309903}, +{501319962,-2088148536}, {446486876,-2100555994}, {391347792,-2111523838}, +{335940246,-2121044593}, {280302715,-2129111646}, {224473078,-2135719516}, +{168489600,-2140863674}, {112390647,-2144540593}, {56214412,-2146747762}, +{2147483647,0}, {2144540595,-112390613}, {2135719506,-224473172}, +{2121044558,-335940465}, {2100555974,-446486968}, {2074309912,-555809682}, +{2042378310,-663608960}, {2004848691,-769589332}, {1961823921,-873460313}, +{1913421927,-974937199}, {1859775377,-1073741851}, {1801031311,-1169603450}, +{1737350743,-1262259248}, {1668908218,-1351455280}, {1595891331,-1436947067}, +{1518500216,-1518500282}, {1436946998,-1595891394}, {1351455207,-1668908277}, +{1262259172,-1737350799}, {1169603371,-1801031362}, {1073741769,-1859775424}, +{974937230,-1913421912}, {873460227,-1961823959}, {769589125,-2004848771}, +{663608871,-2042378339}, {555809715,-2074309903}, {446486876,-2100555994}, +{335940246,-2121044593}, {224473078,-2135719516}, {112390647,-2144540593}, +{-94,-2147483647}, {-112390835,-2144540584}, {-224473265,-2135719496}, +{-335940431,-2121044564}, {-446487060,-2100555955}, {-555809896,-2074309855}, +{-663609049,-2042378281}, {-769589300,-2004848703}, {-873460398,-1961823883}, +{-974937397,-1913421827}, {-1073741932,-1859775330}, {-1169603421,-1801031330}, +{-1262259116,-1737350839}, {-1351455453,-1668908078}, {-1436947137,-1595891268}, +{-1518500258,-1518500240}, {-1595891628,-1436946738}, {-1668908417,-1351455035}, +{-1737350854,-1262259096}, {-1801031344,-1169603400}, {-1859775343,-1073741910}, +{-1913422071,-974936918}, {-1961823997,-873460141}, {-2004848713,-769589276}, +{-2042378447,-663608538}, {-2074309994,-555809377}, {-2100556013,-446486785}, +{-2121044568,-335940406}, {-2135719499,-224473240}, {-2144540612,-112390298}, +{2147483647,0}, {2140863671,-168489630}, {2121044558,-335940465}, +{2088148500,-501320115}, {2042378310,-663608960}, {1984016179,-821806435}, +{1913421927,-974937199}, {1831030826,-1122057097}, {1737350743,-1262259248}, +{1632959307,-1394679144}, {1518500216,-1518500282}, {1394679073,-1632959368}, +{1262259172,-1737350799}, {1122057017,-1831030875}, {974937230,-1913421912}, +{821806407,-1984016190}, {663608871,-2042378339}, {501319962,-2088148536}, +{335940246,-2121044593}, {168489600,-2140863674}, {-94,-2147483647}, +{-168489787,-2140863659}, {-335940431,-2121044564}, {-501320144,-2088148493}, +{-663609049,-2042378281}, {-821806581,-1984016118}, {-974937397,-1913421827}, +{-1122057395,-1831030643}, {-1262259116,-1737350839}, {-1394679021,-1632959413}, +{-1518500258,-1518500240}, {-1632959429,-1394679001}, {-1737350854,-1262259096}, +{-1831030924,-1122056937}, {-1913422071,-974936918}, {-1984016324,-821806084}, +{-2042378447,-663608538}, {-2088148499,-501320119}, {-2121044568,-335940406}, +{-2140863681,-168489506}, {-2147483647,188}, {-2140863651,168489881}, +{-2121044509,335940777}, {-2088148411,501320484}, {-2042378331,663608895}, +{-1984016181,821806431}, {-1913421900,974937252}, {-1831030728,1122057257}, +{-1737350633,1262259400}, {-1632959185,1394679287}, {-1518499993,1518500506}, +{-1394678735,1632959656}, {-1262258813,1737351059}, {-1122056638,1831031107}, +{-974936606,1913422229}, {-821805761,1984016458}, {-663609179,2042378239}, +{-501320277,2088148461}, {-335940566,2121044542}, {-168489668,2140863668}, +}; +static const ne10_fft_cpx_int32_t ne10_twiddles_120[120] = { +{0,0}, {2147483647,0}, {2147483647,0}, +{2147483647,0}, {1961823921,-873460313}, {1436946998,-1595891394}, +{2147483647,0}, {1436946998,-1595891394}, {-224473265,-2135719496}, +{2147483647,0}, {663608871,-2042378339}, {-1737350854,-1262259096}, +{2147483647,0}, {-224473265,-2135719496}, {-2100555935,446487152}, +{2147483647,0}, {2100555974,-446486968}, {1961823921,-873460313}, +{1737350743,-1262259248}, {1436946998,-1595891394}, {1073741769,-1859775424}, +{663608871,-2042378339}, {224473078,-2135719516}, {-224473265,-2135719496}, +{-663609049,-2042378281}, {-1073741932,-1859775330}, {-1436947137,-1595891268}, +{-1737350854,-1262259096}, {-1961823997,-873460141}, {-2100556013,-446486785}, +{2147483647,0}, {2144540595,-112390613}, {2135719506,-224473172}, +{2121044558,-335940465}, {2100555974,-446486968}, {2074309912,-555809682}, +{2042378310,-663608960}, {2004848691,-769589332}, {1961823921,-873460313}, +{1913421927,-974937199}, {1859775377,-1073741851}, {1801031311,-1169603450}, +{1737350743,-1262259248}, {1668908218,-1351455280}, {1595891331,-1436947067}, +{1518500216,-1518500282}, {1436946998,-1595891394}, {1351455207,-1668908277}, +{1262259172,-1737350799}, {1169603371,-1801031362}, {1073741769,-1859775424}, +{974937230,-1913421912}, {873460227,-1961823959}, {769589125,-2004848771}, +{663608871,-2042378339}, {555809715,-2074309903}, {446486876,-2100555994}, +{335940246,-2121044593}, {224473078,-2135719516}, {112390647,-2144540593}, +{2147483647,0}, {2135719506,-224473172}, {2100555974,-446486968}, +{2042378310,-663608960}, {1961823921,-873460313}, {1859775377,-1073741851}, +{1737350743,-1262259248}, {1595891331,-1436947067}, {1436946998,-1595891394}, +{1262259172,-1737350799}, {1073741769,-1859775424}, {873460227,-1961823959}, +{663608871,-2042378339}, {446486876,-2100555994}, {224473078,-2135719516}, +{-94,-2147483647}, {-224473265,-2135719496}, {-446487060,-2100555955}, +{-663609049,-2042378281}, {-873460398,-1961823883}, {-1073741932,-1859775330}, +{-1262259116,-1737350839}, {-1436947137,-1595891268}, {-1595891628,-1436946738}, +{-1737350854,-1262259096}, {-1859775343,-1073741910}, {-1961823997,-873460141}, +{-2042378447,-663608538}, {-2100556013,-446486785}, {-2135719499,-224473240}, +{2147483647,0}, {2121044558,-335940465}, {2042378310,-663608960}, +{1913421927,-974937199}, {1737350743,-1262259248}, {1518500216,-1518500282}, +{1262259172,-1737350799}, {974937230,-1913421912}, {663608871,-2042378339}, +{335940246,-2121044593}, {-94,-2147483647}, {-335940431,-2121044564}, +{-663609049,-2042378281}, {-974937397,-1913421827}, {-1262259116,-1737350839}, +{-1518500258,-1518500240}, {-1737350854,-1262259096}, {-1913422071,-974936918}, +{-2042378447,-663608538}, {-2121044568,-335940406}, {-2147483647,188}, +{-2121044509,335940777}, {-2042378331,663608895}, {-1913421900,974937252}, +{-1737350633,1262259400}, {-1518499993,1518500506}, {-1262258813,1737351059}, +{-974936606,1913422229}, {-663609179,2042378239}, {-335940566,2121044542}, +}; +static const ne10_fft_cpx_int32_t ne10_twiddles_60[60] = { +{0,0}, {2147483647,0}, {2147483647,0}, +{2147483647,0}, {1961823921,-873460313}, {1436946998,-1595891394}, +{2147483647,0}, {1436946998,-1595891394}, {-224473265,-2135719496}, +{2147483647,0}, {663608871,-2042378339}, {-1737350854,-1262259096}, +{2147483647,0}, {-224473265,-2135719496}, {-2100555935,446487152}, +{2147483647,0}, {2135719506,-224473172}, {2100555974,-446486968}, +{2042378310,-663608960}, {1961823921,-873460313}, {1859775377,-1073741851}, +{1737350743,-1262259248}, {1595891331,-1436947067}, {1436946998,-1595891394}, +{1262259172,-1737350799}, {1073741769,-1859775424}, {873460227,-1961823959}, +{663608871,-2042378339}, {446486876,-2100555994}, {224473078,-2135719516}, +{2147483647,0}, {2100555974,-446486968}, {1961823921,-873460313}, +{1737350743,-1262259248}, {1436946998,-1595891394}, {1073741769,-1859775424}, +{663608871,-2042378339}, {224473078,-2135719516}, {-224473265,-2135719496}, +{-663609049,-2042378281}, {-1073741932,-1859775330}, {-1436947137,-1595891268}, +{-1737350854,-1262259096}, {-1961823997,-873460141}, {-2100556013,-446486785}, +{2147483647,0}, {2042378310,-663608960}, {1737350743,-1262259248}, +{1262259172,-1737350799}, {663608871,-2042378339}, {-94,-2147483647}, +{-663609049,-2042378281}, {-1262259116,-1737350839}, {-1737350854,-1262259096}, +{-2042378447,-663608538}, {-2147483647,188}, {-2042378331,663608895}, +{-1737350633,1262259400}, {-1262258813,1737351059}, {-663609179,2042378239}, +}; +static const ne10_fft_state_int32_t ne10_fft_state_int32_t_480 = { +120, +(ne10_int32_t *)ne10_factors_480, +(ne10_fft_cpx_int32_t *)ne10_twiddles_480, +NULL, +(ne10_fft_cpx_int32_t *)&ne10_twiddles_480[120], +}; +static const arch_fft_state cfg_arch_480 = { +1, +(void *)&ne10_fft_state_int32_t_480, +}; + +static const ne10_fft_state_int32_t ne10_fft_state_int32_t_240 = { +60, +(ne10_int32_t *)ne10_factors_240, +(ne10_fft_cpx_int32_t *)ne10_twiddles_240, +NULL, +(ne10_fft_cpx_int32_t *)&ne10_twiddles_240[60], +}; +static const arch_fft_state cfg_arch_240 = { +1, +(void *)&ne10_fft_state_int32_t_240, +}; + +static const ne10_fft_state_int32_t ne10_fft_state_int32_t_120 = { +30, +(ne10_int32_t *)ne10_factors_120, +(ne10_fft_cpx_int32_t *)ne10_twiddles_120, +NULL, +(ne10_fft_cpx_int32_t *)&ne10_twiddles_120[30], +}; +static const arch_fft_state cfg_arch_120 = { +1, +(void *)&ne10_fft_state_int32_t_120, +}; + +static const ne10_fft_state_int32_t ne10_fft_state_int32_t_60 = { +15, +(ne10_int32_t *)ne10_factors_60, +(ne10_fft_cpx_int32_t *)ne10_twiddles_60, +NULL, +(ne10_fft_cpx_int32_t *)&ne10_twiddles_60[15], +}; +static const arch_fft_state cfg_arch_60 = { +1, +(void *)&ne10_fft_state_int32_t_60, +}; + +#endif /* end NE10_FFT_PARAMS48000_960 */ diff --git a/managed_components/78__esp-opus/celt/static_modes_float.h b/managed_components/78__esp-opus/celt/static_modes_float.h new file mode 100644 index 0000000..e102a38 --- /dev/null +++ b/managed_components/78__esp-opus/celt/static_modes_float.h @@ -0,0 +1,888 @@ +/* The contents of this file was automatically generated by dump_modes.c + with arguments: 48000 960 + It contains static definitions for some pre-defined modes. */ +#include "modes.h" +#include "rate.h" + +#ifdef HAVE_ARM_NE10 +#define OVERRIDE_FFT 1 +#include "static_modes_float_arm_ne10.h" +#endif + +#ifndef DEF_WINDOW120 +#define DEF_WINDOW120 +static const opus_val16 window120[120] = { +6.7286966e-05f, 0.00060551348f, 0.0016815970f, 0.0032947962f, 0.0054439943f, +0.0081276923f, 0.011344001f, 0.015090633f, 0.019364886f, 0.024163635f, +0.029483315f, 0.035319905f, 0.041668911f, 0.048525347f, 0.055883718f, +0.063737999f, 0.072081616f, 0.080907428f, 0.090207705f, 0.099974111f, +0.11019769f, 0.12086883f, 0.13197729f, 0.14351214f, 0.15546177f, +0.16781389f, 0.18055550f, 0.19367290f, 0.20715171f, 0.22097682f, +0.23513243f, 0.24960208f, 0.26436860f, 0.27941419f, 0.29472040f, +0.31026818f, 0.32603788f, 0.34200931f, 0.35816177f, 0.37447407f, +0.39092462f, 0.40749142f, 0.42415215f, 0.44088423f, 0.45766484f, +0.47447104f, 0.49127978f, 0.50806798f, 0.52481261f, 0.54149077f, +0.55807973f, 0.57455701f, 0.59090049f, 0.60708841f, 0.62309951f, +0.63891306f, 0.65450896f, 0.66986776f, 0.68497077f, 0.69980010f, +0.71433873f, 0.72857055f, 0.74248043f, 0.75605424f, 0.76927895f, +0.78214257f, 0.79463430f, 0.80674445f, 0.81846456f, 0.82978733f, +0.84070669f, 0.85121779f, 0.86131698f, 0.87100183f, 0.88027111f, +0.88912479f, 0.89756398f, 0.90559094f, 0.91320904f, 0.92042270f, +0.92723738f, 0.93365955f, 0.93969656f, 0.94535671f, 0.95064907f, +0.95558353f, 0.96017067f, 0.96442171f, 0.96834849f, 0.97196334f, +0.97527906f, 0.97830883f, 0.98106616f, 0.98356480f, 0.98581869f, +0.98784191f, 0.98964856f, 0.99125274f, 0.99266849f, 0.99390969f, +0.99499004f, 0.99592297f, 0.99672162f, 0.99739874f, 0.99796667f, +0.99843728f, 0.99882195f, 0.99913147f, 0.99937606f, 0.99956527f, +0.99970802f, 0.99981248f, 0.99988613f, 0.99993565f, 0.99996697f, +0.99998518f, 0.99999457f, 0.99999859f, 0.99999982f, 1.0000000f, +}; +#endif + +#ifndef DEF_LOGN400 +#define DEF_LOGN400 +static const opus_int16 logN400[21] = { +0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 8, 16, 16, 16, 21, 21, 24, 29, 34, 36, }; +#endif + +#ifndef DEF_PULSE_CACHE50 +#define DEF_PULSE_CACHE50 +static const opus_int16 cache_index50[105] = { +-1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 41, 41, 41, +82, 82, 123, 164, 200, 222, 0, 0, 0, 0, 0, 0, 0, 0, 41, +41, 41, 41, 123, 123, 123, 164, 164, 240, 266, 283, 295, 41, 41, 41, +41, 41, 41, 41, 41, 123, 123, 123, 123, 240, 240, 240, 266, 266, 305, +318, 328, 336, 123, 123, 123, 123, 123, 123, 123, 123, 240, 240, 240, 240, +305, 305, 305, 318, 318, 343, 351, 358, 364, 240, 240, 240, 240, 240, 240, +240, 240, 305, 305, 305, 305, 343, 343, 343, 351, 351, 370, 376, 382, 387, +}; +static const unsigned char cache_bits50[392] = { +40, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, +7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, +7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 40, 15, 23, 28, +31, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 47, 47, 49, 50, +51, 52, 53, 54, 55, 55, 57, 58, 59, 60, 61, 62, 63, 63, 65, +66, 67, 68, 69, 70, 71, 71, 40, 20, 33, 41, 48, 53, 57, 61, +64, 66, 69, 71, 73, 75, 76, 78, 80, 82, 85, 87, 89, 91, 92, +94, 96, 98, 101, 103, 105, 107, 108, 110, 112, 114, 117, 119, 121, 123, +124, 126, 128, 40, 23, 39, 51, 60, 67, 73, 79, 83, 87, 91, 94, +97, 100, 102, 105, 107, 111, 115, 118, 121, 124, 126, 129, 131, 135, 139, +142, 145, 148, 150, 153, 155, 159, 163, 166, 169, 172, 174, 177, 179, 35, +28, 49, 65, 78, 89, 99, 107, 114, 120, 126, 132, 136, 141, 145, 149, +153, 159, 165, 171, 176, 180, 185, 189, 192, 199, 205, 211, 216, 220, 225, +229, 232, 239, 245, 251, 21, 33, 58, 79, 97, 112, 125, 137, 148, 157, +166, 174, 182, 189, 195, 201, 207, 217, 227, 235, 243, 251, 17, 35, 63, +86, 106, 123, 139, 152, 165, 177, 187, 197, 206, 214, 222, 230, 237, 250, +25, 31, 55, 75, 91, 105, 117, 128, 138, 146, 154, 161, 168, 174, 180, +185, 190, 200, 208, 215, 222, 229, 235, 240, 245, 255, 16, 36, 65, 89, +110, 128, 144, 159, 173, 185, 196, 207, 217, 226, 234, 242, 250, 11, 41, +74, 103, 128, 151, 172, 191, 209, 225, 241, 255, 9, 43, 79, 110, 138, +163, 186, 207, 227, 246, 12, 39, 71, 99, 123, 144, 164, 182, 198, 214, +228, 241, 253, 9, 44, 81, 113, 142, 168, 192, 214, 235, 255, 7, 49, +90, 127, 160, 191, 220, 247, 6, 51, 95, 134, 170, 203, 234, 7, 47, +87, 123, 155, 184, 212, 237, 6, 52, 97, 137, 174, 208, 240, 5, 57, +106, 151, 192, 231, 5, 59, 111, 158, 202, 243, 5, 55, 103, 147, 187, +224, 5, 60, 113, 161, 206, 248, 4, 65, 122, 175, 224, 4, 67, 127, +182, 234, }; +static const unsigned char cache_caps50[168] = { +224, 224, 224, 224, 224, 224, 224, 224, 160, 160, 160, 160, 185, 185, 185, +178, 178, 168, 134, 61, 37, 224, 224, 224, 224, 224, 224, 224, 224, 240, +240, 240, 240, 207, 207, 207, 198, 198, 183, 144, 66, 40, 160, 160, 160, +160, 160, 160, 160, 160, 185, 185, 185, 185, 193, 193, 193, 183, 183, 172, +138, 64, 38, 240, 240, 240, 240, 240, 240, 240, 240, 207, 207, 207, 207, +204, 204, 204, 193, 193, 180, 143, 66, 40, 185, 185, 185, 185, 185, 185, +185, 185, 193, 193, 193, 193, 193, 193, 193, 183, 183, 172, 138, 65, 39, +207, 207, 207, 207, 207, 207, 207, 207, 204, 204, 204, 204, 201, 201, 201, +188, 188, 176, 141, 66, 40, 193, 193, 193, 193, 193, 193, 193, 193, 193, +193, 193, 193, 194, 194, 194, 184, 184, 173, 139, 65, 39, 204, 204, 204, +204, 204, 204, 204, 204, 201, 201, 201, 201, 198, 198, 198, 187, 187, 175, +140, 66, 40, }; +#endif + +#ifndef FFT_TWIDDLES48000_960 +#define FFT_TWIDDLES48000_960 +static const kiss_twiddle_cpx fft_twiddles48000_960[480] = { +{1.0000000f, -0.0000000f}, {0.99991433f, -0.013089596f}, +{0.99965732f, -0.026176948f}, {0.99922904f, -0.039259816f}, +{0.99862953f, -0.052335956f}, {0.99785892f, -0.065403129f}, +{0.99691733f, -0.078459096f}, {0.99580493f, -0.091501619f}, +{0.99452190f, -0.10452846f}, {0.99306846f, -0.11753740f}, +{0.99144486f, -0.13052619f}, {0.98965139f, -0.14349262f}, +{0.98768834f, -0.15643447f}, {0.98555606f, -0.16934950f}, +{0.98325491f, -0.18223553f}, {0.98078528f, -0.19509032f}, +{0.97814760f, -0.20791169f}, {0.97534232f, -0.22069744f}, +{0.97236992f, -0.23344536f}, {0.96923091f, -0.24615329f}, +{0.96592583f, -0.25881905f}, {0.96245524f, -0.27144045f}, +{0.95881973f, -0.28401534f}, {0.95501994f, -0.29654157f}, +{0.95105652f, -0.30901699f}, {0.94693013f, -0.32143947f}, +{0.94264149f, -0.33380686f}, {0.93819134f, -0.34611706f}, +{0.93358043f, -0.35836795f}, {0.92880955f, -0.37055744f}, +{0.92387953f, -0.38268343f}, {0.91879121f, -0.39474386f}, +{0.91354546f, -0.40673664f}, {0.90814317f, -0.41865974f}, +{0.90258528f, -0.43051110f}, {0.89687274f, -0.44228869f}, +{0.89100652f, -0.45399050f}, {0.88498764f, -0.46561452f}, +{0.87881711f, -0.47715876f}, {0.87249601f, -0.48862124f}, +{0.86602540f, -0.50000000f}, {0.85940641f, -0.51129309f}, +{0.85264016f, -0.52249856f}, {0.84572782f, -0.53361452f}, +{0.83867057f, -0.54463904f}, {0.83146961f, -0.55557023f}, +{0.82412619f, -0.56640624f}, {0.81664156f, -0.57714519f}, +{0.80901699f, -0.58778525f}, {0.80125381f, -0.59832460f}, +{0.79335334f, -0.60876143f}, {0.78531693f, -0.61909395f}, +{0.77714596f, -0.62932039f}, {0.76884183f, -0.63943900f}, +{0.76040597f, -0.64944805f}, {0.75183981f, -0.65934582f}, +{0.74314483f, -0.66913061f}, {0.73432251f, -0.67880075f}, +{0.72537437f, -0.68835458f}, {0.71630194f, -0.69779046f}, +{0.70710678f, -0.70710678f}, {0.69779046f, -0.71630194f}, +{0.68835458f, -0.72537437f}, {0.67880075f, -0.73432251f}, +{0.66913061f, -0.74314483f}, {0.65934582f, -0.75183981f}, +{0.64944805f, -0.76040597f}, {0.63943900f, -0.76884183f}, +{0.62932039f, -0.77714596f}, {0.61909395f, -0.78531693f}, +{0.60876143f, -0.79335334f}, {0.59832460f, -0.80125381f}, +{0.58778525f, -0.80901699f}, {0.57714519f, -0.81664156f}, +{0.56640624f, -0.82412619f}, {0.55557023f, -0.83146961f}, +{0.54463904f, -0.83867057f}, {0.53361452f, -0.84572782f}, +{0.52249856f, -0.85264016f}, {0.51129309f, -0.85940641f}, +{0.50000000f, -0.86602540f}, {0.48862124f, -0.87249601f}, +{0.47715876f, -0.87881711f}, {0.46561452f, -0.88498764f}, +{0.45399050f, -0.89100652f}, {0.44228869f, -0.89687274f}, +{0.43051110f, -0.90258528f}, {0.41865974f, -0.90814317f}, +{0.40673664f, -0.91354546f}, {0.39474386f, -0.91879121f}, +{0.38268343f, -0.92387953f}, {0.37055744f, -0.92880955f}, +{0.35836795f, -0.93358043f}, {0.34611706f, -0.93819134f}, +{0.33380686f, -0.94264149f}, {0.32143947f, -0.94693013f}, +{0.30901699f, -0.95105652f}, {0.29654157f, -0.95501994f}, +{0.28401534f, -0.95881973f}, {0.27144045f, -0.96245524f}, +{0.25881905f, -0.96592583f}, {0.24615329f, -0.96923091f}, +{0.23344536f, -0.97236992f}, {0.22069744f, -0.97534232f}, +{0.20791169f, -0.97814760f}, {0.19509032f, -0.98078528f}, +{0.18223553f, -0.98325491f}, {0.16934950f, -0.98555606f}, +{0.15643447f, -0.98768834f}, {0.14349262f, -0.98965139f}, +{0.13052619f, -0.99144486f}, {0.11753740f, -0.99306846f}, +{0.10452846f, -0.99452190f}, {0.091501619f, -0.99580493f}, +{0.078459096f, -0.99691733f}, {0.065403129f, -0.99785892f}, +{0.052335956f, -0.99862953f}, {0.039259816f, -0.99922904f}, +{0.026176948f, -0.99965732f}, {0.013089596f, -0.99991433f}, +{6.1230318e-17f, -1.0000000f}, {-0.013089596f, -0.99991433f}, +{-0.026176948f, -0.99965732f}, {-0.039259816f, -0.99922904f}, +{-0.052335956f, -0.99862953f}, {-0.065403129f, -0.99785892f}, +{-0.078459096f, -0.99691733f}, {-0.091501619f, -0.99580493f}, +{-0.10452846f, -0.99452190f}, {-0.11753740f, -0.99306846f}, +{-0.13052619f, -0.99144486f}, {-0.14349262f, -0.98965139f}, +{-0.15643447f, -0.98768834f}, {-0.16934950f, -0.98555606f}, +{-0.18223553f, -0.98325491f}, {-0.19509032f, -0.98078528f}, +{-0.20791169f, -0.97814760f}, {-0.22069744f, -0.97534232f}, +{-0.23344536f, -0.97236992f}, {-0.24615329f, -0.96923091f}, +{-0.25881905f, -0.96592583f}, {-0.27144045f, -0.96245524f}, +{-0.28401534f, -0.95881973f}, {-0.29654157f, -0.95501994f}, +{-0.30901699f, -0.95105652f}, {-0.32143947f, -0.94693013f}, +{-0.33380686f, -0.94264149f}, {-0.34611706f, -0.93819134f}, +{-0.35836795f, -0.93358043f}, {-0.37055744f, -0.92880955f}, +{-0.38268343f, -0.92387953f}, {-0.39474386f, -0.91879121f}, +{-0.40673664f, -0.91354546f}, {-0.41865974f, -0.90814317f}, +{-0.43051110f, -0.90258528f}, {-0.44228869f, -0.89687274f}, +{-0.45399050f, -0.89100652f}, {-0.46561452f, -0.88498764f}, +{-0.47715876f, -0.87881711f}, {-0.48862124f, -0.87249601f}, +{-0.50000000f, -0.86602540f}, {-0.51129309f, -0.85940641f}, +{-0.52249856f, -0.85264016f}, {-0.53361452f, -0.84572782f}, +{-0.54463904f, -0.83867057f}, {-0.55557023f, -0.83146961f}, +{-0.56640624f, -0.82412619f}, {-0.57714519f, -0.81664156f}, +{-0.58778525f, -0.80901699f}, {-0.59832460f, -0.80125381f}, +{-0.60876143f, -0.79335334f}, {-0.61909395f, -0.78531693f}, +{-0.62932039f, -0.77714596f}, {-0.63943900f, -0.76884183f}, +{-0.64944805f, -0.76040597f}, {-0.65934582f, -0.75183981f}, +{-0.66913061f, -0.74314483f}, {-0.67880075f, -0.73432251f}, +{-0.68835458f, -0.72537437f}, {-0.69779046f, -0.71630194f}, +{-0.70710678f, -0.70710678f}, {-0.71630194f, -0.69779046f}, +{-0.72537437f, -0.68835458f}, {-0.73432251f, -0.67880075f}, +{-0.74314483f, -0.66913061f}, {-0.75183981f, -0.65934582f}, +{-0.76040597f, -0.64944805f}, {-0.76884183f, -0.63943900f}, +{-0.77714596f, -0.62932039f}, {-0.78531693f, -0.61909395f}, +{-0.79335334f, -0.60876143f}, {-0.80125381f, -0.59832460f}, +{-0.80901699f, -0.58778525f}, {-0.81664156f, -0.57714519f}, +{-0.82412619f, -0.56640624f}, {-0.83146961f, -0.55557023f}, +{-0.83867057f, -0.54463904f}, {-0.84572782f, -0.53361452f}, +{-0.85264016f, -0.52249856f}, {-0.85940641f, -0.51129309f}, +{-0.86602540f, -0.50000000f}, {-0.87249601f, -0.48862124f}, +{-0.87881711f, -0.47715876f}, {-0.88498764f, -0.46561452f}, +{-0.89100652f, -0.45399050f}, {-0.89687274f, -0.44228869f}, +{-0.90258528f, -0.43051110f}, {-0.90814317f, -0.41865974f}, +{-0.91354546f, -0.40673664f}, {-0.91879121f, -0.39474386f}, +{-0.92387953f, -0.38268343f}, {-0.92880955f, -0.37055744f}, +{-0.93358043f, -0.35836795f}, {-0.93819134f, -0.34611706f}, +{-0.94264149f, -0.33380686f}, {-0.94693013f, -0.32143947f}, +{-0.95105652f, -0.30901699f}, {-0.95501994f, -0.29654157f}, +{-0.95881973f, -0.28401534f}, {-0.96245524f, -0.27144045f}, +{-0.96592583f, -0.25881905f}, {-0.96923091f, -0.24615329f}, +{-0.97236992f, -0.23344536f}, {-0.97534232f, -0.22069744f}, +{-0.97814760f, -0.20791169f}, {-0.98078528f, -0.19509032f}, +{-0.98325491f, -0.18223553f}, {-0.98555606f, -0.16934950f}, +{-0.98768834f, -0.15643447f}, {-0.98965139f, -0.14349262f}, +{-0.99144486f, -0.13052619f}, {-0.99306846f, -0.11753740f}, +{-0.99452190f, -0.10452846f}, {-0.99580493f, -0.091501619f}, +{-0.99691733f, -0.078459096f}, {-0.99785892f, -0.065403129f}, +{-0.99862953f, -0.052335956f}, {-0.99922904f, -0.039259816f}, +{-0.99965732f, -0.026176948f}, {-0.99991433f, -0.013089596f}, +{-1.0000000f, -1.2246064e-16f}, {-0.99991433f, 0.013089596f}, +{-0.99965732f, 0.026176948f}, {-0.99922904f, 0.039259816f}, +{-0.99862953f, 0.052335956f}, {-0.99785892f, 0.065403129f}, +{-0.99691733f, 0.078459096f}, {-0.99580493f, 0.091501619f}, +{-0.99452190f, 0.10452846f}, {-0.99306846f, 0.11753740f}, +{-0.99144486f, 0.13052619f}, {-0.98965139f, 0.14349262f}, +{-0.98768834f, 0.15643447f}, {-0.98555606f, 0.16934950f}, +{-0.98325491f, 0.18223553f}, {-0.98078528f, 0.19509032f}, +{-0.97814760f, 0.20791169f}, {-0.97534232f, 0.22069744f}, +{-0.97236992f, 0.23344536f}, {-0.96923091f, 0.24615329f}, +{-0.96592583f, 0.25881905f}, {-0.96245524f, 0.27144045f}, +{-0.95881973f, 0.28401534f}, {-0.95501994f, 0.29654157f}, +{-0.95105652f, 0.30901699f}, {-0.94693013f, 0.32143947f}, +{-0.94264149f, 0.33380686f}, {-0.93819134f, 0.34611706f}, +{-0.93358043f, 0.35836795f}, {-0.92880955f, 0.37055744f}, +{-0.92387953f, 0.38268343f}, {-0.91879121f, 0.39474386f}, +{-0.91354546f, 0.40673664f}, {-0.90814317f, 0.41865974f}, +{-0.90258528f, 0.43051110f}, {-0.89687274f, 0.44228869f}, +{-0.89100652f, 0.45399050f}, {-0.88498764f, 0.46561452f}, +{-0.87881711f, 0.47715876f}, {-0.87249601f, 0.48862124f}, +{-0.86602540f, 0.50000000f}, {-0.85940641f, 0.51129309f}, +{-0.85264016f, 0.52249856f}, {-0.84572782f, 0.53361452f}, +{-0.83867057f, 0.54463904f}, {-0.83146961f, 0.55557023f}, +{-0.82412619f, 0.56640624f}, {-0.81664156f, 0.57714519f}, +{-0.80901699f, 0.58778525f}, {-0.80125381f, 0.59832460f}, +{-0.79335334f, 0.60876143f}, {-0.78531693f, 0.61909395f}, +{-0.77714596f, 0.62932039f}, {-0.76884183f, 0.63943900f}, +{-0.76040597f, 0.64944805f}, {-0.75183981f, 0.65934582f}, +{-0.74314483f, 0.66913061f}, {-0.73432251f, 0.67880075f}, +{-0.72537437f, 0.68835458f}, {-0.71630194f, 0.69779046f}, +{-0.70710678f, 0.70710678f}, {-0.69779046f, 0.71630194f}, +{-0.68835458f, 0.72537437f}, {-0.67880075f, 0.73432251f}, +{-0.66913061f, 0.74314483f}, {-0.65934582f, 0.75183981f}, +{-0.64944805f, 0.76040597f}, {-0.63943900f, 0.76884183f}, +{-0.62932039f, 0.77714596f}, {-0.61909395f, 0.78531693f}, +{-0.60876143f, 0.79335334f}, {-0.59832460f, 0.80125381f}, +{-0.58778525f, 0.80901699f}, {-0.57714519f, 0.81664156f}, +{-0.56640624f, 0.82412619f}, {-0.55557023f, 0.83146961f}, +{-0.54463904f, 0.83867057f}, {-0.53361452f, 0.84572782f}, +{-0.52249856f, 0.85264016f}, {-0.51129309f, 0.85940641f}, +{-0.50000000f, 0.86602540f}, {-0.48862124f, 0.87249601f}, +{-0.47715876f, 0.87881711f}, {-0.46561452f, 0.88498764f}, +{-0.45399050f, 0.89100652f}, {-0.44228869f, 0.89687274f}, +{-0.43051110f, 0.90258528f}, {-0.41865974f, 0.90814317f}, +{-0.40673664f, 0.91354546f}, {-0.39474386f, 0.91879121f}, +{-0.38268343f, 0.92387953f}, {-0.37055744f, 0.92880955f}, +{-0.35836795f, 0.93358043f}, {-0.34611706f, 0.93819134f}, +{-0.33380686f, 0.94264149f}, {-0.32143947f, 0.94693013f}, +{-0.30901699f, 0.95105652f}, {-0.29654157f, 0.95501994f}, +{-0.28401534f, 0.95881973f}, {-0.27144045f, 0.96245524f}, +{-0.25881905f, 0.96592583f}, {-0.24615329f, 0.96923091f}, +{-0.23344536f, 0.97236992f}, {-0.22069744f, 0.97534232f}, +{-0.20791169f, 0.97814760f}, {-0.19509032f, 0.98078528f}, +{-0.18223553f, 0.98325491f}, {-0.16934950f, 0.98555606f}, +{-0.15643447f, 0.98768834f}, {-0.14349262f, 0.98965139f}, +{-0.13052619f, 0.99144486f}, {-0.11753740f, 0.99306846f}, +{-0.10452846f, 0.99452190f}, {-0.091501619f, 0.99580493f}, +{-0.078459096f, 0.99691733f}, {-0.065403129f, 0.99785892f}, +{-0.052335956f, 0.99862953f}, {-0.039259816f, 0.99922904f}, +{-0.026176948f, 0.99965732f}, {-0.013089596f, 0.99991433f}, +{-1.8369095e-16f, 1.0000000f}, {0.013089596f, 0.99991433f}, +{0.026176948f, 0.99965732f}, {0.039259816f, 0.99922904f}, +{0.052335956f, 0.99862953f}, {0.065403129f, 0.99785892f}, +{0.078459096f, 0.99691733f}, {0.091501619f, 0.99580493f}, +{0.10452846f, 0.99452190f}, {0.11753740f, 0.99306846f}, +{0.13052619f, 0.99144486f}, {0.14349262f, 0.98965139f}, +{0.15643447f, 0.98768834f}, {0.16934950f, 0.98555606f}, +{0.18223553f, 0.98325491f}, {0.19509032f, 0.98078528f}, +{0.20791169f, 0.97814760f}, {0.22069744f, 0.97534232f}, +{0.23344536f, 0.97236992f}, {0.24615329f, 0.96923091f}, +{0.25881905f, 0.96592583f}, {0.27144045f, 0.96245524f}, +{0.28401534f, 0.95881973f}, {0.29654157f, 0.95501994f}, +{0.30901699f, 0.95105652f}, {0.32143947f, 0.94693013f}, +{0.33380686f, 0.94264149f}, {0.34611706f, 0.93819134f}, +{0.35836795f, 0.93358043f}, {0.37055744f, 0.92880955f}, +{0.38268343f, 0.92387953f}, {0.39474386f, 0.91879121f}, +{0.40673664f, 0.91354546f}, {0.41865974f, 0.90814317f}, +{0.43051110f, 0.90258528f}, {0.44228869f, 0.89687274f}, +{0.45399050f, 0.89100652f}, {0.46561452f, 0.88498764f}, +{0.47715876f, 0.87881711f}, {0.48862124f, 0.87249601f}, +{0.50000000f, 0.86602540f}, {0.51129309f, 0.85940641f}, +{0.52249856f, 0.85264016f}, {0.53361452f, 0.84572782f}, +{0.54463904f, 0.83867057f}, {0.55557023f, 0.83146961f}, +{0.56640624f, 0.82412619f}, {0.57714519f, 0.81664156f}, +{0.58778525f, 0.80901699f}, {0.59832460f, 0.80125381f}, +{0.60876143f, 0.79335334f}, {0.61909395f, 0.78531693f}, +{0.62932039f, 0.77714596f}, {0.63943900f, 0.76884183f}, +{0.64944805f, 0.76040597f}, {0.65934582f, 0.75183981f}, +{0.66913061f, 0.74314483f}, {0.67880075f, 0.73432251f}, +{0.68835458f, 0.72537437f}, {0.69779046f, 0.71630194f}, +{0.70710678f, 0.70710678f}, {0.71630194f, 0.69779046f}, +{0.72537437f, 0.68835458f}, {0.73432251f, 0.67880075f}, +{0.74314483f, 0.66913061f}, {0.75183981f, 0.65934582f}, +{0.76040597f, 0.64944805f}, {0.76884183f, 0.63943900f}, +{0.77714596f, 0.62932039f}, {0.78531693f, 0.61909395f}, +{0.79335334f, 0.60876143f}, {0.80125381f, 0.59832460f}, +{0.80901699f, 0.58778525f}, {0.81664156f, 0.57714519f}, +{0.82412619f, 0.56640624f}, {0.83146961f, 0.55557023f}, +{0.83867057f, 0.54463904f}, {0.84572782f, 0.53361452f}, +{0.85264016f, 0.52249856f}, {0.85940641f, 0.51129309f}, +{0.86602540f, 0.50000000f}, {0.87249601f, 0.48862124f}, +{0.87881711f, 0.47715876f}, {0.88498764f, 0.46561452f}, +{0.89100652f, 0.45399050f}, {0.89687274f, 0.44228869f}, +{0.90258528f, 0.43051110f}, {0.90814317f, 0.41865974f}, +{0.91354546f, 0.40673664f}, {0.91879121f, 0.39474386f}, +{0.92387953f, 0.38268343f}, {0.92880955f, 0.37055744f}, +{0.93358043f, 0.35836795f}, {0.93819134f, 0.34611706f}, +{0.94264149f, 0.33380686f}, {0.94693013f, 0.32143947f}, +{0.95105652f, 0.30901699f}, {0.95501994f, 0.29654157f}, +{0.95881973f, 0.28401534f}, {0.96245524f, 0.27144045f}, +{0.96592583f, 0.25881905f}, {0.96923091f, 0.24615329f}, +{0.97236992f, 0.23344536f}, {0.97534232f, 0.22069744f}, +{0.97814760f, 0.20791169f}, {0.98078528f, 0.19509032f}, +{0.98325491f, 0.18223553f}, {0.98555606f, 0.16934950f}, +{0.98768834f, 0.15643447f}, {0.98965139f, 0.14349262f}, +{0.99144486f, 0.13052619f}, {0.99306846f, 0.11753740f}, +{0.99452190f, 0.10452846f}, {0.99580493f, 0.091501619f}, +{0.99691733f, 0.078459096f}, {0.99785892f, 0.065403129f}, +{0.99862953f, 0.052335956f}, {0.99922904f, 0.039259816f}, +{0.99965732f, 0.026176948f}, {0.99991433f, 0.013089596f}, +}; +#ifndef FFT_BITREV480 +#define FFT_BITREV480 +static const opus_int16 fft_bitrev480[480] = { +0, 96, 192, 288, 384, 32, 128, 224, 320, 416, 64, 160, 256, 352, 448, +8, 104, 200, 296, 392, 40, 136, 232, 328, 424, 72, 168, 264, 360, 456, +16, 112, 208, 304, 400, 48, 144, 240, 336, 432, 80, 176, 272, 368, 464, +24, 120, 216, 312, 408, 56, 152, 248, 344, 440, 88, 184, 280, 376, 472, +4, 100, 196, 292, 388, 36, 132, 228, 324, 420, 68, 164, 260, 356, 452, +12, 108, 204, 300, 396, 44, 140, 236, 332, 428, 76, 172, 268, 364, 460, +20, 116, 212, 308, 404, 52, 148, 244, 340, 436, 84, 180, 276, 372, 468, +28, 124, 220, 316, 412, 60, 156, 252, 348, 444, 92, 188, 284, 380, 476, +1, 97, 193, 289, 385, 33, 129, 225, 321, 417, 65, 161, 257, 353, 449, +9, 105, 201, 297, 393, 41, 137, 233, 329, 425, 73, 169, 265, 361, 457, +17, 113, 209, 305, 401, 49, 145, 241, 337, 433, 81, 177, 273, 369, 465, +25, 121, 217, 313, 409, 57, 153, 249, 345, 441, 89, 185, 281, 377, 473, +5, 101, 197, 293, 389, 37, 133, 229, 325, 421, 69, 165, 261, 357, 453, +13, 109, 205, 301, 397, 45, 141, 237, 333, 429, 77, 173, 269, 365, 461, +21, 117, 213, 309, 405, 53, 149, 245, 341, 437, 85, 181, 277, 373, 469, +29, 125, 221, 317, 413, 61, 157, 253, 349, 445, 93, 189, 285, 381, 477, +2, 98, 194, 290, 386, 34, 130, 226, 322, 418, 66, 162, 258, 354, 450, +10, 106, 202, 298, 394, 42, 138, 234, 330, 426, 74, 170, 266, 362, 458, +18, 114, 210, 306, 402, 50, 146, 242, 338, 434, 82, 178, 274, 370, 466, +26, 122, 218, 314, 410, 58, 154, 250, 346, 442, 90, 186, 282, 378, 474, +6, 102, 198, 294, 390, 38, 134, 230, 326, 422, 70, 166, 262, 358, 454, +14, 110, 206, 302, 398, 46, 142, 238, 334, 430, 78, 174, 270, 366, 462, +22, 118, 214, 310, 406, 54, 150, 246, 342, 438, 86, 182, 278, 374, 470, +30, 126, 222, 318, 414, 62, 158, 254, 350, 446, 94, 190, 286, 382, 478, +3, 99, 195, 291, 387, 35, 131, 227, 323, 419, 67, 163, 259, 355, 451, +11, 107, 203, 299, 395, 43, 139, 235, 331, 427, 75, 171, 267, 363, 459, +19, 115, 211, 307, 403, 51, 147, 243, 339, 435, 83, 179, 275, 371, 467, +27, 123, 219, 315, 411, 59, 155, 251, 347, 443, 91, 187, 283, 379, 475, +7, 103, 199, 295, 391, 39, 135, 231, 327, 423, 71, 167, 263, 359, 455, +15, 111, 207, 303, 399, 47, 143, 239, 335, 431, 79, 175, 271, 367, 463, +23, 119, 215, 311, 407, 55, 151, 247, 343, 439, 87, 183, 279, 375, 471, +31, 127, 223, 319, 415, 63, 159, 255, 351, 447, 95, 191, 287, 383, 479, +}; +#endif + +#ifndef FFT_BITREV240 +#define FFT_BITREV240 +static const opus_int16 fft_bitrev240[240] = { +0, 48, 96, 144, 192, 16, 64, 112, 160, 208, 32, 80, 128, 176, 224, +4, 52, 100, 148, 196, 20, 68, 116, 164, 212, 36, 84, 132, 180, 228, +8, 56, 104, 152, 200, 24, 72, 120, 168, 216, 40, 88, 136, 184, 232, +12, 60, 108, 156, 204, 28, 76, 124, 172, 220, 44, 92, 140, 188, 236, +1, 49, 97, 145, 193, 17, 65, 113, 161, 209, 33, 81, 129, 177, 225, +5, 53, 101, 149, 197, 21, 69, 117, 165, 213, 37, 85, 133, 181, 229, +9, 57, 105, 153, 201, 25, 73, 121, 169, 217, 41, 89, 137, 185, 233, +13, 61, 109, 157, 205, 29, 77, 125, 173, 221, 45, 93, 141, 189, 237, +2, 50, 98, 146, 194, 18, 66, 114, 162, 210, 34, 82, 130, 178, 226, +6, 54, 102, 150, 198, 22, 70, 118, 166, 214, 38, 86, 134, 182, 230, +10, 58, 106, 154, 202, 26, 74, 122, 170, 218, 42, 90, 138, 186, 234, +14, 62, 110, 158, 206, 30, 78, 126, 174, 222, 46, 94, 142, 190, 238, +3, 51, 99, 147, 195, 19, 67, 115, 163, 211, 35, 83, 131, 179, 227, +7, 55, 103, 151, 199, 23, 71, 119, 167, 215, 39, 87, 135, 183, 231, +11, 59, 107, 155, 203, 27, 75, 123, 171, 219, 43, 91, 139, 187, 235, +15, 63, 111, 159, 207, 31, 79, 127, 175, 223, 47, 95, 143, 191, 239, +}; +#endif + +#ifndef FFT_BITREV120 +#define FFT_BITREV120 +static const opus_int16 fft_bitrev120[120] = { +0, 24, 48, 72, 96, 8, 32, 56, 80, 104, 16, 40, 64, 88, 112, +4, 28, 52, 76, 100, 12, 36, 60, 84, 108, 20, 44, 68, 92, 116, +1, 25, 49, 73, 97, 9, 33, 57, 81, 105, 17, 41, 65, 89, 113, +5, 29, 53, 77, 101, 13, 37, 61, 85, 109, 21, 45, 69, 93, 117, +2, 26, 50, 74, 98, 10, 34, 58, 82, 106, 18, 42, 66, 90, 114, +6, 30, 54, 78, 102, 14, 38, 62, 86, 110, 22, 46, 70, 94, 118, +3, 27, 51, 75, 99, 11, 35, 59, 83, 107, 19, 43, 67, 91, 115, +7, 31, 55, 79, 103, 15, 39, 63, 87, 111, 23, 47, 71, 95, 119, +}; +#endif + +#ifndef FFT_BITREV60 +#define FFT_BITREV60 +static const opus_int16 fft_bitrev60[60] = { +0, 12, 24, 36, 48, 4, 16, 28, 40, 52, 8, 20, 32, 44, 56, +1, 13, 25, 37, 49, 5, 17, 29, 41, 53, 9, 21, 33, 45, 57, +2, 14, 26, 38, 50, 6, 18, 30, 42, 54, 10, 22, 34, 46, 58, +3, 15, 27, 39, 51, 7, 19, 31, 43, 55, 11, 23, 35, 47, 59, +}; +#endif + +#ifndef FFT_STATE48000_960_0 +#define FFT_STATE48000_960_0 +static const kiss_fft_state fft_state48000_960_0 = { +480, /* nfft */ +0.002083333f, /* scale */ +-1, /* shift */ +{5, 96, 3, 32, 4, 8, 2, 4, 4, 1, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev480, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_480, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_1 +#define FFT_STATE48000_960_1 +static const kiss_fft_state fft_state48000_960_1 = { +240, /* nfft */ +0.004166667f, /* scale */ +1, /* shift */ +{5, 48, 3, 16, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev240, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_240, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_2 +#define FFT_STATE48000_960_2 +static const kiss_fft_state fft_state48000_960_2 = { +120, /* nfft */ +0.008333333f, /* scale */ +2, /* shift */ +{5, 24, 3, 8, 2, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev120, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_120, +#else +NULL, +#endif +}; +#endif + +#ifndef FFT_STATE48000_960_3 +#define FFT_STATE48000_960_3 +static const kiss_fft_state fft_state48000_960_3 = { +60, /* nfft */ +0.016666667f, /* scale */ +3, /* shift */ +{5, 12, 3, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev60, /* bitrev */ +fft_twiddles48000_960, /* bitrev */ +#ifdef OVERRIDE_FFT +(arch_fft_state *)&cfg_arch_60, +#else +NULL, +#endif +}; +#endif + +#endif + +#ifndef MDCT_TWIDDLES960 +#define MDCT_TWIDDLES960 +static const opus_val16 mdct_twiddles960[1800] = { +0.99999994f, 0.99999321f, 0.99997580f, 0.99994773f, 0.99990886f, +0.99985933f, 0.99979913f, 0.99972820f, 0.99964654f, 0.99955416f, +0.99945110f, 0.99933738f, 0.99921292f, 0.99907774f, 0.99893188f, +0.99877530f, 0.99860805f, 0.99843007f, 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-0.23662624f, +-0.26197869f, -0.28715160f, -0.31212771f, -0.33688989f, -0.36142120f, +-0.38570482f, -0.40972409f, -0.43346253f, -0.45690393f, -0.48003218f, +-0.50283146f, -0.52528608f, -0.54738069f, -0.56910020f, -0.59042966f, +-0.61135447f, -0.63186026f, -0.65193301f, -0.67155898f, -0.69072473f, +-0.70941705f, -0.72762316f, -0.74533063f, -0.76252723f, -0.77920127f, +-0.79534131f, -0.81093621f, -0.82597536f, -0.84044844f, -0.85434550f, +-0.86765707f, -0.88037395f, -0.89248747f, -0.90398932f, -0.91487163f, +-0.92512697f, -0.93474823f, -0.94372886f, -0.95206273f, -0.95974404f, +-0.96676767f, -0.97312868f, -0.97882277f, -0.98384601f, -0.98819500f, +-0.99186671f, -0.99485862f, -0.99716878f, -0.99879545f, -0.99973762f, +}; +#endif + +static const CELTMode mode48000_960_120 = { +48000, /* Fs */ +120, /* overlap */ +21, /* nbEBands */ +21, /* effEBands */ +{0.85000610f, 0.0000000f, 1.0000000f, 1.0000000f, }, /* preemph */ +eband5ms, /* eBands */ +3, /* maxLM */ +8, /* nbShortMdcts */ +120, /* shortMdctSize */ +11, /* nbAllocVectors */ +band_allocation, /* allocVectors */ +logN400, /* logN */ +window120, /* window */ +{1920, 3, {&fft_state48000_960_0, &fft_state48000_960_1, &fft_state48000_960_2, &fft_state48000_960_3, }, mdct_twiddles960}, /* mdct */ +{392, cache_index50, cache_bits50, cache_caps50}, /* cache */ +}; + +/* List of all the available modes */ +#define TOTAL_MODES 1 +static const CELTMode * const static_mode_list[TOTAL_MODES] = { +&mode48000_960_120, +}; diff --git a/managed_components/78__esp-opus/celt/static_modes_float_arm_ne10.h b/managed_components/78__esp-opus/celt/static_modes_float_arm_ne10.h new file mode 100644 index 0000000..66e1abb --- /dev/null +++ b/managed_components/78__esp-opus/celt/static_modes_float_arm_ne10.h @@ -0,0 +1,404 @@ +/* The contents of this file was automatically generated by + * dump_mode_arm_ne10.c with arguments: 48000 960 + * It contains static definitions for some pre-defined modes. */ +#include + +#ifndef NE10_FFT_PARAMS48000_960 +#define NE10_FFT_PARAMS48000_960 +static const ne10_int32_t ne10_factors_480[64] = { +4, 40, 4, 30, 2, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_240[64] = { +3, 20, 4, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_120[64] = { +3, 10, 2, 15, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_int32_t ne10_factors_60[64] = { +2, 5, 5, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, }; +static const ne10_fft_cpx_float32_t ne10_twiddles_480[480] = { +{1.0000000f,0.0000000f}, {1.0000000f,-0.0000000f}, {1.0000000f,-0.0000000f}, +{1.0000000f,-0.0000000f}, {0.91354543f,-0.40673664f}, {0.66913056f,-0.74314487f}, +{1.0000000f,-0.0000000f}, {0.66913056f,-0.74314487f}, {-0.10452851f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.30901697f,-0.95105654f}, {-0.80901700f,-0.58778518f}, +{1.0000000f,-0.0000000f}, {-0.10452851f,-0.99452192f}, {-0.97814757f,0.20791179f}, +{1.0000000f,-0.0000000f}, {0.97814763f,-0.20791170f}, {0.91354543f,-0.40673664f}, +{0.80901700f,-0.58778524f}, {0.66913056f,-0.74314487f}, {0.49999997f,-0.86602545f}, +{0.30901697f,-0.95105654f}, {0.10452842f,-0.99452192f}, {-0.10452851f,-0.99452192f}, +{-0.30901703f,-0.95105648f}, {-0.50000006f,-0.86602533f}, {-0.66913068f,-0.74314475f}, +{-0.80901700f,-0.58778518f}, {-0.91354549f,-0.40673658f}, {-0.97814763f,-0.20791161f}, +{1.0000000f,-0.0000000f}, {0.99862951f,-0.052335959f}, {0.99452192f,-0.10452846f}, +{0.98768836f,-0.15643448f}, {0.97814763f,-0.20791170f}, {0.96592581f,-0.25881904f}, +{0.95105648f,-0.30901700f}, {0.93358040f,-0.35836795f}, {0.91354543f,-0.40673664f}, +{0.89100653f,-0.45399052f}, {0.86602545f,-0.50000000f}, {0.83867055f,-0.54463905f}, +{0.80901700f,-0.58778524f}, {0.77714598f,-0.62932038f}, {0.74314475f,-0.66913062f}, +{0.70710677f,-0.70710683f}, {0.66913056f,-0.74314487f}, {0.62932038f,-0.77714598f}, +{0.58778524f,-0.80901700f}, {0.54463899f,-0.83867055f}, {0.49999997f,-0.86602545f}, +{0.45399052f,-0.89100653f}, {0.40673661f,-0.91354549f}, {0.35836786f,-0.93358046f}, +{0.30901697f,-0.95105654f}, {0.25881907f,-0.96592581f}, {0.20791166f,-0.97814763f}, +{0.15643437f,-0.98768836f}, {0.10452842f,-0.99452192f}, {0.052335974f,-0.99862951f}, +{1.0000000f,-0.0000000f}, {0.99452192f,-0.10452846f}, {0.97814763f,-0.20791170f}, +{0.95105648f,-0.30901700f}, {0.91354543f,-0.40673664f}, {0.86602545f,-0.50000000f}, +{0.80901700f,-0.58778524f}, {0.74314475f,-0.66913062f}, {0.66913056f,-0.74314487f}, +{0.58778524f,-0.80901700f}, {0.49999997f,-0.86602545f}, {0.40673661f,-0.91354549f}, +{0.30901697f,-0.95105654f}, {0.20791166f,-0.97814763f}, {0.10452842f,-0.99452192f}, +{-4.3711388e-08f,-1.0000000f}, {-0.10452851f,-0.99452192f}, {-0.20791174f,-0.97814757f}, +{-0.30901703f,-0.95105648f}, {-0.40673670f,-0.91354543f}, {-0.50000006f,-0.86602533f}, +{-0.58778518f,-0.80901700f}, {-0.66913068f,-0.74314475f}, {-0.74314493f,-0.66913044f}, +{-0.80901700f,-0.58778518f}, {-0.86602539f,-0.50000006f}, {-0.91354549f,-0.40673658f}, +{-0.95105654f,-0.30901679f}, {-0.97814763f,-0.20791161f}, {-0.99452192f,-0.10452849f}, +{1.0000000f,-0.0000000f}, {0.98768836f,-0.15643448f}, {0.95105648f,-0.30901700f}, +{0.89100653f,-0.45399052f}, {0.80901700f,-0.58778524f}, {0.70710677f,-0.70710683f}, +{0.58778524f,-0.80901700f}, {0.45399052f,-0.89100653f}, {0.30901697f,-0.95105654f}, +{0.15643437f,-0.98768836f}, {-4.3711388e-08f,-1.0000000f}, {-0.15643445f,-0.98768836f}, +{-0.30901703f,-0.95105648f}, {-0.45399061f,-0.89100647f}, {-0.58778518f,-0.80901700f}, +{-0.70710677f,-0.70710677f}, {-0.80901700f,-0.58778518f}, {-0.89100659f,-0.45399037f}, +{-0.95105654f,-0.30901679f}, {-0.98768836f,-0.15643445f}, {-1.0000000f,8.7422777e-08f}, +{-0.98768830f,0.15643461f}, {-0.95105654f,0.30901697f}, {-0.89100653f,0.45399055f}, +{-0.80901694f,0.58778536f}, {-0.70710665f,0.70710689f}, {-0.58778507f,0.80901712f}, +{-0.45399022f,0.89100665f}, {-0.30901709f,0.95105648f}, {-0.15643452f,0.98768830f}, +{1.0000000f,-0.0000000f}, {0.99991435f,-0.013089596f}, {0.99965733f,-0.026176950f}, +{0.99922901f,-0.039259817f}, {0.99862951f,-0.052335959f}, {0.99785894f,-0.065403134f}, +{0.99691731f,-0.078459099f}, {0.99580491f,-0.091501623f}, {0.99452192f,-0.10452846f}, +{0.99306846f,-0.11753740f}, {0.99144489f,-0.13052620f}, {0.98965138f,-0.14349262f}, +{0.98768836f,-0.15643448f}, {0.98555607f,-0.16934951f}, {0.98325491f,-0.18223552f}, +{0.98078525f,-0.19509032f}, {0.97814763f,-0.20791170f}, {0.97534233f,-0.22069745f}, +{0.97236991f,-0.23344538f}, {0.96923089f,-0.24615330f}, {0.96592581f,-0.25881904f}, +{0.96245521f,-0.27144045f}, {0.95881975f,-0.28401536f}, {0.95501995f,-0.29654160f}, +{0.95105648f,-0.30901700f}, {0.94693011f,-0.32143945f}, {0.94264150f,-0.33380687f}, +{0.93819129f,-0.34611708f}, {0.93358040f,-0.35836795f}, {0.92880952f,-0.37055743f}, +{0.92387956f,-0.38268346f}, {0.91879117f,-0.39474389f}, {0.91354543f,-0.40673664f}, +{0.90814316f,-0.41865975f}, {0.90258527f,-0.43051112f}, {0.89687270f,-0.44228873f}, +{0.89100653f,-0.45399052f}, {0.88498765f,-0.46561453f}, {0.87881708f,-0.47715878f}, +{0.87249601f,-0.48862126f}, {0.86602545f,-0.50000000f}, {0.85940641f,-0.51129311f}, +{0.85264015f,-0.52249855f}, {0.84572786f,-0.53361452f}, {0.83867055f,-0.54463905f}, +{0.83146960f,-0.55557024f}, {0.82412618f,-0.56640625f}, {0.81664151f,-0.57714522f}, +{0.80901700f,-0.58778524f}, {0.80125380f,-0.59832460f}, {0.79335332f,-0.60876143f}, +{0.78531694f,-0.61909395f}, {0.77714598f,-0.62932038f}, {0.76884180f,-0.63943899f}, +{0.76040596f,-0.64944810f}, {0.75183982f,-0.65934587f}, {0.74314475f,-0.66913062f}, +{0.73432249f,-0.67880076f}, {0.72537434f,-0.68835455f}, {0.71630192f,-0.69779050f}, +{0.70710677f,-0.70710683f}, {0.69779044f,-0.71630198f}, {0.68835455f,-0.72537440f}, +{0.67880070f,-0.73432255f}, {0.66913056f,-0.74314487f}, {0.65934581f,-0.75183982f}, +{0.64944804f,-0.76040596f}, {0.63943899f,-0.76884186f}, {0.62932038f,-0.77714598f}, +{0.61909395f,-0.78531694f}, {0.60876137f,-0.79335338f}, {0.59832460f,-0.80125386f}, +{0.58778524f,-0.80901700f}, {0.57714516f,-0.81664151f}, {0.56640625f,-0.82412618f}, +{0.55557019f,-0.83146960f}, {0.54463899f,-0.83867055f}, {0.53361452f,-0.84572786f}, +{0.52249849f,-0.85264015f}, {0.51129311f,-0.85940641f}, {0.49999997f,-0.86602545f}, +{0.48862118f,-0.87249601f}, {0.47715876f,-0.87881708f}, {0.46561447f,-0.88498765f}, +{0.45399052f,-0.89100653f}, {0.44228867f,-0.89687276f}, {0.43051103f,-0.90258533f}, +{0.41865975f,-0.90814316f}, {0.40673661f,-0.91354549f}, {0.39474380f,-0.91879129f}, +{0.38268343f,-0.92387956f}, {0.37055740f,-0.92880958f}, {0.35836786f,-0.93358046f}, +{0.34611705f,-0.93819135f}, {0.33380681f,-0.94264150f}, {0.32143947f,-0.94693011f}, +{0.30901697f,-0.95105654f}, {0.29654151f,-0.95501995f}, {0.28401533f,-0.95881975f}, +{0.27144039f,-0.96245527f}, {0.25881907f,-0.96592581f}, {0.24615327f,-0.96923089f}, +{0.23344530f,-0.97236991f}, {0.22069745f,-0.97534233f}, {0.20791166f,-0.97814763f}, +{0.19509023f,-0.98078531f}, {0.18223552f,-0.98325491f}, {0.16934945f,-0.98555607f}, +{0.15643437f,-0.98768836f}, {0.14349259f,-0.98965138f}, {0.13052613f,-0.99144489f}, +{0.11753740f,-0.99306846f}, {0.10452842f,-0.99452192f}, {0.091501534f,-0.99580491f}, +{0.078459084f,-0.99691731f}, {0.065403074f,-0.99785894f}, {0.052335974f,-0.99862951f}, +{0.039259788f,-0.99922901f}, {0.026176875f,-0.99965733f}, {0.013089597f,-0.99991435f}, +{1.0000000f,-0.0000000f}, {0.99965733f,-0.026176950f}, {0.99862951f,-0.052335959f}, +{0.99691731f,-0.078459099f}, {0.99452192f,-0.10452846f}, {0.99144489f,-0.13052620f}, +{0.98768836f,-0.15643448f}, {0.98325491f,-0.18223552f}, {0.97814763f,-0.20791170f}, +{0.97236991f,-0.23344538f}, {0.96592581f,-0.25881904f}, {0.95881975f,-0.28401536f}, +{0.95105648f,-0.30901700f}, {0.94264150f,-0.33380687f}, {0.93358040f,-0.35836795f}, +{0.92387956f,-0.38268346f}, {0.91354543f,-0.40673664f}, {0.90258527f,-0.43051112f}, +{0.89100653f,-0.45399052f}, {0.87881708f,-0.47715878f}, {0.86602545f,-0.50000000f}, +{0.85264015f,-0.52249855f}, {0.83867055f,-0.54463905f}, {0.82412618f,-0.56640625f}, +{0.80901700f,-0.58778524f}, {0.79335332f,-0.60876143f}, {0.77714598f,-0.62932038f}, +{0.76040596f,-0.64944810f}, {0.74314475f,-0.66913062f}, {0.72537434f,-0.68835455f}, +{0.70710677f,-0.70710683f}, {0.68835455f,-0.72537440f}, {0.66913056f,-0.74314487f}, +{0.64944804f,-0.76040596f}, {0.62932038f,-0.77714598f}, {0.60876137f,-0.79335338f}, +{0.58778524f,-0.80901700f}, {0.56640625f,-0.82412618f}, {0.54463899f,-0.83867055f}, +{0.52249849f,-0.85264015f}, {0.49999997f,-0.86602545f}, {0.47715876f,-0.87881708f}, +{0.45399052f,-0.89100653f}, {0.43051103f,-0.90258533f}, {0.40673661f,-0.91354549f}, +{0.38268343f,-0.92387956f}, {0.35836786f,-0.93358046f}, {0.33380681f,-0.94264150f}, +{0.30901697f,-0.95105654f}, {0.28401533f,-0.95881975f}, {0.25881907f,-0.96592581f}, +{0.23344530f,-0.97236991f}, {0.20791166f,-0.97814763f}, {0.18223552f,-0.98325491f}, +{0.15643437f,-0.98768836f}, {0.13052613f,-0.99144489f}, {0.10452842f,-0.99452192f}, +{0.078459084f,-0.99691731f}, {0.052335974f,-0.99862951f}, {0.026176875f,-0.99965733f}, +{-4.3711388e-08f,-1.0000000f}, {-0.026176963f,-0.99965733f}, {-0.052336060f,-0.99862951f}, +{-0.078459173f,-0.99691731f}, {-0.10452851f,-0.99452192f}, {-0.13052621f,-0.99144489f}, +{-0.15643445f,-0.98768836f}, {-0.18223560f,-0.98325491f}, {-0.20791174f,-0.97814757f}, +{-0.23344538f,-0.97236991f}, {-0.25881916f,-0.96592581f}, {-0.28401542f,-0.95881969f}, +{-0.30901703f,-0.95105648f}, {-0.33380687f,-0.94264150f}, {-0.35836795f,-0.93358040f}, +{-0.38268352f,-0.92387950f}, {-0.40673670f,-0.91354543f}, {-0.43051112f,-0.90258527f}, +{-0.45399061f,-0.89100647f}, {-0.47715873f,-0.87881708f}, {-0.50000006f,-0.86602533f}, +{-0.52249867f,-0.85264009f}, {-0.54463905f,-0.83867055f}, {-0.56640631f,-0.82412612f}, +{-0.58778518f,-0.80901700f}, {-0.60876143f,-0.79335332f}, {-0.62932050f,-0.77714586f}, +{-0.64944804f,-0.76040596f}, {-0.66913068f,-0.74314475f}, {-0.68835467f,-0.72537428f}, +{-0.70710677f,-0.70710677f}, {-0.72537446f,-0.68835449f}, {-0.74314493f,-0.66913044f}, +{-0.76040596f,-0.64944804f}, {-0.77714604f,-0.62932026f}, {-0.79335332f,-0.60876143f}, +{-0.80901700f,-0.58778518f}, {-0.82412624f,-0.56640613f}, {-0.83867055f,-0.54463899f}, +{-0.85264021f,-0.52249849f}, {-0.86602539f,-0.50000006f}, {-0.87881714f,-0.47715873f}, +{-0.89100659f,-0.45399037f}, {-0.90258527f,-0.43051112f}, {-0.91354549f,-0.40673658f}, +{-0.92387956f,-0.38268328f}, {-0.93358040f,-0.35836792f}, {-0.94264150f,-0.33380675f}, +{-0.95105654f,-0.30901679f}, {-0.95881975f,-0.28401530f}, {-0.96592587f,-0.25881892f}, +{-0.97236991f,-0.23344538f}, {-0.97814763f,-0.20791161f}, {-0.98325491f,-0.18223536f}, +{-0.98768836f,-0.15643445f}, {-0.99144489f,-0.13052608f}, {-0.99452192f,-0.10452849f}, +{-0.99691737f,-0.078459039f}, {-0.99862957f,-0.052335810f}, {-0.99965733f,-0.026176952f}, +{1.0000000f,-0.0000000f}, {0.99922901f,-0.039259817f}, {0.99691731f,-0.078459099f}, +{0.99306846f,-0.11753740f}, {0.98768836f,-0.15643448f}, {0.98078525f,-0.19509032f}, +{0.97236991f,-0.23344538f}, {0.96245521f,-0.27144045f}, {0.95105648f,-0.30901700f}, +{0.93819129f,-0.34611708f}, {0.92387956f,-0.38268346f}, {0.90814316f,-0.41865975f}, +{0.89100653f,-0.45399052f}, {0.87249601f,-0.48862126f}, {0.85264015f,-0.52249855f}, +{0.83146960f,-0.55557024f}, {0.80901700f,-0.58778524f}, {0.78531694f,-0.61909395f}, +{0.76040596f,-0.64944810f}, {0.73432249f,-0.67880076f}, {0.70710677f,-0.70710683f}, +{0.67880070f,-0.73432255f}, {0.64944804f,-0.76040596f}, {0.61909395f,-0.78531694f}, +{0.58778524f,-0.80901700f}, {0.55557019f,-0.83146960f}, {0.52249849f,-0.85264015f}, +{0.48862118f,-0.87249601f}, {0.45399052f,-0.89100653f}, {0.41865975f,-0.90814316f}, +{0.38268343f,-0.92387956f}, {0.34611705f,-0.93819135f}, {0.30901697f,-0.95105654f}, +{0.27144039f,-0.96245527f}, {0.23344530f,-0.97236991f}, {0.19509023f,-0.98078531f}, +{0.15643437f,-0.98768836f}, {0.11753740f,-0.99306846f}, {0.078459084f,-0.99691731f}, +{0.039259788f,-0.99922901f}, {-4.3711388e-08f,-1.0000000f}, {-0.039259877f,-0.99922901f}, +{-0.078459173f,-0.99691731f}, {-0.11753749f,-0.99306846f}, {-0.15643445f,-0.98768836f}, +{-0.19509032f,-0.98078525f}, {-0.23344538f,-0.97236991f}, {-0.27144048f,-0.96245521f}, +{-0.30901703f,-0.95105648f}, {-0.34611711f,-0.93819129f}, {-0.38268352f,-0.92387950f}, +{-0.41865984f,-0.90814310f}, {-0.45399061f,-0.89100647f}, {-0.48862135f,-0.87249595f}, +{-0.52249867f,-0.85264009f}, {-0.55557036f,-0.83146954f}, {-0.58778518f,-0.80901700f}, +{-0.61909389f,-0.78531694f}, {-0.64944804f,-0.76040596f}, {-0.67880076f,-0.73432249f}, +{-0.70710677f,-0.70710677f}, {-0.73432249f,-0.67880070f}, {-0.76040596f,-0.64944804f}, +{-0.78531694f,-0.61909389f}, {-0.80901700f,-0.58778518f}, {-0.83146966f,-0.55557019f}, +{-0.85264021f,-0.52249849f}, {-0.87249607f,-0.48862115f}, {-0.89100659f,-0.45399037f}, +{-0.90814322f,-0.41865960f}, {-0.92387956f,-0.38268328f}, {-0.93819135f,-0.34611690f}, +{-0.95105654f,-0.30901679f}, {-0.96245521f,-0.27144048f}, {-0.97236991f,-0.23344538f}, +{-0.98078531f,-0.19509031f}, {-0.98768836f,-0.15643445f}, {-0.99306846f,-0.11753736f}, +{-0.99691737f,-0.078459039f}, {-0.99922901f,-0.039259743f}, {-1.0000000f,8.7422777e-08f}, +{-0.99922901f,0.039259918f}, {-0.99691731f,0.078459218f}, {-0.99306846f,0.11753753f}, +{-0.98768830f,0.15643461f}, {-0.98078525f,0.19509049f}, {-0.97236985f,0.23344554f}, +{-0.96245515f,0.27144065f}, {-0.95105654f,0.30901697f}, {-0.93819135f,0.34611705f}, +{-0.92387956f,0.38268346f}, {-0.90814316f,0.41865975f}, {-0.89100653f,0.45399055f}, +{-0.87249601f,0.48862129f}, {-0.85264015f,0.52249861f}, {-0.83146960f,0.55557030f}, +{-0.80901694f,0.58778536f}, {-0.78531688f,0.61909401f}, {-0.76040590f,0.64944816f}, +{-0.73432243f,0.67880082f}, {-0.70710665f,0.70710689f}, {-0.67880058f,0.73432261f}, +{-0.64944792f,0.76040608f}, {-0.61909378f,0.78531706f}, {-0.58778507f,0.80901712f}, +{-0.55557001f,0.83146977f}, {-0.52249837f,0.85264033f}, {-0.48862100f,0.87249613f}, +{-0.45399022f,0.89100665f}, {-0.41865945f,0.90814328f}, {-0.38268313f,0.92387968f}, +{-0.34611672f,0.93819147f}, {-0.30901709f,0.95105648f}, {-0.27144054f,0.96245521f}, +{-0.23344545f,0.97236991f}, {-0.19509038f,0.98078525f}, {-0.15643452f,0.98768830f}, +{-0.11753743f,0.99306846f}, {-0.078459114f,0.99691731f}, {-0.039259821f,0.99922901f}, +}; +static const ne10_fft_cpx_float32_t ne10_twiddles_240[240] = { +{1.0000000f,0.0000000f}, {1.0000000f,-0.0000000f}, {1.0000000f,-0.0000000f}, +{1.0000000f,-0.0000000f}, {0.91354543f,-0.40673664f}, {0.66913056f,-0.74314487f}, +{1.0000000f,-0.0000000f}, {0.66913056f,-0.74314487f}, {-0.10452851f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.30901697f,-0.95105654f}, {-0.80901700f,-0.58778518f}, +{1.0000000f,-0.0000000f}, {-0.10452851f,-0.99452192f}, {-0.97814757f,0.20791179f}, +{1.0000000f,-0.0000000f}, {0.99452192f,-0.10452846f}, {0.97814763f,-0.20791170f}, +{0.95105648f,-0.30901700f}, {0.91354543f,-0.40673664f}, {0.86602545f,-0.50000000f}, +{0.80901700f,-0.58778524f}, {0.74314475f,-0.66913062f}, {0.66913056f,-0.74314487f}, +{0.58778524f,-0.80901700f}, {0.49999997f,-0.86602545f}, {0.40673661f,-0.91354549f}, +{0.30901697f,-0.95105654f}, {0.20791166f,-0.97814763f}, {0.10452842f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.97814763f,-0.20791170f}, {0.91354543f,-0.40673664f}, +{0.80901700f,-0.58778524f}, {0.66913056f,-0.74314487f}, {0.49999997f,-0.86602545f}, +{0.30901697f,-0.95105654f}, {0.10452842f,-0.99452192f}, {-0.10452851f,-0.99452192f}, +{-0.30901703f,-0.95105648f}, {-0.50000006f,-0.86602533f}, {-0.66913068f,-0.74314475f}, +{-0.80901700f,-0.58778518f}, {-0.91354549f,-0.40673658f}, {-0.97814763f,-0.20791161f}, +{1.0000000f,-0.0000000f}, {0.95105648f,-0.30901700f}, {0.80901700f,-0.58778524f}, +{0.58778524f,-0.80901700f}, {0.30901697f,-0.95105654f}, {-4.3711388e-08f,-1.0000000f}, +{-0.30901703f,-0.95105648f}, {-0.58778518f,-0.80901700f}, {-0.80901700f,-0.58778518f}, +{-0.95105654f,-0.30901679f}, {-1.0000000f,8.7422777e-08f}, {-0.95105654f,0.30901697f}, +{-0.80901694f,0.58778536f}, {-0.58778507f,0.80901712f}, {-0.30901709f,0.95105648f}, +{1.0000000f,-0.0000000f}, {0.99965733f,-0.026176950f}, {0.99862951f,-0.052335959f}, +{0.99691731f,-0.078459099f}, {0.99452192f,-0.10452846f}, {0.99144489f,-0.13052620f}, +{0.98768836f,-0.15643448f}, {0.98325491f,-0.18223552f}, {0.97814763f,-0.20791170f}, +{0.97236991f,-0.23344538f}, {0.96592581f,-0.25881904f}, {0.95881975f,-0.28401536f}, +{0.95105648f,-0.30901700f}, {0.94264150f,-0.33380687f}, {0.93358040f,-0.35836795f}, +{0.92387956f,-0.38268346f}, {0.91354543f,-0.40673664f}, {0.90258527f,-0.43051112f}, +{0.89100653f,-0.45399052f}, {0.87881708f,-0.47715878f}, {0.86602545f,-0.50000000f}, +{0.85264015f,-0.52249855f}, {0.83867055f,-0.54463905f}, {0.82412618f,-0.56640625f}, +{0.80901700f,-0.58778524f}, {0.79335332f,-0.60876143f}, {0.77714598f,-0.62932038f}, +{0.76040596f,-0.64944810f}, {0.74314475f,-0.66913062f}, {0.72537434f,-0.68835455f}, +{0.70710677f,-0.70710683f}, {0.68835455f,-0.72537440f}, {0.66913056f,-0.74314487f}, +{0.64944804f,-0.76040596f}, {0.62932038f,-0.77714598f}, {0.60876137f,-0.79335338f}, +{0.58778524f,-0.80901700f}, {0.56640625f,-0.82412618f}, {0.54463899f,-0.83867055f}, +{0.52249849f,-0.85264015f}, {0.49999997f,-0.86602545f}, {0.47715876f,-0.87881708f}, +{0.45399052f,-0.89100653f}, {0.43051103f,-0.90258533f}, {0.40673661f,-0.91354549f}, +{0.38268343f,-0.92387956f}, {0.35836786f,-0.93358046f}, {0.33380681f,-0.94264150f}, +{0.30901697f,-0.95105654f}, {0.28401533f,-0.95881975f}, {0.25881907f,-0.96592581f}, +{0.23344530f,-0.97236991f}, {0.20791166f,-0.97814763f}, {0.18223552f,-0.98325491f}, +{0.15643437f,-0.98768836f}, {0.13052613f,-0.99144489f}, {0.10452842f,-0.99452192f}, +{0.078459084f,-0.99691731f}, {0.052335974f,-0.99862951f}, {0.026176875f,-0.99965733f}, +{1.0000000f,-0.0000000f}, {0.99862951f,-0.052335959f}, {0.99452192f,-0.10452846f}, +{0.98768836f,-0.15643448f}, {0.97814763f,-0.20791170f}, {0.96592581f,-0.25881904f}, +{0.95105648f,-0.30901700f}, {0.93358040f,-0.35836795f}, {0.91354543f,-0.40673664f}, +{0.89100653f,-0.45399052f}, {0.86602545f,-0.50000000f}, {0.83867055f,-0.54463905f}, +{0.80901700f,-0.58778524f}, {0.77714598f,-0.62932038f}, {0.74314475f,-0.66913062f}, +{0.70710677f,-0.70710683f}, {0.66913056f,-0.74314487f}, {0.62932038f,-0.77714598f}, +{0.58778524f,-0.80901700f}, {0.54463899f,-0.83867055f}, {0.49999997f,-0.86602545f}, +{0.45399052f,-0.89100653f}, {0.40673661f,-0.91354549f}, {0.35836786f,-0.93358046f}, +{0.30901697f,-0.95105654f}, {0.25881907f,-0.96592581f}, {0.20791166f,-0.97814763f}, +{0.15643437f,-0.98768836f}, {0.10452842f,-0.99452192f}, {0.052335974f,-0.99862951f}, +{-4.3711388e-08f,-1.0000000f}, {-0.052336060f,-0.99862951f}, {-0.10452851f,-0.99452192f}, +{-0.15643445f,-0.98768836f}, {-0.20791174f,-0.97814757f}, {-0.25881916f,-0.96592581f}, +{-0.30901703f,-0.95105648f}, {-0.35836795f,-0.93358040f}, {-0.40673670f,-0.91354543f}, +{-0.45399061f,-0.89100647f}, {-0.50000006f,-0.86602533f}, {-0.54463905f,-0.83867055f}, +{-0.58778518f,-0.80901700f}, {-0.62932050f,-0.77714586f}, {-0.66913068f,-0.74314475f}, +{-0.70710677f,-0.70710677f}, {-0.74314493f,-0.66913044f}, {-0.77714604f,-0.62932026f}, +{-0.80901700f,-0.58778518f}, {-0.83867055f,-0.54463899f}, {-0.86602539f,-0.50000006f}, +{-0.89100659f,-0.45399037f}, {-0.91354549f,-0.40673658f}, {-0.93358040f,-0.35836792f}, +{-0.95105654f,-0.30901679f}, {-0.96592587f,-0.25881892f}, {-0.97814763f,-0.20791161f}, +{-0.98768836f,-0.15643445f}, {-0.99452192f,-0.10452849f}, {-0.99862957f,-0.052335810f}, +{1.0000000f,-0.0000000f}, {0.99691731f,-0.078459099f}, {0.98768836f,-0.15643448f}, +{0.97236991f,-0.23344538f}, {0.95105648f,-0.30901700f}, {0.92387956f,-0.38268346f}, +{0.89100653f,-0.45399052f}, {0.85264015f,-0.52249855f}, {0.80901700f,-0.58778524f}, +{0.76040596f,-0.64944810f}, {0.70710677f,-0.70710683f}, {0.64944804f,-0.76040596f}, +{0.58778524f,-0.80901700f}, {0.52249849f,-0.85264015f}, {0.45399052f,-0.89100653f}, +{0.38268343f,-0.92387956f}, {0.30901697f,-0.95105654f}, {0.23344530f,-0.97236991f}, +{0.15643437f,-0.98768836f}, {0.078459084f,-0.99691731f}, {-4.3711388e-08f,-1.0000000f}, +{-0.078459173f,-0.99691731f}, {-0.15643445f,-0.98768836f}, {-0.23344538f,-0.97236991f}, +{-0.30901703f,-0.95105648f}, {-0.38268352f,-0.92387950f}, {-0.45399061f,-0.89100647f}, +{-0.52249867f,-0.85264009f}, {-0.58778518f,-0.80901700f}, {-0.64944804f,-0.76040596f}, +{-0.70710677f,-0.70710677f}, {-0.76040596f,-0.64944804f}, {-0.80901700f,-0.58778518f}, +{-0.85264021f,-0.52249849f}, {-0.89100659f,-0.45399037f}, {-0.92387956f,-0.38268328f}, +{-0.95105654f,-0.30901679f}, {-0.97236991f,-0.23344538f}, {-0.98768836f,-0.15643445f}, +{-0.99691737f,-0.078459039f}, {-1.0000000f,8.7422777e-08f}, {-0.99691731f,0.078459218f}, +{-0.98768830f,0.15643461f}, {-0.97236985f,0.23344554f}, {-0.95105654f,0.30901697f}, +{-0.92387956f,0.38268346f}, {-0.89100653f,0.45399055f}, {-0.85264015f,0.52249861f}, +{-0.80901694f,0.58778536f}, {-0.76040590f,0.64944816f}, {-0.70710665f,0.70710689f}, +{-0.64944792f,0.76040608f}, {-0.58778507f,0.80901712f}, {-0.52249837f,0.85264033f}, +{-0.45399022f,0.89100665f}, {-0.38268313f,0.92387968f}, {-0.30901709f,0.95105648f}, +{-0.23344545f,0.97236991f}, {-0.15643452f,0.98768830f}, {-0.078459114f,0.99691731f}, +}; +static const ne10_fft_cpx_float32_t ne10_twiddles_120[120] = { +{1.0000000f,0.0000000f}, {1.0000000f,-0.0000000f}, {1.0000000f,-0.0000000f}, +{1.0000000f,-0.0000000f}, {0.91354543f,-0.40673664f}, {0.66913056f,-0.74314487f}, +{1.0000000f,-0.0000000f}, {0.66913056f,-0.74314487f}, {-0.10452851f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.30901697f,-0.95105654f}, {-0.80901700f,-0.58778518f}, +{1.0000000f,-0.0000000f}, {-0.10452851f,-0.99452192f}, {-0.97814757f,0.20791179f}, +{1.0000000f,-0.0000000f}, {0.97814763f,-0.20791170f}, {0.91354543f,-0.40673664f}, +{0.80901700f,-0.58778524f}, {0.66913056f,-0.74314487f}, {0.49999997f,-0.86602545f}, +{0.30901697f,-0.95105654f}, {0.10452842f,-0.99452192f}, {-0.10452851f,-0.99452192f}, +{-0.30901703f,-0.95105648f}, {-0.50000006f,-0.86602533f}, {-0.66913068f,-0.74314475f}, +{-0.80901700f,-0.58778518f}, {-0.91354549f,-0.40673658f}, {-0.97814763f,-0.20791161f}, +{1.0000000f,-0.0000000f}, {0.99862951f,-0.052335959f}, {0.99452192f,-0.10452846f}, +{0.98768836f,-0.15643448f}, {0.97814763f,-0.20791170f}, {0.96592581f,-0.25881904f}, +{0.95105648f,-0.30901700f}, {0.93358040f,-0.35836795f}, {0.91354543f,-0.40673664f}, +{0.89100653f,-0.45399052f}, {0.86602545f,-0.50000000f}, {0.83867055f,-0.54463905f}, +{0.80901700f,-0.58778524f}, {0.77714598f,-0.62932038f}, {0.74314475f,-0.66913062f}, +{0.70710677f,-0.70710683f}, {0.66913056f,-0.74314487f}, {0.62932038f,-0.77714598f}, +{0.58778524f,-0.80901700f}, {0.54463899f,-0.83867055f}, {0.49999997f,-0.86602545f}, +{0.45399052f,-0.89100653f}, {0.40673661f,-0.91354549f}, {0.35836786f,-0.93358046f}, +{0.30901697f,-0.95105654f}, {0.25881907f,-0.96592581f}, {0.20791166f,-0.97814763f}, +{0.15643437f,-0.98768836f}, {0.10452842f,-0.99452192f}, {0.052335974f,-0.99862951f}, +{1.0000000f,-0.0000000f}, {0.99452192f,-0.10452846f}, {0.97814763f,-0.20791170f}, +{0.95105648f,-0.30901700f}, {0.91354543f,-0.40673664f}, {0.86602545f,-0.50000000f}, +{0.80901700f,-0.58778524f}, {0.74314475f,-0.66913062f}, {0.66913056f,-0.74314487f}, +{0.58778524f,-0.80901700f}, {0.49999997f,-0.86602545f}, {0.40673661f,-0.91354549f}, +{0.30901697f,-0.95105654f}, {0.20791166f,-0.97814763f}, {0.10452842f,-0.99452192f}, +{-4.3711388e-08f,-1.0000000f}, {-0.10452851f,-0.99452192f}, {-0.20791174f,-0.97814757f}, +{-0.30901703f,-0.95105648f}, {-0.40673670f,-0.91354543f}, {-0.50000006f,-0.86602533f}, +{-0.58778518f,-0.80901700f}, {-0.66913068f,-0.74314475f}, {-0.74314493f,-0.66913044f}, +{-0.80901700f,-0.58778518f}, {-0.86602539f,-0.50000006f}, {-0.91354549f,-0.40673658f}, +{-0.95105654f,-0.30901679f}, {-0.97814763f,-0.20791161f}, {-0.99452192f,-0.10452849f}, +{1.0000000f,-0.0000000f}, {0.98768836f,-0.15643448f}, {0.95105648f,-0.30901700f}, +{0.89100653f,-0.45399052f}, {0.80901700f,-0.58778524f}, {0.70710677f,-0.70710683f}, +{0.58778524f,-0.80901700f}, {0.45399052f,-0.89100653f}, {0.30901697f,-0.95105654f}, +{0.15643437f,-0.98768836f}, {-4.3711388e-08f,-1.0000000f}, {-0.15643445f,-0.98768836f}, +{-0.30901703f,-0.95105648f}, {-0.45399061f,-0.89100647f}, {-0.58778518f,-0.80901700f}, +{-0.70710677f,-0.70710677f}, {-0.80901700f,-0.58778518f}, {-0.89100659f,-0.45399037f}, +{-0.95105654f,-0.30901679f}, {-0.98768836f,-0.15643445f}, {-1.0000000f,8.7422777e-08f}, +{-0.98768830f,0.15643461f}, {-0.95105654f,0.30901697f}, {-0.89100653f,0.45399055f}, +{-0.80901694f,0.58778536f}, {-0.70710665f,0.70710689f}, {-0.58778507f,0.80901712f}, +{-0.45399022f,0.89100665f}, {-0.30901709f,0.95105648f}, {-0.15643452f,0.98768830f}, +}; +static const ne10_fft_cpx_float32_t ne10_twiddles_60[60] = { +{1.0000000f,0.0000000f}, {1.0000000f,-0.0000000f}, {1.0000000f,-0.0000000f}, +{1.0000000f,-0.0000000f}, {0.91354543f,-0.40673664f}, {0.66913056f,-0.74314487f}, +{1.0000000f,-0.0000000f}, {0.66913056f,-0.74314487f}, {-0.10452851f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.30901697f,-0.95105654f}, {-0.80901700f,-0.58778518f}, +{1.0000000f,-0.0000000f}, {-0.10452851f,-0.99452192f}, {-0.97814757f,0.20791179f}, +{1.0000000f,-0.0000000f}, {0.99452192f,-0.10452846f}, {0.97814763f,-0.20791170f}, +{0.95105648f,-0.30901700f}, {0.91354543f,-0.40673664f}, {0.86602545f,-0.50000000f}, +{0.80901700f,-0.58778524f}, {0.74314475f,-0.66913062f}, {0.66913056f,-0.74314487f}, +{0.58778524f,-0.80901700f}, {0.49999997f,-0.86602545f}, {0.40673661f,-0.91354549f}, +{0.30901697f,-0.95105654f}, {0.20791166f,-0.97814763f}, {0.10452842f,-0.99452192f}, +{1.0000000f,-0.0000000f}, {0.97814763f,-0.20791170f}, {0.91354543f,-0.40673664f}, +{0.80901700f,-0.58778524f}, {0.66913056f,-0.74314487f}, {0.49999997f,-0.86602545f}, +{0.30901697f,-0.95105654f}, {0.10452842f,-0.99452192f}, {-0.10452851f,-0.99452192f}, +{-0.30901703f,-0.95105648f}, {-0.50000006f,-0.86602533f}, {-0.66913068f,-0.74314475f}, +{-0.80901700f,-0.58778518f}, {-0.91354549f,-0.40673658f}, {-0.97814763f,-0.20791161f}, +{1.0000000f,-0.0000000f}, {0.95105648f,-0.30901700f}, {0.80901700f,-0.58778524f}, +{0.58778524f,-0.80901700f}, {0.30901697f,-0.95105654f}, {-4.3711388e-08f,-1.0000000f}, +{-0.30901703f,-0.95105648f}, {-0.58778518f,-0.80901700f}, {-0.80901700f,-0.58778518f}, +{-0.95105654f,-0.30901679f}, {-1.0000000f,8.7422777e-08f}, {-0.95105654f,0.30901697f}, +{-0.80901694f,0.58778536f}, {-0.58778507f,0.80901712f}, {-0.30901709f,0.95105648f}, +}; +static const ne10_fft_state_float32_t ne10_fft_state_float32_t_480 = { +120, +(ne10_int32_t *)ne10_factors_480, +(ne10_fft_cpx_float32_t *)ne10_twiddles_480, +NULL, +(ne10_fft_cpx_float32_t *)&ne10_twiddles_480[120], +/* is_forward_scaled = true */ +(ne10_int32_t) 1, +/* is_backward_scaled = false */ +(ne10_int32_t) 0, +}; +static const arch_fft_state cfg_arch_480 = { +1, +(void *)&ne10_fft_state_float32_t_480, +}; + +static const ne10_fft_state_float32_t ne10_fft_state_float32_t_240 = { +60, +(ne10_int32_t *)ne10_factors_240, +(ne10_fft_cpx_float32_t *)ne10_twiddles_240, +NULL, +(ne10_fft_cpx_float32_t *)&ne10_twiddles_240[60], +/* is_forward_scaled = true */ +(ne10_int32_t) 1, +/* is_backward_scaled = false */ +(ne10_int32_t) 0, +}; +static const arch_fft_state cfg_arch_240 = { +1, +(void *)&ne10_fft_state_float32_t_240, +}; + +static const ne10_fft_state_float32_t ne10_fft_state_float32_t_120 = { +30, +(ne10_int32_t *)ne10_factors_120, +(ne10_fft_cpx_float32_t *)ne10_twiddles_120, +NULL, +(ne10_fft_cpx_float32_t *)&ne10_twiddles_120[30], +/* is_forward_scaled = true */ +(ne10_int32_t) 1, +/* is_backward_scaled = false */ +(ne10_int32_t) 0, +}; +static const arch_fft_state cfg_arch_120 = { +1, +(void *)&ne10_fft_state_float32_t_120, +}; + +static const ne10_fft_state_float32_t ne10_fft_state_float32_t_60 = { +15, +(ne10_int32_t *)ne10_factors_60, +(ne10_fft_cpx_float32_t *)ne10_twiddles_60, +NULL, +(ne10_fft_cpx_float32_t *)&ne10_twiddles_60[15], +/* is_forward_scaled = true */ +(ne10_int32_t) 1, +/* is_backward_scaled = false */ +(ne10_int32_t) 0, +}; +static const arch_fft_state cfg_arch_60 = { +1, +(void *)&ne10_fft_state_float32_t_60, +}; + +#endif /* end NE10_FFT_PARAMS48000_960 */ diff --git a/managed_components/78__esp-opus/celt/tests/meson.build b/managed_components/78__esp-opus/celt/tests/meson.build new file mode 100644 index 0000000..0e6d2e6 --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/meson.build @@ -0,0 +1,19 @@ +tests = [ + 'test_unit_types', + 'test_unit_mathops', + 'test_unit_entropy', + 'test_unit_laplace', + 'test_unit_dft', + 'test_unit_mdct', + 'test_unit_rotation', + 'test_unit_cwrs32', +] + +foreach test_name : tests + exe = executable(test_name, '@0@.c'.format(test_name), + include_directories : opus_includes, + link_with : [celt_lib, celt_static_libs], + dependencies : libm, + install : false) + test(test_name, exe) +endforeach diff --git a/managed_components/78__esp-opus/celt/tests/test_unit_cwrs32.c b/managed_components/78__esp-opus/celt/tests/test_unit_cwrs32.c new file mode 100644 index 0000000..f6b8ac4 --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/test_unit_cwrs32.c @@ -0,0 +1,162 @@ +/* Copyright (c) 2008-2011 Xiph.Org Foundation, Mozilla Corporation, + Gregory Maxwell + Written by Jean-Marc Valin, Gregory Maxwell, and Timothy B. Terriberry */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include + +#ifndef CUSTOM_MODES +#define CUSTOM_MODES +#else +#define TEST_CUSTOM_MODES +#endif + +#define CELT_C +#include "stack_alloc.h" +#include "entenc.c" +#include "entdec.c" +#include "entcode.c" +#include "cwrs.c" +#include "mathops.c" +#include "rate.h" + +#define NMAX (240) +#define KMAX (128) + +#ifdef TEST_CUSTOM_MODES + +#define NDIMS (44) +static const int pn[NDIMS]={ + 2, 3, 4, 5, 6, 7, 8, 9, 10, + 11, 12, 13, 14, 15, 16, 18, 20, 22, + 24, 26, 28, 30, 32, 36, 40, 44, 48, + 52, 56, 60, 64, 72, 80, 88, 96, 104, + 112, 120, 128, 144, 160, 176, 192, 208 +}; +static const int pkmax[NDIMS]={ + 128, 128, 128, 128, 88, 52, 36, 26, 22, + 18, 16, 15, 13, 12, 12, 11, 10, 9, + 9, 8, 8, 7, 7, 7, 7, 6, 6, + 6, 6, 6, 5, 5, 5, 5, 5, 5, + 4, 4, 4, 4, 4, 4, 4, 4 +}; + +#else /* TEST_CUSTOM_MODES */ + +#define NDIMS (22) +static const int pn[NDIMS]={ + 2, 3, 4, 6, 8, 9, 11, 12, 16, + 18, 22, 24, 32, 36, 44, 48, 64, 72, + 88, 96, 144, 176 +}; +static const int pkmax[NDIMS]={ + 128, 128, 128, 88, 36, 26, 18, 16, 12, + 11, 9, 9, 7, 7, 6, 6, 5, 5, + 5, 5, 4, 4 +}; + +#endif + +int main(void){ + int t; + int n; + ALLOC_STACK; + for(t=0;tpkmax[t])break; + printf("Testing CWRS with N=%i, K=%i...\n",n,k); +#if defined(SMALL_FOOTPRINT) + nc=ncwrs_urow(n,k,uu); +#else + nc=CELT_PVQ_V(n,k); +#endif + inc=nc/20000; + if(inc<1)inc=1; + for(i=0;i");*/ +#if defined(SMALL_FOOTPRINT) + ii=icwrs(n,k,&v,y,u); +#else + ii=icwrs(n,y); + v=CELT_PVQ_V(n,k); +#endif + if(ii!=i){ + fprintf(stderr,"Combination-index mismatch (%lu!=%lu).\n", + (long)ii,(long)i); + return 1; + } + if(v!=nc){ + fprintf(stderr,"Combination count mismatch (%lu!=%lu).\n", + (long)v,(long)nc); + return 2; + } + /*printf(" %6u\n",i);*/ + } + /*printf("\n");*/ + } + } + RESTORE_STACK; + return 0; +} diff --git a/managed_components/78__esp-opus/celt/tests/test_unit_dft.c b/managed_components/78__esp-opus/celt/tests/test_unit_dft.c new file mode 100644 index 0000000..ad6c60a --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/test_unit_dft.c @@ -0,0 +1,181 @@ +/* Copyright (c) 2008 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include + +#include "stack_alloc.h" +#include "kiss_fft.h" +#include "mathops.h" +#include "modes.h" + +#ifndef M_PI +#define M_PI 3.141592653 +#endif + +int ret = 0; + +void check(kiss_fft_cpx * in,kiss_fft_cpx * out,int nfft,int isinverse) +{ + int bin,k; + double errpow=0,sigpow=0, snr; + + for (bin=0;binmdct.kfft[id]; +#endif + + in = (kiss_fft_cpx*)malloc(buflen); + out = (kiss_fft_cpx*)malloc(buflen); + + for (k=0;k1) { + int k; + for (k=1;k +#include +#include +#include +#define CELT_C +#include "entcode.h" +#include "entenc.h" +#include "entdec.h" +#include + +#include "entenc.c" +#include "entdec.c" +#include "entcode.c" + +#ifndef M_LOG2E +# define M_LOG2E 1.4426950408889634074 +#endif +#define DATA_SIZE 10000000 +#define DATA_SIZE2 10000 + +int main(int _argc,char **_argv){ + ec_enc enc; + ec_dec dec; + long nbits; + long nbits2; + double entropy; + int ft; + int ftb; + int sz; + int i; + int ret; + unsigned int sym; + unsigned int seed; + unsigned char *ptr; + const char *env_seed; + ret=0; + entropy=0; + if (_argc > 2) { + fprintf(stderr, "Usage: %s []\n", _argv[0]); + return 1; + } + env_seed = getenv("SEED"); + if (_argc > 1) + seed = atoi(_argv[1]); + else if (env_seed) + seed = atoi(env_seed); + else + seed = time(NULL); + /*Testing encoding of raw bit values.*/ + ptr = (unsigned char *)malloc(DATA_SIZE); + ec_enc_init(&enc,ptr, DATA_SIZE); + for(ft=2;ft<1024;ft++){ + for(i=0;i>(rand()%11U))+1U)+10; + sz=rand()/((RAND_MAX>>(rand()%9U))+1U); + data=(unsigned *)malloc(sz*sizeof(*data)); + tell=(unsigned *)malloc((sz+1)*sizeof(*tell)); + ec_enc_init(&enc,ptr,DATA_SIZE2); + zeros = rand()%13==0; + tell[0]=ec_tell_frac(&enc); + for(j=0;j>(rand()%9U))+1U); + logp1=(unsigned *)malloc(sz*sizeof(*logp1)); + data=(unsigned *)malloc(sz*sizeof(*data)); + tell=(unsigned *)malloc((sz+1)*sizeof(*tell)); + enc_method=(unsigned *)malloc(sz*sizeof(*enc_method)); + ec_enc_init(&enc,ptr,DATA_SIZE2); + tell[0]=ec_tell_frac(&enc); + for(j=0;j>1)+1); + logp1[j]=(rand()%15)+1; + enc_method[j]=rand()/((RAND_MAX>>2)+1); + switch(enc_method[j]){ + case 0:{ + ec_encode(&enc,data[j]?(1<>2)+1); + switch(dec_method){ + case 0:{ + fs=ec_decode(&dec,1<=(1<=(1< +#include +#define CELT_C +#include "laplace.h" +#include "stack_alloc.h" + +#include "entenc.c" +#include "entdec.c" +#include "entcode.c" +#include "laplace.c" + +#define DATA_SIZE 40000 + +int ec_laplace_get_start_freq(int decay) +{ + opus_uint32 ft = 32768 - LAPLACE_MINP*(2*LAPLACE_NMIN+1); + int fs = (ft*(16384-decay))/(16384+decay); + return fs+LAPLACE_MINP; +} + +int main(void) +{ + int i; + int ret = 0; + ec_enc enc; + ec_dec dec; + unsigned char *ptr; + int val[10000], decay[10000]; + ALLOC_STACK; + ptr = (unsigned char *)malloc(DATA_SIZE); + ec_enc_init(&enc,ptr,DATA_SIZE); + + val[0] = 3; decay[0] = 6000; + val[1] = 0; decay[1] = 5800; + val[2] = -1; decay[2] = 5600; + for (i=3;i<10000;i++) + { + val[i] = rand()%15-7; + decay[i] = rand()%11000+5000; + } + for (i=0;i<10000;i++) + ec_laplace_encode(&enc, &val[i], + ec_laplace_get_start_freq(decay[i]), decay[i]); + + ec_enc_done(&enc); + + ec_dec_init(&dec,ec_get_buffer(&enc),ec_range_bytes(&enc)); + + for (i=0;i<10000;i++) + { + int d = ec_laplace_decode(&dec, + ec_laplace_get_start_freq(decay[i]), decay[i]); + if (d != val[i]) + { + fprintf (stderr, "Got %d instead of %d\n", d, val[i]); + ret = 1; + } + } + + free(ptr); + RESTORE_STACK; + return ret; +} diff --git a/managed_components/78__esp-opus/celt/tests/test_unit_mathops.c b/managed_components/78__esp-opus/celt/tests/test_unit_mathops.c new file mode 100644 index 0000000..f1e45e1 --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/test_unit_mathops.c @@ -0,0 +1,539 @@ +/* Copyright (c) 2008-2011 Xiph.Org Foundation, Mozilla Corporation, + Gregory Maxwell + Copyright (c) 2024 Arm Limited + Written by Jean-Marc Valin, Gregory Maxwell, Timothy B. Terriberry, + and Yunho Huh */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#ifndef CUSTOM_MODES +#define CUSTOM_MODES +#endif + +#include +#include +#include "bands.h" +#include "cpu_support.h" +#include "float_cast.h" +#include "mathops.h" + +#ifdef FIXED_POINT +#define WORD "%d" +#define FIX_INT_TO_DOUBLE(x,q) ((double)(x) / (double)(1L << q)) +#define DOUBLE_TO_FIX_INT(x,q) (((double)x * (double)(1L << q))) +#else +#define WORD "%f" +#endif + +int ret = 0; + +void testdiv(void) +{ + opus_int32 i; + for (i=1;i<=327670;i++) + { + double prod; + opus_val32 val; + val = celt_rcp(i); +#ifdef FIXED_POINT + prod = (1./32768./65526.)*val*i; +#else + prod = val*i; +#endif + if (fabs(prod-1) > .00025) + { + fprintf (stderr, "div failed: 1/%d="WORD" (product = %f)\n", i, val, prod); + ret = 1; + } + } +} + +void testsqrt(void) +{ + opus_int32 i; + for (i=1;i<=1000000000;i++) + { + double ratio; + opus_val16 val; + val = celt_sqrt(i); + ratio = val/sqrt(i); + if (fabs(ratio - 1) > .0005 && fabs(val-sqrt(i)) > 2) + { + fprintf (stderr, "sqrt failed: sqrt(%d)="WORD" (ratio = %f)\n", i, val, ratio); + ret = 1; + } + i+= i>>10; + } +} + +void testbitexactcos(void) +{ + int i; + opus_int32 min_d,max_d,last,chk; + chk=max_d=0; + last=min_d=32767; + for(i=64;i<=16320;i++) + { + opus_int32 d; + opus_int32 q=bitexact_cos(i); + chk ^= q*i; + d = last - q; + if (d>max_d)max_d=d; + if (dmax_d)max_d=d; + if (d error_threshold) + { + fprintf (stderr, + "celt_log2 failed: " + "fabs((1.442695040888963387*log(x))-celt_log2(x))>%15.25e " + "(x = %f, error = %15.25e)\n", error_threshold, x, error); + ret = 1; + } + } + fprintf (stdout, "celt_log2 max_error: %15.25e\n", max_error); +} + +void testexp2(void) +{ + float x; + float error_threshold = 2.3e-07; + float max_error = 0; + for (x=-11.0;x<24.0;x+=0.0007f) + { + float error = fabs(x-(1.442695040888963387*log(celt_exp2(x)))); + if (max_error < error) + { + max_error = error; + } + + if (error > error_threshold) + { + fprintf (stderr, + "celt_exp2 failed: " + "fabs(x-(1.442695040888963387*log(celt_exp2(x))))>%15.25e " + "(x = %f, error = %15.25e)\n", error_threshold, x, error); + ret = 1; + } + } + fprintf (stdout, "celt_exp2 max_error: %15.25e\n", max_error); +} + +void testexp2log2(void) +{ + float x; + float error_threshold = 2.0e-06; + float max_error = 0; + for (x=-11.0;x<24.0;x+=0.0007f) + { + float error = fabs(x-(celt_log2(celt_exp2(x)))); + if (max_error < error) + { + max_error = error; + } + + if (error > error_threshold) + { + fprintf (stderr, + "celt_log2/celt_exp2 failed: " + "fabs(x-(celt_log2(celt_exp2(x))))>%15.25e " + "(x = %f, error = %15.25e)\n", error_threshold, x, error); + ret = 1; + } + } + fprintf (stdout, "celt_exp2, celt_log2 max_error: %15.25e\n", max_error); +} +#else + +void testlog2_db(void) +{ +#if defined(ENABLE_QEXT) + /* celt_log2_db test */ + float error = -1; + float max_error = -2; + float error_threshold = 2.e-07; + opus_int32 x = 0; + int q_input = 14; + for (x = 8; x < 1073741824; x += (x >> 3)) + { + error = fabs((1.442695040888963387*log(FIX_INT_TO_DOUBLE(x, q_input))) - + FIX_INT_TO_DOUBLE(celt_log2_db(x), DB_SHIFT)); + if (error > max_error) + { + max_error = error; + } + if (error > error_threshold) + { + fprintf(stderr, "celt_log2_db failed: error: [%.5e > %.5e] (x = %f)\n", + error, error_threshold, FIX_INT_TO_DOUBLE(x, DB_SHIFT)); + ret = 1; + } + } + fprintf(stdout, "celt_log2_db max_error: %.7e\n", max_error); +#endif /* defined(ENABLE_QEXT) */ +} + +void testlog2(void) +{ + opus_val32 x; + for (x=8;x<1073741824;x+=(x>>3)) + { + float error = fabs((1.442695040888963387*log(x/16384.0))-celt_log2(x)/1024.0); + if (error>0.003) + { + fprintf (stderr, "celt_log2 failed: x = %ld, error = %f\n", (long)x,error); + ret = 1; + } + } +} + +void testexp2(void) +{ + opus_val16 x; + for (x=-32768;x<15360;x++) + { + float error1 = fabs(x/1024.0-(1.442695040888963387*log(celt_exp2(x)/65536.0))); + float error2 = fabs(exp(0.6931471805599453094*x/1024.0)-celt_exp2(x)/65536.0); + if (error1>0.0002&&error2>0.00004) + { + fprintf (stderr, "celt_exp2 failed: x = "WORD", error1 = %f, error2 = %f\n", x,error1,error2); + ret = 1; + } + } +} + +void testexp2_db(void) +{ +#if defined(ENABLE_QEXT) + float absolute_error = -1; + float absolute_error_threshold = FIX_INT_TO_DOUBLE(2, 16); + float relative_error_threshold = -2; + float fx; + float quantized_fx; + opus_val32 x_32; + + for (fx = -32.0; fx < 15.0; fx += 0.0007) + { + double ground_truth; + x_32 = DOUBLE_TO_FIX_INT(fx, DB_SHIFT); + quantized_fx = FIX_INT_TO_DOUBLE(x_32, DB_SHIFT); + + ground_truth = (exp(0.6931471805599453094 * quantized_fx)); + absolute_error = fabs(ground_truth - + FIX_INT_TO_DOUBLE(celt_exp2_db(x_32), 16)); + + relative_error_threshold = 1.24e-7 * ground_truth; + if (absolute_error > absolute_error_threshold && + absolute_error > relative_error_threshold) + { + fprintf(stderr, + "celt_exp2_db failed: " + "absolute_error: [%.5e > %.5e] " + "relative_error: [%.5e > %.5e] (x = %f)\n", + absolute_error, absolute_error_threshold, + absolute_error, relative_error_threshold, quantized_fx); + ret = 1; + } + } +#endif /* defined(ENABLE_QEXT) */ +} + +void testexp2log2(void) +{ + opus_val32 x; + for (x=8;x<65536;x+=(x>>3)) + { + float error = fabs(x-0.25*celt_exp2(celt_log2(x)))/16384; + if (error>0.004) + { + fprintf (stderr, "celt_log2/celt_exp2 failed: fabs(x-(celt_exp2(celt_log2(x))))>0.001 (x = %ld, error = %f)\n", (long)x,error); + ret = 1; + } + } +} + +void testilog2(void) +{ + opus_val32 x; + for (x=1;x<=268435455;x+=127) + { + opus_val32 lg; + opus_val32 y; + + lg = celt_ilog2(x); + if (lg<0 || lg>=31) + { + printf("celt_ilog2 failed: 0<=celt_ilog2(x)<31 (x = %d, celt_ilog2(x) = %d)\n",x,lg); + ret = 1; + } + y = 1<>1)>=y) + { + printf("celt_ilog2 failed: 2**celt_ilog2(x)<=x<2**(celt_ilog2(x)+1) (x = %d, 2**celt_ilog2(x) = %d)\n",x,y); + ret = 1; + } + } +} +#endif + + +#ifndef DISABLE_FLOAT_API + +void testcelt_float2int16(int use_ref_impl, int buffer_size) +{ + +#define MAX_BUFFER_SIZE 2080 + int i, cnt; + float floatsToConvert[MAX_BUFFER_SIZE]; + short results[MAX_BUFFER_SIZE] = { 0 }; + float scaleInt16RangeTo01; + + celt_assert(buffer_size <= MAX_BUFFER_SIZE); + + scaleInt16RangeTo01 = 1.f / 32768.f; + cnt = 0; + + while (cnt + 15 < buffer_size && cnt < buffer_size / 2) + { + floatsToConvert[cnt++] = 77777.0f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = 33000.0f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = 32768.0f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = 32767.4f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = 32766.6f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = .501 * scaleInt16RangeTo01; + floatsToConvert[cnt++] = .499f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = .0f; + floatsToConvert[cnt++] = -.499f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -.501f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -32767.6f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -32768.4f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -32769.0f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -33000.0f * scaleInt16RangeTo01; + floatsToConvert[cnt++] = -77777.0f * scaleInt16RangeTo01; + + celt_assert(cnt < buffer_size); + } + + while (cnt < buffer_size) + { + float inInt16Range = cnt * 7 + .5; + inInt16Range += (cnt & 0x01) ? .1 : -.1; + inInt16Range *= (cnt & 0x02) ? 1 : -1; + floatsToConvert[cnt++] = inInt16Range * scaleInt16RangeTo01; + } + + for (i = 0; i < MAX_BUFFER_SIZE; ++i) + { + results[i] = 42; + } + + if (use_ref_impl) + { + celt_float2int16_c(floatsToConvert, results, cnt); + } else { + celt_float2int16(floatsToConvert, results, cnt, opus_select_arch()); + } + + for (i = 0; i < cnt; ++i) + { + const float expected = FLOAT2INT16(floatsToConvert[i]); + if (results[i] != expected) + { + fprintf (stderr, "testcelt_float2int16 failed: celt_float2int16 converted %f (index: %d) to %d (x*32768=%f, expected: %d, cnt: %d, ref: %d)\n", + floatsToConvert[i], i, (int)results[i], floatsToConvert[i] * 32768.0f, (int)expected, buffer_size, use_ref_impl); + ret = 1; + } + } + + for (i = cnt; i < MAX_BUFFER_SIZE; ++i) + { + if (results[i] != 42) + { + fprintf (stderr, "testcelt_float2int16 failed: buffer overflow (cnt: %d, ref: %d)\n", buffer_size, use_ref_impl); + ret = 1; + break; + } + } +#undef MAX_BUFFER_SIZE +} + +void testopus_limit2_checkwithin1(int use_ref_impl) +{ +#define BUFFER_SIZE 37 /* strange float count to trigger residue loop of SIMD implementation */ +#define BYTE_COUNT (BUFFER_SIZE * sizeof(float)) + int i, within1; + const int arch = opus_select_arch(); + + float pattern[BUFFER_SIZE], buffer[BUFFER_SIZE]; + + for (i = 0; i < BUFFER_SIZE; ++i) + { + pattern[i] = i % 2 ? -1.f : 1.f; + } + + /* All values within -1..1: + Nothing changed. Return value is implementation-dependent (not expected to recognise nothing exceeds -1..1) */ + memcpy(buffer, pattern, BYTE_COUNT); + within1 = use_ref_impl ? opus_limit2_checkwithin1_c(buffer, BUFFER_SIZE) : opus_limit2_checkwithin1(buffer, BUFFER_SIZE, arch); + if (memcmp(buffer, pattern, BYTE_COUNT) != 0) + { + fprintf (stderr, "opus_limit2_checkwithin1() modified values not exceeding -1..1 (ref=%d)\n", use_ref_impl); + ret = 1; + } + + /* One value exceeds -1..1, within -2..2: + Values unchanged. Return value says not all values are within -1..1 */ + for (i = 0; i < BUFFER_SIZE; ++i) + { + const float replace_value = pattern[i] * 1.001f; + + memcpy(buffer, pattern, BYTE_COUNT); + buffer[i] = replace_value; + within1 = use_ref_impl ? opus_limit2_checkwithin1_c(buffer, BUFFER_SIZE) : opus_limit2_checkwithin1(buffer, BUFFER_SIZE, arch); + if (within1 || buffer[i] != replace_value) + { + fprintf (stderr, "opus_limit2_checkwithin1() handled value exceeding -1..1 erroneously (ref=%d, i=%d)\n", use_ref_impl, i); + ret = 1; + } + buffer[i] = pattern[i]; + if (memcmp(buffer, pattern, BYTE_COUNT) != 0) + { + fprintf (stderr, "opus_limit2_checkwithin1() modified value within -2..2 (ref=%d, i=%d)\n", use_ref_impl, i); + ret = 1; + } + } + + /* One value exceeds -2..2: + One value is hardclipped, others are unchanged. Return value says not all values are within -1..1 */ + for (i = 0; i < BUFFER_SIZE; ++i) + { + const float replace_value = pattern[i] * 2.1; + + memcpy(buffer, pattern, BYTE_COUNT); + buffer[i] = replace_value; + within1 = use_ref_impl ? opus_limit2_checkwithin1_c(buffer, BUFFER_SIZE) : opus_limit2_checkwithin1(buffer, BUFFER_SIZE, arch); + if (within1 || buffer[i] != (replace_value > 0.f ? 2.f : -2.f)) + { + fprintf (stderr, "opus_limit2_checkwithin1() handled value exceeding -2..2 erroneously (ref=%d, i=%d)\n", use_ref_impl, i); + ret = 1; + } + buffer[i] = pattern[i]; + if (memcmp(buffer, pattern, BYTE_COUNT) != 0) + { + fprintf (stderr, "opus_limit2_checkwithin1() modified value within -2..2 (ref=%d, i=%d)\n", use_ref_impl, i); + ret = 1; + } + } +#undef BUFFER_SIZE +#undef BYTE_COUNT +} + +#endif + +int main(void) +{ + int i; + int use_ref_impl[2] = { 0, 1 }; + + testbitexactcos(); + testbitexactlog2tan(); + testdiv(); + testsqrt(); + testlog2(); + testexp2(); + testexp2log2(); +#ifdef FIXED_POINT + testilog2(); + testlog2_db(); + testexp2_db(); +#endif +#ifndef DISABLE_FLOAT_API + for (i = 0; i <= 1; ++i) + { + testcelt_float2int16(use_ref_impl[i], 1); + testcelt_float2int16(use_ref_impl[i], 32); + testcelt_float2int16(use_ref_impl[i], 127); + testcelt_float2int16(use_ref_impl[i], 1031); + testopus_limit2_checkwithin1(use_ref_impl[i]); + } +#endif + return ret; +} diff --git a/managed_components/78__esp-opus/celt/tests/test_unit_mdct.c b/managed_components/78__esp-opus/celt/tests/test_unit_mdct.c new file mode 100644 index 0000000..00b3840 --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/test_unit_mdct.c @@ -0,0 +1,233 @@ +/* Copyright (c) 2008-2011 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include + +#include "mdct.h" +#include "stack_alloc.h" +#include "kiss_fft.h" +#include "mdct.h" +#include "modes.h" + +#ifndef M_PI +#define M_PI 3.141592653 +#endif + +int ret = 0; +void check(kiss_fft_scalar * in,kiss_fft_scalar * out,int nfft,int isinverse) +{ + int bin,k; + double errpow=0,sigpow=0; + double snr; + for (bin=0;binmdct; +#endif + + in = (kiss_fft_scalar*)malloc(buflen); + in_copy = (kiss_fft_scalar*)malloc(buflen); + out = (kiss_fft_scalar*)malloc(buflen); + window = (celt_coef*)malloc(sizeof(*window)*nfft/2); + + for (k=0;k1) { + int k; + for (k=1;k +#include +#include "vq.h" +#include "bands.h" +#include "stack_alloc.h" +#include + + +#define MAX_SIZE 100 + +int ret=0; +void test_rotation(int N, int K) +{ + int i; + double err = 0, ener = 0, snr, snr0; + opus_val16 x0[MAX_SIZE]; + opus_val16 x1[MAX_SIZE]; + for (i=0;i 20) + { + fprintf(stderr, "FAIL!\n"); + ret = 1; + } +} + +int main(void) +{ + ALLOC_STACK; + test_rotation(15, 3); + test_rotation(23, 5); + test_rotation(50, 3); + test_rotation(80, 1); + RESTORE_STACK; + return ret; +} diff --git a/managed_components/78__esp-opus/celt/tests/test_unit_types.c b/managed_components/78__esp-opus/celt/tests/test_unit_types.c new file mode 100644 index 0000000..67a0fb8 --- /dev/null +++ b/managed_components/78__esp-opus/celt/tests/test_unit_types.c @@ -0,0 +1,50 @@ +/* Copyright (c) 2008-2011 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "opus_types.h" +#include + +int main(void) +{ + opus_int16 i = 1; + i <<= 14; + if (i>>14 != 1) + { + fprintf(stderr, "opus_int16 isn't 16 bits\n"); + return 1; + } + if (sizeof(opus_int16)*2 != sizeof(opus_int32)) + { + fprintf(stderr, "16*2 != 32\n"); + return 1; + } + return 0; +} diff --git a/managed_components/78__esp-opus/celt/vq.c b/managed_components/78__esp-opus/celt/vq.c new file mode 100644 index 0000000..df8754d --- /dev/null +++ b/managed_components/78__esp-opus/celt/vq.c @@ -0,0 +1,442 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "mathops.h" +#include "cwrs.h" +#include "vq.h" +#include "arch.h" +#include "os_support.h" +#include "bands.h" +#include "rate.h" +#include "pitch.h" + +#if defined(MIPSr1_ASM) +#include "mips/vq_mipsr1.h" +#endif + +#ifndef OVERRIDE_vq_exp_rotation1 +static void exp_rotation1(celt_norm *X, int len, int stride, opus_val16 c, opus_val16 s) +{ + int i; + opus_val16 ms; + celt_norm *Xptr; + Xptr = X; + ms = NEG16(s); + for (i=0;i=0;i--) + { + celt_norm x1, x2; + x1 = Xptr[0]; + x2 = Xptr[stride]; + Xptr[stride] = EXTRACT16(PSHR32(MAC16_16(MULT16_16(c, x2), s, x1), 15)); + *Xptr-- = EXTRACT16(PSHR32(MAC16_16(MULT16_16(c, x1), ms, x2), 15)); + } +} +#endif /* OVERRIDE_vq_exp_rotation1 */ + +void exp_rotation(celt_norm *X, int len, int dir, int stride, int K, int spread) +{ + static const int SPREAD_FACTOR[3]={15,10,5}; + int i; + opus_val16 c, s; + opus_val16 gain, theta; + int stride2=0; + int factor; + + if (2*K>=len || spread==SPREAD_NONE) + return; + factor = SPREAD_FACTOR[spread-1]; + + gain = celt_div((opus_val32)MULT16_16(Q15_ONE,len),(opus_val32)(len+factor*K)); + theta = HALF16(MULT16_16_Q15(gain,gain)); + + c = celt_cos_norm(EXTEND32(theta)); + s = celt_cos_norm(EXTEND32(SUB16(Q15ONE,theta))); /* sin(theta) */ + + if (len>=8*stride) + { + stride2 = 1; + /* This is just a simple (equivalent) way of computing sqrt(len/stride) with rounding. + It's basically incrementing long as (stride2+0.5)^2 < len/stride. */ + while ((stride2*stride2+stride2)*stride + (stride>>2) < len) + stride2++; + } + /*NOTE: As a minor optimization, we could be passing around log2(B), not B, for both this and for + extract_collapse_mask().*/ + len = celt_udiv(len, stride); + for (i=0;i>1; +#endif + t = VSHR32(Ryy, 2*(k-7)); + g = MULT32_32_Q31(celt_rsqrt_norm(t),gain); + + i=0; + do + X[i] = EXTRACT16(PSHR32(MULT16_16(g, iy[i]), k+1)); + while (++i < N); +} + +static unsigned extract_collapse_mask(int *iy, int N, int B) +{ + unsigned collapse_mask; + int N0; + int i; + if (B<=1) + return 1; + /*NOTE: As a minor optimization, we could be passing around log2(B), not B, for both this and for + exp_rotation().*/ + N0 = celt_udiv(N, B); + collapse_mask = 0; + i=0; do { + int j; + unsigned tmp=0; + j=0; do { + tmp |= iy[i*N0+j]; + } while (++j (N>>1)) + { + opus_val16 rcp; + j=0; do { + sum += X[j]; + } while (++j EPSILON && sum < 64)) +#endif + { + X[0] = QCONST16(1.f,14); + j=1; do + X[j]=0; + while (++j=0); + + /* This should never happen, but just in case it does (e.g. on silence) + we fill the first bin with pulses. */ +#ifdef FIXED_POINT_DEBUG + celt_sig_assert(pulsesLeft<=N+3); +#endif + if (pulsesLeft > N+3) + { + opus_val16 tmp = (opus_val16)pulsesLeft; + yy = MAC16_16(yy, tmp, tmp); + yy = MAC16_16(yy, tmp, y[0]); + iy[0] += pulsesLeft; + pulsesLeft=0; + } + + for (i=0;i= best_num/best_den, but that way + we can do it without any division */ + /* OPT: It's not clear whether a cmov is faster than a branch here + since the condition is more often false than true and using + a cmov introduces data dependencies across iterations. The optimal + choice may be architecture-dependent. */ + if (opus_unlikely(MULT16_16(best_den, Rxy) > MULT16_16(Ryy, best_num))) + { + best_den = Ryy; + best_num = Rxy; + best_id = j; + } + } while (++j0, "alg_quant() needs at least one pulse"); + celt_assert2(N>1, "alg_quant() needs at least two dimensions"); + + /* Covers vectorization by up to 4. */ + ALLOC(iy, N+3, int); + + exp_rotation(X, N, 1, B, K, spread); + + yy = op_pvq_search(X, iy, K, N, arch); + + encode_pulses(iy, N, K, enc); + + if (resynth) + { + normalise_residual(iy, X, N, yy, gain); + exp_rotation(X, N, -1, B, K, spread); + } + + collapse_mask = extract_collapse_mask(iy, N, B); + RESTORE_STACK; + return collapse_mask; +} + +/** Decode pulse vector and combine the result with the pitch vector to produce + the final normalised signal in the current band. */ +unsigned alg_unquant(celt_norm *X, int N, int K, int spread, int B, + ec_dec *dec, opus_val32 gain) +{ + opus_val32 Ryy; + unsigned collapse_mask; + VARDECL(int, iy); + SAVE_STACK; + + celt_assert2(K>0, "alg_unquant() needs at least one pulse"); + celt_assert2(N>1, "alg_unquant() needs at least two dimensions"); + ALLOC(iy, N, int); + Ryy = decode_pulses(iy, N, K, dec); + normalise_residual(iy, X, N, Ryy, gain); + exp_rotation(X, N, -1, B, K, spread); + collapse_mask = extract_collapse_mask(iy, N, B); + RESTORE_STACK; + return collapse_mask; +} + +#ifndef OVERRIDE_renormalise_vector +void renormalise_vector(celt_norm *X, int N, opus_val32 gain, int arch) +{ + int i; +#ifdef FIXED_POINT + int k; +#endif + opus_val32 E; + opus_val16 g; + opus_val32 t; + celt_norm *xptr; + E = EPSILON + celt_inner_prod(X, X, N, arch); +#ifdef FIXED_POINT + k = celt_ilog2(E)>>1; +#endif + t = VSHR32(E, 2*(k-7)); + g = MULT32_32_Q31(celt_rsqrt_norm(t),gain); + + xptr = X; + for (i=0;i +#include +#include +#include "celt_lpc.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "pitch.h" +#include "x86cpu.h" + +#if defined(FIXED_POINT) + +void celt_fir_sse4_1(const opus_val16 *x, + const opus_val16 *num, + opus_val16 *y, + int N, + int ord, + int arch) +{ + int i,j; + VARDECL(opus_val16, rnum); + + __m128i vecNoA; + opus_int32 noA ; + SAVE_STACK; + + ALLOC(rnum, ord, opus_val16); + for(i=0;i> 1; + vecNoA = _mm_set_epi32(noA, noA, noA, noA); + + for (i=0;i +#include "x86cpu.h" +#include "pitch.h" + +#if defined(OPUS_X86_MAY_HAVE_AVX2) && !defined(FIXED_POINT) + +/* Like the "regular" xcorr_kernel(), but computes 8 results at a time. */ +static void xcorr_kernel_avx(const float *x, const float *y, float sum[8], int len) +{ + __m256 xsum0, xsum1, xsum2, xsum3, xsum4, xsum5, xsum6, xsum7; + xsum7 = xsum6 = xsum5 = xsum4 = xsum3 = xsum2 = xsum1 = xsum0 = _mm256_setzero_ps(); + int i; + __m256 x0; + /* Compute 8 inner products using partial sums. */ + for (i=0;i0); + (void)arch; + for (i=0;i +#include "arch.h" + +void xcorr_kernel_sse(const opus_val16 *x, const opus_val16 *y, opus_val32 sum[4], int len) +{ + int j; + __m128 xsum1, xsum2; + xsum1 = _mm_loadu_ps(sum); + xsum2 = _mm_setzero_ps(); + + for (j = 0; j < len-3; j += 4) + { + __m128 x0 = _mm_loadu_ps(x+j); + __m128 yj = _mm_loadu_ps(y+j); + __m128 y3 = _mm_loadu_ps(y+j+3); + + xsum1 = _mm_add_ps(xsum1,_mm_mul_ps(_mm_shuffle_ps(x0,x0,0x00),yj)); + xsum2 = _mm_add_ps(xsum2,_mm_mul_ps(_mm_shuffle_ps(x0,x0,0x55), + _mm_shuffle_ps(yj,y3,0x49))); + xsum1 = _mm_add_ps(xsum1,_mm_mul_ps(_mm_shuffle_ps(x0,x0,0xaa), + _mm_shuffle_ps(yj,y3,0x9e))); + xsum2 = _mm_add_ps(xsum2,_mm_mul_ps(_mm_shuffle_ps(x0,x0,0xff),y3)); + } + if (j < len) + { + xsum1 = _mm_add_ps(xsum1,_mm_mul_ps(_mm_load1_ps(x+j),_mm_loadu_ps(y+j))); + if (++j < len) + { + xsum2 = _mm_add_ps(xsum2,_mm_mul_ps(_mm_load1_ps(x+j),_mm_loadu_ps(y+j))); + if (++j < len) + { + xsum1 = _mm_add_ps(xsum1,_mm_mul_ps(_mm_load1_ps(x+j),_mm_loadu_ps(y+j))); + } + } + } + _mm_storeu_ps(sum,_mm_add_ps(xsum1,xsum2)); +} + + +void dual_inner_prod_sse(const opus_val16 *x, const opus_val16 *y01, const opus_val16 *y02, + int N, opus_val32 *xy1, opus_val32 *xy2) +{ + int i; + __m128 xsum1, xsum2; + xsum1 = _mm_setzero_ps(); + xsum2 = _mm_setzero_ps(); + for (i=0;i +#include + +#include "macros.h" +#include "celt_lpc.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "pitch.h" + +#if defined(OPUS_X86_MAY_HAVE_SSE2) && defined(FIXED_POINT) +opus_val32 celt_inner_prod_sse2(const opus_val16 *x, const opus_val16 *y, + int N) +{ + opus_int i, dataSize16; + opus_int32 sum; + + __m128i inVec1_76543210, inVec1_FEDCBA98, acc1; + __m128i inVec2_76543210, inVec2_FEDCBA98, acc2; + + sum = 0; + dataSize16 = N & ~15; + + acc1 = _mm_setzero_si128(); + acc2 = _mm_setzero_si128(); + + for (i=0;i= 8) + { + inVec1_76543210 = _mm_loadu_si128((__m128i *)(&x[i + 0])); + inVec2_76543210 = _mm_loadu_si128((__m128i *)(&y[i + 0])); + + inVec1_76543210 = _mm_madd_epi16(inVec1_76543210, inVec2_76543210); + + acc1 = _mm_add_epi32(acc1, inVec1_76543210); + i += 8; + } + + acc1 = _mm_add_epi32(acc1, _mm_unpackhi_epi64( acc1, acc1)); + acc1 = _mm_add_epi32(acc1, _mm_shufflelo_epi16( acc1, 0x0E)); + sum += _mm_cvtsi128_si32(acc1); + + for (;i +#include + +#include "macros.h" +#include "celt_lpc.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "pitch.h" + +#if defined(OPUS_X86_MAY_HAVE_SSE4_1) && defined(FIXED_POINT) +#include +#include "x86cpu.h" + +opus_val32 celt_inner_prod_sse4_1(const opus_val16 *x, const opus_val16 *y, + int N) +{ + opus_int i, dataSize16; + opus_int32 sum; + __m128i inVec1_76543210, inVec1_FEDCBA98, acc1; + __m128i inVec2_76543210, inVec2_FEDCBA98, acc2; + __m128i inVec1_3210, inVec2_3210; + + sum = 0; + dataSize16 = N & ~15; + + acc1 = _mm_setzero_si128(); + acc2 = _mm_setzero_si128(); + + for (i=0;i= 8) + { + inVec1_76543210 = _mm_loadu_si128((__m128i *)(&x[i + 0])); + inVec2_76543210 = _mm_loadu_si128((__m128i *)(&y[i + 0])); + + inVec1_76543210 = _mm_madd_epi16(inVec1_76543210, inVec2_76543210); + + acc1 = _mm_add_epi32(acc1, inVec1_76543210); + i += 8; + } + + if (N - i >= 4) + { + inVec1_3210 = OP_CVTEPI16_EPI32_M64(&x[i + 0]); + inVec2_3210 = OP_CVTEPI16_EPI32_M64(&y[i + 0]); + + inVec1_3210 = _mm_mullo_epi32(inVec1_3210, inVec2_3210); + + acc1 = _mm_add_epi32(acc1, inVec1_3210); + i += 4; + } + + acc1 = _mm_add_epi32(acc1, _mm_unpackhi_epi64(acc1, acc1)); + acc1 = _mm_add_epi32(acc1, _mm_shufflelo_epi16(acc1, 0x0E)); + + sum += _mm_cvtsi128_si32(acc1); + + for (;i= 3); + + sum0 = _mm_setzero_si128(); + sum1 = _mm_setzero_si128(); + sum2 = _mm_setzero_si128(); + sum3 = _mm_setzero_si128(); + + for (j=0;j<(len-7);j+=8) + { + vecX = _mm_loadu_si128((__m128i *)(&x[j + 0])); + vecY0 = _mm_loadu_si128((__m128i *)(&y[j + 0])); + vecY1 = _mm_loadu_si128((__m128i *)(&y[j + 1])); + vecY2 = _mm_loadu_si128((__m128i *)(&y[j + 2])); + vecY3 = _mm_loadu_si128((__m128i *)(&y[j + 3])); + + sum0 = _mm_add_epi32(sum0, _mm_madd_epi16(vecX, vecY0)); + sum1 = _mm_add_epi32(sum1, _mm_madd_epi16(vecX, vecY1)); + sum2 = _mm_add_epi32(sum2, _mm_madd_epi16(vecX, vecY2)); + sum3 = _mm_add_epi32(sum3, _mm_madd_epi16(vecX, vecY3)); + } + + sum0 = _mm_add_epi32(sum0, _mm_unpackhi_epi64( sum0, sum0)); + sum0 = _mm_add_epi32(sum0, _mm_shufflelo_epi16( sum0, 0x0E)); + + sum1 = _mm_add_epi32(sum1, _mm_unpackhi_epi64( sum1, sum1)); + sum1 = _mm_add_epi32(sum1, _mm_shufflelo_epi16( sum1, 0x0E)); + + sum2 = _mm_add_epi32(sum2, _mm_unpackhi_epi64( sum2, sum2)); + sum2 = _mm_add_epi32(sum2, _mm_shufflelo_epi16( sum2, 0x0E)); + + sum3 = _mm_add_epi32(sum3, _mm_unpackhi_epi64( sum3, sum3)); + sum3 = _mm_add_epi32(sum3, _mm_shufflelo_epi16( sum3, 0x0E)); + + vecSum = _mm_unpacklo_epi64(_mm_unpacklo_epi32(sum0, sum1), + _mm_unpacklo_epi32(sum2, sum3)); + + for (;j<(len-3);j+=4) + { + vecX = OP_CVTEPI16_EPI32_M64(&x[j + 0]); + vecX0 = _mm_shuffle_epi32(vecX, 0x00); + vecX1 = _mm_shuffle_epi32(vecX, 0x55); + vecX2 = _mm_shuffle_epi32(vecX, 0xaa); + vecX3 = _mm_shuffle_epi32(vecX, 0xff); + + vecY0 = OP_CVTEPI16_EPI32_M64(&y[j + 0]); + vecY1 = OP_CVTEPI16_EPI32_M64(&y[j + 1]); + vecY2 = OP_CVTEPI16_EPI32_M64(&y[j + 2]); + vecY3 = OP_CVTEPI16_EPI32_M64(&y[j + 3]); + + sum0 = _mm_mullo_epi32(vecX0, vecY0); + sum1 = _mm_mullo_epi32(vecX1, vecY1); + sum2 = _mm_mullo_epi32(vecX2, vecY2); + sum3 = _mm_mullo_epi32(vecX3, vecY3); + + sum0 = _mm_add_epi32(sum0, sum1); + sum2 = _mm_add_epi32(sum2, sum3); + vecSum = _mm_add_epi32(vecSum, sum0); + vecSum = _mm_add_epi32(vecSum, sum2); + } + + vecX = OP_CVTEPI16_EPI32_M64(&x[len - 4]); + if (len - j == 3) + { + vecX0 = _mm_shuffle_epi32(vecX, 0x55); + vecX1 = _mm_shuffle_epi32(vecX, 0xaa); + vecX2 = _mm_shuffle_epi32(vecX, 0xff); + + vecY0 = OP_CVTEPI16_EPI32_M64(&y[j + 0]); + vecY1 = OP_CVTEPI16_EPI32_M64(&y[j + 1]); + vecY2 = OP_CVTEPI16_EPI32_M64(&y[j + 2]); + + sum0 = _mm_mullo_epi32(vecX0, vecY0); + sum1 = _mm_mullo_epi32(vecX1, vecY1); + sum2 = _mm_mullo_epi32(vecX2, vecY2); + + vecSum = _mm_add_epi32(vecSum, sum0); + vecSum = _mm_add_epi32(vecSum, sum1); + vecSum = _mm_add_epi32(vecSum, sum2); + } + else if (len - j == 2) + { + vecX0 = _mm_shuffle_epi32(vecX, 0xaa); + vecX1 = _mm_shuffle_epi32(vecX, 0xff); + + vecY0 = OP_CVTEPI16_EPI32_M64(&y[j + 0]); + vecY1 = OP_CVTEPI16_EPI32_M64(&y[j + 1]); + + sum0 = _mm_mullo_epi32(vecX0, vecY0); + sum1 = _mm_mullo_epi32(vecX1, vecY1); + + vecSum = _mm_add_epi32(vecSum, sum0); + vecSum = _mm_add_epi32(vecSum, sum1); + } + else if (len - j == 1) + { + vecX0 = _mm_shuffle_epi32(vecX, 0xff); + + vecY0 = OP_CVTEPI16_EPI32_M64(&y[j + 0]); + + sum0 = _mm_mullo_epi32(vecX0, vecY0); + + vecSum = _mm_add_epi32(vecSum, sum0); + } + + initSum = _mm_loadu_si128((__m128i *)(&sum[0])); + initSum = _mm_add_epi32(initSum, vecSum); + _mm_storeu_si128((__m128i *)sum, initSum); + +#ifdef OPUS_CHECK_ASM + celt_assert(!memcmp(sum_c, sum, sizeof(sum_c))); +#endif +} +#endif diff --git a/managed_components/78__esp-opus/celt/x86/vq_sse.h b/managed_components/78__esp-opus/celt/x86/vq_sse.h new file mode 100644 index 0000000..444503b --- /dev/null +++ b/managed_components/78__esp-opus/celt/x86/vq_sse.h @@ -0,0 +1,52 @@ +/* Copyright (c) 2016 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef VQ_SSE_H +#define VQ_SSE_H + +#if defined(OPUS_X86_MAY_HAVE_SSE2) && !defined(FIXED_POINT) + +opus_val16 op_pvq_search_sse2(celt_norm *_X, int *iy, int K, int N, int arch); + +#if defined(OPUS_X86_PRESUME_SSE2) + +#define OVERRIDE_OP_PVQ_SEARCH +#define op_pvq_search(x, iy, K, N, arch) \ + (op_pvq_search_sse2(x, iy, K, N, arch)) + +#elif defined(OPUS_HAVE_RTCD) + +#define OVERRIDE_OP_PVQ_SEARCH +extern opus_val16 (*const OP_PVQ_SEARCH_IMPL[OPUS_ARCHMASK + 1])( + celt_norm *_X, int *iy, int K, int N, int arch); + +# define op_pvq_search(X, iy, K, N, arch) \ + ((*OP_PVQ_SEARCH_IMPL[(arch) & OPUS_ARCHMASK])(X, iy, K, N, arch)) + +#endif +#endif + +#endif diff --git a/managed_components/78__esp-opus/celt/x86/vq_sse2.c b/managed_components/78__esp-opus/celt/x86/vq_sse2.c new file mode 100644 index 0000000..4c4ebf8 --- /dev/null +++ b/managed_components/78__esp-opus/celt/x86/vq_sse2.c @@ -0,0 +1,217 @@ +/* Copyright (c) 2007-2008 CSIRO + Copyright (c) 2007-2009 Xiph.Org Foundation + Copyright (c) 2007-2016 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include "celt_lpc.h" +#include "stack_alloc.h" +#include "mathops.h" +#include "vq.h" +#include "x86cpu.h" + + +#ifndef FIXED_POINT + +opus_val16 op_pvq_search_sse2(celt_norm *_X, int *iy, int K, int N, int arch) +{ + int i, j; + int pulsesLeft; + float xy, yy; + VARDECL(celt_norm, y); + VARDECL(celt_norm, X); + VARDECL(float, signy); + __m128 signmask; + __m128 sums; + __m128i fours; + SAVE_STACK; + + (void)arch; + /* All bits set to zero, except for the sign bit. */ + signmask = _mm_set_ps1(-0.f); + fours = _mm_set_epi32(4, 4, 4, 4); + ALLOC(y, N+3, celt_norm); + ALLOC(X, N+3, celt_norm); + ALLOC(signy, N+3, float); + + OPUS_COPY(X, _X, N); + X[N] = X[N+1] = X[N+2] = 0; + sums = _mm_setzero_ps(); + for (j=0;j (N>>1)) + { + __m128i pulses_sum; + __m128 yy4, xy4; + __m128 rcp4; + opus_val32 sum = _mm_cvtss_f32(sums); + /* If X is too small, just replace it with a pulse at 0 */ + /* Prevents infinities and NaNs from causing too many pulses + to be allocated. 64 is an approximation of infinity here. */ + if (!(sum > EPSILON && sum < 64)) + { + X[0] = QCONST16(1.f,14); + j=1; do + X[j]=0; + while (++j=0); + + /* This should never happen, but just in case it does (e.g. on silence) + we fill the first bin with pulses. */ + if (pulsesLeft > N+3) + { + opus_val16 tmp = (opus_val16)pulsesLeft; + yy = MAC16_16(yy, tmp, tmp); + yy = MAC16_16(yy, tmp, y[0]); + iy[0] += pulsesLeft; + pulsesLeft=0; + } + + for (i=0;i +static _inline void cpuid(unsigned int CPUInfo[4], unsigned int InfoType) +{ + __cpuid((int*)CPUInfo, InfoType); +} + +#else + +#if defined(CPU_INFO_BY_C) +#include +#endif + +static void cpuid(unsigned int CPUInfo[4], unsigned int InfoType) +{ +#if defined(CPU_INFO_BY_ASM) +#if defined(__i386__) && defined(__PIC__) +/* %ebx is PIC register in 32-bit, so mustn't clobber it. */ + __asm__ __volatile__ ( + "xchg %%ebx, %1\n" + "cpuid\n" + "xchg %%ebx, %1\n": + "=a" (CPUInfo[0]), + "=r" (CPUInfo[1]), + "=c" (CPUInfo[2]), + "=d" (CPUInfo[3]) : + /* We clear ECX to avoid a valgrind false-positive prior to v3.17.0. */ + "0" (InfoType), "2" (0) + ); +#else + __asm__ __volatile__ ( + "cpuid": + "=a" (CPUInfo[0]), + "=b" (CPUInfo[1]), + "=c" (CPUInfo[2]), + "=d" (CPUInfo[3]) : + /* We clear ECX to avoid a valgrind false-positive prior to v3.17.0. */ + "0" (InfoType), "2" (0) + ); +#endif +#elif defined(CPU_INFO_BY_C) + /* We use __get_cpuid_count to clear ECX to avoid a valgrind false-positive + prior to v3.17.0.*/ + if (!__get_cpuid_count(InfoType, 0, &(CPUInfo[0]), &(CPUInfo[1]), &(CPUInfo[2]), &(CPUInfo[3]))) { + /* Our function cannot fail, but __get_cpuid{_count} can. + Returning all zeroes will effectively disable all SIMD, which is + what we want on CPUs that don't support CPUID. */ + CPUInfo[3] = CPUInfo[2] = CPUInfo[1] = CPUInfo[0] = 0; + } +#else +# error "Configured to use x86 RTCD, but no CPU detection method available. " \ + "Reconfigure with --disable-rtcd (or send patches)." +#endif +} + +#endif + +typedef struct CPU_Feature{ + /* SIMD: 128-bit */ + int HW_SSE; + int HW_SSE2; + int HW_SSE41; + /* SIMD: 256-bit */ + int HW_AVX2; +} CPU_Feature; + +static void opus_cpu_feature_check(CPU_Feature *cpu_feature) +{ + unsigned int info[4]; + unsigned int nIds = 0; + + cpuid(info, 0); + nIds = info[0]; + + if (nIds >= 1){ + cpuid(info, 1); + cpu_feature->HW_SSE = (info[3] & (1 << 25)) != 0; + cpu_feature->HW_SSE2 = (info[3] & (1 << 26)) != 0; + cpu_feature->HW_SSE41 = (info[2] & (1 << 19)) != 0; + cpu_feature->HW_AVX2 = (info[2] & (1 << 28)) != 0 && (info[2] & (1 << 12)) != 0; + if (cpu_feature->HW_AVX2 && nIds >= 7) { + cpuid(info, 7); + cpu_feature->HW_AVX2 = cpu_feature->HW_AVX2 && (info[1] & (1 << 5)) != 0; + } else { + cpu_feature->HW_AVX2 = 0; + } + } + else { + cpu_feature->HW_SSE = 0; + cpu_feature->HW_SSE2 = 0; + cpu_feature->HW_SSE41 = 0; + cpu_feature->HW_AVX2 = 0; + } +} + +static int opus_select_arch_impl(void) +{ + CPU_Feature cpu_feature; + int arch; + + opus_cpu_feature_check(&cpu_feature); + + arch = 0; + if (!cpu_feature.HW_SSE) + { + return arch; + } + arch++; + + if (!cpu_feature.HW_SSE2) + { + return arch; + } + arch++; + + if (!cpu_feature.HW_SSE41) + { + return arch; + } + arch++; + + if (!cpu_feature.HW_AVX2) + { + return arch; + } + arch++; + + return arch; +} + +int opus_select_arch(void) { + int arch = opus_select_arch_impl(); +#ifdef FUZZING + /* Randomly downgrade the architecture. */ + arch = rand()%(arch+1); +#endif + return arch; +} + +#endif diff --git a/managed_components/78__esp-opus/celt/x86/x86cpu.h b/managed_components/78__esp-opus/celt/x86/x86cpu.h new file mode 100644 index 0000000..1e5b6a4 --- /dev/null +++ b/managed_components/78__esp-opus/celt/x86/x86cpu.h @@ -0,0 +1,112 @@ +/* Copyright (c) 2014, Cisco Systems, INC + Written by XiangMingZhu WeiZhou MinPeng YanWang + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#if !defined(X86CPU_H) +# define X86CPU_H + +# if defined(OPUS_X86_MAY_HAVE_SSE) +# define MAY_HAVE_SSE(name) name ## _sse +# else +# define MAY_HAVE_SSE(name) name ## _c +# endif + +# if defined(OPUS_X86_MAY_HAVE_SSE2) +# define MAY_HAVE_SSE2(name) name ## _sse2 +# else +# define MAY_HAVE_SSE2(name) name ## _c +# endif + +# if defined(OPUS_X86_MAY_HAVE_SSE4_1) +# define MAY_HAVE_SSE4_1(name) name ## _sse4_1 +# else +# define MAY_HAVE_SSE4_1(name) name ## _c +# endif + +# if defined(OPUS_X86_MAY_HAVE_AVX2) +# define MAY_HAVE_AVX2(name) name ## _avx2 +# else +# define MAY_HAVE_AVX2(name) name ## _c +# endif + +# if defined(OPUS_HAVE_RTCD) && \ + ((defined(OPUS_X86_MAY_HAVE_SSE) && !defined(OPUS_X86_PRESUME_SSE)) || \ + (defined(OPUS_X86_MAY_HAVE_SSE2) && !defined(OPUS_X86_PRESUME_SSE2)) || \ + (defined(OPUS_X86_MAY_HAVE_SSE4_1) && !defined(OPUS_X86_PRESUME_SSE4_1)) || \ + (defined(OPUS_X86_MAY_HAVE_AVX2) && !defined(OPUS_X86_PRESUME_AVX2))) +int opus_select_arch(void); +# endif + +# if defined(OPUS_X86_MAY_HAVE_SSE2) +# include "opus_defines.h" + +/*MOVD should not impose any alignment restrictions, but the C standard does, + and UBSan will report errors if we actually make unaligned accesses. + Use this to work around those restrictions (which should hopefully all get + optimized to a single MOVD instruction). + GCC implemented _mm_loadu_si32() since GCC 11; HOWEVER, there is a bug! + https://gcc.gnu.org/bugzilla/show_bug.cgi?id=99754 + LLVM implemented _mm_loadu_si32() since Clang 8.0, however the + __clang_major__ version number macro is unreliable, as vendors + (specifically, Apple) will use different numbering schemes than upstream. + Clang's advice is "use feature detection", but they do not provide feature + detection support for specific SIMD functions. + We follow the approach from the SIMDe project and instead detect unrelated + features that should be available in the version we want (see + ).*/ +# if defined(__clang__) +# if __has_warning("-Wextra-semi-stmt") || \ + __has_builtin(__builtin_rotateleft32) +# define OPUS_CLANG_8 (1) +# endif +# endif +# if !defined(_MSC_VER) && !OPUS_GNUC_PREREQ(11,3) && !defined(OPUS_CLANG_8) +# include +# include + +# ifdef _mm_loadu_si32 +# undef _mm_loadu_si32 +# endif +# define _mm_loadu_si32 WORKAROUND_mm_loadu_si32 +static inline __m128i WORKAROUND_mm_loadu_si32(void const* mem_addr) { + int val; + memcpy(&val, mem_addr, sizeof(val)); + return _mm_cvtsi32_si128(val); +} +# elif defined(_MSC_VER) + /* MSVC needs this for _mm_loadu_si32 */ +# include +# endif + +# define OP_CVTEPI8_EPI32_M32(x) \ + (_mm_cvtepi8_epi32(_mm_loadu_si32(x))) + +# define OP_CVTEPI16_EPI32_M64(x) \ + (_mm_cvtepi16_epi32(_mm_loadl_epi64((__m128i *)(void*)(x)))) + +# endif + +#endif diff --git a/managed_components/78__esp-opus/celt_headers.mk b/managed_components/78__esp-opus/celt_headers.mk new file mode 100644 index 0000000..267b418 --- /dev/null +++ b/managed_components/78__esp-opus/celt_headers.mk @@ -0,0 +1,55 @@ +CELT_HEAD = \ +celt/arch.h \ +celt/bands.h \ +celt/celt.h \ +celt/cpu_support.h \ +include/opus_types.h \ +include/opus_defines.h \ +include/opus_custom.h \ +celt/cwrs.h \ +celt/ecintrin.h \ +celt/entcode.h \ +celt/entdec.h \ +celt/entenc.h \ +celt/fixed_debug.h \ +celt/fixed_generic.h \ +celt/float_cast.h \ +celt/_kiss_fft_guts.h \ +celt/kiss_fft.h \ +celt/laplace.h \ +celt/mathops.h \ +celt/mdct.h \ +celt/mfrngcod.h \ +celt/modes.h \ +celt/os_support.h \ +celt/pitch.h \ +celt/celt_lpc.h \ +celt/x86/celt_lpc_sse.h \ +celt/quant_bands.h \ +celt/rate.h \ +celt/stack_alloc.h \ +celt/vq.h \ +celt/static_modes_float.h \ +celt/static_modes_fixed.h \ +celt/static_modes_float_arm_ne10.h \ +celt/static_modes_fixed_arm_ne10.h \ +celt/arm/armcpu.h \ +celt/arm/fixed_armv4.h \ +celt/arm/fixed_armv5e.h \ +celt/arm/fixed_arm64.h \ +celt/arm/kiss_fft_armv4.h \ +celt/arm/kiss_fft_armv5e.h \ +celt/arm/mathops_arm.h \ +celt/arm/pitch_arm.h \ +celt/arm/fft_arm.h \ +celt/arm/mdct_arm.h \ +celt/mips/celt_mipsr1.h \ +celt/mips/fixed_generic_mipsr1.h \ +celt/mips/kiss_fft_mipsr1.h \ +celt/mips/mdct_mipsr1.h \ +celt/mips/pitch_mipsr1.h \ +celt/mips/vq_mipsr1.h \ +celt/x86/pitch_sse.h \ +celt/x86/vq_sse.h \ +celt/x86/x86_arch_macros.h \ +celt/x86/x86cpu.h diff --git a/managed_components/78__esp-opus/celt_sources.mk b/managed_components/78__esp-opus/celt_sources.mk new file mode 100644 index 0000000..6b73d48 --- /dev/null +++ b/managed_components/78__esp-opus/celt_sources.mk @@ -0,0 +1,55 @@ +CELT_SOURCES = \ +celt/bands.c \ +celt/celt.c \ +celt/celt_encoder.c \ +celt/celt_decoder.c \ +celt/cwrs.c \ +celt/entcode.c \ +celt/entdec.c \ +celt/entenc.c \ +celt/kiss_fft.c \ +celt/laplace.c \ +celt/mathops.c \ +celt/mdct.c \ +celt/modes.c \ +celt/pitch.c \ +celt/celt_lpc.c \ +celt/quant_bands.c \ +celt/rate.c \ +celt/vq.c + +CELT_SOURCES_X86_RTCD = \ +celt/x86/x86cpu.c \ +celt/x86/x86_celt_map.c + +CELT_SOURCES_SSE = \ +celt/x86/pitch_sse.c + +CELT_SOURCES_SSE2 = \ +celt/x86/pitch_sse2.c \ +celt/x86/vq_sse2.c + +CELT_SOURCES_SSE4_1 = \ +celt/x86/celt_lpc_sse4_1.c \ +celt/x86/pitch_sse4_1.c + +CELT_SOURCES_AVX2 = \ +celt/x86/pitch_avx.c + +CELT_SOURCES_ARM_RTCD = \ +celt/arm/armcpu.c \ +celt/arm/arm_celt_map.c + +CELT_SOURCES_ARM_ASM = \ +celt/arm/celt_pitch_xcorr_arm.s + +CELT_AM_SOURCES_ARM_ASM = \ +celt/arm/armopts.s.in + +CELT_SOURCES_ARM_NEON_INTR = \ +celt/arm/celt_neon_intr.c \ +celt/arm/pitch_neon_intr.c + +CELT_SOURCES_ARM_NE10 = \ +celt/arm/celt_fft_ne10.c \ +celt/arm/celt_mdct_ne10.c diff --git a/managed_components/78__esp-opus/cmake/CFeatureCheck.cmake b/managed_components/78__esp-opus/cmake/CFeatureCheck.cmake new file mode 100644 index 0000000..08828f5 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/CFeatureCheck.cmake @@ -0,0 +1,39 @@ +# - Compile and run code to check for C features +# +# This functions compiles a source file under the `cmake` folder +# and adds the corresponding `HAVE_[FILENAME]` flag to the CMake +# environment +# +# c_feature_check( []) +# +# - Example +# +# include(CFeatureCheck) +# c_feature_check(VLA) + +if(__c_feature_check) + return() +endif() +set(__c_feature_check INCLUDED) + +function(c_feature_check FILE) + string(TOLOWER ${FILE} FILE) + string(TOUPPER ${FILE} VAR) + string(TOUPPER "${VAR}_SUPPORTED" FEATURE) + if (DEFINED ${VAR}_SUPPORTED) + set(${VAR}_SUPPORTED 1 PARENT_SCOPE) + return() + endif() + + if (NOT DEFINED COMPILE_${FEATURE}) + message(STATUS "Performing Test ${FEATURE}") + try_compile(COMPILE_${FEATURE} ${PROJECT_BINARY_DIR} ${PROJECT_SOURCE_DIR}/cmake/${FILE}.c) + endif() + + if(COMPILE_${FEATURE}) + message(STATUS "Performing Test ${FEATURE} -- success") + set(${VAR}_SUPPORTED 1 PARENT_SCOPE) + else() + message(STATUS "Performing Test ${FEATURE} -- failed to compile") + endif() +endfunction() diff --git a/managed_components/78__esp-opus/cmake/OpusBuildtype.cmake b/managed_components/78__esp-opus/cmake/OpusBuildtype.cmake new file mode 100644 index 0000000..557cc89 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusBuildtype.cmake @@ -0,0 +1,27 @@ +# Set a default build type if none was specified +if(__opus_buildtype) + return() +endif() +set(__opus_buildtype INCLUDED) + +if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) + if(CMAKE_C_FLAGS) + message(STATUS "CMAKE_C_FLAGS: " ${CMAKE_C_FLAGS}) + else() + set(default_build_type "Release") + message( + STATUS + "Setting build type to '${default_build_type}' as none was specified and no CFLAGS was exported." + ) + set(CMAKE_BUILD_TYPE "${default_build_type}" + CACHE STRING "Choose the type of build." + FORCE) + # Set the possible values of build type for cmake-gui + set_property(CACHE CMAKE_BUILD_TYPE + PROPERTY STRINGS + "Debug" + "Release" + "MinSizeRel" + "RelWithDebInfo") + endif() +endif() diff --git a/managed_components/78__esp-opus/cmake/OpusConfig.cmake b/managed_components/78__esp-opus/cmake/OpusConfig.cmake new file mode 100644 index 0000000..9fd814c --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusConfig.cmake @@ -0,0 +1,118 @@ +if(__opus_config) + return() +endif() +set(__opus_config INCLUDED) + +include(OpusFunctions) + +configure_file(cmake/config.h.cmake.in config.h @ONLY) +add_definitions(-DHAVE_CONFIG_H) + +set_property(GLOBAL PROPERTY USE_FOLDERS ON) + +if(MSVC) + # For compilers that have no notion of a C standard level, + # such as Microsoft Visual C++ before VS 16.7, + # this property has no effect. + set(CMAKE_C_STANDARD 11) +else() + set(CMAKE_C_STANDARD 99) +endif() + +if(MSVC) + add_definitions(-D_CRT_SECURE_NO_WARNINGS) +endif() + +include(CFeatureCheck) +c_feature_check(VLA) + +include(CheckIncludeFile) +check_include_file(alloca.h HAVE_ALLOCA_H) + +include(CheckSymbolExists) +if(HAVE_ALLOCA_H) + add_definitions(-DHAVE_ALLOCA_H) + check_symbol_exists(alloca "alloca.h" USE_ALLOCA_SUPPORTED) +else() + check_symbol_exists(alloca "stdlib.h;malloc.h" USE_ALLOCA_SUPPORTED) +endif() + +include(CMakePushCheckState) +cmake_push_check_state(RESET) +include(CheckLibraryExists) +check_library_exists(m floor "" HAVE_LIBM) +if(HAVE_LIBM) + list(APPEND OPUS_REQUIRED_LIBRARIES m) + set(CMAKE_REQUIRED_LIBRARIES m) +endif() + +check_symbol_exists(lrintf "math.h" HAVE_LRINTF) +check_symbol_exists(lrint "math.h" HAVE_LRINT) +cmake_pop_check_state() + +if(CMAKE_SYSTEM_PROCESSOR MATCHES "(i[0-9]86|x86|X86|amd64|AMD64|x86_64)") + if(CMAKE_SIZEOF_VOID_P EQUAL 8) + set(OPUS_CPU_X64 1) + else() + set(OPUS_CPU_X86 1) + endif() +elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "(arm|aarch64)") + set(OPUS_CPU_ARM 1) +endif() + +if(NOT OPUS_DISABLE_INTRINSICS) + opus_supports_cpu_detection(RUNTIME_CPU_CAPABILITY_DETECTION) +endif() + +if(OPUS_CPU_X86 OR OPUS_CPU_X64 AND NOT OPUS_DISABLE_INTRINSICS) + opus_detect_sse(COMPILER_SUPPORT_SIMD) +elseif(OPUS_CPU_ARM AND NOT OPUS_DISABLE_INTRINSICS) + opus_detect_neon(COMPILER_SUPPORT_NEON) + if(COMPILER_SUPPORT_NEON) + option(OPUS_USE_NEON "Option to enable NEON" ON) + option(OPUS_MAY_HAVE_NEON "Does runtime check for neon support" ON) + option(OPUS_PRESUME_NEON "Assume target CPU has NEON support" OFF) + if(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + set(OPUS_PRESUME_NEON ON) + elseif(CMAKE_SYSTEM_NAME MATCHES "iOS") + set(OPUS_PRESUME_NEON ON) + endif() + endif() +endif() + +if(MSVC) + check_flag(FAST_MATH /fp:fast) + check_flag(STACK_PROTECTOR /GS) + check_flag(STACK_PROTECTOR_DISABLED /GS-) +else() + check_flag(FAST_MATH -ffast-math) + check_flag(STACK_PROTECTOR -fstack-protector-strong) + check_flag(HIDDEN_VISIBILITY -fvisibility=hidden) + set(FORTIFY_SOURCE_SUPPORTED 1) +endif() + +if(MINGW) + # For MINGW we need to link ssp lib for security features such as + # stack protector and fortify_sources + check_library_exists(ssp __stack_chk_fail "" HAVE_LIBSSP) + if(NOT HAVE_LIBSSP) + message(WARNING "Could not find libssp in MinGW, disabling STACK_PROTECTOR and FORTIFY_SOURCE") + set(STACK_PROTECTOR_SUPPORTED 0) + set(FORTIFY_SOURCE_SUPPORTED 0) + endif() +endif() + +if(MSVC) + # move cosmetic warnings to level 4 + add_compile_options(/w44244 /w44305 /w44267) +else() + set(WARNING_LIST -Wall -W -Wstrict-prototypes -Wextra -Wcast-align -Wnested-externs -Wshadow) + include(CheckCCompilerFlag) + foreach(WARNING_FLAG ${WARNING_LIST}) + string(REPLACE - "" WARNING_VAR ${WARNING_FLAG}) + check_c_compiler_flag(${WARNING_FLAG} ${WARNING_VAR}_SUPPORTED) + if(${WARNING_VAR}_SUPPORTED) + add_compile_options(${WARNING_FLAG}) + endif() + endforeach() +endif() diff --git a/managed_components/78__esp-opus/cmake/OpusConfig.cmake.in b/managed_components/78__esp-opus/cmake/OpusConfig.cmake.in new file mode 100644 index 0000000..0b21231 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusConfig.cmake.in @@ -0,0 +1,20 @@ +set(OPUS_VERSION @PROJECT_VERSION@) +set(OPUS_VERSION_STRING @PROJECT_VERSION@) +set(OPUS_VERSION_MAJOR @PROJECT_VERSION_MAJOR@) +set(OPUS_VERSION_MINOR @PROJECT_VERSION_MINOR@) +set(OPUS_VERSION_PATCH @PROJECT_VERSION_PATCH@) + +@PACKAGE_INIT@ + +set_and_check(OPUS_INCLUDE_DIR "@PACKAGE_INCLUDE_INSTALL_DIR@") +set(OPUS_INCLUDE_DIR ${OPUS_INCLUDE_DIR};${OPUS_INCLUDE_DIR}/opus) +set(OPUS_INCLUDE_DIRS "@PACKAGE_INCLUDE_INSTALL_DIR@;@PACKAGE_INCLUDE_INSTALL_DIR@/opus") + +include(${CMAKE_CURRENT_LIST_DIR}/OpusTargets.cmake) + +set(OPUS_LIBRARY Opus::opus) +set(OPUS_LIBRARIES Opus::opus) + +check_required_components(Opus) + +set(OPUS_FOUND 1) diff --git a/managed_components/78__esp-opus/cmake/OpusFunctions.cmake b/managed_components/78__esp-opus/cmake/OpusFunctions.cmake new file mode 100644 index 0000000..a265c47 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusFunctions.cmake @@ -0,0 +1,231 @@ +if(__opus_functions) + return() +endif() +set(__opus_functions INCLUDED) + +function(get_library_version OPUS_LIBRARY_VERSION OPUS_LIBRARY_VERSION_MAJOR) + file(STRINGS configure.ac opus_lt_current_string + LIMIT_COUNT 1 + REGEX "OPUS_LT_CURRENT=") + string(REGEX MATCH + "OPUS_LT_CURRENT=([0-9]*)" + _ + ${opus_lt_current_string}) + set(OPUS_LT_CURRENT ${CMAKE_MATCH_1}) + + file(STRINGS configure.ac opus_lt_revision_string + LIMIT_COUNT 1 + REGEX "OPUS_LT_REVISION=") + string(REGEX MATCH + "OPUS_LT_REVISION=([0-9]*)" + _ + ${opus_lt_revision_string}) + set(OPUS_LT_REVISION ${CMAKE_MATCH_1}) + + file(STRINGS configure.ac opus_lt_age_string + LIMIT_COUNT 1 + REGEX "OPUS_LT_AGE=") + string(REGEX MATCH + "OPUS_LT_AGE=([0-9]*)" + _ + ${opus_lt_age_string}) + set(OPUS_LT_AGE ${CMAKE_MATCH_1}) + + math(EXPR OPUS_LIBRARY_VERSION_MAJOR "${OPUS_LT_CURRENT} - ${OPUS_LT_AGE}") + set(OPUS_LIBRARY_VERSION_MINOR ${OPUS_LT_AGE}) + set(OPUS_LIBRARY_VERSION_PATCH ${OPUS_LT_REVISION}) + set( + OPUS_LIBRARY_VERSION + "${OPUS_LIBRARY_VERSION_MAJOR}.${OPUS_LIBRARY_VERSION_MINOR}.${OPUS_LIBRARY_VERSION_PATCH}" + PARENT_SCOPE) + set(OPUS_LIBRARY_VERSION_MAJOR ${OPUS_LIBRARY_VERSION_MAJOR} PARENT_SCOPE) +endfunction() + +function(check_flag NAME FLAG) + include(CheckCCompilerFlag) + check_c_compiler_flag(${FLAG} ${NAME}_SUPPORTED) +endfunction() + +include(CheckIncludeFile) + +# This function determines if the compiler has support for SSE, SSE2, SSE4.1, AVX, +# AVX2 and FMA. Should the target systems potentially lack SSE support, the +# OPUS_MAY_HAVE_SSE option is recommended for use. If, however, the target system is +# assured to support SSE, the OPUS_PRESUME_SSE option can be employed, thus +# eliminating the necessity for an SSE runtime check. +function(opus_detect_sse COMPILER_SUPPORT_SIMD) + message(STATUS "Check SIMD support by compiler") + check_include_file(xmmintrin.h HAVE_XMMINTRIN_H) # SSE1 + if(HAVE_XMMINTRIN_H) + if(MSVC) + # different arch options for 32 and 64 bit target for MSVC + if(CMAKE_SIZEOF_VOID_P EQUAL 4) + check_flag(SSE1 /arch:SSE) + else() + set(SSE1_SUPPORTED + 1 + PARENT_SCOPE) + endif() + else() + check_flag(SSE1 -msse) + endif() + else() + set(SSE1_SUPPORTED + 0 + PARENT_SCOPE) + endif() + + check_include_file(emmintrin.h HAVE_EMMINTRIN_H) # SSE2 + if(HAVE_EMMINTRIN_H) + if(MSVC) + if(CMAKE_SIZEOF_VOID_P EQUAL 4) + check_flag(SSE2 /arch:SSE2) + else() + set(SSE2_SUPPORTED + 1 + PARENT_SCOPE) + endif() + else() + check_flag(SSE2 -msse2) + endif() + else() + set(SSE2_SUPPORTED + 0 + PARENT_SCOPE) + endif() + + check_include_file(smmintrin.h HAVE_SMMINTRIN_H) # SSE4.1 + if(HAVE_SMMINTRIN_H) + if(MSVC) + if(CMAKE_SIZEOF_VOID_P EQUAL 4) + check_flag(SSE4_1 /arch:SSE2) # SSE2 and above + else() + set(SSE4_1_SUPPORTED + 1 + PARENT_SCOPE) + endif() + else() + check_flag(SSE4_1 -msse4.1) + endif() + else() + set(SSE4_1_SUPPORTED + 0 + PARENT_SCOPE) + endif() + + check_include_file(immintrin.h HAVE_IMMINTRIN_H) # AVX2 + if(HAVE_IMMINTRIN_H) + if(MSVC) + check_flag(AVX2 /arch:AVX2) + else() + check_flag(AVX2 -mavx2 -mfma -mavx) + endif() + else() + set(AVX2_SUPPORTED + 0 + PARENT_SCOPE) + endif() + + if(SSE1_SUPPORTED OR SSE2_SUPPORTED OR SSE4_1_SUPPORTED OR AVX2_SUPPORTED) + set(COMPILER_SUPPORT_SIMD 1 PARENT_SCOPE) + else() + message(STATUS "No SIMD support in compiler") + endif() +endfunction() + +function(opus_detect_neon COMPILER_SUPPORT_NEON) + if(CMAKE_SYSTEM_PROCESSOR MATCHES "(arm|aarch64)") + message(STATUS "Check NEON support by compiler") + check_include_file(arm_neon.h HAVE_ARM_NEON_H) + if(HAVE_ARM_NEON_H) + set(COMPILER_SUPPORT_NEON ${HAVE_ARM_NEON_H} PARENT_SCOPE) + endif() + endif() +endfunction() + +function(opus_supports_cpu_detection RUNTIME_CPU_CAPABILITY_DETECTION) + set(RUNTIME_CPU_CAPABILITY_DETECTION 0 PARENT_SCOPE) + if(OPUS_CPU_X86 OR OPUS_CPU_X64) + if(MSVC) + check_include_file(intrin.h HAVE_INTRIN_H) + if(HAVE_INTRIN_H) + # if intrin.h is available we assume __cpuid is there + set(RUNTIME_CPU_CAPABILITY_DETECTION 1 PARENT_SCOPE) + endif() + else() + include(CFeatureCheck) + c_feature_check(CPU_INFO_BY_ASM) + set(CPU_INFO_BY_ASM_SUPPORTED ${CPU_INFO_BY_ASM_SUPPORTED} PARENT_SCOPE) + check_include_file(cpuid.h HAVE_CPUID_H) + if(HAVE_CPUID_H) + c_feature_check(CPU_INFO_BY_C) + set(CPU_INFO_BY_C_SUPPORTED ${CPU_INFO_BY_C_SUPPORTED} PARENT_SCOPE) + endif() + if(CPU_INFO_BY_ASM_SUPPORTED OR CPU_INFO_BY_C_SUPPORTED) + set(RUNTIME_CPU_CAPABILITY_DETECTION 1 PARENT_SCOPE) + endif() + endif() + elseif(OPUS_CPU_ARM) + # ARM cpu detection is implemented for Windows and anything + # using a Linux kernel (such as Android). + if (CMAKE_SYSTEM_NAME MATCHES "(Windows|Linux|Android)") + set(RUNTIME_CPU_CAPABILITY_DETECTION 1 PARENT_SCOPE) + endif () + else() + set(RUNTIME_CPU_CAPABILITY_DETECTION 0 PARENT_SCOPE) + endif() +endfunction() + +function(add_sources_group target group) + target_sources(${target} PRIVATE ${ARGN}) + source_group(${group} FILES ${ARGN}) +endfunction() + +function(get_opus_sources SOURCE_GROUP MAKE_FILE SOURCES) + # read file, each item in list is one group + file(STRINGS ${MAKE_FILE} opus_sources) + + # add wildcard for regex match + string(CONCAT SOURCE_GROUP ${SOURCE_GROUP} ".*$") + + # find group + foreach(val IN LISTS opus_sources) + if(val MATCHES ${SOURCE_GROUP}) + list(LENGTH val list_length) + if(${list_length} EQUAL 1) + # for tests split by '=' and clean up the rest into a list + string(FIND ${val} "=" index) + math(EXPR index "${index} + 1") + string(SUBSTRING ${val} + ${index} + -1 + sources) + string(REPLACE " " + ";" + sources + ${sources}) + else() + # discard the group + list(REMOVE_AT val 0) + set(sources ${val}) + endif() + break() + endif() + endforeach() + + list(LENGTH sources list_length) + if(${list_length} LESS 1) + message( + FATAL_ERROR + "No files parsed successfully from ${SOURCE_GROUP} in ${MAKE_FILE}") + endif() + + # remove trailing whitespaces + set(list_var "") + foreach(source ${sources}) + string(STRIP "${source}" source) + list(APPEND list_var "${source}") + endforeach() + + set(${SOURCES} ${list_var} PARENT_SCOPE) +endfunction() diff --git a/managed_components/78__esp-opus/cmake/OpusPackageVersion.cmake b/managed_components/78__esp-opus/cmake/OpusPackageVersion.cmake new file mode 100644 index 0000000..447ce3b --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusPackageVersion.cmake @@ -0,0 +1,70 @@ +if(__opus_version) + return() +endif() +set(__opus_version INCLUDED) + +function(get_package_version PACKAGE_VERSION PROJECT_VERSION) + + find_package(Git) + if(GIT_FOUND AND EXISTS "${CMAKE_CURRENT_LIST_DIR}/.git") + execute_process(COMMAND ${GIT_EXECUTABLE} + --git-dir=${CMAKE_CURRENT_LIST_DIR}/.git describe + --tags --match "v*" OUTPUT_VARIABLE OPUS_PACKAGE_VERSION) + if(OPUS_PACKAGE_VERSION) + string(STRIP ${OPUS_PACKAGE_VERSION}, OPUS_PACKAGE_VERSION) + string(REPLACE \n + "" + OPUS_PACKAGE_VERSION + ${OPUS_PACKAGE_VERSION}) + string(REPLACE , + "" + OPUS_PACKAGE_VERSION + ${OPUS_PACKAGE_VERSION}) + + string(SUBSTRING ${OPUS_PACKAGE_VERSION} + 1 + -1 + OPUS_PACKAGE_VERSION) + message(STATUS "Opus package version from git repo: ${OPUS_PACKAGE_VERSION}") + endif() + + elseif(EXISTS "${CMAKE_CURRENT_LIST_DIR}/package_version" + AND NOT OPUS_PACKAGE_VERSION) + # Not a git repo, lets' try to parse it from package_version file if exists + file(STRINGS package_version OPUS_PACKAGE_VERSION + LIMIT_COUNT 1 + REGEX "PACKAGE_VERSION=") + string(REPLACE "PACKAGE_VERSION=" + "" + OPUS_PACKAGE_VERSION + ${OPUS_PACKAGE_VERSION}) + string(REPLACE "\"" + "" + OPUS_PACKAGE_VERSION + ${OPUS_PACKAGE_VERSION}) + # In case we have a unknown dist here we just replace it with 0 + string(REPLACE "unknown" + "0" + OPUS_PACKAGE_VERSION + ${OPUS_PACKAGE_VERSION}) + message(STATUS "Opus package version from package_version file: ${OPUS_PACKAGE_VERSION}") + endif() + + if(OPUS_PACKAGE_VERSION) + string(REGEX + REPLACE "^([0-9]+.[0-9]+\\.?([0-9]+)?).*" + "\\1" + OPUS_PROJECT_VERSION + ${OPUS_PACKAGE_VERSION}) + else() + # fail to parse version from git and package version + message(WARNING "Could not get package version.") + set(OPUS_PACKAGE_VERSION 0) + set(OPUS_PROJECT_VERSION 0) + endif() + + message(STATUS "Opus project version: ${OPUS_PROJECT_VERSION}") + + set(PACKAGE_VERSION ${OPUS_PACKAGE_VERSION} PARENT_SCOPE) + set(PROJECT_VERSION ${OPUS_PROJECT_VERSION} PARENT_SCOPE) +endfunction() diff --git a/managed_components/78__esp-opus/cmake/OpusSources.cmake b/managed_components/78__esp-opus/cmake/OpusSources.cmake new file mode 100644 index 0000000..53518ff --- /dev/null +++ b/managed_components/78__esp-opus/cmake/OpusSources.cmake @@ -0,0 +1,71 @@ +if(__opus_sources) + return() +endif() +set(__opus_sources INCLUDED) + +include(OpusFunctions) + +get_opus_sources(SILK_HEAD silk_headers.mk silk_headers) +get_opus_sources(SILK_SOURCES silk_sources.mk silk_sources) +get_opus_sources(SILK_SOURCES_FLOAT silk_sources.mk silk_sources_float) +get_opus_sources(SILK_SOURCES_FIXED silk_sources.mk silk_sources_fixed) +get_opus_sources(SILK_SOURCES_X86_RTCD silk_sources.mk silk_sources_x86_rtcd) +get_opus_sources(SILK_SOURCES_SSE4_1 silk_sources.mk silk_sources_sse4_1) +get_opus_sources(SILK_SOURCES_FIXED_SSE4_1 silk_sources.mk + silk_sources_fixed_sse4_1) +get_opus_sources(SILK_SOURCES_AVX2 silk_sources.mk silk_sources_avx2) +get_opus_sources(SILK_SOURCES_FLOAT_AVX2 silk_sources.mk silk_sources_float_avx2) +get_opus_sources(SILK_SOURCES_ARM_RTCD silk_sources.mk silk_sources_arm_rtcd) +get_opus_sources(SILK_SOURCES_ARM_NEON_INTR silk_sources.mk + silk_sources_arm_neon_intr) +get_opus_sources(SILK_SOURCES_FIXED_ARM_NEON_INTR silk_sources.mk + silk_sources_fixed_arm_neon_intr) + +get_opus_sources(OPUS_HEAD opus_headers.mk opus_headers) +get_opus_sources(OPUS_SOURCES opus_sources.mk opus_sources) +get_opus_sources(OPUS_SOURCES_FLOAT opus_sources.mk opus_sources_float) + +get_opus_sources(CELT_HEAD celt_headers.mk celt_headers) +get_opus_sources(CELT_SOURCES celt_sources.mk celt_sources) +get_opus_sources(CELT_SOURCES_X86_RTCD celt_sources.mk celt_sources_x86_rtcd) +get_opus_sources(CELT_SOURCES_SSE celt_sources.mk celt_sources_sse) +get_opus_sources(CELT_SOURCES_SSE2 celt_sources.mk celt_sources_sse2) +get_opus_sources(CELT_SOURCES_SSE4_1 celt_sources.mk celt_sources_sse4_1) +get_opus_sources(CELT_SOURCES_AVX2 celt_sources.mk celt_sources_avx2) +get_opus_sources(CELT_SOURCES_ARM_RTCD celt_sources.mk celt_sources_arm_rtcd) +get_opus_sources(CELT_SOURCES_ARM_ASM celt_sources.mk celt_sources_arm_asm) +get_opus_sources(CELT_AM_SOURCES_ARM_ASM celt_sources.mk + celt_am_sources_arm_asm) +get_opus_sources(CELT_SOURCES_ARM_NEON_INTR celt_sources.mk + celt_sources_arm_neon_intr) +get_opus_sources(CELT_SOURCES_ARM_NE10 celt_sources.mk celt_sources_arm_ne10) + +get_opus_sources(DEEP_PLC_HEAD lpcnet_headers.mk deep_plc_headers) +get_opus_sources(DRED_HEAD lpcnet_headers.mk dred_headers) +get_opus_sources(OSCE_HEAD lpcnet_headers.mk osce_headers) +get_opus_sources(DEEP_PLC_SOURCES lpcnet_sources.mk deep_plc_sources) +get_opus_sources(DRED_SOURCES lpcnet_sources.mk dred_sources) +get_opus_sources(OSCE_SOURCES lpcnet_sources.mk osce_sources) +get_opus_sources(DNN_SOURCES_X86_RTCD lpcnet_sources.mk dnn_sources_x86_rtcd) +get_opus_sources(DNN_SOURCES_SSE2 lpcnet_sources.mk dnn_sources_sse2) +get_opus_sources(DNN_SOURCES_SSE4_1 lpcnet_sources.mk dnn_sources_sse4_1) +get_opus_sources(DNN_SOURCES_AVX2 lpcnet_sources.mk dnn_sources_avx2) +get_opus_sources(DNN_SOURCES_NEON lpcnet_sources.mk dnn_sources_arm_neon) +get_opus_sources(DNN_SOURCES_DOTPROD lpcnet_sources.mk dnn_sources_arm_dotprod) + +get_opus_sources(opus_demo_SOURCES Makefile.am opus_demo_sources) +get_opus_sources(opus_custom_demo_SOURCES Makefile.am opus_custom_demo_sources) +get_opus_sources(opus_compare_SOURCES Makefile.am opus_compare_sources) +get_opus_sources(tests_test_opus_api_SOURCES Makefile.am test_opus_api_sources) +get_opus_sources(tests_test_opus_encode_SOURCES Makefile.am + test_opus_encode_sources) +get_opus_sources(tests_test_opus_extensions_SOURCES Makefile.am + test_opus_extensions_sources) +get_opus_sources(tests_test_opus_decode_SOURCES Makefile.am + test_opus_decode_sources) +get_opus_sources(tests_test_opus_padding_SOURCES Makefile.am + test_opus_padding_sources) +get_opus_sources(tests_test_opus_dred_SOURCES Makefile.am + test_opus_dred_sources) +get_opus_sources(tests_test_opus_custom_SOURCES Makefile.am + test_opus_custom_sources) diff --git a/managed_components/78__esp-opus/cmake/README.md b/managed_components/78__esp-opus/cmake/README.md new file mode 100644 index 0000000..d5f0337 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/README.md @@ -0,0 +1,132 @@ +# Using CMake for the Opus Project + +This guide provides instructions for using CMake to build the Opus project with various configuration options. CMake is a widely used build system generator that helps manage the build process across different platforms. + +Note: Please keep in mind that software documentation can sometimes go out of date as new versions are released. It is always recommended to refer to the official CMake documentation for the most up-to-date and accurate information. You can find the official CMake documentation at [cmake.org/documentation](https://cmake.org/documentation/). + +## Prerequisites + +Before proceeding, make sure you have the following prerequisites installed: + +- CMake +- Git (optional, but recommended for version control integration) +- Working C compiler + +## Build Instructions + +Follow the steps below to build the Opus project using CMake: + +1. Clone the Opus repository using Git: + + ```shell + git clone https://gitlab.xiph.org/xiph/opus + ``` + +2. Create a build directory within the Opus repository: + + ```shell + cd opus + mkdir build + cd build + ``` + +3. Configure the build with CMake. You can set the desired configuration options using CMake's `-D` flag. Here are some available options: + + - `OPUS_BUILD_SHARED_LIBRARY`: build shared library. + - `OPUS_BUILD_TESTING`: build tests. + - `OPUS_BUILD_PROGRAMS`: build programs. + - `OPUS_CUSTOM_MODES`, enable non-Opus modes, e.g. 44.1 kHz & 2^n frames. + + For example, to enable the custom modes and build programs, use the following command: + + ```shell + cmake .. -DOPUS_BUILD_PROGRAMS=ON -DOPUS_BUILD_TESTING=ON + ``` + +4. Build the Opus project: + + ```shell + cmake --build . + ``` + +5. After a successful build, you can find the compiled Opus library and associated files in the build directory. + +## Testing with CTest + +Opus provides a comprehensive test suite to ensure the functionality and correctness of the project. You can execute the tests using CTest, a part of the CMake build system. CTest allows for automated testing and provides useful features for managing and evaluating the test results. + +To run the Opus tests using CTest, follow these steps: + +1. Navigate to the build directory after configuring and building the project with CMake: + + ```shell + cd build + ``` + +2. Execute the tests using CTest: + + ```shell + ctest + ``` + +Note: For Windows you need to specify which configuration to test + +```shell +ctest -C Debug +``` + +## Platform Support and Bug Reporting + +CMake aims to provide broad platform support, allowing the Opus project to be built and used on major operating systems and platforms. The supported platforms include: + +- Windows +- macOS +- Linux +- Android +- iOS + +CMake achieves platform support by generating platform-specific build files (e.g., Makefiles, Visual Studio projects) based on the target platform. This allows developers to build and configure the Opus project consistently across different operating systems and environments. + +While CMake strives to ensure compatibility and stability across platforms, bugs or issues may still arise in specific configurations. If you encounter any problems during the configuration process or while building the Opus project, we encourage you to file an issue in the [project's issue tracker](https://gitlab.xiph.org/xiph/opus/-/issues). + +When reporting an issue, please provide the following information to help us understand and reproduce the configuration problem effectively: + +1. Detailed description of the issue, including any error messages or unexpected behavior observed. +2. Steps to reproduce the problem, including the CMake command and any specific configuration options used. +3. Operating system and version (e.g., Windows 10, macOS Big Sur, Ubuntu 20.04). +4. CMake version (e.g., CMake 3.21.1). +5. Any relevant information about the platform, toolchain, or dependencies used. +6. Additional context or details that might assist in troubleshooting the issue. + +By providing thorough information when reporting configuration issues, you contribute to improving the Opus project's compatibility and reliability across different platforms. + +We appreciate your help in identifying and addressing any configuration-related problems, ensuring a better experience for all users of the Opus project. + +## Platform Specific Examples + +Note: Examples can go out of date. Always refer to documentation for latest reference. + +### Cross compiling for Android + +```shell +cmake .. -DCMAKE_TOOLCHAIN_FILE=${ANDROID_HOME}/ndk/25.2.9519653/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a +``` + +For more information about cross compiling for android, you can refer to the [Cross compiling for Android documentation](https://cmake.org/cmake/help/latest/manual/cmake-toolchains.7.html#cross-compiling-for-android). + +### Cross compiling for iOS + +```shell +cmake .. -G "Unix Makefiles" -DCMAKE_SYSTEM_NAME=iOS -DCMAKE_OSX_ARCHITECTURES=arm64 +``` + +For more information about cross compilation for iOS, you can refer to the [Cross compiling for iOS documentation](https://cmake.org/cmake/help/latest/manual/cmake-toolchains.7.html#cross-compiling-for-ios-tvos-or-watchos). + + +### Windows Visual Studio + +```shell +cmake .. -G "Visual Studio 17 2022" -A x64 +``` + +For more information about the Visual Studio generator options and additional customization, you can refer to the [Visual Studio Generator documentation](https://cmake.org/cmake/help/latest/generator/Visual%20Studio%2017%202022.html). diff --git a/managed_components/78__esp-opus/cmake/RunTest.cmake b/managed_components/78__esp-opus/cmake/RunTest.cmake new file mode 100644 index 0000000..f6f8b4a --- /dev/null +++ b/managed_components/78__esp-opus/cmake/RunTest.cmake @@ -0,0 +1,61 @@ +if(NOT EXISTS ${TEST_EXECUTABLE}) + message(FATAL_ERROR "Error could not find ${TEST_EXECUTABLE}, ensure that you built the test binary") +endif() + +if(CMAKE_SYSTEM_NAME STREQUAL "Android") + + # support to run plain old binary on android devices + # requires android debug bridge to be installed + + find_program(adb_executable adb) + if(NOT adb_executable) + message(FATAL_ERROR "Error could not find adb") + endif() + + # check if any device emulator is attached + execute_process(COMMAND ${adb_executable} shell echo RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error adb: no devices/emulators found") + endif() + + # push binary + set(android_path /data/local/tmp) + execute_process(COMMAND ${adb_executable} push ${TEST_EXECUTABLE} ${android_path} RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error running ${adb_executable} push ${TEST_EXECUTABLE} ${android_path} failed with result ${CMD_RESULT}") + endif() + + # set permissions + get_filename_component(test_executable ${TEST_EXECUTABLE} NAME) + set(test_executable_on_android /data/local/tmp/${test_executable}) + execute_process(COMMAND ${adb_executable} shell chmod 555 ${test_executable_on_android} RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error running ${adb_executable} shell chmod 555 ${test_executable_on_android} failed with result ${CMD_RESULT}") + endif() + + # run executable + execute_process(COMMAND ${adb_executable} shell ${test_executable_on_android} RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error running ${adb_executable} shell ${test_executable_on_android} failed with result ${CMD_RESULT}") + endif() + + # clean up binary + execute_process(COMMAND ${adb_executable} shell rm ${test_executable_on_android} RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error running ${adb_executable} shell rm ${test_executable_on_android} failed with result ${CMD_RESULT}") + endif() + +elseif(CMAKE_SYSTEM_NAME STREQUAL "iOS") + # CTest doesn't support iOS + + message(FATAL_ERROR "Error CTest is not supported on iOS") + +else() + # for other platforms just execute test binary on host + + execute_process(COMMAND ${TEST_EXECUTABLE} RESULT_VARIABLE CMD_RESULT) + if(CMD_RESULT) + message(FATAL_ERROR "Error running ${TEST_EXECUTABLE} failed with result ${CMD_RESULT}") + endif() + +endif() \ No newline at end of file diff --git a/managed_components/78__esp-opus/cmake/config.h.cmake.in b/managed_components/78__esp-opus/cmake/config.h.cmake.in new file mode 100644 index 0000000..5550842 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/config.h.cmake.in @@ -0,0 +1 @@ +#cmakedefine PACKAGE_VERSION "@PACKAGE_VERSION@" \ No newline at end of file diff --git a/managed_components/78__esp-opus/cmake/cpu_info_by_asm.c b/managed_components/78__esp-opus/cmake/cpu_info_by_asm.c new file mode 100644 index 0000000..1a70a81 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/cpu_info_by_asm.c @@ -0,0 +1,31 @@ +#include +int main() { + unsigned int CPUInfo0; + unsigned int CPUInfo1; + unsigned int CPUInfo2; + unsigned int CPUInfo3; + unsigned int InfoType; +#if defined(__i386__) && defined(__PIC__) +/* %ebx is PIC register in 32-bit, so mustn't clobber it. */ + __asm__ __volatile__ ( + "xchg %%ebx, %1\n" + "cpuid\n" + "xchg %%ebx, %1\n": + "=a" (CPUInfo0), + "=r" (CPUInfo1), + "=c" (CPUInfo2), + "=d" (CPUInfo3) : + "0" (InfoType), "2" (0) + ); +#else + __asm__ __volatile__ ( + "cpuid": + "=a" (CPUInfo0), + "=b" (CPUInfo1), + "=c" (CPUInfo2), + "=d" (CPUInfo3) : + "0" (InfoType), "2" (0) + ); +#endif + return 0; +} diff --git a/managed_components/78__esp-opus/cmake/cpu_info_by_c.c b/managed_components/78__esp-opus/cmake/cpu_info_by_c.c new file mode 100644 index 0000000..117084e --- /dev/null +++ b/managed_components/78__esp-opus/cmake/cpu_info_by_c.c @@ -0,0 +1,9 @@ +#include +int main() { + unsigned int CPUInfo0; + unsigned int CPUInfo1; + unsigned int CPUInfo2; + unsigned int CPUInfo3; + unsigned int InfoType; + return __get_cpuid_count(InfoType, 0, &CPUInfo0, &CPUInfo1, &CPUInfo2, &CPUInfo3); +} diff --git a/managed_components/78__esp-opus/cmake/vla.c b/managed_components/78__esp-opus/cmake/vla.c new file mode 100644 index 0000000..05b2119 --- /dev/null +++ b/managed_components/78__esp-opus/cmake/vla.c @@ -0,0 +1,7 @@ +int main() { + static int x; + char a[++x]; + a[sizeof a - 1] = 0; + int N; + return a[0]; +} \ No newline at end of file diff --git a/managed_components/78__esp-opus/configure.ac b/managed_components/78__esp-opus/configure.ac new file mode 100644 index 0000000..2e9c756 --- /dev/null +++ b/managed_components/78__esp-opus/configure.ac @@ -0,0 +1,1091 @@ +dnl Process this file with autoconf to produce a configure script. -*-m4-*- + +dnl The package_version file will be automatically synced to the git revision +dnl by the update_version script when configured in the repository, but will +dnl remain constant in tarball releases unless it is manually edited. +m4_define([CURRENT_VERSION], + m4_esyscmd([ ./update_version 2>/dev/null || true + if test -e package_version; then + . ./package_version + printf "$PACKAGE_VERSION" + else + printf "unknown" + fi ])) + +AC_INIT([opus],[CURRENT_VERSION],[opus@xiph.org]) + +AC_CONFIG_SRCDIR(src/opus_encoder.c) +AC_CONFIG_MACRO_DIR([m4]) + +dnl enable silent rules on automake 1.11 and later +m4_ifdef([AM_SILENT_RULES], [AM_SILENT_RULES([yes])]) + +# For libtool. +dnl Please update these for releases. +OPUS_LT_CURRENT=10 +OPUS_LT_REVISION=1 +OPUS_LT_AGE=10 + +AC_SUBST(OPUS_LT_CURRENT) +AC_SUBST(OPUS_LT_REVISION) +AC_SUBST(OPUS_LT_AGE) + +AM_INIT_AUTOMAKE([no-define]) +AM_MAINTAINER_MODE([enable]) + +AC_CANONICAL_HOST +AC_MINGW32 +AM_PROG_LIBTOOL +AM_PROG_CC_C_O + +AC_PROG_CC_C99 +AC_C_CONST +AC_C_INLINE + +AM_PROG_AS + +AC_DEFINE([OPUS_BUILD], [], [This is a build of OPUS]) + +#Use a hacked up version of autoconf's AC_C_RESTRICT because it's not +#strong enough a test to detect old buggy versions of GCC (e.g. 2.95.3) +#Note: Both this and the test for variable-size arrays below are also +# done by AC_PROG_CC_C99, but not thoroughly enough apparently. +AC_CACHE_CHECK([for C/C++ restrict keyword], ac_cv_c_restrict, + [ac_cv_c_restrict=no + # The order here caters to the fact that C++ does not require restrict. + for ac_kw in __restrict __restrict__ _Restrict restrict; do + AC_COMPILE_IFELSE([AC_LANG_PROGRAM( + [[typedef int * int_ptr; + int foo (int_ptr $ac_kw ip, int * $ac_kw baz[]) { + return ip[0]; + }]], + [[int s[1]; + int * $ac_kw t = s; + t[0] = 0; + return foo(t, (void *)0)]])], + [ac_cv_c_restrict=$ac_kw]) + test "$ac_cv_c_restrict" != no && break + done + ]) + +AH_VERBATIM([restrict], +[/* Define to the equivalent of the C99 'restrict' keyword, or to + nothing if this is not supported. Do not define if restrict is + supported directly. */ +#undef restrict +/* Work around a bug in Sun C++: it does not support _Restrict or + __restrict__, even though the corresponding Sun C compiler ends up with + "#define restrict _Restrict" or "#define restrict __restrict__" in the + previous line. Perhaps some future version of Sun C++ will work with + restrict; if so, hopefully it defines __RESTRICT like Sun C does. */ +#if defined __SUNPRO_CC && !defined __RESTRICT +# define _Restrict +# define __restrict__ +#endif]) + +case $ac_cv_c_restrict in + restrict) ;; + no) AC_DEFINE([restrict], []) ;; + *) AC_DEFINE_UNQUOTED([restrict], [$ac_cv_c_restrict]) ;; +esac + +AC_MSG_CHECKING(for C99 variable-size arrays) +AC_COMPILE_IFELSE([AC_LANG_PROGRAM([], + [[static int x; char a[++x]; a[sizeof a - 1] = 0; int N; return a[0];]])], + [ has_var_arrays=yes + use_alloca="no (using var arrays)" + AC_DEFINE([VAR_ARRAYS], [1], [Use C99 variable-size arrays]) + ],[ + has_var_arrays=no + ]) +AC_MSG_RESULT([$has_var_arrays]) + +AS_IF([test "$has_var_arrays" = "no"], + [ + AC_CHECK_HEADERS([alloca.h]) + AC_MSG_CHECKING(for alloca) + AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[#include ]], + [[int foo=10; int *array = alloca(foo);]])], + [ use_alloca=yes; + AC_DEFINE([USE_ALLOCA], [], [Make use of alloca]) + ],[ + use_alloca=no + ]) + AC_MSG_RESULT([$use_alloca]) + ]) + +LT_LIB_M + +AC_ARG_ENABLE([fixed-point], + [AS_HELP_STRING([--enable-fixed-point], + [compile without floating point (for machines without a fast enough FPU)])],, + [enable_fixed_point=no]) + +AS_IF([test "$enable_fixed_point" = "yes"],[ + enable_float="no" + AC_DEFINE([FIXED_POINT], [1], [Compile as fixed-point (for machines without a fast enough FPU)]) + PC_BUILD="fixed-point" +],[ + enable_float="yes"; + PC_BUILD="floating-point" +]) + +AM_CONDITIONAL([FIXED_POINT], [test "$enable_fixed_point" = "yes"]) + +AC_ARG_ENABLE([fixed-point-debug], + [AS_HELP_STRING([--enable-fixed-point-debug], [debug fixed-point implementation])],, + [enable_fixed_point_debug=no]) + +AS_IF([test "$enable_fixed_point_debug" = "yes"],[ + AC_DEFINE([FIXED_DEBUG], [1], [Debug fixed-point implementation]) +]) + +AC_ARG_ENABLE([float_api], + [AS_HELP_STRING([--disable-float-api], + [compile without the floating point API (for machines with no float library)])],, + [enable_float_api=yes]) + +AM_CONDITIONAL([DISABLE_FLOAT_API], [test "$enable_float_api" = "no"]) + +AS_IF([test "$enable_float_api" = "no"],[ + AC_DEFINE([DISABLE_FLOAT_API], [1], [Do not build the float API]) +]) + +AC_ARG_ENABLE([fixed-res24], + [AS_HELP_STRING([--enable-fixed-res24], [Use 24-bit internal resolution for fixed-point implementation])],, + [enable_fixed_res24=no]) + +AS_IF([test "$enable_fixed_res24" = "yes"],[ + AC_DEFINE([ENABLE_RES24], [1], [24-bit internal resolution for fixed-point]) +]) + +AC_ARG_ENABLE([custom-modes], + [AS_HELP_STRING([--enable-custom-modes], [enable non-Opus modes, e.g. 44.1 kHz & 2^n frames])],, + [enable_custom_modes=no]) + +AS_IF([test "$enable_custom_modes" = "yes"],[ + AC_DEFINE([CUSTOM_MODES], [1], [Custom modes]) + PC_BUILD="$PC_BUILD, custom modes" +]) + +AM_CONDITIONAL([CUSTOM_MODES], [test "$enable_custom_modes" = "yes"]) + +AC_ARG_ENABLE([dred], + [AS_HELP_STRING([--enable-dred], [Use Deep REDundancy (DRED)])],, + [enable_dred=no]) + +AS_IF([test "$enable_dred" = "yes"],[ + AC_DEFINE([ENABLE_DRED], [1], [DRED]) +]) +AM_CONDITIONAL([ENABLE_DRED], [test "$enable_dred" = "yes"]) + +AC_ARG_ENABLE([deep-plc], + [AS_HELP_STRING([--enable-deep-plc], [Use deep PLC for SILK])],, + [enable_deep_plc=no]) + +AS_IF([test "$enable_deep_plc" = "yes" || test "$enable_dred" = "yes" || test "$enable_osce" = "yes" || test "$enable_osce_training_data" = "yes"],[ + AC_DEFINE([ENABLE_DEEP_PLC], [1], [Deep PLC]) +]) +AM_CONDITIONAL([ENABLE_DEEP_PLC], [test "$enable_deep_plc" = "yes" || test "$enable_dred" = "yes" || test "$enable_osce" = "yes" || test "$enable_osce_training_data" = "yes"]) + +AC_ARG_ENABLE([lossgen], + [AS_HELP_STRING([--enable-lossgen], [Build opus_demo with packet loss simulator])],, + [enable_lossgen=no]) + +AS_IF([test "$enable_lossgen" = "yes"],[ + AC_DEFINE([ENABLE_LOSSGEN], [1], [LOSSGEN]) +]) +AM_CONDITIONAL([ENABLE_LOSSGEN], [test "$enable_lossgen" = "yes"]) + +has_float_approx=no +case "$host_cpu" in +i[[3456]]86 | x86_64 | arm* | aarch64* | powerpc64 | powerpc32 | ia64) + has_float_approx=yes + ;; +esac + +AC_ARG_ENABLE([float-approx], + [AS_HELP_STRING([--enable-float-approx], [enable fast approximations for floating point])], + [if test "$enable_float_approx" = "yes"; then + AC_WARN([Floating point approximations are not supported on all platforms.]) + fi + ], + [enable_float_approx=$has_float_approx]) + +AS_IF([test "$enable_float_approx" = "yes"],[ + AC_DEFINE([FLOAT_APPROX], [1], [Float approximations]) +]) + +AC_ARG_ENABLE([asm], + [AS_HELP_STRING([--disable-asm], [Disable assembly optimizations])],, + [enable_asm=yes]) + +AC_ARG_ENABLE([rtcd], + [AS_HELP_STRING([--disable-rtcd], [Disable run-time CPU capabilities detection])],, + [enable_rtcd=yes]) + +AC_ARG_ENABLE([intrinsics], + [AS_HELP_STRING([--disable-intrinsics], [Disable intrinsics optimizations])],, + [enable_intrinsics=yes]) + +rtcd_support=no +cpu_arm=no +cpu_x86=no + +AS_IF([test x"${enable_asm}" = x"yes"],[ + inline_optimization="No inline ASM for your platform, please send patches" + case $host_cpu in + arm*) + dnl Currently we only have asm for fixed-point + #AS_IF([test "$enable_float" != "yes"],[ + cpu_arm=yes + AC_DEFINE([OPUS_ARM_ASM], [], [Make use of ARM asm optimization]) + AS_GCC_INLINE_ASSEMBLY( + [inline_optimization="ARM"], + [inline_optimization="disabled"] + ) + AS_ASM_ARM_EDSP([OPUS_ARM_INLINE_EDSP=1],[OPUS_ARM_INLINE_EDSP=0]) + AS_ASM_ARM_MEDIA([OPUS_ARM_INLINE_MEDIA=1], + [OPUS_ARM_INLINE_MEDIA=0]) + AS_ASM_ARM_NEON([OPUS_ARM_INLINE_NEON=1],[OPUS_ARM_INLINE_NEON=0]) + AS_IF([test x"$inline_optimization" = x"ARM"],[ + AM_CONDITIONAL([OPUS_ARM_INLINE_ASM],[true]) + AC_DEFINE([OPUS_ARM_INLINE_ASM], 1, + [Use generic ARMv4 inline asm optimizations]) + AS_IF([test x"$OPUS_ARM_INLINE_EDSP" = x"1"],[ + AC_DEFINE([OPUS_ARM_INLINE_EDSP], [1], + [Use ARMv5E inline asm optimizations]) + inline_optimization="$inline_optimization (EDSP)" + ]) + AS_IF([test x"$OPUS_ARM_INLINE_MEDIA" = x"1"],[ + AC_DEFINE([OPUS_ARM_INLINE_MEDIA], [1], + [Use ARMv6 inline asm optimizations]) + inline_optimization="$inline_optimization (Media)" + ]) + AS_IF([test x"$OPUS_ARM_INLINE_NEON" = x"1"],[ + AC_DEFINE([OPUS_ARM_INLINE_NEON], 1, + [Use ARM NEON inline asm optimizations]) + inline_optimization="$inline_optimization (NEON)" + ]) + ]) + dnl We need Perl to translate RVCT-syntax asm to gas syntax. + AC_CHECK_PROG([HAVE_PERL], perl, yes, no) + AS_IF([test x"$HAVE_PERL" = x"yes"],[ + AM_CONDITIONAL([OPUS_ARM_EXTERNAL_ASM],[true]) + asm_optimization="ARM" + AS_IF([test x"$OPUS_ARM_INLINE_EDSP" = x"1"], [ + OPUS_ARM_PRESUME_EDSP=1 + OPUS_ARM_MAY_HAVE_EDSP=1 + ], + [ + OPUS_ARM_PRESUME_EDSP=0 + OPUS_ARM_MAY_HAVE_EDSP=0 + ]) + AS_IF([test x"$OPUS_ARM_INLINE_MEDIA" = x"1"], [ + OPUS_ARM_PRESUME_MEDIA=1 + OPUS_ARM_MAY_HAVE_MEDIA=1 + ], + [ + OPUS_ARM_PRESUME_MEDIA=0 + OPUS_ARM_MAY_HAVE_MEDIA=0 + ]) + AS_IF([test x"$OPUS_ARM_INLINE_NEON" = x"1"], [ + OPUS_ARM_PRESUME_NEON=1 + OPUS_ARM_MAY_HAVE_NEON=1 + ], + [ + OPUS_ARM_PRESUME_NEON=0 + OPUS_ARM_MAY_HAVE_NEON=0 + ]) + AS_IF([test x"$enable_rtcd" = x"yes"],[ + AS_IF([test x"$OPUS_ARM_MAY_HAVE_EDSP" != x"1"],[ + AC_MSG_NOTICE( + [Trying to force-enable armv5e EDSP instructions...]) + AS_ASM_ARM_EDSP_FORCE([OPUS_ARM_MAY_HAVE_EDSP=1]) + ]) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_MEDIA" != x"1"],[ + AC_MSG_NOTICE( + [Trying to force-enable ARMv6 media instructions...]) + AS_ASM_ARM_MEDIA_FORCE([OPUS_ARM_MAY_HAVE_MEDIA=1]) + ]) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_NEON" != x"1"],[ + AC_MSG_NOTICE( + [Trying to force-enable NEON instructions...]) + AS_ASM_ARM_NEON_FORCE([OPUS_ARM_MAY_HAVE_NEON=1]) + ]) + ]) + rtcd_support= + AS_IF([test x"$OPUS_ARM_MAY_HAVE_EDSP" = x"1"],[ + AC_DEFINE(OPUS_ARM_MAY_HAVE_EDSP, 1, + [Define if assembler supports EDSP instructions]) + AS_IF([test x"$OPUS_ARM_PRESUME_EDSP" = x"1"],[ + AC_DEFINE(OPUS_ARM_PRESUME_EDSP, 1, + [Define if binary requires EDSP instruction support]) + asm_optimization="$asm_optimization (EDSP)" + ], + [rtcd_support="$rtcd_support (EDSP)"] + ) + ]) + AC_SUBST(OPUS_ARM_MAY_HAVE_EDSP) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_MEDIA" = x"1"],[ + AC_DEFINE(OPUS_ARM_MAY_HAVE_MEDIA, 1, + [Define if assembler supports ARMv6 media instructions]) + AS_IF([test x"$OPUS_ARM_PRESUME_MEDIA" = x"1"],[ + AC_DEFINE(OPUS_ARM_PRESUME_MEDIA, 1, + [Define if binary requires ARMv6 media instruction support]) + asm_optimization="$asm_optimization (Media)" + ], + [rtcd_support="$rtcd_support (Media)"] + ) + ]) + AC_SUBST(OPUS_ARM_MAY_HAVE_MEDIA) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_NEON" = x"1"],[ + AC_DEFINE(OPUS_ARM_MAY_HAVE_NEON, 1, + [Define if compiler supports NEON instructions]) + AS_IF([test x"$OPUS_ARM_PRESUME_NEON" = x"1"], [ + AC_DEFINE(OPUS_ARM_PRESUME_NEON, 1, + [Define if binary requires NEON instruction support]) + asm_optimization="$asm_optimization (NEON)" + ], + [rtcd_support="$rtcd_support (NEON)"] + ) + ]) + AC_SUBST(OPUS_ARM_MAY_HAVE_NEON) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_DOTPROD" = x"1"],[ + AC_DEFINE(OPUS_ARM_MAY_HAVE_DOTPROD, 1, + [Define if compiler supports DOTPROD instructions]) + AS_IF([test x"$OPUS_ARM_PRESUME_DOTPROD" = x"1"], [ + AC_DEFINE(OPUS_ARM_PRESUME_DOTPROD, 1, + [Define if binary requires DOTPROD instruction support]) + asm_optimization="$asm_optimization (DOTPROD)" + ], + [rtcd_support="$rtcd_support (DOTPROD)"] + ) + ]) + AC_SUBST(OPUS_ARM_MAY_HAVE_DOTPROD) + dnl Make sure turning on RTCD gets us at least one + dnl instruction set. + AS_IF([test x"$rtcd_support" != x""], + [rtcd_support=ARM"$rtcd_support"], + [rtcd_support="no"] + ) + AC_MSG_CHECKING([for apple style tools]) + AC_PREPROC_IFELSE([AC_LANG_PROGRAM([ +#ifndef __APPLE__ +#error 1 +#endif],[])], + [AC_MSG_RESULT([yes]); ARM2GNU_PARAMS="--apple"], + [AC_MSG_RESULT([no]); ARM2GNU_PARAMS=""]) + AC_SUBST(ARM2GNU_PARAMS) + ], + [ + AC_MSG_WARN( + [*** ARM assembly requires perl -- disabling optimizations]) + asm_optimization="(missing perl dependency for ARM)" + ]) + #]) + ;; + esac +],[ + inline_optimization="disabled" + asm_optimization="disabled" +]) + +AM_CONDITIONAL([OPUS_ARM_INLINE_ASM], + [test x"${inline_optimization%% *}" = x"ARM"]) +AM_CONDITIONAL([OPUS_ARM_EXTERNAL_ASM], + [test x"${asm_optimization%% *}" = x"ARM"]) + +AM_CONDITIONAL([HAVE_SSE], [false]) +AM_CONDITIONAL([HAVE_SSE2], [false]) +AM_CONDITIONAL([HAVE_SSE4_1], [false]) +AM_CONDITIONAL([HAVE_AVX2], [false]) + +m4_define([DEFAULT_X86_SSE_CFLAGS], [-msse]) +m4_define([DEFAULT_X86_SSE2_CFLAGS], [-msse2]) +m4_define([DEFAULT_X86_SSE4_1_CFLAGS], [-msse4.1]) +m4_define([DEFAULT_X86_AVX2_CFLAGS], [-mavx -mfma -mavx2]) +m4_define([DEFAULT_ARM_NEON_INTR_CFLAGS], [-mfpu=neon]) +m4_define([DEFAULT_ARM_DOTPROD_INTR_CFLAGS], ["-march=armv8.2-a+dotprod"]) +# With GCC on ARM32 softfp architectures (e.g. Android, or older Ubuntu) you need to specify +# -mfloat-abi=softfp for -mfpu=neon to work. However, on ARM32 hardfp architectures (e.g. newer Ubuntu), +# this option will break things. + +# As a heuristic, if host matches arm*eabi* but not arm*hf*, it's probably soft-float. +m4_define([DEFAULT_ARM_NEON_SOFTFP_INTR_CFLAGS], [-mfpu=neon -mfloat-abi=softfp]) + +AS_CASE([$host], + [arm*hf*], [AS_VAR_SET([RESOLVED_DEFAULT_ARM_NEON_INTR_CFLAGS], "DEFAULT_ARM_NEON_INTR_CFLAGS")], + [arm*eabi*], [AS_VAR_SET([RESOLVED_DEFAULT_ARM_NEON_INTR_CFLAGS], "DEFAULT_ARM_NEON_SOFTFP_INTR_CFLAGS")], + [AS_VAR_SET([RESOLVED_DEFAULT_ARM_NEON_INTR_CFLAGS], "DEFAULT_ARM_NEON_INTR_CFLAGS")]) + +AC_ARG_VAR([X86_SSE_CFLAGS], [C compiler flags to compile SSE intrinsics @<:@default=]DEFAULT_X86_SSE_CFLAGS[@:>@]) +AC_ARG_VAR([X86_SSE2_CFLAGS], [C compiler flags to compile SSE2 intrinsics @<:@default=]DEFAULT_X86_SSE2_CFLAGS[@:>@]) +AC_ARG_VAR([X86_SSE4_1_CFLAGS], [C compiler flags to compile SSE4.1 intrinsics @<:@default=]DEFAULT_X86_SSE4_1_CFLAGS[@:>@]) +AC_ARG_VAR([X86_AVX2_CFLAGS], [C compiler flags to compile AVX2 intrinsics @<:@default=]DEFAULT_X86_AVX2_CFLAGS[@:>@]) +AC_ARG_VAR([ARM_NEON_INTR_CFLAGS], [C compiler flags to compile ARM NEON intrinsics @<:@default=]DEFAULT_ARM_NEON_INTR_CFLAGS / DEFAULT_ARM_NEON_SOFTFP_INTR_CFLAGS[@:>@]) +AC_ARG_VAR([ARM_DOTPROD_INTR_CFLAGS], [C compiler flags to compile ARM DOTPROD intrinsics @<:@default=]DEFAULT_ARM_DOTPROD_INTR_CFLAGS[@:>@]) + +AS_VAR_SET_IF([X86_SSE_CFLAGS], [], [AS_VAR_SET([X86_SSE_CFLAGS], "DEFAULT_X86_SSE_CFLAGS")]) +AS_VAR_SET_IF([X86_SSE2_CFLAGS], [], [AS_VAR_SET([X86_SSE2_CFLAGS], "DEFAULT_X86_SSE2_CFLAGS")]) +AS_VAR_SET_IF([X86_SSE4_1_CFLAGS], [], [AS_VAR_SET([X86_SSE4_1_CFLAGS], "DEFAULT_X86_SSE4_1_CFLAGS")]) +AS_VAR_SET_IF([X86_AVX2_CFLAGS], [], [AS_VAR_SET([X86_AVX2_CFLAGS], "DEFAULT_X86_AVX2_CFLAGS")]) +AS_VAR_SET_IF([ARM_NEON_INTR_CFLAGS], [], [AS_VAR_SET([ARM_NEON_INTR_CFLAGS], ["$RESOLVED_DEFAULT_ARM_NEON_INTR_CFLAGS"])]) +AS_VAR_SET_IF([ARM_DOTPROD_INTR_CFLAGS], [], [AS_VAR_SET([ARM_DOTPROD_INTR_CFLAGS], ["DEFAULT_ARM_DOTPROD_INTR_CFLAGS"])]) + +AC_DEFUN([OPUS_PATH_NE10], + [ + AC_ARG_WITH(NE10, + AC_HELP_STRING([--with-NE10=PFX],[Prefix where libNE10 is installed (optional)]), + NE10_prefix="$withval", NE10_prefix="") + AC_ARG_WITH(NE10-libraries, + AC_HELP_STRING([--with-NE10-libraries=DIR], + [Directory where libNE10 library is installed (optional)]), + NE10_libraries="$withval", NE10_libraries="") + AC_ARG_WITH(NE10-includes, + AC_HELP_STRING([--with-NE10-includes=DIR], + [Directory where libNE10 header files are installed (optional)]), + NE10_includes="$withval", NE10_includes="") + + if test "x$NE10_libraries" != "x" ; then + NE10_LIBS="-L$NE10_libraries" + elif test "x$NE10_prefix" = "xno" || test "x$NE10_prefix" = "xyes" ; then + NE10_LIBS="" + elif test "x$NE10_prefix" != "x" ; then + NE10_LIBS="-L$NE10_prefix/lib" + elif test "x$prefix" != "xNONE" ; then + NE10_LIBS="-L$prefix/lib" + fi + + if test "x$NE10_prefix" != "xno" ; then + NE10_LIBS="$NE10_LIBS -lNE10" + fi + + if test "x$NE10_includes" != "x" ; then + NE10_CFLAGS="-I$NE10_includes" + elif test "x$NE10_prefix" = "xno" || test "x$NE10_prefix" = "xyes" ; then + NE10_CFLAGS="" + elif test "x$NE10_prefix" != "x" ; then + NE10_CFLAGS="-I$NE10_prefix/include" + elif test "x$prefix" != "xNONE"; then + NE10_CFLAGS="-I$prefix/include" + fi + + AC_MSG_CHECKING(for NE10) + save_CFLAGS="$CFLAGS"; CFLAGS="$CFLAGS $NE10_CFLAGS" + save_LIBS="$LIBS"; LIBS="$LIBS $NE10_LIBS $LIBM" + AC_LINK_IFELSE( + [ + AC_LANG_PROGRAM( + [[#include + ]], + [[ + ne10_fft_cfg_float32_t cfg; + cfg = ne10_fft_alloc_c2c_float32_neon(480); + ]] + ) + ],[ + HAVE_ARM_NE10=1 + AC_MSG_RESULT([yes]) + ],[ + HAVE_ARM_NE10=0 + AC_MSG_RESULT([no]) + NE10_CFLAGS="" + NE10_LIBS="" + ] + ) + CFLAGS="$save_CFLAGS"; LIBS="$save_LIBS" + #Now we know if libNE10 is installed or not + AS_IF([test x"$HAVE_ARM_NE10" = x"1"], + [ + AC_DEFINE([HAVE_ARM_NE10], 1, [NE10 library is installed on host. Make sure it is on target!]) + AC_SUBST(HAVE_ARM_NE10) + AC_SUBST(NE10_CFLAGS) + AC_SUBST(NE10_LIBS) + ] + ) + ] +) + +AS_IF([test x"$enable_intrinsics" = x"yes"],[ + intrinsics_support="" + AS_CASE([$host_cpu], + [arm*|aarch64*], + [ + cpu_arm=yes + OPUS_CHECK_INTRINSICS( + [ARM Neon], + [$ARM_NEON_INTR_CFLAGS], + [OPUS_ARM_MAY_HAVE_NEON_INTR], + [OPUS_ARM_PRESUME_NEON_INTR], + [[#include + ]], + [[ + static float32x4_t A0, A1, SUMM; + SUMM = vmlaq_f32(SUMM, A0, A1); + return (int)vgetq_lane_f32(SUMM, 0); + ]] + ) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_NEON_INTR" = x"1" && test x"$OPUS_ARM_PRESUME_NEON_INTR" != x"1"], + [ + OPUS_ARM_NEON_INTR_CFLAGS="$ARM_NEON_INTR_CFLAGS" + AC_SUBST([OPUS_ARM_NEON_INTR_CFLAGS]) + ] + ) + + AS_IF([test x"$OPUS_ARM_MAY_HAVE_NEON_INTR" = x"1"], + [ + AC_DEFINE([OPUS_ARM_MAY_HAVE_NEON_INTR], 1, [Compiler supports ARMv7/Aarch64 Neon Intrinsics]) + intrinsics_support="$intrinsics_support (NEON)" + + AS_IF([test x"$enable_rtcd" != x"no" && test x"$OPUS_ARM_PRESUME_NEON_INTR" != x"1"], + [AS_IF([test x"$rtcd_support" = x"no"], + [rtcd_support="ARM (NEON Intrinsics)"], + [rtcd_support="$rtcd_support (NEON Intrinsics)"])]) + + AS_IF([test x"$OPUS_ARM_PRESUME_NEON_INTR" = x"1"], + [AC_DEFINE([OPUS_ARM_PRESUME_NEON_INTR], 1, [Define if binary requires NEON intrinsics support])]) + + OPUS_PATH_NE10() + AS_IF([test x"$NE10_LIBS" != x""], + [ + intrinsics_support="$intrinsics_support (NE10)" + AS_IF([test x"enable_rtcd" != x"" \ + && test x"$OPUS_ARM_PRESUME_NEON_INTR" != x"1"], + [rtcd_support="$rtcd_support (NE10)"]) + ]) + + OPUS_CHECK_INTRINSICS( + [Aarch64 Neon], + [$ARM_NEON_INTR_CFLAGS], + [OPUS_ARM_MAY_HAVE_AARCH64_NEON_INTR], + [OPUS_ARM_PRESUME_AARCH64_NEON_INTR], + [[#include + ]], + [[ + static int32_t IN; + static int16_t OUT; + OUT = vqmovns_s32(IN); + ]] + ) + + AS_IF([test x"$OPUS_ARM_PRESUME_AARCH64_NEON_INTR" = x"1"], + [ + AC_DEFINE([OPUS_ARM_PRESUME_AARCH64_NEON_INTR], 1, [Define if binary requires Aarch64 Neon Intrinsics]) + intrinsics_support="$intrinsics_support (NEON [Aarch64])" + ]) + + OPUS_CHECK_INTRINSICS( + [Aarch64 dotprod], + [$ARM_DOTPROD_INTR_CFLAGS], + [OPUS_ARM_MAY_HAVE_DOTPROD], + [OPUS_ARM_PRESUME_DOTPROD], + [[#include + ]], + [[ + static int32x4_t acc; + static int8x16_t a, b; + acc = vdotq_s32(acc, a, b); + ]] + ) + AS_IF([test x"$OPUS_ARM_MAY_HAVE_DOTPROD" = x"1" && test x"$OPUS_ARM_PRESUME_DOTPROD" != x"1"], + [ + OPUS_ARM_DOTPROD_INTR_CFLAGS="$ARM_NEON_DOTPROD_CFLAGS" + AC_SUBST([OPUS_ARM_DOTPROD_INTR_CFLAGS]) + ] + ) + + AS_IF([test x"$OPUS_ARM_MAY_HAVE_DOTPROD" = x"1"], + [ + AC_DEFINE([OPUS_ARM_MAY_HAVE_DOTPROD], 1, [Compiler supports Aarch64 DOTPROD Intrinsics]) + intrinsics_support="$intrinsics_support (DOTPROD)" + + AS_IF([test x"$OPUS_ARM_PRESUME_DOTPROD" = x"1"], + [ + AC_DEFINE([OPUS_ARM_PRESUME_DOTPROD], 1, [Define if binary requires Aarch64 dotprod Intrinsics]) + intrinsics_support="$intrinsics_support (DOTPROD [Aarch64])" + ]) + + AS_IF([test x"$enable_rtcd" != x"no" && test x"$OPUS_ARM_PRESUME_DOTPROD" != x"1"], + [AS_IF([test x"$rtcd_support" = x"no"], + [rtcd_support="ARM (DOTPROD Intrinsics)"], + [rtcd_support="$rtcd_support (DOTPROD Intrinsics)"])]) + + ] + ) + + + AS_IF([test x"$intrinsics_support" = x""], + [intrinsics_support=no], + [intrinsics_support="ARM$intrinsics_support"]) + ], + [ + AC_MSG_WARN([Compiler does not support ARM intrinsics]) + intrinsics_support=no + ]) + ], + [i?86|x86_64], + [ + cpu_x86=yes + OPUS_CHECK_INTRINSICS( + [SSE], + [$X86_SSE_CFLAGS], + [OPUS_X86_MAY_HAVE_SSE], + [OPUS_X86_PRESUME_SSE], + [[#include + #include + ]], + [[ + __m128 mtest; + mtest = _mm_set1_ps((float)time(NULL)); + mtest = _mm_mul_ps(mtest, mtest); + return _mm_cvtss_si32(mtest); + ]] + ) + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE" = x"1" && test x"$OPUS_X86_PRESUME_SSE" != x"1"], + [ + OPUS_X86_SSE_CFLAGS="$X86_SSE_CFLAGS" + AC_SUBST([OPUS_X86_SSE_CFLAGS]) + ] + ) + OPUS_CHECK_INTRINSICS( + [SSE2], + [$X86_SSE2_CFLAGS], + [OPUS_X86_MAY_HAVE_SSE2], + [OPUS_X86_PRESUME_SSE2], + [[#include + #include + ]], + [[ + __m128i mtest; + mtest = _mm_set1_epi32((int)time(NULL)); + mtest = _mm_mul_epu32(mtest, mtest); + return _mm_cvtsi128_si32(mtest); + ]] + ) + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE2" = x"1" && test x"$OPUS_X86_PRESUME_SSE2" != x"1"], + [ + OPUS_X86_SSE2_CFLAGS="$X86_SSE2_CFLAGS" + AC_SUBST([OPUS_X86_SSE2_CFLAGS]) + ] + ) + OPUS_CHECK_INTRINSICS( + [SSE4.1], + [$X86_SSE4_1_CFLAGS], + [OPUS_X86_MAY_HAVE_SSE4_1], + [OPUS_X86_PRESUME_SSE4_1], + [[#include + #include + ]], + [[ + __m128i mtest; + mtest = _mm_set1_epi32((int)time(NULL)); + mtest = _mm_mul_epi32(mtest, mtest); + return _mm_cvtsi128_si32(mtest); + ]] + ) + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE4_1" = x"1" && test x"$OPUS_X86_PRESUME_SSE4_1" != x"1"], + [ + OPUS_X86_SSE4_1_CFLAGS="$X86_SSE4_1_CFLAGS" + AC_SUBST([OPUS_X86_SSE4_1_CFLAGS]) + ] + ) + OPUS_CHECK_INTRINSICS( + [AVX2], + [$X86_AVX2_CFLAGS], + [OPUS_X86_MAY_HAVE_AVX2], + [OPUS_X86_PRESUME_AVX2], + [[#include + #include + ]], + [[ + unsigned char utest[[16]] = {1}; + __m256 mtest; + __m256i mtest1; + __m256i mtest2; + mtest = _mm256_set1_ps((float)time(NULL)); + mtest = _mm256_fmadd_ps(mtest, mtest, mtest); + mtest1 = _mm256_set_m128i(_mm_loadu_si64(utest), _mm_loadu_si64(utest)); + mtest2 = + _mm256_cvtepi16_epi32(_mm_loadu_si128(utest)); + return _mm256_extract_epi16(_mm256_xor_si256( + _mm256_xor_si256(mtest1, mtest2), _mm256_cvttps_epi32(mtest)), 0); + ]] + ) + AS_IF([test x"$OPUS_X86_MAY_HAVE_AVX2" = x"1" && test x"$OPUS_X86_PRESUME_AVX2" != x"1"], + [ + OPUS_X86_AVX2_CFLAGS="$X86_AVX2_CFLAGS" + AC_SUBST([OPUS_X86_AVX2_CFLAGS]) + ] + ) + AS_IF([test x"$rtcd_support" = x"no"], [rtcd_support=""]) + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE" = x"1"], + [ + AC_DEFINE([OPUS_X86_MAY_HAVE_SSE], 1, [Compiler supports X86 SSE Intrinsics]) + intrinsics_support="$intrinsics_support SSE" + + AS_IF([test x"$OPUS_X86_PRESUME_SSE" = x"1"], + [AC_DEFINE([OPUS_X86_PRESUME_SSE], 1, [Define if binary requires SSE intrinsics support])], + [rtcd_support="$rtcd_support SSE"]) + ], + [ + AC_MSG_WARN([Compiler does not support SSE intrinsics]) + ]) + + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE2" = x"1"], + [ + AC_DEFINE([OPUS_X86_MAY_HAVE_SSE2], 1, [Compiler supports X86 SSE2 Intrinsics]) + intrinsics_support="$intrinsics_support SSE2" + + AS_IF([test x"$OPUS_X86_PRESUME_SSE2" = x"1"], + [AC_DEFINE([OPUS_X86_PRESUME_SSE2], 1, [Define if binary requires SSE2 intrinsics support])], + [rtcd_support="$rtcd_support SSE2"]) + ], + [ + AC_MSG_WARN([Compiler does not support SSE2 intrinsics]) + ]) + + AS_IF([test x"$OPUS_X86_MAY_HAVE_SSE4_1" = x"1"], + [ + AC_DEFINE([OPUS_X86_MAY_HAVE_SSE4_1], 1, [Compiler supports X86 SSE4.1 Intrinsics]) + intrinsics_support="$intrinsics_support SSE4.1" + + AS_IF([test x"$OPUS_X86_PRESUME_SSE4_1" = x"1"], + [AC_DEFINE([OPUS_X86_PRESUME_SSE4_1], 1, [Define if binary requires SSE4.1 intrinsics support])], + [rtcd_support="$rtcd_support SSE4.1"]) + ], + [ + AC_MSG_WARN([Compiler does not support SSE4.1 intrinsics]) + ]) + AS_IF([test x"$OPUS_X86_MAY_HAVE_AVX2" = x"1"], + [ + AC_DEFINE([OPUS_X86_MAY_HAVE_AVX2], 1, [Compiler supports X86 AVX2 Intrinsics]) + intrinsics_support="$intrinsics_support AVX2" + + AS_IF([test x"$OPUS_X86_PRESUME_AVX2" = x"1"], + [AC_DEFINE([OPUS_X86_PRESUME_AVX2], 1, [Define if binary requires AVX2 intrinsics support])], + [rtcd_support="$rtcd_support AVX2"]) + ], + [ + AC_MSG_WARN([Compiler does not support AVX2 intrinsics]) + ]) + + AS_IF([test x"$intrinsics_support" = x""], + [intrinsics_support=no], + [intrinsics_support="x86$intrinsics_support"] + ) + AS_IF([test x"$rtcd_support" = x""], + [rtcd_support=no], + [rtcd_support="x86$rtcd_support"], + ) + + AS_IF([test x"$enable_rtcd" = x"yes" && test x"$rtcd_support" != x""],[ + get_cpuid_by_asm="no" + AC_MSG_CHECKING([How to get X86 CPU Info]) + AC_LINK_IFELSE([AC_LANG_PROGRAM([[ + #include + ]],[[ + unsigned int CPUInfo0; + unsigned int CPUInfo1; + unsigned int CPUInfo2; + unsigned int CPUInfo3; + unsigned int InfoType; + #if defined(__i386__) && defined(__PIC__) + __asm__ __volatile__ ( + "xchg %%ebx, %1\n" + "cpuid\n" + "xchg %%ebx, %1\n": + "=a" (CPUInfo0), + "=r" (CPUInfo1), + "=c" (CPUInfo2), + "=d" (CPUInfo3) : + "a" (InfoType), "c" (0) + ); + #else + __asm__ __volatile__ ( + "cpuid": + "=a" (CPUInfo0), + "=b" (CPUInfo1), + "=c" (CPUInfo2), + "=d" (CPUInfo3) : + "a" (InfoType), "c" (0) + ); + #endif + ]])], + [get_cpuid_by_asm="yes" + AC_MSG_RESULT([Inline Assembly]) + AC_DEFINE([CPU_INFO_BY_ASM], [1], [Get CPU Info by asm method])], + [AC_LINK_IFELSE([AC_LANG_PROGRAM([[ + #include + ]],[[ + unsigned int CPUInfo0; + unsigned int CPUInfo1; + unsigned int CPUInfo2; + unsigned int CPUInfo3; + unsigned int InfoType; + __get_cpuid_count(InfoType, 0, &CPUInfo0, &CPUInfo1, &CPUInfo2, &CPUInfo3); + ]])], + [AC_MSG_RESULT([C method]) + AC_DEFINE([CPU_INFO_BY_C], [1], [Get CPU Info by c method])], + [AC_MSG_ERROR([no supported Get CPU Info method, please disable run-time CPU capabilities detection or intrinsics])])])]) + ], + [ + AC_MSG_WARN([No intrinsics support for your architecture]) + intrinsics_support="no" + ]) +], +[ + intrinsics_support="no" +]) + +AM_CONDITIONAL([CPU_ARM], [test "$cpu_arm" = "yes"]) +AM_CONDITIONAL([HAVE_ARM_DOTPROD], + [test x"$OPUS_ARM_MAY_HAVE_DOTPROD" = x"1"]) +AM_CONDITIONAL([HAVE_ARM_NEON_INTR], + [test x"$OPUS_ARM_MAY_HAVE_NEON_INTR" = x"1"]) +AM_CONDITIONAL([HAVE_ARM_NE10], + [test x"$HAVE_ARM_NE10" = x"1"]) +AM_CONDITIONAL([CPU_X86], [test "$cpu_x86" = "yes"]) +AM_CONDITIONAL([HAVE_SSE], + [test x"$OPUS_X86_MAY_HAVE_SSE" = x"1"]) +AM_CONDITIONAL([HAVE_SSE2], + [test x"$OPUS_X86_MAY_HAVE_SSE2" = x"1"]) +AM_CONDITIONAL([HAVE_SSE4_1], + [test x"$OPUS_X86_MAY_HAVE_SSE4_1" = x"1"]) +AM_CONDITIONAL([HAVE_AVX2], + [test x"$OPUS_X86_MAY_HAVE_AVX2" = x"1"]) + +AM_CONDITIONAL([HAVE_RTCD], + [test x"$enable_rtcd" = x"yes" -a x"$rtcd_support" != x"no"]) +AS_IF([test x"$enable_rtcd" = x"yes"],[ + AS_IF([test x"$rtcd_support" != x"no"],[ + AC_DEFINE([OPUS_HAVE_RTCD], [1], + [Use run-time CPU capabilities detection]) + OPUS_HAVE_RTCD=1 + AC_SUBST(OPUS_HAVE_RTCD) + ]) +],[ + rtcd_support="disabled" +]) + +AC_ARG_ENABLE([assertions], + [AS_HELP_STRING([--enable-assertions],[enable additional software error checking])],, + [enable_assertions=no]) + +AS_IF([test "$enable_assertions" = "yes"], [ + AC_DEFINE([ENABLE_ASSERTIONS], [1], [Assertions]) +]) + +AC_ARG_ENABLE([hardening], + [AS_HELP_STRING([--disable-hardening],[disable run-time checks that are cheap and safe for use in production])],, + [enable_hardening=yes]) + +AS_IF([test "$enable_hardening" = "yes"], [ + AC_DEFINE([ENABLE_HARDENING], [1], [Hardening]) +]) + +AC_ARG_ENABLE([fuzzing], + [AS_HELP_STRING([--enable-fuzzing],[causes the encoder to make random decisions (do not use in production)])],, + [enable_fuzzing=no]) + +AS_IF([test "$enable_fuzzing" = "yes"], [ + AC_DEFINE([FUZZING], [1], [Fuzzing]) +]) + +AC_ARG_ENABLE([check-asm], + [AS_HELP_STRING([--enable-check-asm], + [enable bit-exactness checks between optimized and c implementations])],, + [enable_check_asm=no]) + +AS_IF([test "$enable_check_asm" = "yes"], [ + AC_DEFINE([OPUS_CHECK_ASM], [1], [Run bit-exactness checks between optimized and c implementations]) +]) + +AC_ARG_ENABLE([doc], + [AS_HELP_STRING([--disable-doc], [Do not build API documentation])],, + [enable_doc=yes]) + +AS_IF([test "$enable_doc" = "yes"], [ + AC_CHECK_PROG(HAVE_DOXYGEN, [doxygen], [yes], [no]) + AC_CHECK_PROG(HAVE_DOT, [dot], [yes], [no]) +],[ + HAVE_DOXYGEN=no +]) + +AC_ARG_ENABLE([dot-product], + AS_HELP_STRING([--disable-dot-product], [Disable dot product implementation]),, + enable_dot_product=yes) + +AS_IF([test "$enable_dot_product" = "no"], [ + AC_DEFINE([DISABLE_DOT_PROD], [1], [Disable dot product instructions]) +]) + +AC_ARG_ENABLE([dnn-debug-float], + AS_HELP_STRING([--enable-dnn-debug-float], [Use floating-point DNN computation everywhere]),, + enable_dnn_debug_float=no) + +AS_IF([test "$enable_dnn_debug_float" = "no"], [ + AC_DEFINE([DISABLE_DEBUG_FLOAT], [1], [Disable DNN debug float]) +]) + +AC_ARG_ENABLE([osce-training-data], + AS_HELP_STRING([--enable-osce-training-data], [enables feature output for SILK enhancement]),, + [enable_osc_training_data=no] +) + +AS_IF([test "$enable_osce_training_data" = "yes"], [ + AC_DEFINE([ENABLE_OSCE_TRAINING_DATA], [1], [Enable dumping of OSCE training data]) +]) + +AC_MSG_CHECKING([argument osce training data]) +AS_IF([test "$enable_osce_training_data" = "yes"], [ + AC_MSG_RESULT([yes]) +], [AC_MSG_RESULT([no])]) + +AC_ARG_ENABLE([osce], + AS_HELP_STRING([--enable-osce], [enables speech coding enhancement]),, + [enable_osce=no] +) + +AS_IF([test "$enable_osce" = "yes" || test "$enable_osce_training_data" = "yes"], [ + AC_DEFINE([ENABLE_OSCE], [1], [Enable Opus Speech Coding Enhancement]) +]) + +AM_CONDITIONAL([ENABLE_OSCE], [test "$enable_osce" = "yes" || test "$enable_osce_training_data" = "yes"]) + +AM_CONDITIONAL([HAVE_DOXYGEN], [test "$HAVE_DOXYGEN" = "yes"]) + +AC_ARG_ENABLE([extra-programs], + [AS_HELP_STRING([--disable-extra-programs], [Do not build extra programs (demo and tests)])],, + [enable_extra_programs=yes]) + +AM_CONDITIONAL([EXTRA_PROGRAMS], [test "$enable_extra_programs" = "yes"]) + + +AC_ARG_ENABLE([rfc8251], + AS_HELP_STRING([--disable-rfc8251], [Disable bitstream fixes from RFC 8251]),, + [enable_rfc8251=yes]) + +AS_IF([test "$enable_rfc8251" = "no"], [ + AC_DEFINE([DISABLE_UPDATE_DRAFT], [1], [Disable bitstream fixes from RFC 8251]) +]) + + +saved_CFLAGS="$CFLAGS" +CFLAGS="$CFLAGS -fvisibility=hidden" +AC_MSG_CHECKING([if ${CC} supports -fvisibility=hidden]) +AC_COMPILE_IFELSE([AC_LANG_SOURCE([[char foo;]])], + [ AC_MSG_RESULT([yes]) ], + [ AC_MSG_RESULT([no]) + CFLAGS="$saved_CFLAGS" + ]) + +on_x86=no +case "$host_cpu" in +i[[3456]]86 | x86_64) + on_x86=yes + ;; +esac + +on_windows=no +case $host in +*cygwin*|*mingw*) + on_windows=yes + ;; +esac + +dnl Enable stack-protector-all only on x86 where it's well supported. +dnl on some platforms it causes crashes. Hopefully the OS's default's +dnl include this on platforms that work but have been missed here. +AC_ARG_ENABLE([stack-protector], + [AS_HELP_STRING([--disable-stack-protector],[Disable compiler stack hardening])],, + [ + AS_IF([test "$ac_cv_c_compiler_gnu" = "yes" && test "$on_x86" = "yes" && test "$on_windows" = "no"], + [enable_stack_protector=yes],[enable_stack_protector=no]) + ]) + +AS_IF([test "$enable_stack_protector" = "yes"], + [ + saved_CFLAGS="$CFLAGS" + CFLAGS="$CFLAGS -fstack-protector-strong" + AC_MSG_CHECKING([if ${CC} supports -fstack-protector-strong]) + AC_LINK_IFELSE([AC_LANG_PROGRAM([],[[char foo;]])], + [ AC_MSG_RESULT([yes]) ], + [ + AC_MSG_RESULT([no]) + enable_stack_protector=no + CFLAGS="$saved_CFLAGS" + ]) + ]) + +AS_IF([test x$ac_cv_c_compiler_gnu = xyes], + [AX_ADD_FORTIFY_SOURCE] +) + +CFLAGS="$CFLAGS -W" + +warn_CFLAGS="-Wall -Wextra -Wcast-align -Wnested-externs -Wshadow -Wstrict-prototypes" +saved_CFLAGS="$CFLAGS" +CFLAGS="$CFLAGS $warn_CFLAGS" +AC_MSG_CHECKING([if ${CC} supports ${warn_CFLAGS}]) +AC_COMPILE_IFELSE([AC_LANG_SOURCE([[char foo;]])], + [ AC_MSG_RESULT([yes]) ], + [ AC_MSG_RESULT([no]) + CFLAGS="$saved_CFLAGS" + ]) + +saved_LIBS="$LIBS" +LIBS="$LIBS $LIBM" +AC_CHECK_FUNCS([lrintf]) +AC_CHECK_FUNCS([lrint]) +LIBS="$saved_LIBS" + +AC_CHECK_FUNCS([__malloc_hook]) + +AC_SUBST([PC_BUILD]) + +AC_CONFIG_FILES([ + Makefile + opus.pc + opus-uninstalled.pc + celt/arm/armopts.s + doc/Makefile + doc/Doxyfile +]) +AC_CONFIG_HEADERS([config.h]) + +AC_OUTPUT + +AC_MSG_NOTICE([ +------------------------------------------------------------------------ + $PACKAGE_NAME $PACKAGE_VERSION: Automatic configuration OK. + + Compiler support: + + C99 var arrays: ................ ${has_var_arrays} + C99 lrintf: .................... ${ac_cv_func_lrintf} + Use alloca: .................... ${use_alloca} + + General configuration: + + Floating point support: ........ ${enable_float} + Fast float approximations: ..... ${enable_float_approx} + Fixed point debugging: ......... ${enable_fixed_point_debug} + Inline Assembly Optimizations: . ${inline_optimization} + External Assembly Optimizations: ${asm_optimization} + Intrinsics Optimizations: ...... ${intrinsics_support} + Run-time CPU detection: ........ ${rtcd_support} + Custom modes: .................. ${enable_custom_modes} + Assertion checking: ............ ${enable_assertions} + Hardening: ..................... ${enable_hardening} + Fuzzing: ....................... ${enable_fuzzing} + Check ASM: ..................... ${enable_check_asm} + + API documentation: ............. ${enable_doc} + Extra programs: ................ ${enable_extra_programs} +------------------------------------------------------------------------ + + Type "make; make install" to compile and install + Type "make check" to run the test suite +]) + diff --git a/managed_components/78__esp-opus/dnn/LPCNet.yml b/managed_components/78__esp-opus/dnn/LPCNet.yml new file mode 100644 index 0000000..3a72665 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/LPCNet.yml @@ -0,0 +1,24 @@ +# +# install +# conda env create -f=LPCNet.yml +# +# update +# conda env update -f=LPCNet.yml +# +# activate +# conda activate LPCNet +# +# remove +# conda remove --name LPCNet --all +# +name: LPCNet +channels: + - anaconda + - conda-forge +dependencies: + - keras==2.2.4 + - python>=3.6 + - tensorflow-gpu==1.12.0 + - cudatoolkit + - h5py + - numpy diff --git a/managed_components/78__esp-opus/dnn/README b/managed_components/78__esp-opus/dnn/README new file mode 100644 index 0000000..96dc92f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/README @@ -0,0 +1 @@ +See README.md diff --git a/managed_components/78__esp-opus/dnn/README.md b/managed_components/78__esp-opus/dnn/README.md new file mode 100644 index 0000000..ad4a672 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/README.md @@ -0,0 +1,126 @@ +# LPCNet + +Low complexity implementation of the WaveRNN-based LPCNet algorithm, as described in: + +- J.-M. Valin, J. Skoglund, [LPCNet: Improving Neural Speech Synthesis Through Linear Prediction](https://jmvalin.ca/papers/lpcnet_icassp2019.pdf), *Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, arXiv:1810.11846, 2019. +- J.-M. Valin, U. Isik, P. Smaragdis, A. Krishnaswamy, [Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet](https://jmvalin.ca/papers/improved_lpcnet.pdf), *Proc. ICASSP*, arxiv:2106.04129, 2022. +- K. Subramani, J.-M. Valin, U. Isik, P. Smaragdis, A. Krishnaswamy, [End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation](https://jmvalin.ca/papers/lpcnet_end2end.pdf), *Proc. INTERSPEECH*, arxiv:2106.04129, 2022. + +For coding/PLC applications of LPCNet, see: + +- J.-M. Valin, J. Skoglund, [A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet](https://jmvalin.ca/papers/lpcnet_codec.pdf), *Proc. INTERSPEECH*, arxiv:1903.12087, 2019. +- J. Skoglund, J.-M. Valin, [Improving Opus Low Bit Rate Quality with Neural Speech Synthesis](https://jmvalin.ca/papers/opusnet.pdf), *Proc. INTERSPEECH*, arxiv:1905.04628, 2020. +- J.-M. Valin, A. Mustafa, C. Montgomery, T.B. Terriberry, M. Klingbeil, P. Smaragdis, A. Krishnaswamy, [Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model](https://jmvalin.ca/papers/lpcnet_plc.pdf), *Proc. INTERSPEECH*, arxiv:2205.05785, 2022. +- J.-M. Valin, J. Büthe, A. Mustafa, [Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder](https://jmvalin.ca/papers/valin_dred.pdf), *Proc. ICASSP*, arXiv:2212.04453, 2023. ([blog post](https://www.amazon.science/blog/neural-encoding-enables-more-efficient-recovery-of-lost-audio-packets)) + +# Introduction + +Work in progress software for researching low CPU complexity algorithms for speech synthesis and compression by applying Linear Prediction techniques to WaveRNN. High quality speech can be synthesised on regular CPUs (around 3 GFLOP) with SIMD support (SSE2, SSSE3, AVX, AVX2/FMA, NEON currently supported). The code also supports very low bitrate compression at 1.6 kb/s. + +The BSD licensed software is written in C and Python/Keras. For training, a GTX 1080 Ti or better is recommended. + +This software is an open source starting point for LPCNet/WaveRNN-based speech synthesis and coding. + +# Using the existing software + +You can build the code using: + +``` +./autogen.sh +./configure +make +``` +Note that the autogen.sh script is used when building from Git and will automatically download the latest model +(models are too large to put in Git). By default, LPCNet will attempt to use 8-bit dot product instructions on AVX\*/Neon to +speed up inference. To disable that (e.g. to avoid quantization effects when retraining), add --disable-dot-product to the +configure script. LPCNet does not yet have a complete implementation for some of the integer operations on the ARMv7 +architecture so for now you will also need --disable-dot-product to successfully compile on 32-bit ARM. + +It is highly recommended to set the CFLAGS environment variable to enable AVX or NEON *prior* to running configure, otherwise +no vectorization will take place and the code will be very slow. On a recent x86 CPU, something like +``` +export CFLAGS='-Ofast -g -march=native' +``` +should work. On ARM, you can enable Neon with: +``` +export CFLAGS='-Ofast -g -mfpu=neon' +``` +While not strictly required, the -Ofast flag will help with auto-vectorization, especially for dot products that +cannot be optimized without -ffast-math (which -Ofast enables). Additionally, -falign-loops=32 has been shown to +help on x86. + +You can test the capabilities of LPCNet using the lpcnet\_demo application. To encode a file: +``` +./lpcnet_demo -encode input.pcm compressed.bin +``` +where input.pcm is a 16-bit (machine endian) PCM file sampled at 16 kHz. The raw compressed data (no header) +is written to compressed.bin and consists of 8 bytes per 40-ms packet. + +To decode: +``` +./lpcnet_demo -decode compressed.bin output.pcm +``` +where output.pcm is also 16-bit, 16 kHz PCM. + +Alternatively, you can run the uncompressed analysis/synthesis using -features +instead of -encode and -synthesis instead of -decode. +The same functionality is available in the form of a library. See include/lpcnet.h for the API. + +To try packet loss concealment (PLC), you first need a PLC model, which you can get with: +``` +./download_model.sh plc-3b1eab4 +``` +or (for the PLC challenge submission): +``` +./download_model.sh plc_challenge +``` +PLC can be tested with: +``` +./lpcnet_demo -plc_file noncausal_dc error_pattern.txt input.pcm output.pcm +``` +where error_pattern.txt is a text file with one entry per 20-ms packet, with 1 meaning "packet lost" and 0 meaning "packet not lost". +noncausal_dc is the non-causal (5-ms look-ahead) with special handling for DC offsets. It's also possible to use "noncausal", "causal", +or "causal_dc". + +# Training a new model + +This codebase is also meant for research and it is possible to train new models. These are the steps to do that: + +1. Set up a Keras system with GPU. + +1. Generate training data: + ``` + ./dump_data -train input.s16 features.f32 data.s16 + ``` + where the first file contains 16 kHz 16-bit raw PCM audio (no header) and the other files are output files. This program makes several passes over the data with different filters to generate a large amount of training data. + +1. Now that you have your files, train with: + ``` + python3 training_tf2/train_lpcnet.py features.f32 data.s16 model_name + ``` + and it will generate an h5 file for each iteration, with model\_name as prefix. If it stops with a + "Failed to allocate RNN reserve space" message try specifying a smaller --batch-size for train\_lpcnet.py. + +1. You can synthesise speech with Python and your GPU card (very slow): + ``` + ./dump_data -test test_input.s16 test_features.f32 + ./training_tf2/test_lpcnet.py lpcnet_model_name.h5 test_features.f32 test.s16 + ``` + +1. Or with C on a CPU (C inference is much faster): + First extract the model files nnet\_data.h and nnet\_data.c + ``` + ./training_tf2/dump_lpcnet.py lpcnet_model_name.h5 + ``` + and move the generated nnet\_data.\* files to the src/ directory. + Then you just need to rebuild the software and use lpcnet\_demo as explained above. + +# Speech Material for Training + +Suitable training material can be obtained from [Open Speech and Language Resources](https://www.openslr.org/). See the datasets.txt file for details on suitable training data. + +# Reading Further + +1. [LPCNet: DSP-Boosted Neural Speech Synthesis](https://people.xiph.org/~jm/demo/lpcnet/) +1. [A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet](https://people.xiph.org/~jm/demo/lpcnet_codec/) +1. Sample model files (check compatibility): https://media.xiph.org/lpcnet/data/ diff --git a/managed_components/78__esp-opus/dnn/adaconvtest.c b/managed_components/78__esp-opus/dnn/adaconvtest.c new file mode 100644 index 0000000..722e4af --- /dev/null +++ b/managed_components/78__esp-opus/dnn/adaconvtest.c @@ -0,0 +1,449 @@ +#include "lace_data.h" +#include "nolace_data.h" +#include "osce.h" +#include "nndsp.h" + + +#include +#include +#include + + +extern const WeightArray lacelayers_arrays[]; +extern const WeightArray nolacelayers_arrays[]; + +void adaconv_compare( + const char * prefix, + int num_frames, + AdaConvState* hAdaConv, + LinearLayer *kernel_layer, + LinearLayer *gain_layer, + int feature_dim, + int frame_size, + int overlap_size, + int in_channels, + int out_channels, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float shape_gain +) +{ + char feature_file[256]; + char x_in_file[256]; + char x_out_file[256]; + char message[512]; + int i_frame, i_sample; + float mse; + float features[512]; + float x_in[512]; + float x_out_ref[512]; + float x_out[512]; + float window[40]; + + init_adaconv_state(hAdaConv); + compute_overlap_window(window, 40); + + FILE *f_features, *f_x_in, *f_x_out; + + strcpy(feature_file, prefix); + strcat(feature_file, "_features.f32"); + f_features = fopen(feature_file, "r"); + if (f_features == NULL) + { + sprintf(message, "could not open file %s", feature_file); + perror(message); + exit(1); + } + + strcpy(x_in_file, prefix); + strcat(x_in_file, "_x_in.f32"); + f_x_in = fopen(x_in_file, "r"); + if (f_x_in == NULL) + { + sprintf(message, "could not open file %s", x_in_file); + perror(message); + exit(1); + } + + strcpy(x_out_file, prefix); + strcat(x_out_file, "_x_out.f32"); + f_x_out = fopen(x_out_file, "r"); + if (f_x_out == NULL) + { + sprintf(message, "could not open file %s", x_out_file); + perror(message); + exit(1); + } + + for (i_frame = 0; i_frame < num_frames; i_frame ++) + { + if (fread(features, sizeof(float), feature_dim, f_features) != feature_dim) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, feature_file); + exit(1); + } + + if (fread(x_in, sizeof(float), frame_size * in_channels, f_x_in) != frame_size * in_channels) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_in_file); + exit(1); + } + + if (fread(x_out_ref, sizeof(float), frame_size * out_channels, f_x_out) != frame_size * out_channels) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_out_file); + exit(1); + } + + adaconv_process_frame(hAdaConv, x_out, x_in, features, kernel_layer, gain_layer, feature_dim, + frame_size, overlap_size, in_channels, out_channels, kernel_size, left_padding, + filter_gain_a, filter_gain_b, shape_gain, window, 0); + + mse = 0; + for (i_sample = 0; i_sample < frame_size * out_channels; i_sample ++) + { + mse += pow(x_out_ref[i_sample] - x_out[i_sample], 2); + } + mse = sqrt(mse / (frame_size * out_channels)); + printf("rmse[%d] %f\n", i_frame, mse); + + } +} + + +void adacomb_compare( + const char * prefix, + int num_frames, + AdaCombState* hAdaComb, + LinearLayer *kernel_layer, + LinearLayer *gain_layer, + LinearLayer *global_gain_layer, + int feature_dim, + int frame_size, + int overlap_size, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float log_gain_limit +) +{ + char feature_file[256]; + char x_in_file[256]; + char p_in_file[256]; + char x_out_file[256]; + char message[512]; + int i_frame, i_sample; + float mse; + float features[512]; + float x_in[512]; + float x_out_ref[512]; + float x_out[512]; + int pitch_lag; + float window[40]; + + init_adacomb_state(hAdaComb); + compute_overlap_window(window, 40); + + FILE *f_features, *f_x_in, *f_p_in, *f_x_out; + + strcpy(feature_file, prefix); + strcat(feature_file, "_features.f32"); + f_features = fopen(feature_file, "r"); + if (f_features == NULL) + { + sprintf(message, "could not open file %s", feature_file); + perror(message); + exit(1); + } + + strcpy(x_in_file, prefix); + strcat(x_in_file, "_x_in.f32"); + f_x_in = fopen(x_in_file, "r"); + if (f_x_in == NULL) + { + sprintf(message, "could not open file %s", x_in_file); + perror(message); + exit(1); + } + + strcpy(p_in_file, prefix); + strcat(p_in_file, "_p_in.s32"); + f_p_in = fopen(p_in_file, "r"); + if (f_p_in == NULL) + { + sprintf(message, "could not open file %s", p_in_file); + perror(message); + exit(1); + } + + strcpy(x_out_file, prefix); + strcat(x_out_file, "_x_out.f32"); + f_x_out = fopen(x_out_file, "r"); + if (f_x_out == NULL) + { + sprintf(message, "could not open file %s", x_out_file); + perror(message); + exit(1); + } + + for (i_frame = 0; i_frame < num_frames; i_frame ++) + { + if (fread(features, sizeof(float), feature_dim, f_features) != feature_dim) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, feature_file); + exit(1); + } + + if (fread(x_in, sizeof(float), frame_size, f_x_in) != frame_size) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_in_file); + exit(1); + } + + if (fread(&pitch_lag, sizeof(int), 1, f_p_in) != 1) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, p_in_file); + exit(1); + } + + if (fread(x_out_ref, sizeof(float), frame_size, f_x_out) != frame_size) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_out_file); + exit(1); + } + + adacomb_process_frame(hAdaComb, x_out, x_in, features, kernel_layer, gain_layer, global_gain_layer, + pitch_lag, feature_dim, frame_size, overlap_size, kernel_size, left_padding, filter_gain_a, filter_gain_b, log_gain_limit, window, 0); + + + mse = 0; + for (i_sample = 0; i_sample < frame_size; i_sample ++) + { + mse += pow(x_out_ref[i_sample] - x_out[i_sample], 2); + } + mse = sqrt(mse / (frame_size)); + printf("rmse[%d] %f\n", i_frame, mse); + + } +} + +void adashape_compare( + const char * prefix, + int num_frames, + AdaShapeState* hAdaShape, + LinearLayer *alpha1, + LinearLayer *alpha2, + int feature_dim, + int frame_size, + int avg_pool_k +) +{ + char feature_file[256]; + char x_in_file[256]; + char x_out_file[256]; + char message[512]; + int i_frame, i_sample; + float mse; + float features[512]; + float x_in[512]; + float x_out_ref[512]; + float x_out[512]; + + init_adashape_state(hAdaShape); + + FILE *f_features, *f_x_in, *f_x_out; + + strcpy(feature_file, prefix); + strcat(feature_file, "_features.f32"); + f_features = fopen(feature_file, "r"); + if (f_features == NULL) + { + sprintf(message, "could not open file %s", feature_file); + perror(message); + exit(1); + } + + strcpy(x_in_file, prefix); + strcat(x_in_file, "_x_in.f32"); + f_x_in = fopen(x_in_file, "r"); + if (f_x_in == NULL) + { + sprintf(message, "could not open file %s", x_in_file); + perror(message); + exit(1); + } + + strcpy(x_out_file, prefix); + strcat(x_out_file, "_x_out.f32"); + f_x_out = fopen(x_out_file, "r"); + if (f_x_out == NULL) + { + sprintf(message, "could not open file %s", x_out_file); + perror(message); + exit(1); + } + + for (i_frame = 0; i_frame < num_frames; i_frame ++) + { + if (fread(features, sizeof(float), feature_dim, f_features) != feature_dim) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, feature_file); + exit(1); + } + + if (fread(x_in, sizeof(float), frame_size, f_x_in) != frame_size) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_in_file); + exit(1); + } + + if (fread(x_out_ref, sizeof(float), frame_size, f_x_out) != frame_size) + { + fprintf(stderr, "could not read frame %d from %s\n", i_frame, x_out_file); + exit(1); + } + + adashape_process_frame(hAdaShape, x_out, x_in, features, alpha1, alpha2, feature_dim, + frame_size, avg_pool_k, 0); + + mse = 0; + for (i_sample = 0; i_sample < frame_size; i_sample ++) + { + mse += pow(x_out_ref[i_sample] - x_out[i_sample], 2); + } + mse = sqrt(mse / (frame_size)); + printf("rmse[%d] %f\n", i_frame, mse); + + } +} + + +int main() +{ + LACELayers hLACE; + NOLACELayers hNoLACE; + + AdaConvState hAdaConv; + AdaCombState hAdaComb; + AdaShapeState hAdaShape; + + init_adaconv_state(&hAdaConv); + + init_lacelayers(&hLACE, lacelayers_arrays); + init_nolacelayers(&hNoLACE, nolacelayers_arrays); + + printf("\ntesting lace.af1 (1 in, 1 out)...\n"); + adaconv_compare( + "testvectors/lace_af1", + 5, + &hAdaConv, + &hLACE.lace_af1_kernel, + &hLACE.lace_af1_gain, + LACE_AF1_FEATURE_DIM, + LACE_AF1_FRAME_SIZE, + LACE_AF1_OVERLAP_SIZE, + LACE_AF1_IN_CHANNELS, + LACE_AF1_OUT_CHANNELS, + LACE_AF1_KERNEL_SIZE, + LACE_AF1_LEFT_PADDING, + LACE_AF1_FILTER_GAIN_A, + LACE_AF1_FILTER_GAIN_B, + LACE_AF1_SHAPE_GAIN + ); + + + printf("\ntesting nolace.af1 (1 in, 2 out)...\n"); + adaconv_compare( + "testvectors/nolace_af1", + 5, + &hAdaConv, + &hNoLACE.nolace_af1_kernel, + &hNoLACE.nolace_af1_gain, + NOLACE_AF1_FEATURE_DIM, + NOLACE_AF1_FRAME_SIZE, + NOLACE_AF1_OVERLAP_SIZE, + NOLACE_AF1_IN_CHANNELS, + NOLACE_AF1_OUT_CHANNELS, + NOLACE_AF1_KERNEL_SIZE, + NOLACE_AF1_LEFT_PADDING, + NOLACE_AF1_FILTER_GAIN_A, + NOLACE_AF1_FILTER_GAIN_B, + NOLACE_AF1_SHAPE_GAIN + ); + + + printf("testing nolace.af4 (2 in, 1 out)...\n"); + adaconv_compare( + "testvectors/nolace_af4", + 5, + &hAdaConv, + &hNoLACE.nolace_af4_kernel, + &hNoLACE.nolace_af4_gain, + NOLACE_AF4_FEATURE_DIM, + NOLACE_AF4_FRAME_SIZE, + NOLACE_AF4_OVERLAP_SIZE, + NOLACE_AF4_IN_CHANNELS, + NOLACE_AF4_OUT_CHANNELS, + NOLACE_AF4_KERNEL_SIZE, + NOLACE_AF4_LEFT_PADDING, + NOLACE_AF4_FILTER_GAIN_A, + NOLACE_AF4_FILTER_GAIN_B, + NOLACE_AF4_SHAPE_GAIN + ); + + printf("\ntesting nolace.af2 (2 in, 2 out)...\n"); + adaconv_compare( + "testvectors/nolace_af2", + 5, + &hAdaConv, + &hNoLACE.nolace_af2_kernel, + &hNoLACE.nolace_af2_gain, + NOLACE_AF2_FEATURE_DIM, + NOLACE_AF2_FRAME_SIZE, + NOLACE_AF2_OVERLAP_SIZE, + NOLACE_AF2_IN_CHANNELS, + NOLACE_AF2_OUT_CHANNELS, + NOLACE_AF2_KERNEL_SIZE, + NOLACE_AF2_LEFT_PADDING, + NOLACE_AF2_FILTER_GAIN_A, + NOLACE_AF2_FILTER_GAIN_B, + NOLACE_AF2_SHAPE_GAIN + ); + + printf("\ntesting lace.cf1...\n"); + adacomb_compare( + "testvectors/lace_cf1", + 5, + &hAdaComb, + &hLACE.lace_cf1_kernel, + &hLACE.lace_cf1_gain, + &hLACE.lace_cf1_global_gain, + LACE_CF1_FEATURE_DIM, + LACE_CF1_FRAME_SIZE, + LACE_CF1_OVERLAP_SIZE, + LACE_CF1_KERNEL_SIZE, + LACE_CF1_LEFT_PADDING, + LACE_CF1_FILTER_GAIN_A, + LACE_CF1_FILTER_GAIN_B, + LACE_CF1_LOG_GAIN_LIMIT + ); + + printf("\ntesting nolace.tdshape1...\n"); + adashape_compare( + "testvectors/nolace_tdshape1", + 5, + &hAdaShape, + &hNoLACE.nolace_tdshape1_alpha1, + &hNoLACE.nolace_tdshape1_alpha2, + NOLACE_TDSHAPE1_FEATURE_DIM, + NOLACE_TDSHAPE1_FRAME_SIZE, + NOLACE_TDSHAPE1_AVG_POOL_K + ); + + return 0; +} + +/* gcc -DVAR_ARRAYS -DENABLE_OSCE -I ../include -I ../silk -I . -I ../celt adaconvtest.c nndsp.c lace_data.c nolace_data.c nnet.c nnet_default.c ../celt/pitch.c ../celt/celt_lpc.c parse_lpcnet_weights.c -lm -o adaconvtest */ \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/arm/arm_dnn_map.c b/managed_components/78__esp-opus/dnn/arm/arm_dnn_map.c new file mode 100644 index 0000000..98a2a31 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/arm/arm_dnn_map.c @@ -0,0 +1,88 @@ +/* Copyright (c) 2018-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "arm/armcpu.h" +#include "nnet.h" + +#if defined(OPUS_HAVE_RTCD) + +#if (defined(OPUS_ARM_MAY_HAVE_DOTPROD) && !defined(OPUS_ARM_PRESUME_DOTPROD)) + +void (*const DNN_COMPUTE_LINEAR_IMPL[OPUS_ARCHMASK + 1])( + const LinearLayer *linear, + float *out, + const float *in +) = { + compute_linear_c, /* default */ + compute_linear_c, + compute_linear_c, + MAY_HAVE_NEON(compute_linear), /* neon */ + MAY_HAVE_DOTPROD(compute_linear) /* dotprod */ +}; + +#endif + +#if (defined(OPUS_ARM_MAY_HAVE_DOTPROD) || defined(OPUS_ARM_MAY_HAVE_NEON)) && !defined(OPUS_ARM_PRESUME_NEON) + +void (*const DNN_COMPUTE_ACTIVATION_IMPL[OPUS_ARCHMASK + 1])( + float *output, + const float *input, + int N, + int activation +) = { + compute_activation_c, /* default */ + compute_activation_c, + compute_activation_c, + MAY_HAVE_NEON(compute_activation), /* neon */ + MAY_HAVE_DOTPROD(compute_activation) /* dotprod */ +}; + +void (*const DNN_COMPUTE_CONV2D_IMPL[OPUS_ARCHMASK + 1])( + const Conv2dLayer *conv, + float *out, + float *mem, + const float *in, + int height, + int hstride, + int activation +) = { + compute_conv2d_c, /* default */ + compute_conv2d_c, + compute_conv2d_c, + MAY_HAVE_NEON(compute_conv2d), /* neon */ + MAY_HAVE_DOTPROD(compute_conv2d) /* dotprod */ +}; + + +#endif + + +#endif diff --git a/managed_components/78__esp-opus/dnn/arm/dnn_arm.h b/managed_components/78__esp-opus/dnn/arm/dnn_arm.h new file mode 100644 index 0000000..d7ac745 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/arm/dnn_arm.h @@ -0,0 +1,104 @@ +/* Copyright (c) 2011-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DNN_ARM_H +#define DNN_ARM_H + +#include "cpu_support.h" +#include "opus_types.h" + +void compute_linear_dotprod(const LinearLayer *linear, float *out, const float *in); +void compute_linear_neon(const LinearLayer *linear, float *out, const float *in); + +void compute_activation_neon(float *output, const float *input, int N, int activation); +void compute_activation_dotprod(float *output, const float *input, int N, int activation); + +void compute_conv2d_neon(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation); +void compute_conv2d_dotprod(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation); + +#if defined(OPUS_ARM_PRESUME_DOTPROD) + +#define OVERRIDE_COMPUTE_LINEAR +#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_dotprod(linear, out, in)) + +#elif defined(OPUS_ARM_PRESUME_NEON_INTR) && !defined(OPUS_ARM_MAY_HAVE_DOTPROD) + +#define OVERRIDE_COMPUTE_LINEAR +#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_neon(linear, out, in)) + +#elif defined(OPUS_HAVE_RTCD) && (defined(OPUS_ARM_MAY_HAVE_DOTPROD) || defined(OPUS_ARM_MAY_HAVE_NEON)) + +extern void (*const DNN_COMPUTE_LINEAR_IMPL[OPUS_ARCHMASK + 1])( + const LinearLayer *linear, + float *out, + const float *in + ); +#define OVERRIDE_COMPUTE_LINEAR +#define compute_linear(linear, out, in, arch) \ + ((*DNN_COMPUTE_LINEAR_IMPL[(arch) & OPUS_ARCHMASK])(linear, out, in)) + + +#endif + +#if defined(OPUS_ARM_PRESUME_NEON) + +#define OVERRIDE_COMPUTE_ACTIVATION +#define compute_activation(output, input, N, activation, arch) ((void)(arch),compute_activation_neon(output, input, N, activation)) +#define OVERRIDE_COMPUTE_CONV2D +#define compute_conv2d(conv, out, mem, in, height, hstride, activation, arch) ((void)(arch),compute_conv2d_neon(conv, out, mem, in, height, hstride, activation)) + +#elif defined(OPUS_HAVE_RTCD) && (defined(OPUS_ARM_MAY_HAVE_DOTPROD) || defined(OPUS_ARM_MAY_HAVE_NEON)) + +extern void (*const DNN_COMPUTE_ACTIVATION_IMPL[OPUS_ARCHMASK + 1])( + float *output, + const float *input, + int N, + int activation + ); +#define OVERRIDE_COMPUTE_ACTIVATION +#define compute_activation(output, input, N, activation, arch) \ + ((*DNN_COMPUTE_ACTIVATION_IMPL[(arch) & OPUS_ARCHMASK])(output, input, N, activation)) + + +extern void (*const DNN_COMPUTE_CONV2D_IMPL[OPUS_ARCHMASK + 1])( + const Conv2dLayer *conv, + float *out, + float *mem, + const float *in, + int height, + int hstride, + int activation + ); +#define OVERRIDE_COMPUTE_CONV2D +#define compute_conv2d(conv, out, mem, in, height, hstride, activation, arch) \ + ((*DNN_COMPUTE_CONV2D_IMPL[(arch) & OPUS_ARCHMASK])(conv, out, mem, in, height, hstride, activation)) + + +#endif + + +#endif /* DNN_ARM_H */ diff --git a/managed_components/78__esp-opus/dnn/arm/nnet_dotprod.c b/managed_components/78__esp-opus/dnn/arm/nnet_dotprod.c new file mode 100644 index 0000000..1354ed3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/arm/nnet_dotprod.c @@ -0,0 +1,38 @@ +/* Copyright (c) 2018-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#ifndef __ARM_FEATURE_DOTPROD +#error nnet_dotprod.c is being compiled without DOTPROD enabled +#endif + +#define RTCD_ARCH dotprod + +#include "nnet_arch.h" diff --git a/managed_components/78__esp-opus/dnn/arm/nnet_neon.c b/managed_components/78__esp-opus/dnn/arm/nnet_neon.c new file mode 100644 index 0000000..fb636f8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/arm/nnet_neon.c @@ -0,0 +1,38 @@ +/* Copyright (c) 2018-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#if !(defined(__ARM_NEON__) || defined(__ARM_NEON)) +#error nnet_neon.c is being compiled without Neon enabled +#endif + +#define RTCD_ARCH neon + +#include "nnet_arch.h" diff --git a/managed_components/78__esp-opus/dnn/burg.c b/managed_components/78__esp-opus/dnn/burg.c new file mode 100644 index 0000000..3d6b23b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/burg.c @@ -0,0 +1,246 @@ +/*********************************************************************** +Copyright (c) 2006-2011, Skype Limited. All rights reserved. +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +- Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. +- Neither the name of Internet Society, IETF or IETF Trust, nor the +names of specific contributors, may be used to endorse or promote +products derived from this software without specific prior written +permission. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. +***********************************************************************/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include + +#include "arch.h" +#include "burg.h" + +#define MAX_FRAME_SIZE 384 /* subfr_length * nb_subfr = ( 0.005 * 16000 + 16 ) * 4 = 384*/ +#define SILK_MAX_ORDER_LPC 16 +#define FIND_LPC_COND_FAC 1e-5f + +/* sum of squares of a silk_float array, with result as double */ +static double silk_energy_FLP( + const float *data, + int dataSize +) +{ + int i; + double result; + + /* 4x unrolled loop */ + result = 0.0; + for( i = 0; i < dataSize - 3; i += 4 ) { + result += data[ i + 0 ] * (double)data[ i + 0 ] + + data[ i + 1 ] * (double)data[ i + 1 ] + + data[ i + 2 ] * (double)data[ i + 2 ] + + data[ i + 3 ] * (double)data[ i + 3 ]; + } + + /* add any remaining products */ + for( ; i < dataSize; i++ ) { + result += data[ i ] * (double)data[ i ]; + } + + assert( result >= 0.0 ); + return result; +} + +/* inner product of two silk_float arrays, with result as double */ +static double silk_inner_product_FLP( + const float *data1, + const float *data2, + int dataSize +) +{ + int i; + double result; + + /* 4x unrolled loop */ + result = 0.0; + for( i = 0; i < dataSize - 3; i += 4 ) { + result += data1[ i + 0 ] * (double)data2[ i + 0 ] + + data1[ i + 1 ] * (double)data2[ i + 1 ] + + data1[ i + 2 ] * (double)data2[ i + 2 ] + + data1[ i + 3 ] * (double)data2[ i + 3 ]; + } + + /* add any remaining products */ + for( ; i < dataSize; i++ ) { + result += data1[ i ] * (double)data2[ i ]; + } + + return result; +} + + +/* Compute reflection coefficients from input signal */ +float silk_burg_analysis( /* O returns residual energy */ + float A[], /* O prediction coefficients (length order) */ + const float x[], /* I input signal, length: nb_subfr*(D+L_sub) */ + const float minInvGain, /* I minimum inverse prediction gain */ + const int subfr_length, /* I input signal subframe length (incl. D preceding samples) */ + const int nb_subfr, /* I number of subframes stacked in x */ + const int D /* I order */ +) +{ + int k, n, s, reached_max_gain; + double C0, invGain, num, nrg_f, nrg_b, rc, Atmp, tmp1, tmp2; + const float *x_ptr; + double C_first_row[ SILK_MAX_ORDER_LPC ], C_last_row[ SILK_MAX_ORDER_LPC ]; + double CAf[ SILK_MAX_ORDER_LPC + 1 ], CAb[ SILK_MAX_ORDER_LPC + 1 ]; + double Af[ SILK_MAX_ORDER_LPC ]; + + assert( subfr_length * nb_subfr <= MAX_FRAME_SIZE ); + + /* Compute autocorrelations, added over subframes */ + C0 = silk_energy_FLP( x, nb_subfr * subfr_length ); + memset( C_first_row, 0, SILK_MAX_ORDER_LPC * sizeof( double ) ); + for( s = 0; s < nb_subfr; s++ ) { + x_ptr = x + s * subfr_length; + for( n = 1; n < D + 1; n++ ) { + C_first_row[ n - 1 ] += silk_inner_product_FLP( x_ptr, x_ptr + n, subfr_length - n ); + } + } + memcpy( C_last_row, C_first_row, SILK_MAX_ORDER_LPC * sizeof( double ) ); + + /* Initialize */ + CAb[ 0 ] = CAf[ 0 ] = C0 + FIND_LPC_COND_FAC * C0 + 1e-9f; + invGain = 1.0f; + reached_max_gain = 0; + for( n = 0; n < D; n++ ) { + /* Update first row of correlation matrix (without first element) */ + /* Update last row of correlation matrix (without last element, stored in reversed order) */ + /* Update C * Af */ + /* Update C * flipud(Af) (stored in reversed order) */ + for( s = 0; s < nb_subfr; s++ ) { + x_ptr = x + s * subfr_length; + tmp1 = x_ptr[ n ]; + tmp2 = x_ptr[ subfr_length - n - 1 ]; + for( k = 0; k < n; k++ ) { + C_first_row[ k ] -= x_ptr[ n ] * x_ptr[ n - k - 1 ]; + C_last_row[ k ] -= x_ptr[ subfr_length - n - 1 ] * x_ptr[ subfr_length - n + k ]; + Atmp = Af[ k ]; + tmp1 += x_ptr[ n - k - 1 ] * Atmp; + tmp2 += x_ptr[ subfr_length - n + k ] * Atmp; + } + for( k = 0; k <= n; k++ ) { + CAf[ k ] -= tmp1 * x_ptr[ n - k ]; + CAb[ k ] -= tmp2 * x_ptr[ subfr_length - n + k - 1 ]; + } + } + tmp1 = C_first_row[ n ]; + tmp2 = C_last_row[ n ]; + for( k = 0; k < n; k++ ) { + Atmp = Af[ k ]; + tmp1 += C_last_row[ n - k - 1 ] * Atmp; + tmp2 += C_first_row[ n - k - 1 ] * Atmp; + } + CAf[ n + 1 ] = tmp1; + CAb[ n + 1 ] = tmp2; + + /* Calculate nominator and denominator for the next order reflection (parcor) coefficient */ + num = CAb[ n + 1 ]; + nrg_b = CAb[ 0 ]; + nrg_f = CAf[ 0 ]; + for( k = 0; k < n; k++ ) { + Atmp = Af[ k ]; + num += CAb[ n - k ] * Atmp; + nrg_b += CAb[ k + 1 ] * Atmp; + nrg_f += CAf[ k + 1 ] * Atmp; + } + assert( nrg_f > 0.0 ); + assert( nrg_b > 0.0 ); + + /* Calculate the next order reflection (parcor) coefficient */ + rc = -2.0 * num / ( nrg_f + nrg_b ); + assert( rc > -1.0 && rc < 1.0 ); + + /* Update inverse prediction gain */ + tmp1 = invGain * ( 1.0 - rc * rc ); + if( tmp1 <= minInvGain ) { + /* Max prediction gain exceeded; set reflection coefficient such that max prediction gain is exactly hit */ + rc = sqrt( 1.0 - minInvGain / invGain ); + if( num > 0 ) { + /* Ensure adjusted reflection coefficients has the original sign */ + rc = -rc; + } + invGain = minInvGain; + reached_max_gain = 1; + } else { + invGain = tmp1; + } + + /* Update the AR coefficients */ + for( k = 0; k < (n + 1) >> 1; k++ ) { + tmp1 = Af[ k ]; + tmp2 = Af[ n - k - 1 ]; + Af[ k ] = tmp1 + rc * tmp2; + Af[ n - k - 1 ] = tmp2 + rc * tmp1; + } + Af[ n ] = rc; + + if( reached_max_gain ) { + /* Reached max prediction gain; set remaining coefficients to zero and exit loop */ + for( k = n + 1; k < D; k++ ) { + Af[ k ] = 0.0; + } + break; + } + + /* Update C * Af and C * Ab */ + for( k = 0; k <= n + 1; k++ ) { + tmp1 = CAf[ k ]; + CAf[ k ] += rc * CAb[ n - k + 1 ]; + CAb[ n - k + 1 ] += rc * tmp1; + } + } + + if( reached_max_gain ) { + /* Convert to float */ + for( k = 0; k < D; k++ ) { + A[ k ] = (float)( -Af[ k ] ); + } + /* Subtract energy of preceding samples from C0 */ + for( s = 0; s < nb_subfr; s++ ) { + C0 -= silk_energy_FLP( x + s * subfr_length, D ); + } + /* Approximate residual energy */ + nrg_f = C0 * invGain; + } else { + /* Compute residual energy and store coefficients as float */ + nrg_f = CAf[ 0 ]; + tmp1 = 1.0; + for( k = 0; k < D; k++ ) { + Atmp = Af[ k ]; + nrg_f += CAf[ k + 1 ] * Atmp; + tmp1 += Atmp * Atmp; + A[ k ] = (float)(-Atmp); + } + nrg_f -= FIND_LPC_COND_FAC * C0 * tmp1; + } + + /* Return residual energy */ + return MAX32(0, (float)nrg_f); +} diff --git a/managed_components/78__esp-opus/dnn/burg.h b/managed_components/78__esp-opus/dnn/burg.h new file mode 100644 index 0000000..f5998d2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/burg.h @@ -0,0 +1,41 @@ +/*********************************************************************** +Copyright (c) 2006-2011, Skype Limited. All rights reserved. +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +- Redistributions of source code must retain the above copyright notice, +this list of conditions and the following disclaimer. +- Redistributions in binary form must reproduce the above copyright +notice, this list of conditions and the following disclaimer in the +documentation and/or other materials provided with the distribution. +- Neither the name of Internet Society, IETF or IETF Trust, nor the +names of specific contributors, may be used to endorse or promote +products derived from this software without specific prior written +permission. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. +***********************************************************************/ + +#ifndef BURG_H +#define BURG_H + + +float silk_burg_analysis( /* O returns residual energy */ + float A[], /* O prediction coefficients (length order) */ + const float x[], /* I input signal, length: nb_subfr*(D+L_sub) */ + const float minInvGain, /* I minimum inverse prediction gain */ + const int subfr_length, /* I input signal subframe length (incl. D preceding samples) */ + const int nb_subfr, /* I number of subframes stacked in x */ + const int D /* I order */ +); + +#endif diff --git a/managed_components/78__esp-opus/dnn/common.h b/managed_components/78__esp-opus/dnn/common.h new file mode 100644 index 0000000..b3bec73 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/common.h @@ -0,0 +1,56 @@ + + +#ifndef COMMON_H +#define COMMON_H + +#include +#include +#include +#include "opus_defines.h" + +#define LOG256 5.5451774445f +static OPUS_INLINE float log2_approx(float x) +{ + int integer; + float frac; + union { + float f; + int i; + } in; + in.f = x; + integer = (in.i>>23)-127; + in.i -= integer<<23; + frac = in.f - 1.5f; + frac = -0.41445418f + frac*(0.95909232f + + frac*(-0.33951290f + frac*0.16541097f)); + return 1+integer+frac; +} + +#define log_approx(x) (0.69315f*log2_approx(x)) + +static OPUS_INLINE float ulaw2lin(float u) +{ + float s; + float scale_1 = 32768.f/255.f; + u = u - 128.f; + s = u >= 0.f ? 1.f : -1.f; + u = fabs(u); + return s*scale_1*(exp(u/128.*LOG256)-1); +} + +static OPUS_INLINE int lin2ulaw(float x) +{ + float u; + float scale = 255.f/32768.f; + int s = x >= 0 ? 1 : -1; + x = fabs(x); + u = (s*(128*log_approx(1+scale*x)/LOG256)); + u = 128 + u; + if (u < 0) u = 0; + if (u > 255) u = 255; + return (int)floor(.5 + u); +} + + + +#endif diff --git a/managed_components/78__esp-opus/dnn/datasets.txt b/managed_components/78__esp-opus/dnn/datasets.txt new file mode 100644 index 0000000..09c48bb --- /dev/null +++ b/managed_components/78__esp-opus/dnn/datasets.txt @@ -0,0 +1,163 @@ +The following datasets can be used to train a language-independent FARGAN model +and a Deep REDundancy (DRED) model. Note that this data typically needs to be +resampled before it can be used. + +https://www.openslr.org/resources/30/si_lk.tar.gz +https://www.openslr.org/resources/32/af_za.tar.gz +https://www.openslr.org/resources/32/st_za.tar.gz +https://www.openslr.org/resources/32/tn_za.tar.gz +https://www.openslr.org/resources/32/xh_za.tar.gz +https://www.openslr.org/resources/37/bn_bd.zip +https://www.openslr.org/resources/37/bn_in.zip +https://www.openslr.org/resources/41/jv_id_female.zip +https://www.openslr.org/resources/41/jv_id_male.zip +https://www.openslr.org/resources/42/km_kh_male.zip +https://www.openslr.org/resources/43/ne_np_female.zip +https://www.openslr.org/resources/44/su_id_female.zip +https://www.openslr.org/resources/44/su_id_male.zip +https://www.openslr.org/resources/61/es_ar_female.zip +https://www.openslr.org/resources/61/es_ar_male.zip +https://www.openslr.org/resources/63/ml_in_female.zip +https://www.openslr.org/resources/63/ml_in_male.zip +https://www.openslr.org/resources/64/mr_in_female.zip +https://www.openslr.org/resources/65/ta_in_female.zip +https://www.openslr.org/resources/65/ta_in_male.zip +https://www.openslr.org/resources/66/te_in_female.zip +https://www.openslr.org/resources/66/te_in_male.zip +https://www.openslr.org/resources/69/ca_es_female.zip +https://www.openslr.org/resources/69/ca_es_male.zip +https://www.openslr.org/resources/70/en_ng_female.zip +https://www.openslr.org/resources/70/en_ng_male.zip +https://www.openslr.org/resources/71/es_cl_female.zip +https://www.openslr.org/resources/71/es_cl_male.zip +https://www.openslr.org/resources/72/es_co_female.zip +https://www.openslr.org/resources/72/es_co_male.zip +https://www.openslr.org/resources/73/es_pe_female.zip +https://www.openslr.org/resources/73/es_pe_male.zip +https://www.openslr.org/resources/74/es_pr_female.zip +https://www.openslr.org/resources/75/es_ve_female.zip +https://www.openslr.org/resources/75/es_ve_male.zip +https://www.openslr.org/resources/76/eu_es_female.zip +https://www.openslr.org/resources/76/eu_es_male.zip +https://www.openslr.org/resources/77/gl_es_female.zip +https://www.openslr.org/resources/77/gl_es_male.zip +https://www.openslr.org/resources/78/gu_in_female.zip +https://www.openslr.org/resources/78/gu_in_male.zip +https://www.openslr.org/resources/79/kn_in_female.zip +https://www.openslr.org/resources/79/kn_in_male.zip +https://www.openslr.org/resources/80/my_mm_female.zip +https://www.openslr.org/resources/83/irish_english_male.zip +https://www.openslr.org/resources/83/midlands_english_female.zip +https://www.openslr.org/resources/83/midlands_english_male.zip +https://www.openslr.org/resources/83/northern_english_female.zip +https://www.openslr.org/resources/83/northern_english_male.zip +https://www.openslr.org/resources/83/scottish_english_female.zip +https://www.openslr.org/resources/83/scottish_english_male.zip +https://www.openslr.org/resources/83/southern_english_female.zip +https://www.openslr.org/resources/83/southern_english_male.zip +https://www.openslr.org/resources/83/welsh_english_female.zip +https://www.openslr.org/resources/83/welsh_english_male.zip +https://www.openslr.org/resources/86/yo_ng_female.zip +https://www.openslr.org/resources/86/yo_ng_male.zip + +The corresponding citations for all these datasets are: + + @inproceedings{demirsahin-etal-2020-open, + title = {{Open-source Multi-speaker Corpora of the English Accents in the British Isles}}, + author = {Demirsahin, Isin and Kjartansson, Oddur and Gutkin, Alexander and Rivera, Clara}, + booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, + month = may, + year = {2020}, + pages = {6532--6541}, + address = {Marseille, France}, + publisher = {European Language Resources Association (ELRA)}, + url = {https://www.aclweb.org/anthology/2020.lrec-1.804}, + ISBN = {979-10-95546-34-4}, + } + @inproceedings{kjartansson-etal-2020-open, + title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, + author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, + booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)}, + year = {2020}, + pages = {21--27}, + month = may, + address = {Marseille, France}, + publisher = {European Language Resources association (ELRA)}, + url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, + ISBN = {979-10-95546-35-1}, + } + + + @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, + title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, + author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, + booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, + year = {2020}, + month = may, + address = {Marseille, France}, + publisher = {European Language Resources Association (ELRA)}, + url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, + pages = {6504--6513}, + ISBN = {979-10-95546-34-4}, + } + @inproceedings{he-etal-2020-open, + title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, + author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, + booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, + month = may, + year = {2020}, + address = {Marseille, France}, + publisher = {European Language Resources Association (ELRA)}, + pages = {6494--6503}, + url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, + ISBN = "{979-10-95546-34-4}", + } + + + @inproceedings{kjartansson-etal-tts-sltu2018, + title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, + author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, + booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, + year = {2018}, + address = {Gurugram, India}, + month = aug, + pages = {66--70}, + URL = {http://dx.doi.org/10.21437/SLTU.2018-14} + } + + + @inproceedings{oo-etal-2020-burmese, + title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}}, + author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, + booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, + month = may, + year = {2020}, + pages = "6328--6339", + address = {Marseille, France}, + publisher = {European Language Resources Association (ELRA)}, + url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, + ISBN = {979-10-95546-34-4}, + } + @inproceedings{van-niekerk-etal-2017, + title = {{Rapid development of TTS corpora for four South African languages}}, + author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha}, + booktitle = {Proc. Interspeech 2017}, + pages = {2178--2182}, + address = {Stockholm, Sweden}, + month = aug, + year = {2017}, + URL = {http://dx.doi.org/10.21437/Interspeech.2017-1139} + } + + @inproceedings{gutkin-et-al-yoruba2020, + title = {{Developing an Open-Source Corpus of Yoruba Speech}}, + author = {Alexander Gutkin and I{\c{s}}{\i}n Demir{\c{s}}ahin and Oddur Kjartansson and Clara Rivera and K\d{\'o}lá Túb\d{\`o}sún}, + booktitle = {Proceedings of Interspeech 2020}, + pages = {404--408}, + month = {October}, + year = {2020}, + address = {Shanghai, China}, + publisher = {International Speech and Communication Association (ISCA)}, + doi = {10.21437/Interspeech.2020-1096}, + url = {http://dx.doi.org/10.21437/Interspeech.2020-1096}, + } diff --git a/managed_components/78__esp-opus/dnn/download_model.bat b/managed_components/78__esp-opus/dnn/download_model.bat new file mode 100644 index 0000000..f49eaa5 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/download_model.bat @@ -0,0 +1,9 @@ +@echo off +set model=opus_data-%1.tar.gz + +if not exist %model% ( + echo Downloading latest model + powershell -Command "(New-Object System.Net.WebClient).DownloadFile('https://media.xiph.org/opus/models/%model%', '%model%')" +) + +tar -xvzf %model% diff --git a/managed_components/78__esp-opus/dnn/download_model.sh b/managed_components/78__esp-opus/dnn/download_model.sh new file mode 100644 index 0000000..75108e3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/download_model.sh @@ -0,0 +1,30 @@ +#!/bin/sh +set -e + +model=opus_data-$1.tar.gz + +if [ ! -f $model ]; then + echo "Downloading latest model" + wget https://media.xiph.org/opus/models/$model +fi + +if command -v sha256sum +then + echo "Validating checksum" + checksum="$1" + checksum2=$(sha256sum $model | awk '{print $1}') + if [ "$checksum" != "$checksum2" ] + then + echo "Aborting due to mismatching checksums. This could be caused by a corrupted download of $model." + echo "Consider deleting local copy of $model and running this script again." + exit 1 + else + echo "checksums match" + fi +else + echo "Could not find sha256 sum; skipping verification. Please verify manually that sha256 hash of ${model} matches ${1}." +fi + + + +tar xvomf $model diff --git a/managed_components/78__esp-opus/dnn/dred_coding.c b/managed_components/78__esp-opus/dnn/dred_coding.c new file mode 100644 index 0000000..669ddc4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_coding.c @@ -0,0 +1,44 @@ +/* Copyright (c) 2022 Amazon + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include + +#include "celt/entenc.h" +#include "os_support.h" +#include "dred_config.h" +#include "dred_coding.h" + +int compute_quantizer(int q0, int dQ, int qmax, int i) { + int quant; + static const int dQ_table[8] = {0, 2, 3, 4, 6, 8, 12, 16}; + quant = q0 + (dQ_table[dQ]*i + 8)/16; + return quant > qmax ? qmax : quant; +} diff --git a/managed_components/78__esp-opus/dnn/dred_coding.h b/managed_components/78__esp-opus/dnn/dred_coding.h new file mode 100644 index 0000000..1ce040c --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_coding.h @@ -0,0 +1,36 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_CODING_H +#define DRED_CODING_H + +#include "opus_types.h" +#include "entcode.h" + +int compute_quantizer(int q0, int dQ, int qmax, int i); + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_config.h b/managed_components/78__esp-opus/dnn/dred_config.h new file mode 100644 index 0000000..88b2261 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_config.h @@ -0,0 +1,54 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_CONFIG_H +#define DRED_CONFIG_H + +/* Change this once DRED gets an extension number assigned. */ +#define DRED_EXTENSION_ID 126 + +/* Remove these two completely once DRED gets an extension number assigned. */ +#define DRED_EXPERIMENTAL_VERSION 10 +#define DRED_EXPERIMENTAL_BYTES 2 + + +#define DRED_MIN_BYTES 8 + +/* these are inpart duplicates to the values defined in dred_rdovae_constants.h */ +#define DRED_SILK_ENCODER_DELAY (79+12-80) +#define DRED_FRAME_SIZE 160 +#define DRED_DFRAME_SIZE (2 * (DRED_FRAME_SIZE)) +#define DRED_MAX_DATA_SIZE 1000 +#define DRED_ENC_Q0 6 +#define DRED_ENC_Q1 15 + +/* Covers 1.04 second so we can cover one second, after the lookahead. */ +#define DRED_MAX_LATENTS 26 +#define DRED_NUM_REDUNDANCY_FRAMES (2*DRED_MAX_LATENTS) +#define DRED_MAX_FRAMES (4*DRED_MAX_LATENTS) + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_decoder.c b/managed_components/78__esp-opus/dnn/dred_decoder.c new file mode 100644 index 0000000..1b28433 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_decoder.c @@ -0,0 +1,129 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "os_support.h" +#include "dred_decoder.h" +#include "dred_coding.h" +#include "celt/entdec.h" +#include "celt/laplace.h" +#include "dred_rdovae_stats_data.h" +#include "dred_rdovae_constants.h" + +static void dred_decode_latents(ec_dec *dec, float *x, const opus_uint8 *scale, const opus_uint8 *r, const opus_uint8 *p0, int dim) { + int i; + for (i=0;idred_offset = 16 - ec_dec_uint(&ec, 32) - extra_offset + dred_frame_offset; + /*printf("%d %d %d\n", dred_offset, q0, dQ);*/ + qmax = 15; + if (q0 < 14 && dQ > 0) { + int nvals; + int ft; + int s; + /* The distribution for the dQmax symbol is split evenly between zero + (which implies qmax == 15) and larger values, with the probability of + all larger values being uniform. + This is equivalent to coding 1 bit to decide if the maximum is less than + 15 followed by a uint to decide the actual value if it is less than + 15, but combined into a single symbol. */ + nvals = 15 - (q0 + 1); + ft = 2*nvals; + s = ec_decode(&ec, ft); + if (s >= nvals) { + qmax = q0 + (s - nvals) + 1; + ec_dec_update(&ec, s, s + 1, ft); + } + else { + ec_dec_update(&ec, 0, nvals, ft); + } + } + state_qoffset = q0*DRED_STATE_DIM; + dred_decode_latents( + &ec, + dec->state, + dred_state_quant_scales_q8 + state_qoffset, + dred_state_r_q8 + state_qoffset, + dred_state_p0_q8 + state_qoffset, + DRED_STATE_DIM); + + /* decode newest to oldest and store oldest to newest */ + for (i = 0; i < IMIN(DRED_NUM_REDUNDANCY_FRAMES, (min_feature_frames+1)/2); i += 2) + { + /* FIXME: Figure out how to avoid missing a last frame that would take up < 8 bits. */ + if (8*num_bytes - ec_tell(&ec) <= 7) + break; + q_level = compute_quantizer(q0, dQ, qmax, i/2); + offset = q_level*DRED_LATENT_DIM; + dred_decode_latents( + &ec, + &dec->latents[(i/2)*DRED_LATENT_DIM], + dred_latent_quant_scales_q8 + offset, + dred_latent_r_q8 + offset, + dred_latent_p0_q8 + offset, + DRED_LATENT_DIM + ); + + offset = 2 * i * DRED_NUM_FEATURES; + } + dec->process_stage = 1; + dec->nb_latents = i/2; + return i/2; +} diff --git a/managed_components/78__esp-opus/dnn/dred_decoder.h b/managed_components/78__esp-opus/dnn/dred_decoder.h new file mode 100644 index 0000000..dcd8121 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_decoder.h @@ -0,0 +1,49 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_DECODER_H +#define DRED_DECODER_H + +#include "opus.h" +#include "dred_config.h" +#include "dred_rdovae.h" +#include "entcode.h" +#include "dred_rdovae_constants.h" + +struct OpusDRED { + float fec_features[2*DRED_NUM_REDUNDANCY_FRAMES*DRED_NUM_FEATURES]; + float state[DRED_STATE_DIM]; + float latents[(DRED_NUM_REDUNDANCY_FRAMES/2)*DRED_LATENT_DIM]; + int nb_latents; + int process_stage; + int dred_offset; +}; + + +int dred_ec_decode(OpusDRED *dec, const opus_uint8 *bytes, int num_bytes, int min_feature_frames, int dred_frame_offset); + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_encoder.c b/managed_components/78__esp-opus/dnn/dred_encoder.c new file mode 100644 index 0000000..edb49cc --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_encoder.c @@ -0,0 +1,363 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include + +#if 0 +#include +#include +#endif + +#include "dred_encoder.h" +#include "dred_coding.h" +#include "celt/entenc.h" + +#include "dred_decoder.h" +#include "float_cast.h" +#include "os_support.h" +#include "celt/laplace.h" +#include "dred_rdovae_stats_data.h" + + +static void DRED_rdovae_init_encoder(RDOVAEEncState *enc_state) +{ + memset(enc_state, 0, sizeof(*enc_state)); +} + +int dred_encoder_load_model(DREDEnc* enc, const void *data, int len) +{ + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_rdovaeenc(&enc->model, list); + opus_free(list); + if (ret == 0) { + ret = lpcnet_encoder_load_model(&enc->lpcnet_enc_state, data, len); + } + if (ret == 0) enc->loaded = 1; + return (ret == 0) ? OPUS_OK : OPUS_BAD_ARG; +} + +void dred_encoder_reset(DREDEnc* enc) +{ + OPUS_CLEAR((char*)&enc->DREDENC_RESET_START, + sizeof(DREDEnc)- + ((char*)&enc->DREDENC_RESET_START - (char*)enc)); + enc->input_buffer_fill = DRED_SILK_ENCODER_DELAY; + lpcnet_encoder_init(&enc->lpcnet_enc_state); + DRED_rdovae_init_encoder(&enc->rdovae_enc); +} + +void dred_encoder_init(DREDEnc* enc, opus_int32 Fs, int channels) +{ + enc->Fs = Fs; + enc->channels = channels; + enc->loaded = 0; +#ifndef USE_WEIGHTS_FILE + if (init_rdovaeenc(&enc->model, rdovaeenc_arrays) == 0) enc->loaded = 1; +#endif + dred_encoder_reset(enc); +} + +static void dred_process_frame(DREDEnc *enc, int arch) +{ + float feature_buffer[2 * 36]; + float input_buffer[2*DRED_NUM_FEATURES] = {0}; + + celt_assert(enc->loaded); + /* shift latents buffer */ + OPUS_MOVE(enc->latents_buffer + DRED_LATENT_DIM, enc->latents_buffer, (DRED_MAX_FRAMES - 1) * DRED_LATENT_DIM); + OPUS_MOVE(enc->state_buffer + DRED_STATE_DIM, enc->state_buffer, (DRED_MAX_FRAMES - 1) * DRED_STATE_DIM); + + /* calculate LPCNet features */ + lpcnet_compute_single_frame_features_float(&enc->lpcnet_enc_state, enc->input_buffer, feature_buffer, arch); + lpcnet_compute_single_frame_features_float(&enc->lpcnet_enc_state, enc->input_buffer + DRED_FRAME_SIZE, feature_buffer + 36, arch); + + /* prepare input buffer (discard LPC coefficients) */ + OPUS_COPY(input_buffer, feature_buffer, DRED_NUM_FEATURES); + OPUS_COPY(input_buffer + DRED_NUM_FEATURES, feature_buffer + 36, DRED_NUM_FEATURES); + + /* run RDOVAE encoder */ + dred_rdovae_encode_dframe(&enc->rdovae_enc, &enc->model, enc->latents_buffer, enc->state_buffer, input_buffer, arch); + enc->latents_buffer_fill = IMIN(enc->latents_buffer_fill+1, DRED_NUM_REDUNDANCY_FRAMES); +} + +void filter_df2t(const float *in, float *out, int len, float b0, const float *b, const float *a, int order, float *mem) +{ + int i; + for (i=0;ichannels*in_len <= MAX_DOWNMIX_BUFFER); + celt_assert(in_len * (opus_int32)16000 == out_len * enc->Fs); + switch(enc->Fs) { + case 8000: + up = 2; + break; + case 12000: + up = 4; + break; + case 16000: + up = 1; + break; + case 24000: + up = 2; + break; + case 48000: + up = 1; + break; + default: + celt_assert(0); + } + OPUS_CLEAR(downmix, up*in_len); + if (enc->channels == 1) { + for (i=0;iFs == 16000) { + OPUS_COPY(out, downmix, out_len); + } else if (enc->Fs == 48000 || enc->Fs == 24000) { + /* ellip(7, .2, 70, 7750/24000) */ + + static const float filter_b[8] = { 0.005873358047f, 0.012980854831f, 0.014531340042f, 0.014531340042f, 0.012980854831f, 0.005873358047f, 0.004523418224f, 0.f}; + static const float filter_a[8] = {-3.878718597768f, 7.748834257468f, -9.653651699533f, 8.007342726666f, -4.379450178552f, 1.463182111810f, -0.231720677804f, 0.f}; + float b0 = 0.004523418224f; + filter_df2t(downmix, downmix, up*in_len, b0, filter_b, filter_a, RESAMPLING_ORDER, enc->resample_mem); + for (i=0;iFs == 12000) { + /* ellip(7, .2, 70, 7750/24000) */ + static const float filter_b[8] = {-0.001017101081f, 0.003673127243f, 0.001009165267f, 0.001009165267f, 0.003673127243f, -0.001017101081f, 0.002033596776f, 0.f}; + static const float filter_a[8] = {-4.930414411612f, 11.291643096504f, -15.322037343815f, 13.216403930898f, -7.220409219553f, 2.310550142771f, -0.334338618782f, 0.f}; + float b0 = 0.002033596776f; + filter_df2t(downmix, downmix, up*in_len, b0, filter_b, filter_a, RESAMPLING_ORDER, enc->resample_mem); + for (i=0;iFs == 8000) { + /* ellip(7, .2, 70, 3900/8000) */ + static const float filter_b[8] = { 0.081670120929f, 0.180401598565f, 0.259391051971f, 0.259391051971f, 0.180401598565f, 0.081670120929f, 0.020109185709f, 0.f}; + static const float filter_a[8] = {-1.393651933659f, 2.609789872676f, -2.403541968806f, 2.056814957331f, -1.148908574570f, 0.473001413788f, -0.110359852412f, 0.f}; + float b0 = 0.020109185709f; + filter_df2t(downmix, out, out_len, b0, filter_b, filter_a, RESAMPLING_ORDER, enc->resample_mem); + } else { + celt_assert(0); + } +} + +void dred_compute_latents(DREDEnc *enc, const float *pcm, int frame_size, int extra_delay, int arch) +{ + int curr_offset16k; + int frame_size16k = frame_size * 16000 / enc->Fs; + celt_assert(enc->loaded); + curr_offset16k = 40 + extra_delay*16000/enc->Fs - enc->input_buffer_fill; + enc->dred_offset = (int)floor((curr_offset16k+20.f)/40.f); + enc->latent_offset = 0; + while (frame_size16k > 0) { + int process_size16k; + int process_size; + process_size16k = IMIN(2*DRED_FRAME_SIZE, frame_size16k); + process_size = process_size16k * enc->Fs / 16000; + dred_convert_to_16k(enc, pcm, process_size, &enc->input_buffer[enc->input_buffer_fill], process_size16k); + enc->input_buffer_fill += process_size16k; + if (enc->input_buffer_fill >= 2*DRED_FRAME_SIZE) + { + curr_offset16k += 320; + dred_process_frame(enc, arch); + enc->input_buffer_fill -= 2*DRED_FRAME_SIZE; + OPUS_MOVE(&enc->input_buffer[0], &enc->input_buffer[2*DRED_FRAME_SIZE], enc->input_buffer_fill); + /* 15 ms (6*2.5 ms) is the ideal offset for DRED because it corresponds to our vocoder look-ahead. */ + if (enc->dred_offset < 6) { + enc->dred_offset += 8; + } else { + enc->latent_offset++; + } + } + + pcm += process_size; + frame_size16k -= process_size16k; + } +} + +static void dred_encode_latents(ec_enc *enc, const float *x, const opus_uint8 *scale, const opus_uint8 *dzone, const opus_uint8 *r, const opus_uint8 *p0, int dim, int arch) { + int i; + int q[IMAX(DRED_LATENT_DIM,DRED_STATE_DIM)]; + float xq[IMAX(DRED_LATENT_DIM,DRED_STATE_DIM)]; + float delta[IMAX(DRED_LATENT_DIM,DRED_STATE_DIM)]; + float deadzone[IMAX(DRED_LATENT_DIM,DRED_STATE_DIM)]; + float eps = .1f; + /* This is split into multiple loops (with temporary arrays) so that the compiler + can vectorize all of it, and so we can call the vector tanh(). */ + for (i=0;ilatent_offset; + /* Delaying new DRED data when just out of silence because we already have the + main Opus payload for that frame. */ + if (activity_mem[0] && enc->last_extra_dred_offset>0) { + latent_offset = enc->last_extra_dred_offset; + delayed_dred = 1; + enc->last_extra_dred_offset = 0; + } + while (latent_offset < enc->latents_buffer_fill && !dred_voice_active(activity_mem, latent_offset)) { + latent_offset++; + extra_dred_offset++; + } + if (!delayed_dred) enc->last_extra_dred_offset = extra_dred_offset; + + /* entropy coding of state and latents */ + ec_enc_init(&ec_encoder, buf, max_bytes); + ec_enc_uint(&ec_encoder, q0, 16); + ec_enc_uint(&ec_encoder, dQ, 8); + total_offset = 16 - (enc->dred_offset - extra_dred_offset*8); + celt_assert(total_offset>=0); + if (total_offset > 31) { + ec_enc_uint(&ec_encoder, 1, 2); + ec_enc_uint(&ec_encoder, total_offset>>5, 256); + ec_enc_uint(&ec_encoder, total_offset&31, 32); + } else { + ec_enc_uint(&ec_encoder, 0, 2); + ec_enc_uint(&ec_encoder, total_offset, 32); + } + celt_assert(qmax >= q0); + if (q0 < 14 && dQ > 0) { + int nvals; + /* If you want to use qmax == q0, you should have set dQ = 0. */ + celt_assert(qmax > q0); + nvals = 15 - (q0 + 1); + ec_encode(&ec_encoder, qmax >= 15 ? 0 : nvals + qmax - (q0 + 1), + qmax >= 15 ? nvals : nvals + qmax - q0, 2*nvals); + } + state_qoffset = q0*DRED_STATE_DIM; + dred_encode_latents( + &ec_encoder, + &enc->state_buffer[latent_offset*DRED_STATE_DIM], + dred_state_quant_scales_q8 + state_qoffset, + dred_state_dead_zone_q8 + state_qoffset, + dred_state_r_q8 + state_qoffset, + dred_state_p0_q8 + state_qoffset, + DRED_STATE_DIM, + arch); + if (ec_tell(&ec_encoder) > 8*max_bytes) { + return 0; + } + ec_bak = ec_encoder; + for (i = 0; i < IMIN(2*max_chunks, enc->latents_buffer_fill-latent_offset-1); i += 2) + { + int active; + q_level = compute_quantizer(q0, dQ, qmax, i/2); + offset = q_level * DRED_LATENT_DIM; + + dred_encode_latents( + &ec_encoder, + enc->latents_buffer + (i+latent_offset) * DRED_LATENT_DIM, + dred_latent_quant_scales_q8 + offset, + dred_latent_dead_zone_q8 + offset, + dred_latent_r_q8 + offset, + dred_latent_p0_q8 + offset, + DRED_LATENT_DIM, + arch + ); + if (ec_tell(&ec_encoder) > 8*max_bytes) { + /* If we haven't been able to code one chunk, give up on DRED completely. */ + if (i==0) return 0; + break; + } + active = dred_voice_active(activity_mem, i+latent_offset); + if (active || prev_active) { + ec_bak = ec_encoder; + dred_encoded = i+2; + } + prev_active = active; + } + /* Avoid sending empty DRED packets. */ + if (dred_encoded==0 || (dred_encoded<=2 && extra_dred_offset)) return 0; + ec_encoder = ec_bak; + + ec_buffer_fill = (ec_tell(&ec_encoder)+7)/8; + ec_enc_shrink(&ec_encoder, ec_buffer_fill); + ec_enc_done(&ec_encoder); + return ec_buffer_fill; +} diff --git a/managed_components/78__esp-opus/dnn/dred_encoder.h b/managed_components/78__esp-opus/dnn/dred_encoder.h new file mode 100644 index 0000000..6987222 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_encoder.h @@ -0,0 +1,71 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_ENCODER_H +#define DRED_ENCODER_H + +#include "lpcnet.h" +#include "dred_config.h" +#include "dred_rdovae.h" +#include "entcode.h" +#include "lpcnet_private.h" +#include "dred_rdovae_enc.h" +#include "dred_rdovae_enc_data.h" + +#define RESAMPLING_ORDER 8 + +typedef struct { + RDOVAEEnc model; + LPCNetEncState lpcnet_enc_state; + RDOVAEEncState rdovae_enc; + int loaded; + opus_int32 Fs; + int channels; + +#define DREDENC_RESET_START input_buffer + float input_buffer[2*DRED_DFRAME_SIZE]; + int input_buffer_fill; + int dred_offset; + int latent_offset; + int last_extra_dred_offset; + float latents_buffer[DRED_MAX_FRAMES * DRED_LATENT_DIM]; + int latents_buffer_fill; + float state_buffer[DRED_MAX_FRAMES * DRED_STATE_DIM]; + float resample_mem[RESAMPLING_ORDER + 1]; +} DREDEnc; + +int dred_encoder_load_model(DREDEnc* enc, const void *data, int len); +void dred_encoder_init(DREDEnc* enc, opus_int32 Fs, int channels); +void dred_encoder_reset(DREDEnc* enc); + +void dred_deinit_encoder(DREDEnc *enc); + +void dred_compute_latents(DREDEnc *enc, const float *pcm, int frame_size, int extra_delay, int arch); + +int dred_encode_silk_frame(DREDEnc *enc, unsigned char *buf, int max_chunks, int max_bytes, int q0, int dQ, int qmax, unsigned char *activity_mem, int arch); + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_rdovae.h b/managed_components/78__esp-opus/dnn/dred_rdovae.h new file mode 100644 index 0000000..89ea39a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_rdovae.h @@ -0,0 +1,42 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_RDOVAE_H +#define DRED_RDOVAE_H + +#include + +#include "opus_types.h" + +typedef struct RDOVAEDec RDOVAEDec; +typedef struct RDOVAEEnc RDOVAEEnc; +typedef struct RDOVAEDecStruct RDOVAEDecState; +typedef struct RDOVAEEncStruct RDOVAEEncState; + + + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_rdovae_dec.c b/managed_components/78__esp-opus/dnn/dred_rdovae_dec.c new file mode 100644 index 0000000..7797ee7 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_rdovae_dec.c @@ -0,0 +1,139 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "dred_rdovae_dec.h" +#include "dred_rdovae_constants.h" +#include "os_support.h" + +static void conv1_cond_init(float *mem, int len, int dilation, int *init) +{ + if (!*init) { + int i; + for (i=0;idec_hidden_init, hidden, initial_state, ACTIVATION_TANH, arch); + compute_generic_dense(&model->dec_gru_init, state_init, hidden, ACTIVATION_TANH, arch); + OPUS_COPY(h->gru1_state, state_init, DEC_GRU1_STATE_SIZE); + counter += DEC_GRU1_STATE_SIZE; + OPUS_COPY(h->gru2_state, &state_init[counter], DEC_GRU2_STATE_SIZE); + counter += DEC_GRU2_STATE_SIZE; + OPUS_COPY(h->gru3_state, &state_init[counter], DEC_GRU3_STATE_SIZE); + counter += DEC_GRU3_STATE_SIZE; + OPUS_COPY(h->gru4_state, &state_init[counter], DEC_GRU4_STATE_SIZE); + counter += DEC_GRU4_STATE_SIZE; + OPUS_COPY(h->gru5_state, &state_init[counter], DEC_GRU5_STATE_SIZE); + h->initialized = 0; +} + + +void dred_rdovae_decode_qframe( + RDOVAEDecState *dec_state, /* io: state buffer handle */ + const RDOVAEDec *model, + float *qframe, /* o: quadruple feature frame (four concatenated frames in reverse order) */ + const float *input, /* i: latent vector */ + int arch + ) +{ + float buffer[DEC_DENSE1_OUT_SIZE + DEC_GRU1_OUT_SIZE + DEC_GRU2_OUT_SIZE + DEC_GRU3_OUT_SIZE + DEC_GRU4_OUT_SIZE + DEC_GRU5_OUT_SIZE + + DEC_CONV1_OUT_SIZE + DEC_CONV2_OUT_SIZE + DEC_CONV3_OUT_SIZE + DEC_CONV4_OUT_SIZE + DEC_CONV5_OUT_SIZE]; + int output_index = 0; + + /* run encoder stack and concatenate output in buffer*/ + compute_generic_dense(&model->dec_dense1, &buffer[output_index], input, ACTIVATION_TANH, arch); + output_index += DEC_DENSE1_OUT_SIZE; + + compute_generic_gru(&model->dec_gru1_input, &model->dec_gru1_recurrent, dec_state->gru1_state, buffer, arch); + compute_glu(&model->dec_glu1, &buffer[output_index], dec_state->gru1_state, arch); + output_index += DEC_GRU1_OUT_SIZE; + conv1_cond_init(dec_state->conv1_state, output_index, 1, &dec_state->initialized); + compute_generic_conv1d(&model->dec_conv1, &buffer[output_index], dec_state->conv1_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += DEC_CONV1_OUT_SIZE; + + compute_generic_gru(&model->dec_gru2_input, &model->dec_gru2_recurrent, dec_state->gru2_state, buffer, arch); + compute_glu(&model->dec_glu2, &buffer[output_index], dec_state->gru2_state, arch); + output_index += DEC_GRU2_OUT_SIZE; + conv1_cond_init(dec_state->conv2_state, output_index, 1, &dec_state->initialized); + compute_generic_conv1d(&model->dec_conv2, &buffer[output_index], dec_state->conv2_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += DEC_CONV2_OUT_SIZE; + + compute_generic_gru(&model->dec_gru3_input, &model->dec_gru3_recurrent, dec_state->gru3_state, buffer, arch); + compute_glu(&model->dec_glu3, &buffer[output_index], dec_state->gru3_state, arch); + output_index += DEC_GRU3_OUT_SIZE; + conv1_cond_init(dec_state->conv3_state, output_index, 1, &dec_state->initialized); + compute_generic_conv1d(&model->dec_conv3, &buffer[output_index], dec_state->conv3_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += DEC_CONV3_OUT_SIZE; + + compute_generic_gru(&model->dec_gru4_input, &model->dec_gru4_recurrent, dec_state->gru4_state, buffer, arch); + compute_glu(&model->dec_glu4, &buffer[output_index], dec_state->gru4_state, arch); + output_index += DEC_GRU4_OUT_SIZE; + conv1_cond_init(dec_state->conv4_state, output_index, 1, &dec_state->initialized); + compute_generic_conv1d(&model->dec_conv4, &buffer[output_index], dec_state->conv4_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += DEC_CONV4_OUT_SIZE; + + compute_generic_gru(&model->dec_gru5_input, &model->dec_gru5_recurrent, dec_state->gru5_state, buffer, arch); + compute_glu(&model->dec_glu5, &buffer[output_index], dec_state->gru5_state, arch); + output_index += DEC_GRU5_OUT_SIZE; + conv1_cond_init(dec_state->conv5_state, output_index, 1, &dec_state->initialized); + compute_generic_conv1d(&model->dec_conv5, &buffer[output_index], dec_state->conv5_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += DEC_CONV5_OUT_SIZE; + + compute_generic_dense(&model->dec_output, qframe, buffer, ACTIVATION_LINEAR, arch); +} diff --git a/managed_components/78__esp-opus/dnn/dred_rdovae_dec.h b/managed_components/78__esp-opus/dnn/dred_rdovae_dec.h new file mode 100644 index 0000000..4e66911 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_rdovae_dec.h @@ -0,0 +1,53 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_RDOVAE_DEC_H +#define DRED_RDOVAE_DEC_H + +#include "dred_rdovae.h" +#include "dred_rdovae_dec_data.h" +#include "dred_rdovae_stats_data.h" + +struct RDOVAEDecStruct { + int initialized; + float gru1_state[DEC_GRU1_STATE_SIZE]; + float gru2_state[DEC_GRU2_STATE_SIZE]; + float gru3_state[DEC_GRU3_STATE_SIZE]; + float gru4_state[DEC_GRU4_STATE_SIZE]; + float gru5_state[DEC_GRU5_STATE_SIZE]; + float conv1_state[DEC_CONV1_STATE_SIZE]; + float conv2_state[DEC_CONV2_STATE_SIZE]; + float conv3_state[DEC_CONV3_STATE_SIZE]; + float conv4_state[DEC_CONV4_STATE_SIZE]; + float conv5_state[DEC_CONV5_STATE_SIZE]; +}; + +void dred_rdovae_dec_init_states(RDOVAEDecState *h, const RDOVAEDec *model, const float * initial_state, int arch); +void dred_rdovae_decode_qframe(RDOVAEDecState *h, const RDOVAEDec *model, float *qframe, const float * z, int arch); +void DRED_rdovae_decode_all(const RDOVAEDec *model, float *features, const float *state, const float *latents, int nb_latents, int arch); + +#endif diff --git a/managed_components/78__esp-opus/dnn/dred_rdovae_enc.c b/managed_components/78__esp-opus/dnn/dred_rdovae_enc.c new file mode 100644 index 0000000..4f13ae2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_rdovae_enc.c @@ -0,0 +1,110 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + + +#include "dred_rdovae_enc.h" +#include "os_support.h" +#include "dred_rdovae_constants.h" + +static void conv1_cond_init(float *mem, int len, int dilation, int *init) +{ + if (!*init) { + int i; + for (i=0;ienc_dense1, &buffer[output_index], input, ACTIVATION_TANH, arch); + output_index += ENC_DENSE1_OUT_SIZE; + + compute_generic_gru(&model->enc_gru1_input, &model->enc_gru1_recurrent, enc_state->gru1_state, buffer, arch); + OPUS_COPY(&buffer[output_index], enc_state->gru1_state, ENC_GRU1_OUT_SIZE); + output_index += ENC_GRU1_OUT_SIZE; + conv1_cond_init(enc_state->conv1_state, output_index, 1, &enc_state->initialized); + compute_generic_conv1d(&model->enc_conv1, &buffer[output_index], enc_state->conv1_state, buffer, output_index, ACTIVATION_TANH, arch); + output_index += ENC_CONV1_OUT_SIZE; + + compute_generic_gru(&model->enc_gru2_input, &model->enc_gru2_recurrent, enc_state->gru2_state, buffer, arch); + OPUS_COPY(&buffer[output_index], enc_state->gru2_state, ENC_GRU2_OUT_SIZE); + output_index += ENC_GRU2_OUT_SIZE; + conv1_cond_init(enc_state->conv2_state, output_index, 2, &enc_state->initialized); + compute_generic_conv1d_dilation(&model->enc_conv2, &buffer[output_index], enc_state->conv2_state, buffer, output_index, 2, ACTIVATION_TANH, arch); + output_index += ENC_CONV2_OUT_SIZE; + + compute_generic_gru(&model->enc_gru3_input, &model->enc_gru3_recurrent, enc_state->gru3_state, buffer, arch); + OPUS_COPY(&buffer[output_index], enc_state->gru3_state, ENC_GRU3_OUT_SIZE); + output_index += ENC_GRU3_OUT_SIZE; + conv1_cond_init(enc_state->conv3_state, output_index, 2, &enc_state->initialized); + compute_generic_conv1d_dilation(&model->enc_conv3, &buffer[output_index], enc_state->conv3_state, buffer, output_index, 2, ACTIVATION_TANH, arch); + output_index += ENC_CONV3_OUT_SIZE; + + compute_generic_gru(&model->enc_gru4_input, &model->enc_gru4_recurrent, enc_state->gru4_state, buffer, arch); + OPUS_COPY(&buffer[output_index], enc_state->gru4_state, ENC_GRU4_OUT_SIZE); + output_index += ENC_GRU4_OUT_SIZE; + conv1_cond_init(enc_state->conv4_state, output_index, 2, &enc_state->initialized); + compute_generic_conv1d_dilation(&model->enc_conv4, &buffer[output_index], enc_state->conv4_state, buffer, output_index, 2, ACTIVATION_TANH, arch); + output_index += ENC_CONV4_OUT_SIZE; + + compute_generic_gru(&model->enc_gru5_input, &model->enc_gru5_recurrent, enc_state->gru5_state, buffer, arch); + OPUS_COPY(&buffer[output_index], enc_state->gru5_state, ENC_GRU5_OUT_SIZE); + output_index += ENC_GRU5_OUT_SIZE; + conv1_cond_init(enc_state->conv5_state, output_index, 2, &enc_state->initialized); + compute_generic_conv1d_dilation(&model->enc_conv5, &buffer[output_index], enc_state->conv5_state, buffer, output_index, 2, ACTIVATION_TANH, arch); + output_index += ENC_CONV5_OUT_SIZE; + + compute_generic_dense(&model->enc_zdense, padded_latents, buffer, ACTIVATION_LINEAR, arch); + OPUS_COPY(latents, padded_latents, DRED_LATENT_DIM); + + /* next, calculate initial state */ + compute_generic_dense(&model->gdense1, state_hidden, buffer, ACTIVATION_TANH, arch); + compute_generic_dense(&model->gdense2, padded_state, state_hidden, ACTIVATION_LINEAR, arch); + OPUS_COPY(initial_state, padded_state, DRED_STATE_DIM); +} diff --git a/managed_components/78__esp-opus/dnn/dred_rdovae_enc.h b/managed_components/78__esp-opus/dnn/dred_rdovae_enc.h new file mode 100644 index 0000000..6fe537e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dred_rdovae_enc.h @@ -0,0 +1,52 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef DRED_RDOVAE_ENC_H +#define DRED_RDOVAE_ENC_H + +#include "dred_rdovae.h" + +#include "dred_rdovae_enc_data.h" + +struct RDOVAEEncStruct { + int initialized; + float gru1_state[ENC_GRU1_STATE_SIZE]; + float gru2_state[ENC_GRU2_STATE_SIZE]; + float gru3_state[ENC_GRU3_STATE_SIZE]; + float gru4_state[ENC_GRU4_STATE_SIZE]; + float gru5_state[ENC_GRU5_STATE_SIZE]; + float conv1_state[ENC_CONV1_STATE_SIZE]; + float conv2_state[2*ENC_CONV2_STATE_SIZE]; + float conv3_state[2*ENC_CONV3_STATE_SIZE]; + float conv4_state[2*ENC_CONV4_STATE_SIZE]; + float conv5_state[2*ENC_CONV5_STATE_SIZE]; +}; + +void dred_rdovae_encode_dframe(RDOVAEEncState *enc_state, const RDOVAEEnc *model, float *latents, float *initial_state, const float *input, int arch); + + +#endif diff --git a/managed_components/78__esp-opus/dnn/dump_data.c b/managed_components/78__esp-opus/dnn/dump_data.c new file mode 100644 index 0000000..65b9639 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/dump_data.c @@ -0,0 +1,238 @@ +/* Copyright (c) 2017-2018 Mozilla */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include +#include +#include "kiss_fft.h" +#include "common.h" +#include +#include "freq.h" +#include "pitch.h" +#include "arch.h" +#include +#include "lpcnet.h" +#include "lpcnet_private.h" +#include "os_support.h" +#include "cpu_support.h" + + +static void biquad(float *y, float mem[2], const float *x, const float *b, const float *a, int N) { + int i; + for (i=0;i \n", argv0); + fprintf(stderr, " or %s -test \n", argv0); + return 1; + } + f1 = fopen(argv[2], "r"); + if (f1 == NULL) { + fprintf(stderr,"Error opening input .s16 16kHz speech input file: %s\n", argv[2]); + exit(1); + } + ffeat = fopen(argv[3], "wb"); + if (ffeat == NULL) { + fprintf(stderr,"Error opening output feature file: %s\n", argv[3]); + exit(1); + } + if (training && !pitch) { + fpcm = fopen(argv[4], "wb"); + if (fpcm == NULL) { + fprintf(stderr,"Error opening output PCM file: %s\n", argv[4]); + exit(1); + } + } + while (1) { + size_t ret; + ret = fread(tmp, sizeof(opus_int16), FRAME_SIZE, f1); + if (feof(f1) || ret != FRAME_SIZE) { + if (!training) break; + rewind(f1); + ret = fread(tmp, sizeof(opus_int16), FRAME_SIZE, f1); + if (ret != FRAME_SIZE) { + fprintf(stderr, "error reading\n"); + exit(1); + } + one_pass_completed = 1; + } + for (i=0;i=10000000 && one_pass_completed) break; + if (training && ++gain_change_count > 2821) { + speech_gain = pow(10., (-30+(rand()%40))/20.); + if (rand()&1) speech_gain = -speech_gain; + if (rand()%20==0) speech_gain *= .01; + if (!pitch && rand()%100==0) speech_gain = 0; + gain_change_count = 0; + rand_resp(a_sig, b_sig); + if (fnoise != NULL) { + long pos; + /* Randomize the fraction because rand() only gives us 31 bits. */ + float frac_pos = rand()/(float)RAND_MAX; + pos = (long)(frac_pos*noise_size); + /* 32-bit alignment. */ + pos = pos/4 * 4; + if (pos > noise_size-500000) pos = noise_size-500000; + noise_gain = pow(10., (-15+(rand()%40))/20.); + if (rand()%10==0) noise_gain = 0; + fseek(fnoise, pos, SEEK_SET); + } + } + if (fnoise != NULL) { + opus_int16 noise[FRAME_SIZE]; + ret = fread(noise, sizeof(opus_int16), FRAME_SIZE, fnoise); + for (i=0;ixcorr_features[i]); + } + for (i=0;iif_features[i]); + } + fwrite(pitch_features, PITCH_MAX_PERIOD-PITCH_MIN_PERIOD+PITCH_IF_FEATURES, 1, ffeat); + } else { + fwrite(st->features, sizeof(float), NB_TOTAL_FEATURES, ffeat); + } + /*if(pitch) fwrite(pcm, FRAME_SIZE, 2, stdout);*/ + if (fpcm) fwrite(pcm, FRAME_SIZE, 2, fpcm); + /*if (fpcm) fwrite(pcm, sizeof(opus_int16), FRAME_SIZE, fpcm);*/ + for (i=0;i +#include +#include "freq.h" +#include "kiss_fft.h" + + +int main(void) { + int i; + FILE *file; + kiss_fft_state *kfft; + float half_window[OVERLAP_SIZE]; + float dct_table[NB_BANDS*NB_BANDS]; + + file=fopen("lpcnet_tables.c", "wb"); + fprintf(file, "/* The contents of this file was automatically generated by dump_lpcnet_tables.c*/\n\n"); + fprintf(file, "#ifdef HAVE_CONFIG_H\n"); + fprintf(file, "#include \"config.h\"\n"); + fprintf(file, "#endif\n"); + + fprintf(file, "#include \"kiss_fft.h\"\n\n"); + + kfft = opus_fft_alloc_twiddles(WINDOW_SIZE, NULL, NULL, NULL, 0); + + fprintf(file, "static const arch_fft_state arch_fft = {0, NULL};\n\n"); + + fprintf (file, "static const opus_int16 fft_bitrev[%d] = {\n", kfft->nfft); + for (i=0;infft;i++) + fprintf (file, "%d,%c", kfft->bitrev[i],(i+16)%15==0?'\n':' '); + fprintf (file, "};\n\n"); + + fprintf (file, "static const kiss_twiddle_cpx fft_twiddles[%d] = {\n", kfft->nfft); + for (i=0;infft;i++) + fprintf (file, "{%#0.9gf, %#0.9gf},%c", kfft->twiddles[i].r, kfft->twiddles[i].i,(i+3)%2==0?'\n':' '); + fprintf (file, "};\n\n"); + + + fprintf(file, "const kiss_fft_state kfft = {\n"); + fprintf(file, "%d, /* nfft */\n", kfft->nfft); + fprintf(file, "%#0.8gf, /* scale */\n", kfft->scale); + fprintf(file, "%d, /* shift */\n", kfft->shift); + fprintf(file, "{"); + for (i=0;i<2*MAXFACTORS;i++) { + fprintf(file, "%d, ", kfft->factors[i]); + } + fprintf(file, "}, /* factors */\n"); + fprintf(file, "fft_bitrev, /* bitrev*/\n"); + fprintf(file, "fft_twiddles, /* twiddles*/\n"); + fprintf(file, "(arch_fft_state *)&arch_fft, /* arch_fft*/\n"); + + fprintf(file, "};\n\n"); + + for (i=0;imodel; + celt_assert(FARGAN_FEATURES+COND_NET_PEMBED_OUT_SIZE == model->cond_net_fdense1.nb_inputs); + celt_assert(COND_NET_FCONV1_IN_SIZE == model->cond_net_fdense1.nb_outputs); + celt_assert(COND_NET_FCONV1_OUT_SIZE == model->cond_net_fconv1.nb_outputs); + OPUS_COPY(&dense_in[NB_FEATURES], &model->cond_net_pembed.float_weights[IMAX(0,IMIN(period-32, 223))*COND_NET_PEMBED_OUT_SIZE], COND_NET_PEMBED_OUT_SIZE); + OPUS_COPY(dense_in, features, NB_FEATURES); + + compute_generic_dense(&model->cond_net_fdense1, conv1_in, dense_in, ACTIVATION_TANH, st->arch); + compute_generic_conv1d(&model->cond_net_fconv1, fdense2_in, st->cond_conv1_state, conv1_in, COND_NET_FCONV1_IN_SIZE, ACTIVATION_TANH, st->arch); + compute_generic_dense(&model->cond_net_fdense2, cond, fdense2_in, ACTIVATION_TANH, st->arch); +} + +static void fargan_deemphasis(float *pcm, float *deemph_mem) { + int i; + for (i=0;icont_initialized); + model = &st->model; + + compute_generic_dense(&model->sig_net_cond_gain_dense, &gain, cond, ACTIVATION_LINEAR, st->arch); + gain = exp(gain); + gain_1 = 1.f/(1e-5f + gain); + + pos = PITCH_MAX_PERIOD-period-2; + for (i=0;ipitch_buf[IMAX(0, pos)])); + pos++; + if (pos == PITCH_MAX_PERIOD) pos -= period; + } + for (i=0;ipitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE+i])); + + OPUS_COPY(&fwc0_in[0], &cond[0], FARGAN_COND_SIZE); + OPUS_COPY(&fwc0_in[FARGAN_COND_SIZE], pred, FARGAN_SUBFRAME_SIZE+4); + OPUS_COPY(&fwc0_in[FARGAN_COND_SIZE+FARGAN_SUBFRAME_SIZE+4], prev, FARGAN_SUBFRAME_SIZE); + + compute_generic_conv1d(&model->sig_net_fwc0_conv, gru1_in, st->fwc0_mem, fwc0_in, SIG_NET_INPUT_SIZE, ACTIVATION_TANH, st->arch); + celt_assert(SIG_NET_FWC0_GLU_GATE_OUT_SIZE == model->sig_net_fwc0_glu_gate.nb_outputs); + compute_glu(&model->sig_net_fwc0_glu_gate, gru1_in, gru1_in, st->arch); + + compute_generic_dense(&model->sig_net_gain_dense_out, pitch_gate, gru1_in, ACTIVATION_SIGMOID, st->arch); + + for (i=0;isig_net_gru1_input, &model->sig_net_gru1_recurrent, st->gru1_state, gru1_in, st->arch); + compute_glu(&model->sig_net_gru1_glu_gate, gru2_in, st->gru1_state, st->arch); + + for (i=0;isig_net_gru2_input, &model->sig_net_gru2_recurrent, st->gru2_state, gru2_in, st->arch); + compute_glu(&model->sig_net_gru2_glu_gate, gru3_in, st->gru2_state, st->arch); + + for (i=0;isig_net_gru3_input, &model->sig_net_gru3_recurrent, st->gru3_state, gru3_in, st->arch); + compute_glu(&model->sig_net_gru3_glu_gate, &skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE], st->gru3_state, st->arch); + + OPUS_COPY(skip_cat, gru2_in, SIG_NET_GRU1_OUT_SIZE); + OPUS_COPY(&skip_cat[SIG_NET_GRU1_OUT_SIZE], gru3_in, SIG_NET_GRU2_OUT_SIZE); + OPUS_COPY(&skip_cat[SIG_NET_GRU1_OUT_SIZE+SIG_NET_GRU2_OUT_SIZE+SIG_NET_GRU3_OUT_SIZE], gru1_in, SIG_NET_FWC0_CONV_OUT_SIZE); + for (i=0;isig_net_skip_dense, skip_out, skip_cat, ACTIVATION_TANH, st->arch); + compute_glu(&model->sig_net_skip_glu_gate, skip_out, skip_out, st->arch); + + compute_generic_dense(&model->sig_net_sig_dense_out, pcm, skip_out, ACTIVATION_TANH, st->arch); + for (i=0;ipitch_buf, &st->pitch_buf[FARGAN_SUBFRAME_SIZE], PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE); + OPUS_COPY(&st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE], pcm, FARGAN_SUBFRAME_SIZE); + fargan_deemphasis(pcm, &st->deemph_mem); +} + +void fargan_cont(FARGANState *st, const float *pcm0, const float *features0) +{ + int i; + float cond[COND_NET_FDENSE2_OUT_SIZE]; + float x0[FARGAN_CONT_SAMPLES]; + float dummy[FARGAN_SUBFRAME_SIZE]; + int period=0; + + /* Pre-load features. */ + for (i=0;i<5;i++) { + const float *features = &features0[i*NB_FEATURES]; + st->last_period = period; + period = (int)floor(.5+256./pow(2.f,((1./60.)*((features[NB_BANDS]+1.5)*60)))); + compute_fargan_cond(st, cond, features, period); + } + + x0[0] = 0; + for (i=1;ipitch_buf[PITCH_MAX_PERIOD-FARGAN_FRAME_SIZE], x0, FARGAN_FRAME_SIZE); + st->cont_initialized = 1; + + for (i=0;ilast_period); + OPUS_COPY(&st->pitch_buf[PITCH_MAX_PERIOD-FARGAN_SUBFRAME_SIZE], &x0[FARGAN_FRAME_SIZE+i*FARGAN_SUBFRAME_SIZE], FARGAN_SUBFRAME_SIZE); + } + st->deemph_mem = pcm0[FARGAN_CONT_SAMPLES-1]; +} + + +void fargan_init(FARGANState *st) +{ + int ret; + OPUS_CLEAR(st, 1); + st->arch = opus_select_arch(); +#ifndef USE_WEIGHTS_FILE + ret = init_fargan(&st->model, fargan_arrays); +#else + ret = 0; +#endif + celt_assert(ret == 0); +} + +int fargan_load_model(FARGANState *st, const void *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_fargan(&st->model, list); + opus_free(list); + if (ret == 0) return 0; + else return -1; +} + +static void fargan_synthesize_impl(FARGANState *st, float *pcm, const float *features) +{ + int subframe; + float cond[COND_NET_FDENSE2_OUT_SIZE]; + int period; + celt_assert(st->cont_initialized); + + period = (int)floor(.5+256./pow(2.f,((1./60.)*((features[NB_BANDS]+1.5)*60)))); + compute_fargan_cond(st, cond, features, period); + for (subframe=0;subframelast_period); + } + st->last_period = period; +} + +void fargan_synthesize(FARGANState *st, float *pcm, const float *features) +{ + fargan_synthesize_impl(st, pcm, features); +} + +void fargan_synthesize_int(FARGANState *st, opus_int16 *pcm, const float *features) +{ + int i; + float fpcm[FARGAN_FRAME_SIZE]; + fargan_synthesize(st, fpcm, features); + for (i=0;i +#include +#include +#include +#include "arch.h" +#include "lpcnet.h" +#include "freq.h" +#include "os_support.h" +#include "fargan.h" +#include "cpu_support.h" + +#ifdef USE_WEIGHTS_FILE +# if __unix__ +# include +# include +# include +# include +/* When available, mmap() is preferable to reading the file, as it leads to + better resource utilization, especially if multiple processes are using the same + file (mapping will be shared in cache). */ +void *load_blob(const char *filename, int *len) { + int fd; + void *data; + struct stat st; + if (stat(filename, &st)) { + *len = 0; + return NULL; + } + *len = st.st_size; + fd = open(filename, O_RDONLY); + if (fd<0) { + *len = 0; + return NULL; + } + data = mmap(NULL, *len, PROT_READ, MAP_SHARED, fd, 0); + if (data == MAP_FAILED) { + *len = 0; + data = NULL; + } + close(fd); + return data; +} +void free_blob(void *blob, int len) { + if (blob) munmap(blob, len); +} +# else +void *load_blob(const char *filename, int *len) { + FILE *file; + void *data; + file = fopen(filename, "r"); + if (file == NULL) + { + perror("could not open blob file"); + *len = 0; + return NULL; + } + fseek(file, 0L, SEEK_END); + *len = ftell(file); + fseek(file, 0L, SEEK_SET); + if (*len <= 0) { + *len = 0; + return NULL; + } + data = malloc(*len); + if (!data) { + *len = 0; + return NULL; + } + *len = fread(data, 1, *len, file); + return data; +} +void free_blob(void *blob, int len) { + free(blob); + (void)len; +} +# endif +#endif + +#define MODE_FEATURES 2 +/*#define MODE_SYNTHESIS 3*/ +#define MODE_ADDLPC 5 +#define MODE_FWGAN_SYNTHESIS 6 +#define MODE_FARGAN_SYNTHESIS 7 + +void usage(void) { + fprintf(stderr, "usage: lpcnet_demo -features \n"); + fprintf(stderr, " lpcnet_demo -fargan-synthesis \n"); + fprintf(stderr, " lpcnet_demo -addlpc \n\n"); + fprintf(stderr, " plc_options:\n"); + fprintf(stderr, " causal: normal (causal) PLC\n"); + fprintf(stderr, " codec: normal (causal) PLC without cross-fade (will glitch)\n"); + exit(1); +} + +int main(int argc, char **argv) { + int mode=0; + int arch; + FILE *fin, *fout; +#ifdef USE_WEIGHTS_FILE + int len; + void *data; + const char *filename = "weights_blob.bin"; +#endif + arch = opus_select_arch(); + if (argc < 4) usage(); + if (strcmp(argv[1], "-features") == 0) mode=MODE_FEATURES; + else if (strcmp(argv[1], "-fargan-synthesis") == 0) mode=MODE_FARGAN_SYNTHESIS; + else if (strcmp(argv[1], "-addlpc") == 0){ + mode=MODE_ADDLPC; + } else { + usage(); + } + if (argc != 4) usage(); + fin = fopen(argv[2], "rb"); + if (fin == NULL) { + fprintf(stderr, "Can't open %s\n", argv[2]); + exit(1); + } + + fout = fopen(argv[3], "wb"); + if (fout == NULL) { + fprintf(stderr, "Can't open %s\n", argv[3]); + exit(1); + } +#ifdef USE_WEIGHTS_FILE + data = load_blob(filename, &len); +#endif + if (mode == MODE_FEATURES) { + LPCNetEncState *net; + net = lpcnet_encoder_create(); + while (1) { + float features[NB_TOTAL_FEATURES]; + opus_int16 pcm[LPCNET_FRAME_SIZE]; + size_t ret; + ret = fread(pcm, sizeof(pcm[0]), LPCNET_FRAME_SIZE, fin); + if (feof(fin) || ret != LPCNET_FRAME_SIZE) break; + lpcnet_compute_single_frame_features(net, pcm, features, arch); + fwrite(features, sizeof(float), NB_TOTAL_FEATURES, fout); + } + lpcnet_encoder_destroy(net); + } else if (mode == MODE_FARGAN_SYNTHESIS) { + FARGANState fargan; + size_t ret, i; + float in_features[5*NB_TOTAL_FEATURES]; + float zeros[320] = {0}; + fargan_init(&fargan); +#ifdef USE_WEIGHTS_FILE + fargan_load_model(&fargan, data, len); +#endif + /* uncomment the following to align with Python code */ + /*ret = fread(&in_features[0], sizeof(in_features[0]), NB_TOTAL_FEATURES, fin);*/ + for (i=0;i<5;i++) { + ret = fread(&in_features[i*NB_FEATURES], sizeof(in_features[0]), NB_TOTAL_FEATURES, fin); + } + fargan_cont(&fargan, zeros, in_features); + while (1) { + float features[NB_FEATURES]; + float fpcm[LPCNET_FRAME_SIZE]; + opus_int16 pcm[LPCNET_FRAME_SIZE]; + ret = fread(in_features, sizeof(features[0]), NB_TOTAL_FEATURES, fin); + if (feof(fin) || ret != NB_TOTAL_FEATURES) break; + OPUS_COPY(features, in_features, NB_FEATURES); + fargan_synthesize(&fargan, fpcm, features); + for (i=0;i +#include +#include +#include "kiss_fft.h" +#include +#include "freq.h" +#include "pitch.h" +#include "arch.h" +#include "burg.h" +#include +#include "os_support.h" + +#define SQUARE(x) ((x)*(x)) + +static const opus_int16 eband5ms[] = { +/*0 200 400 600 800 1k 1.2 1.4 1.6 2k 2.4 2.8 3.2 4k 4.8 5.6 6.8 8k*/ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40 +}; + +static const float compensation[] = { + 0.8f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 1.f, 0.666667f, 0.5f, 0.5f, 0.5f, 0.333333f, 0.25f, 0.25f, 0.2f, 0.166667f, 0.173913f +}; + + +extern const kiss_fft_state kfft; +extern const float half_window[OVERLAP_SIZE]; +extern const float dct_table[NB_BANDS*NB_BANDS]; + + +static void compute_band_energy_inverse(float *bandE, const kiss_fft_cpx *X) { + int i; + float sum[NB_BANDS] = {0}; + for (i=0;i>1; j++) + { + opus_val32 tmp1, tmp2; + tmp1 = lpc[j]; + tmp2 = lpc[i-1-j]; + lpc[j] = tmp1 + MULT32_32_Q31(r,tmp2); + lpc[i-1-j] = tmp2 + MULT32_32_Q31(r,tmp1); + } + + error = error - MULT32_32_Q31(MULT32_32_Q31(r,r),error); + /* Bail out once we get 30 dB gain */ + if (error<.001f*ac[0]) + break; + } + } + return error; +} + + + +void lpcn_compute_band_energy(float *bandE, const kiss_fft_cpx *X) { + int i; + float sum[NB_BANDS] = {0}; + for (i=0;imodel; + celt_assert(FWGAN_FEATURES == model->bfcc_with_corr_upsampler_fc.nb_inputs); + celt_assert(BFCC_WITH_CORR_UPSAMPLER_FC_OUT_SIZE == model->bfcc_with_corr_upsampler_fc.nb_outputs); + compute_generic_dense(&model->bfcc_with_corr_upsampler_fc, cond, features, ACTIVATION_TANH); +} + +static void fwgan_synthesize_impl(FWGANState *st, float *pcm, const float *lpc, const float *features); +void fwgan_cont(FWGANState *st, const float *pcm0, const float *features0) +{ + int i; + float norm2, norm_1; + float wpcm0[CONT_PCM_INPUTS]; + float cont_inputs[CONT_PCM_INPUTS+1]; + float tmp1[MAX_CONT_SIZE]; + float tmp2[MAX_CONT_SIZE]; + float lpc[LPC_ORDER]; + float new_pcm[FWGAN_FRAME_SIZE]; + FWGAN *model; + st->embed_phase[0] = 1; + model = &st->model; + compute_wlpc(lpc, features0); + /* Deemphasis memory is just the last continuation sample. */ + st->deemph_mem = pcm0[CONT_PCM_INPUTS-1]; + + /* Apply analysis filter, considering that the preemphasis and deemphasis filter + cancel each other in this case since the LPC filter is constant across that boundary. + */ + for (i=LPC_ORDER;ipreemph_mem = wpcm0[CONT_PCM_INPUTS-1]; + /* The memory of the synthesis filter is the pre-emphasized continuation. */ + for (i=0;isyn_mem[i] = pcm0[CONT_PCM_INPUTS-1-i] - FWGAN_DEEMPHASIS*pcm0[CONT_PCM_INPUTS-2-i]; + + norm2 = celt_inner_prod(wpcm0, wpcm0, CONT_PCM_INPUTS, st->arch); + norm_1 = 1.f/sqrt(1e-8f + norm2); + for (i=0;icont_net_0, tmp1, cont_inputs, ACTIVATION_TANH); + compute_generic_dense(&model->cont_net_2, tmp2, tmp1, ACTIVATION_TANH); + compute_generic_dense(&model->cont_net_4, tmp1, tmp2, ACTIVATION_TANH); + compute_generic_dense(&model->cont_net_6, tmp2, tmp1, ACTIVATION_TANH); + compute_generic_dense(&model->cont_net_8, tmp1, tmp2, ACTIVATION_TANH); + celt_assert(CONT_NET_10_OUT_SIZE == model->cont_net_10.nb_outputs); + compute_generic_dense(&model->cont_net_10, st->cont, tmp1, ACTIVATION_TANH); + + /* Computing continuation for each layer. */ + celt_assert(RNN_GRU_STATE_SIZE == model->rnn_cont_fc_0.nb_outputs); + compute_generic_dense(&model->rnn_cont_fc_0, st->rnn_state, st->cont, ACTIVATION_TANH); + + celt_assert(FWC1_STATE_SIZE == model->fwc1_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc1_cont_fc_0, st->fwc1_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC2_STATE_SIZE == model->fwc2_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc2_cont_fc_0, st->fwc2_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC3_STATE_SIZE == model->fwc3_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc3_cont_fc_0, st->fwc3_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC4_STATE_SIZE == model->fwc4_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc4_cont_fc_0, st->fwc4_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC5_STATE_SIZE == model->fwc5_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc5_cont_fc_0, st->fwc5_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC6_STATE_SIZE == model->fwc6_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc6_cont_fc_0, st->fwc6_state, st->cont, ACTIVATION_TANH); + celt_assert(FWC7_STATE_SIZE == model->fwc7_cont_fc_0.nb_outputs); + compute_generic_dense(&model->fwc7_cont_fc_0, st->fwc7_state, st->cont, ACTIVATION_TANH); + + st->cont_initialized = 1; + /* Process the first frame, discard the first subframe, and keep the rest for the first + synthesis call. */ + fwgan_synthesize_impl(st, new_pcm, lpc, features0); + OPUS_COPY(st->pcm_buf, &new_pcm[SUBFRAME_SIZE], FWGAN_FRAME_SIZE-SUBFRAME_SIZE); +} + +static void apply_gain(float *pcm, float c0, float *last_gain) { + int i; + float gain = pow(10.f, (0.5f*c0/sqrt(18.f))); + for (i=0;imodel; + + pitch_embeddings(pembed, st->embed_phase, w0); + /* Interleave bfcc_cond and pembed for each subframe in feat_in. */ + OPUS_COPY(&feat_in[BFCC_WITH_CORR_UPSAMPLER_FC_OUT_SIZE/4], &cond[0], BFCC_WITH_CORR_UPSAMPLER_FC_OUT_SIZE/4); + OPUS_COPY(&feat_in[0], &pembed[0], FWGAN_FRAME_SIZE/2); + + compute_generic_conv1d(&model->feat_in_conv1_conv, rnn_in, st->cont_conv1_mem, feat_in, FEAT_IN_CONV1_CONV_IN_SIZE, ACTIVATION_LINEAR); + celt_assert(FEAT_IN_NL1_GATE_OUT_SIZE == model->feat_in_nl1_gate.nb_outputs); + compute_gated_activation(&model->feat_in_nl1_gate, rnn_in, rnn_in, ACTIVATION_TANH); + + if (st->cont_initialized == 1) { + /* On the very first subframe we stop here. We only want to run the feat_in layer since the + others are initialized via the continuation network. */ + OPUS_CLEAR(pcm, SUBFRAME_SIZE); + st->cont_initialized = 2; + apply_gain(pcm, c0, &st->last_gain); + OPUS_COPY(st->last_lpc, lpc, LPC_ORDER); + return; + } + + compute_generic_gru(&model->rnn_gru_input, &model->rnn_gru_recurrent, st->rnn_state, rnn_in); + celt_assert(IMAX(RNN_GRU_STATE_SIZE, FWC2_FC_0_OUT_SIZE) >= model->rnn_nl_gate.nb_outputs); + compute_gated_activation(&model->rnn_nl_gate, tmp2, st->rnn_state, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc1_fc_0, tmp1, st->fwc1_state, tmp2, RNN_GRU_STATE_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc1_fc_1_gate, tmp1, tmp1, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc2_fc_0, tmp2, st->fwc2_state, tmp1, FWC1_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc2_fc_1_gate, tmp2, tmp2, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc3_fc_0, tmp1, st->fwc3_state, tmp2, FWC2_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc3_fc_1_gate, tmp1, tmp1, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc4_fc_0, tmp2, st->fwc4_state, tmp1, FWC3_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc4_fc_1_gate, tmp2, tmp2, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc5_fc_0, tmp1, st->fwc5_state, tmp2, FWC4_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc5_fc_1_gate, tmp1, tmp1, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc6_fc_0, tmp2, st->fwc6_state, tmp1, FWC5_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc6_fc_1_gate, tmp2, tmp2, ACTIVATION_TANH); + + compute_generic_conv1d(&model->fwc7_fc_0, tmp1, st->fwc7_state, tmp2, FWC6_FC_0_OUT_SIZE, ACTIVATION_LINEAR); + compute_gated_activation(&model->fwc7_fc_1_gate, pcm, tmp1, ACTIVATION_TANH); + + apply_gain(pcm, c0, &st->last_gain); + fwgan_preemphasis(pcm, &st->preemph_mem); + fwgan_lpc_syn(pcm, st->syn_mem, lpc, st->last_lpc); + fwgan_deemphasis(pcm, &st->deemph_mem); +} + +void fwgan_init(FWGANState *st) +{ + int ret; + OPUS_CLEAR(st, 1); + ret = init_fwgan(&st->model, fwgan_arrays); + celt_assert(ret == 0); + /* FIXME: perform arch detection. */ +} + +int fwgan_load_model(FWGANState *st, const unsigned char *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_fwgan(&st->model, list); + opus_free(list); + if (ret == 0) return 0; + else return -1; +} + +static void fwgan_synthesize_impl(FWGANState *st, float *pcm, const float *lpc, const float *features) +{ + int subframe; + float cond[BFCC_WITH_CORR_UPSAMPLER_FC_OUT_SIZE]; + double w0; + int period; + float fwgan_features[NB_FEATURES-1]; + celt_assert(st->cont_initialized); + OPUS_COPY(fwgan_features, features, NB_FEATURES-2); + fwgan_features[NB_FEATURES-2] = features[NB_FEATURES-1]+.5; + + period = (int)floor(.1 + 50*features[NB_BANDS]+100); + w0 = 2*M_PI/period; + run_fwgan_upsampler(st, cond, fwgan_features); + for (subframe=0;subframepcm_buf, FWGAN_FRAME_SIZE-SUBFRAME_SIZE); + OPUS_COPY(&pcm[FWGAN_FRAME_SIZE-SUBFRAME_SIZE], new_pcm, SUBFRAME_SIZE); + OPUS_COPY(st->pcm_buf, &new_pcm[SUBFRAME_SIZE], FWGAN_FRAME_SIZE-SUBFRAME_SIZE); +} + +void fwgan_synthesize_int(FWGANState *st, opus_int16 *pcm, const float *features) +{ + int i; + float fpcm[FWGAN_FRAME_SIZE]; + fwgan_synthesize(st, fpcm, features); + for (i=0;iz=362436069; + _this->w=521288629; + _this->jsr=123456789; + _this->jcong=380116160; + for(i=3;i<_ndata;i+=4){ + _this->z^=_data[i-3]; + _this->w^=_data[i-2]; + _this->jsr^=_data[i-1]; + _this->jcong^=_data[i]; + kiss99_rand(_this); + } + if(i-3<_ndata)_this->z^=_data[i-3]; + if(i-2<_ndata)_this->w^=_data[i-2]; + if(i-1<_ndata)_this->jsr^=_data[i-1]; + /*Fix any potential short cycles that show up. + These are not too likely, given the way we initialize the state, but they + are technically possible, so let us go ahead and eliminate that + possibility. + See Gregory G. Rose: "KISS: A Bit Too Simple", Cryptographic Communications + No. 10, pp. 123---137, 2018.*/ + if(_this->z==0||_this->z==0x9068FFFF)_this->z++; + if(_this->w==0||_this->w==0x464FFFFF)_this->w++; + if(_this->jsr==0)_this->jsr++; +} + +uint32_t kiss99_rand(kiss99_ctx *_this){ + uint32_t znew; + uint32_t wnew; + uint32_t mwc; + uint32_t shr3; + uint32_t cong; + znew=36969*(_this->z&0xFFFF)+(_this->z>>16); + wnew=18000*(_this->w&0xFFFF)+(_this->w>>16); + mwc=(znew<<16)+wnew; + /*We swap the 13 and 17 from the original 1999 algorithm to produce a single + cycle of maximal length, matching KISS11. + We are not actually using KISS11 because of the impractically large (16 MB) + internal state of the full algorithm.*/ + shr3=_this->jsr^(_this->jsr<<13); + shr3^=shr3>>17; + shr3^=shr3<<5; + cong=69069*_this->jcong+1234567; + _this->z=znew; + _this->w=wnew; + _this->jsr=shr3; + _this->jcong=cong; + return (mwc^cong)+shr3; +} diff --git a/managed_components/78__esp-opus/dnn/kiss99.h b/managed_components/78__esp-opus/dnn/kiss99.h new file mode 100644 index 0000000..28646db --- /dev/null +++ b/managed_components/78__esp-opus/dnn/kiss99.h @@ -0,0 +1,46 @@ +/*Daala video codec +Copyright (c) 2012 Daala project contributors. All rights reserved. +Author: Timothy B. Terriberry + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +- Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +- Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.*/ + +#if !defined(_kiss99_H) +# define _kiss99_H (1) +# include + +/*KISS PRNG from George Marsaglia (1999 version). + See https://en.wikipedia.org/wiki/KISS_(algorithm) for details. + This is suitable for simulations, but not for use in crytographic contexts.*/ + +typedef struct kiss99_ctx kiss99_ctx; + +struct kiss99_ctx{ + uint32_t z; + uint32_t w; + uint32_t jsr; + uint32_t jcong; +}; + +void kiss99_srand(kiss99_ctx *_this,const unsigned char *_data,int _ndata); +uint32_t kiss99_rand(kiss99_ctx *_this); + +#endif diff --git a/managed_components/78__esp-opus/dnn/lossgen.c b/managed_components/78__esp-opus/dnn/lossgen.c new file mode 100644 index 0000000..8c1a2ad --- /dev/null +++ b/managed_components/78__esp-opus/dnn/lossgen.c @@ -0,0 +1,192 @@ +/* Copyright (c) 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +/* This packet loss simulator can be used independently of the Opus codebase. + To do that, you need to compile the following files: + dnn/lossgen.c + dnn/lossgen_data.c + + with the following files needed as #include + dnn/lossgen_data.h + dnn/lossgen.h + dnn/nnet_arch.h + dnn/nnet.h + dnn/parse_lpcnet_weights.c (included despite being a C file) + dnn/vec_avx.h + dnn/vec.h + celt/os_support.h + celt/arch.h + celt/x86/x86_arch_macros.h + include/opus_defines.h + include/opus_types.h + + Additionally, the code in dnn/lossgen_demo.c can be used to generate losses from + the command line. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "arch.h" + +#include +#include "lossgen.h" +#include "os_support.h" +#include "nnet.h" +#include "assert.h" + +/* Disable RTCD for this. */ +#define RTCD_ARCH c + +/* Override assert to avoid undefined/redefined symbols. */ +#undef celt_assert +#define celt_assert assert + +/* Directly include the C files we need since the symbols won't be exposed if we link in a shared object. */ +#include "parse_lpcnet_weights.c" +#include "nnet_arch.h" + +#undef compute_linear +#undef compute_activation + +/* Force the C version since the SIMD versions may be hidden. */ +#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_c(linear, out, in)) +#define compute_activation(output, input, N, activation, arch) ((void)(arch),compute_activation_c(output, input, N, activation)) + +#define MAX_RNN_NEURONS_ALL IMAX(LOSSGEN_GRU1_STATE_SIZE, LOSSGEN_GRU2_STATE_SIZE) + +/* These two functions are copied from nnet.c to make sure we don't have linking issues. */ +void compute_generic_gru_lossgen(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch) +{ + int i; + int N; + float zrh[3*MAX_RNN_NEURONS_ALL]; + float recur[3*MAX_RNN_NEURONS_ALL]; + float *z; + float *r; + float *h; + celt_assert(3*recurrent_weights->nb_inputs == recurrent_weights->nb_outputs); + celt_assert(input_weights->nb_outputs == recurrent_weights->nb_outputs); + N = recurrent_weights->nb_inputs; + z = zrh; + r = &zrh[N]; + h = &zrh[2*N]; + celt_assert(recurrent_weights->nb_outputs <= 3*MAX_RNN_NEURONS_ALL); + celt_assert(in != state); + compute_linear(input_weights, zrh, in, arch); + compute_linear(recurrent_weights, recur, state, arch); + for (i=0;i<2*N;i++) + zrh[i] += recur[i]; + compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID, arch); + for (i=0;inb_outputs, activation, arch); +} + + +static int sample_loss_impl( + LossGenState *st, + float percent_loss) +{ + float input[2]; + float tmp[LOSSGEN_DENSE_IN_OUT_SIZE]; + float out; + int loss; + LossGen *model = &st->model; + input[0] = st->last_loss; + input[1] = percent_loss; + compute_generic_dense_lossgen(&model->lossgen_dense_in, tmp, input, ACTIVATION_TANH, 0); + compute_generic_gru_lossgen(&model->lossgen_gru1_input, &model->lossgen_gru1_recurrent, st->gru1_state, tmp, 0); + compute_generic_gru_lossgen(&model->lossgen_gru2_input, &model->lossgen_gru2_recurrent, st->gru2_state, st->gru1_state, 0); + compute_generic_dense_lossgen(&model->lossgen_dense_out, &out, st->gru2_state, ACTIVATION_SIGMOID, 0); + loss = (float)rand()/RAND_MAX < out; + st->last_loss = loss; + return loss; +} + +int sample_loss( + LossGenState *st, + float percent_loss) +{ + /* Due to GRU being initialized with zeros, the first packets aren't quite random, + so we skip them. */ + if (!st->used) { + int i; + for (i=0;i<1000;i++) sample_loss_impl(st, percent_loss); + st->used = 1; + } + return sample_loss_impl(st, percent_loss); +} + +void lossgen_init(LossGenState *st) +{ + int ret; + OPUS_CLEAR(st, 1); + ret = init_lossgen(&st->model, lossgen_arrays); + celt_assert(ret == 0); + (void)ret; +} + +int lossgen_load_model(LossGenState *st, const void *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_lossgen(&st->model, list); + opus_free(list); + if (ret == 0) return 0; + else return -1; +} + +#if 0 +#include +int main(int argc, char **argv) { + int i, N; + float p; + LossGenState st; + if (argc!=3) { + fprintf(stderr, "usage: lossgen \n"); + return 1; + } + lossgen_init(&st); + p = atof(argv[1]); + N = atoi(argv[2]); + for (i=0;i +#include +#include "lossgen.h" +int main(int argc, char **argv) +{ + LossGenState st; + long num_packets; + long i; + float percent; + if (argc != 3) { + fprintf(stderr, "usage: %s \n", argv[0]); + return 1; + } + lossgen_init(&st); + percent = atof(argv[1]); + num_packets = atol(argv[2]); + /*printf("loss: %f %d\n", percent, num_packets);*/ + for (i=0;i +#include +#include "nnet_data.h" +#include "nnet.h" +#include "common.h" +#include "arch.h" +#include "lpcnet.h" +#include "lpcnet_private.h" +#include "os_support.h" + +#define PREEMPH 0.85f + +#define PDF_FLOOR 0.002 + +#define FRAME_INPUT_SIZE (NB_FEATURES + EMBED_PITCH_OUT_SIZE) + + +#if 0 +static void print_vector(float *x, int N) +{ + int i; + for (i=0;innet; + OPUS_COPY(in, features, NB_FEATURES); + compute_embedding(&lpcnet->model.embed_pitch, &in[NB_FEATURES], pitch); + compute_conv1d(&lpcnet->model.feature_conv1, conv1_out, net->feature_conv1_state, in); + if (lpcnet->frame_count < FEATURE_CONV1_DELAY) OPUS_CLEAR(conv1_out, FEATURE_CONV1_OUT_SIZE); + compute_conv1d(&lpcnet->model.feature_conv2, conv2_out, net->feature_conv2_state, conv1_out); + if (lpcnet->frame_count < FEATURES_DELAY) OPUS_CLEAR(conv2_out, FEATURE_CONV2_OUT_SIZE); + _lpcnet_compute_dense(&lpcnet->model.feature_dense1, dense1_out, conv2_out); + _lpcnet_compute_dense(&lpcnet->model.feature_dense2, condition, dense1_out); + OPUS_COPY(rc, condition, LPC_ORDER); + _lpcnet_compute_dense(&lpcnet->model.gru_a_dense_feature, gru_a_condition, condition); + _lpcnet_compute_dense(&lpcnet->model.gru_b_dense_feature, gru_b_condition, condition); +#ifdef END2END + rc2lpc(lpc, rc); +#elif FEATURES_DELAY>0 + memcpy(lpc, lpcnet->old_lpc[FEATURES_DELAY-1], LPC_ORDER*sizeof(lpc[0])); + memmove(lpcnet->old_lpc[1], lpcnet->old_lpc[0], (FEATURES_DELAY-1)*LPC_ORDER*sizeof(lpc[0])); + lpc_from_cepstrum(lpcnet->old_lpc[0], features); +#else + lpc_from_cepstrum(lpc, features); +#endif +#ifdef LPC_GAMMA + lpc_weighting(lpc, LPC_GAMMA); +#endif + if (lpcnet->frame_count < 1000) lpcnet->frame_count++; +} + +void run_frame_network_deferred(LPCNetState *lpcnet, const float *features) +{ + int max_buffer_size = lpcnet->model.feature_conv1.kernel_size + lpcnet->model.feature_conv2.kernel_size - 2; + celt_assert(max_buffer_size <= MAX_FEATURE_BUFFER_SIZE); + if (lpcnet->feature_buffer_fill == max_buffer_size) { + OPUS_MOVE(lpcnet->feature_buffer, &lpcnet->feature_buffer[NB_FEATURES], (max_buffer_size-1)*NB_FEATURES); + } else { + lpcnet->feature_buffer_fill++; + } + OPUS_COPY(&lpcnet->feature_buffer[(lpcnet->feature_buffer_fill-1)*NB_FEATURES], features, NB_FEATURES); +} + +void run_frame_network_flush(LPCNetState *lpcnet) +{ + int i; + for (i=0;ifeature_buffer_fill;i++) { + float lpc[LPC_ORDER]; + float gru_a_condition[3*GRU_A_STATE_SIZE]; + float gru_b_condition[3*GRU_B_STATE_SIZE]; + run_frame_network(lpcnet, gru_a_condition, gru_b_condition, lpc, &lpcnet->feature_buffer[i*NB_FEATURES]); + } + lpcnet->feature_buffer_fill = 0; +} + +int run_sample_network(LPCNetState *lpcnet, const float *gru_a_condition, const float *gru_b_condition, int last_exc, int last_sig, int pred, const float *sampling_logit_table, kiss99_ctx *rng) +{ + NNetState *net; + float gru_a_input[3*GRU_A_STATE_SIZE]; + float in_b[GRU_A_STATE_SIZE+FEATURE_DENSE2_OUT_SIZE]; + float gru_b_input[3*GRU_B_STATE_SIZE]; + net = &lpcnet->nnet; +#if 1 + compute_gru_a_input(gru_a_input, gru_a_condition, GRU_A_STATE_SIZE, &lpcnet->model.gru_a_embed_sig, last_sig, &lpcnet->model.gru_a_embed_pred, pred, &lpcnet->model.gru_a_embed_exc, last_exc); +#else + OPUS_COPY(gru_a_input, gru_a_condition, 3*GRU_A_STATE_SIZE); + accum_embedding(&lpcnet->model.gru_a_embed_sig, gru_a_input, last_sig); + accum_embedding(&lpcnet->model.gru_a_embed_pred, gru_a_input, pred); + accum_embedding(&lpcnet->model.gru_a_embed_exc, gru_a_input, last_exc); +#endif + /*compute_gru3(&gru_a, net->gru_a_state, gru_a_input);*/ + compute_sparse_gru(&lpcnet->model.sparse_gru_a, net->gru_a_state, gru_a_input); + OPUS_COPY(in_b, net->gru_a_state, GRU_A_STATE_SIZE); + OPUS_COPY(gru_b_input, gru_b_condition, 3*GRU_B_STATE_SIZE); + compute_gruB(&lpcnet->model.gru_b, gru_b_input, net->gru_b_state, in_b); + return sample_mdense(&lpcnet->model.dual_fc, net->gru_b_state, sampling_logit_table, rng); +} + +int lpcnet_get_size() +{ + return sizeof(LPCNetState); +} + +void lpcnet_reset(LPCNetState *lpcnet) +{ + const char* rng_string="LPCNet"; + OPUS_CLEAR((char*)&lpcnet->LPCNET_RESET_START, + sizeof(LPCNetState)- + ((char*)&lpcnet->LPCNET_RESET_START - (char*)lpcnet)); + lpcnet->last_exc = lin2ulaw(0.f); + kiss99_srand(&lpcnet->rng, (const unsigned char *)rng_string, strlen(rng_string)); +} + +int lpcnet_init(LPCNetState *lpcnet) +{ + int i; + int ret; + for (i=0;i<256;i++) { + float prob = .025f+.95f*i/255.f; + lpcnet->sampling_logit_table[i] = -log((1-prob)/prob); + } +#ifndef USE_WEIGHTS_FILE + ret = init_lpcnet_model(&lpcnet->model, lpcnet_arrays); +#else + ret = 0; +#endif + lpcnet_reset(lpcnet); + celt_assert(ret == 0); + return ret; +} + +int lpcnet_load_model(LPCNetState *st, const unsigned char *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_lpcnet_model(&st->model, list); + opus_free(list); + if (ret == 0) return 0; + else return -1; +} + + +LPCNetState *lpcnet_create() +{ + LPCNetState *lpcnet; + lpcnet = (LPCNetState *)opus_alloc(lpcnet_get_size(), 1); + OPUS_CLEAR(lpcnet, 1); + lpcnet_init(lpcnet); + return lpcnet; +} + +void lpcnet_destroy(LPCNetState *lpcnet) +{ + opus_free(lpcnet); +} + +void lpcnet_reset_signal(LPCNetState *lpcnet) +{ + lpcnet->deemph_mem = 0; + lpcnet->last_exc = lin2ulaw(0.f); + OPUS_CLEAR(lpcnet->last_sig, LPC_ORDER); + OPUS_CLEAR(lpcnet->nnet.gru_a_state, GRU_A_STATE_SIZE); + OPUS_CLEAR(lpcnet->nnet.gru_b_state, GRU_B_STATE_SIZE); +} + +void lpcnet_synthesize_tail_impl(LPCNetState *lpcnet, opus_int16 *output, int N, int preload) +{ + int i; + + if (lpcnet->frame_count <= FEATURES_DELAY) + { + OPUS_CLEAR(output, N); + return; + } + for (i=0;ilast_sig[j]*lpcnet->lpc[j]; + last_sig_ulaw = lin2ulaw(lpcnet->last_sig[0]); + pred_ulaw = lin2ulaw(pred); + exc = run_sample_network(lpcnet, lpcnet->gru_a_condition, lpcnet->gru_b_condition, lpcnet->last_exc, last_sig_ulaw, pred_ulaw, lpcnet->sampling_logit_table, &lpcnet->rng); + if (i < preload) { + exc = lin2ulaw(output[i]-PREEMPH*lpcnet->deemph_mem - pred); + pcm = output[i]-PREEMPH*lpcnet->deemph_mem; + } else { + pcm = pred + ulaw2lin(exc); + } + OPUS_MOVE(&lpcnet->last_sig[1], &lpcnet->last_sig[0], LPC_ORDER-1); + lpcnet->last_sig[0] = pcm; + lpcnet->last_exc = exc; + pcm += PREEMPH*lpcnet->deemph_mem; + lpcnet->deemph_mem = pcm; + if (pcm<-32767) pcm = -32767; + if (pcm>32767) pcm = 32767; + if (i >= preload) output[i] = (int)floor(.5 + pcm); + } +} + +void lpcnet_synthesize_impl(LPCNetState *lpcnet, const float *features, opus_int16 *output, int N, int preload) +{ + run_frame_network(lpcnet, lpcnet->gru_a_condition, lpcnet->gru_b_condition, lpcnet->lpc, features); + lpcnet_synthesize_tail_impl(lpcnet, output, N, preload); +} + +void lpcnet_synthesize(LPCNetState *lpcnet, const float *features, opus_int16 *output, int N) { + lpcnet_synthesize_impl(lpcnet, features, output, N, 0); +} diff --git a/managed_components/78__esp-opus/dnn/lpcnet.h b/managed_components/78__esp-opus/dnn/lpcnet.h new file mode 100644 index 0000000..4a98124 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/lpcnet.h @@ -0,0 +1,183 @@ +/* Copyright (c) 2018 Mozilla */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef LPCNET_H_ +#define LPCNET_H_ + +#include "opus_types.h" + +#define NB_FEATURES 20 +#define NB_TOTAL_FEATURES 36 + +/** Number of audio samples in a feature frame (not for encoding/decoding). */ +#define LPCNET_FRAME_SIZE (160) + +typedef struct LPCNetState LPCNetState; + +typedef struct LPCNetDecState LPCNetDecState; + +typedef struct LPCNetEncState LPCNetEncState; + +typedef struct LPCNetPLCState LPCNetPLCState; + + +/** Gets the size of an LPCNetDecState structure. + * @returns The size in bytes. + */ +int lpcnet_decoder_get_size(void); + +/** Initializes a previously allocated decoder state + * The memory pointed to by st must be at least the size returned by lpcnet_decoder_get_size(). + * This is intended for applications which use their own allocator instead of malloc. + * @see lpcnet_decoder_create(),lpcnet_decoder_get_size() + * @param [in] st LPCNetDecState*: Decoder state + * @retval 0 Success + */ +int lpcnet_decoder_init(LPCNetDecState *st); + +void lpcnet_reset(LPCNetState *lpcnet); + +/** Allocates and initializes a decoder state. + * @returns The newly created state + */ +LPCNetDecState *lpcnet_decoder_create(void); + +/** Frees an LPCNetDecState allocated by lpcnet_decoder_create(). + * @param[in] st LPCNetDecState*: State to be freed. + */ +void lpcnet_decoder_destroy(LPCNetDecState *st); + +/** Decodes a packet of LPCNET_COMPRESSED_SIZE bytes (currently 8) into LPCNET_PACKET_SAMPLES samples (currently 640). + * @param [in] st LPCNetDecState*: Decoder state + * @param [in] buf const unsigned char *: Compressed packet + * @param [out] pcm opus_int16 *: Decoded audio + * @retval 0 Success + */ +int lpcnet_decode(LPCNetDecState *st, const unsigned char *buf, opus_int16 *pcm); + + + +/** Gets the size of an LPCNetEncState structure. + * @returns The size in bytes. + */ +int lpcnet_encoder_get_size(void); + +/** Initializes a previously allocated encoder state + * The memory pointed to by st must be at least the size returned by lpcnet_encoder_get_size(). + * This is intended for applications which use their own allocator instead of malloc. + * @see lpcnet_encoder_create(),lpcnet_encoder_get_size() + * @param [in] st LPCNetEncState*: Encoder state + * @retval 0 Success + */ +int lpcnet_encoder_init(LPCNetEncState *st); + +int lpcnet_encoder_load_model(LPCNetEncState *st, const void *data, int len); + +/** Allocates and initializes an encoder state. + * @returns The newly created state + */ +LPCNetEncState *lpcnet_encoder_create(void); + +/** Frees an LPCNetEncState allocated by lpcnet_encoder_create(). + * @param[in] st LPCNetEncState*: State to be freed. + */ +void lpcnet_encoder_destroy(LPCNetEncState *st); + +/** Encodes LPCNET_PACKET_SAMPLES speech samples (currently 640) into a packet of LPCNET_COMPRESSED_SIZE bytes (currently 8). + * @param [in] st LPCNetDecState*: Encoder state + * @param [in] pcm opus_int16 *: Input speech to be encoded + * @param [out] buf const unsigned char *: Compressed packet + * @retval 0 Success + */ +int lpcnet_encode(LPCNetEncState *st, const opus_int16 *pcm, unsigned char *buf); + +/** Compute features on LPCNET_FRAME_SIZE speech samples (currently 160) and output features for one 10-ms frame. + * @param [in] st LPCNetDecState*: Encoder state + * @param [in] pcm opus_int16 *: Input speech to be analyzed + * @param [out] features float[NB_TOTAL_FEATURES]: Four feature vectors + * @retval 0 Success + */ +int lpcnet_compute_single_frame_features(LPCNetEncState *st, const opus_int16 *pcm, float features[NB_TOTAL_FEATURES], int arch); + + +/** Compute features on LPCNET_FRAME_SIZE speech samples (currently 160) and output features for one 10-ms frame. + * @param [in] st LPCNetDecState*: Encoder state + * @param [in] pcm float *: Input speech to be analyzed + * @param [out] features float[NB_TOTAL_FEATURES]: Four feature vectors + * @retval 0 Success + */ +int lpcnet_compute_single_frame_features_float(LPCNetEncState *st, const float *pcm, float features[NB_TOTAL_FEATURES], int arch); + +/** Gets the size of an LPCNetState structure. + * @returns The size in bytes. + */ +int lpcnet_get_size(void); + +/** Initializes a previously allocated synthesis state + * The memory pointed to by st must be at least the size returned by lpcnet_get_size(). + * This is intended for applications which use their own allocator instead of malloc. + * @see lpcnet_create(),lpcnet_get_size() + * @param [in] st LPCNetState*: Synthesis state + * @retval 0 Success + */ +int lpcnet_init(LPCNetState *st); + +/** Allocates and initializes a synthesis state. + * @returns The newly created state + */ +LPCNetState *lpcnet_create(void); + +/** Frees an LPCNetState allocated by lpcnet_create(). + * @param[in] st LPCNetState*: State to be freed. + */ +void lpcnet_destroy(LPCNetState *st); + +/** Synthesizes speech from an LPCNet feature vector. + * @param [in] st LPCNetState*: Synthesis state + * @param [in] features const float *: Compressed packet + * @param [out] output opus_int16 **: Synthesized speech + * @param [in] N int: Number of samples to generate + * @retval 0 Success + */ +void lpcnet_synthesize(LPCNetState *st, const float *features, opus_int16 *output, int N); + + + +int lpcnet_plc_init(LPCNetPLCState *st); +void lpcnet_plc_reset(LPCNetPLCState *st); + +int lpcnet_plc_update(LPCNetPLCState *st, opus_int16 *pcm); + +int lpcnet_plc_conceal(LPCNetPLCState *st, opus_int16 *pcm); + +void lpcnet_plc_fec_add(LPCNetPLCState *st, const float *features); + +void lpcnet_plc_fec_clear(LPCNetPLCState *st); + +int lpcnet_load_model(LPCNetState *st, const void *data, int len); +int lpcnet_plc_load_model(LPCNetPLCState *st, const void *data, int len); + +#endif diff --git a/managed_components/78__esp-opus/dnn/lpcnet_enc.c b/managed_components/78__esp-opus/dnn/lpcnet_enc.c new file mode 100644 index 0000000..5293041 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/lpcnet_enc.c @@ -0,0 +1,230 @@ +/* Copyright (c) 2017-2019 Mozilla */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include +#include "kiss_fft.h" +#include "common.h" +#include +#include "freq.h" +#include "pitch.h" +#include "arch.h" +#include +#include "lpcnet_private.h" +#include "lpcnet.h" +#include "os_support.h" +#include "_kiss_fft_guts.h" +#include "celt_lpc.h" +#include "mathops.h" + + +int lpcnet_encoder_get_size(void) { + return sizeof(LPCNetEncState); +} + +int lpcnet_encoder_init(LPCNetEncState *st) { + memset(st, 0, sizeof(*st)); + pitchdnn_init(&st->pitchdnn); + return 0; +} + +int lpcnet_encoder_load_model(LPCNetEncState *st, const void *data, int len) { + return pitchdnn_load_model(&st->pitchdnn, data, len); +} + +LPCNetEncState *lpcnet_encoder_create(void) { + LPCNetEncState *st; + st = opus_alloc(lpcnet_encoder_get_size()); + lpcnet_encoder_init(st); + return st; +} + +void lpcnet_encoder_destroy(LPCNetEncState *st) { + opus_free(st); +} + +static void frame_analysis(LPCNetEncState *st, kiss_fft_cpx *X, float *Ex, const float *in) { + float x[WINDOW_SIZE]; + OPUS_COPY(x, st->analysis_mem, OVERLAP_SIZE); + OPUS_COPY(&x[OVERLAP_SIZE], in, FRAME_SIZE); + OPUS_COPY(st->analysis_mem, &in[FRAME_SIZE-OVERLAP_SIZE], OVERLAP_SIZE); + apply_window(x); + forward_transform(X, x); + lpcn_compute_band_energy(Ex, X); +} + +static void biquad(float *y, float mem[2], const float *x, const float *b, const float *a, int N) { + int i; + float mem0, mem1; + mem0 = mem[0]; + mem1 = mem[1]; + for (i=0;ianalysis_mem[OVERLAP_SIZE-TRAINING_OFFSET], TRAINING_OFFSET); + frame_analysis(st, X, Ex, in); + st->if_features[0] = MAX16(-1.f, MIN16(1.f, (1.f/64)*(10.f*celt_log10(1e-15f + X[0].r*X[0].r)-6.f))); + for (i=1;iprev_if[i]); + norm_1 = 1.f/sqrt(1e-15f + prod.r*prod.r + prod.i*prod.i); + C_MULBYSCALAR(prod, norm_1); + st->if_features[3*i-2] = prod.r; + st->if_features[3*i-1] = prod.i; + st->if_features[3*i] = MAX16(-1.f, MIN16(1.f, (1.f/64)*(10.f*celt_log10(1e-15f + X[i].r*X[i].r + X[i].i*X[i].i)-6.f))); + } + OPUS_COPY(st->prev_if, X, PITCH_IF_MAX_FREQ); + /*for (i=0;i<88;i++) printf("%f ", st->if_features[i]);printf("\n");*/ + logMax = -2; + follow = -2; + for (i=0;ifeatures, Ly); + st->features[0] -= 4; + lpc_from_cepstrum(st->lpc, st->features); + for (i=0;ifeatures[NB_BANDS+2+i] = st->lpc[i]; + OPUS_MOVE(st->exc_buf, &st->exc_buf[FRAME_SIZE], PITCH_MAX_PERIOD); + OPUS_MOVE(st->lp_buf, &st->lp_buf[FRAME_SIZE], PITCH_MAX_PERIOD); + OPUS_COPY(&aligned_in[TRAINING_OFFSET], in, FRAME_SIZE-TRAINING_OFFSET); + OPUS_COPY(&x[0], st->pitch_mem, LPC_ORDER); + OPUS_COPY(&x[LPC_ORDER], aligned_in, FRAME_SIZE); + OPUS_COPY(st->pitch_mem, &aligned_in[FRAME_SIZE-LPC_ORDER], LPC_ORDER); + celt_fir(&x[LPC_ORDER], st->lpc, &st->lp_buf[PITCH_MAX_PERIOD], FRAME_SIZE, LPC_ORDER, arch); + for (i=0;iexc_buf[PITCH_MAX_PERIOD+i] = st->lp_buf[PITCH_MAX_PERIOD+i] + .7f*st->pitch_filt; + st->pitch_filt = st->lp_buf[PITCH_MAX_PERIOD+i]; + /*printf("%f\n", st->exc_buf[PITCH_MAX_PERIOD+i]);*/ + } + biquad(&st->lp_buf[PITCH_MAX_PERIOD], st->lp_mem, &st->lp_buf[PITCH_MAX_PERIOD], lp_b, lp_a, FRAME_SIZE); + { + double ener1; + float *buf = st->exc_buf; + celt_pitch_xcorr(&buf[PITCH_MAX_PERIOD], buf, xcorr, FRAME_SIZE, PITCH_MAX_PERIOD-PITCH_MIN_PERIOD, arch); + ener0 = celt_inner_prod(&buf[PITCH_MAX_PERIOD], &buf[PITCH_MAX_PERIOD], FRAME_SIZE, arch); + ener1 = celt_inner_prod(&buf[0], &buf[0], FRAME_SIZE, arch); + /*printf("%f\n", st->frame_weight[sub]);*/ + for (i=0;ixcorr_features[i] = 2*xcorr[i]; + ener_norm[i] = ener; + ener1 += buf[i+FRAME_SIZE]*(double)buf[i+FRAME_SIZE] - buf[i]*(double)buf[i]; + /*printf("%f ", st->xcorr_features[i]);*/ + } + /* Split in a separate loop so the compiler can vectorize it */ + for (i=0;ixcorr_features[i] /= ener_norm[i]; + } + /*printf("\n");*/ + } + st->dnn_pitch = compute_pitchdnn(&st->pitchdnn, st->if_features, st->xcorr_features, arch); + pitch = (int)floor(.5+256./pow(2.f,((1./60.)*((st->dnn_pitch+1.5)*60)))); + xx = celt_inner_prod(&st->lp_buf[PITCH_MAX_PERIOD], &st->lp_buf[PITCH_MAX_PERIOD], FRAME_SIZE, arch); + yy = celt_inner_prod(&st->lp_buf[PITCH_MAX_PERIOD-pitch], &st->lp_buf[PITCH_MAX_PERIOD-pitch], FRAME_SIZE, arch); + xy = celt_inner_prod(&st->lp_buf[PITCH_MAX_PERIOD], &st->lp_buf[PITCH_MAX_PERIOD-pitch], FRAME_SIZE, arch); + /*printf("%f %f\n", frame_corr, xy/sqrt(1e-15+xx*yy));*/ + frame_corr = xy/sqrt(1+xx*yy); + frame_corr = log(1.f+exp(5.f*frame_corr))/log(1+exp(5.f)); + st->features[NB_BANDS] = st->dnn_pitch; + st->features[NB_BANDS + 1] = frame_corr-.5f; +} + +void preemphasis(float *y, float *mem, const float *x, float coef, int N) { + int i; + for (i=0;imem_preemph, x, PREEMPHASIS, FRAME_SIZE); + compute_frame_features(st, x, arch); + OPUS_COPY(features, &st->features[0], NB_TOTAL_FEATURES); + return 0; +} + +int lpcnet_compute_single_frame_features(LPCNetEncState *st, const opus_int16 *pcm, float features[NB_TOTAL_FEATURES], int arch) { + int i; + float x[FRAME_SIZE]; + for (i=0;iLPCNET_PLC_RESET_START, + sizeof(LPCNetPLCState)- + ((char*)&st->LPCNET_PLC_RESET_START - (char*)st)); + lpcnet_encoder_init(&st->enc); + OPUS_CLEAR(st->pcm, PLC_BUF_SIZE); + st->blend = 0; + st->loss_count = 0; + st->analysis_gap = 1; + st->analysis_pos = PLC_BUF_SIZE; + st->predict_pos = PLC_BUF_SIZE; +} + +int lpcnet_plc_init(LPCNetPLCState *st) { + int ret; + st->arch = opus_select_arch(); + fargan_init(&st->fargan); + lpcnet_encoder_init(&st->enc); + st->loaded = 0; +#ifndef USE_WEIGHTS_FILE + ret = init_plcmodel(&st->model, plcmodel_arrays); + if (ret == 0) st->loaded = 1; +#else + ret = 0; +#endif + celt_assert(ret == 0); + lpcnet_plc_reset(st); + return ret; +} + +int lpcnet_plc_load_model(LPCNetPLCState *st, const void *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_plcmodel(&st->model, list); + opus_free(list); + if (ret == 0) { + ret = lpcnet_encoder_load_model(&st->enc, data, len); + } + if (ret == 0) { + ret = fargan_load_model(&st->fargan, data, len); + } + if (ret == 0) st->loaded = 1; + return ret; +} + +void lpcnet_plc_fec_add(LPCNetPLCState *st, const float *features) { + if (features == NULL) { + st->fec_skip++; + return; + } + if (st->fec_fill_pos == PLC_MAX_FEC) { + OPUS_MOVE(&st->fec[0][0], &st->fec[st->fec_read_pos][0], (st->fec_fill_pos-st->fec_read_pos)*NB_FEATURES); + st->fec_fill_pos = st->fec_fill_pos-st->fec_read_pos; + st->fec_read_pos -= st->fec_read_pos; + } + OPUS_COPY(&st->fec[st->fec_fill_pos][0], features, NB_FEATURES); + st->fec_fill_pos++; +} + +void lpcnet_plc_fec_clear(LPCNetPLCState *st) { + st->fec_read_pos = st->fec_fill_pos = st->fec_skip = 0; +} + + +static void compute_plc_pred(LPCNetPLCState *st, float *out, const float *in) { + float tmp[PLC_DENSE_IN_OUT_SIZE]; + PLCModel *model = &st->model; + PLCNetState *net = &st->plc_net; + celt_assert(st->loaded); + compute_generic_dense(&model->plc_dense_in, tmp, in, ACTIVATION_TANH, st->arch); + compute_generic_gru(&model->plc_gru1_input, &model->plc_gru1_recurrent, net->gru1_state, tmp, st->arch); + compute_generic_gru(&model->plc_gru2_input, &model->plc_gru2_recurrent, net->gru2_state, net->gru1_state, st->arch); + compute_generic_dense(&model->plc_dense_out, out, net->gru2_state, ACTIVATION_LINEAR, st->arch); +} + +static int get_fec_or_pred(LPCNetPLCState *st, float *out) { + if (st->fec_read_pos != st->fec_fill_pos && st->fec_skip==0) { + float plc_features[2*NB_BANDS+NB_FEATURES+1] = {0}; + float discard[NB_FEATURES]; + OPUS_COPY(out, &st->fec[st->fec_read_pos][0], NB_FEATURES); + st->fec_read_pos++; + /* Update PLC state using FEC, so without Burg features. */ + OPUS_COPY(&plc_features[2*NB_BANDS], out, NB_FEATURES); + plc_features[2*NB_BANDS+NB_FEATURES] = -1; + compute_plc_pred(st, discard, plc_features); + return 1; + } else { + float zeros[2*NB_BANDS+NB_FEATURES+1] = {0}; + compute_plc_pred(st, out, zeros); + if (st->fec_skip > 0) st->fec_skip--; + return 0; + } +} + +static void queue_features(LPCNetPLCState *st, const float *features) { + OPUS_MOVE(&st->cont_features[0], &st->cont_features[NB_FEATURES], (CONT_VECTORS-1)*NB_FEATURES); + OPUS_COPY(&st->cont_features[(CONT_VECTORS-1)*NB_FEATURES], features, NB_FEATURES); +} + +/* In this causal version of the code, the DNN model implemented by compute_plc_pred() + needs to generate two feature vectors to conceal the first lost packet.*/ + +int lpcnet_plc_update(LPCNetPLCState *st, opus_int16 *pcm) { + int i; + if (st->analysis_pos - FRAME_SIZE >= 0) st->analysis_pos -= FRAME_SIZE; + else st->analysis_gap = 1; + if (st->predict_pos - FRAME_SIZE >= 0) st->predict_pos -= FRAME_SIZE; + OPUS_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE-FRAME_SIZE); + for (i=0;ipcm[PLC_BUF_SIZE-FRAME_SIZE+i] = (1.f/32768.f)*pcm[i]; + st->loss_count = 0; + st->blend = 0; + return 0; +} + +static const float att_table[10] = {0, 0, -.2, -.2, -.4, -.4, -.8, -.8, -1.6, -1.6}; +int lpcnet_plc_conceal(LPCNetPLCState *st, opus_int16 *pcm) { + int i; + celt_assert(st->loaded); + if (st->blend == 0) { + int count = 0; + st->plc_net = st->plc_bak[0]; + while (st->analysis_pos + FRAME_SIZE <= PLC_BUF_SIZE) { + float x[FRAME_SIZE]; + float plc_features[2*NB_BANDS+NB_FEATURES+1]; + celt_assert(st->analysis_pos >= 0); + for (i=0;ipcm[st->analysis_pos+i]; + burg_cepstral_analysis(plc_features, x); + lpcnet_compute_single_frame_features_float(&st->enc, x, st->features, st->arch); + if ((!st->analysis_gap || count>0) && st->analysis_pos >= st->predict_pos) { + queue_features(st, st->features); + OPUS_COPY(&plc_features[2*NB_BANDS], st->features, NB_FEATURES); + plc_features[2*NB_BANDS+NB_FEATURES] = 1; + st->plc_bak[0] = st->plc_bak[1]; + st->plc_bak[1] = st->plc_net; + compute_plc_pred(st, st->features, plc_features); + } + st->analysis_pos += FRAME_SIZE; + count++; + } + st->plc_bak[0] = st->plc_bak[1]; + st->plc_bak[1] = st->plc_net; + get_fec_or_pred(st, st->features); + queue_features(st, st->features); + st->plc_bak[0] = st->plc_bak[1]; + st->plc_bak[1] = st->plc_net; + get_fec_or_pred(st, st->features); + queue_features(st, st->features); + fargan_cont(&st->fargan, &st->pcm[PLC_BUF_SIZE-FARGAN_CONT_SAMPLES], st->cont_features); + st->analysis_gap = 0; + } + st->plc_bak[0] = st->plc_bak[1]; + st->plc_bak[1] = st->plc_net; + if (get_fec_or_pred(st, st->features)) st->loss_count = 0; + else st->loss_count++; + if (st->loss_count >= 10) st->features[0] = MAX16(-10, st->features[0]+att_table[9] - 2*(st->loss_count-9)); + else st->features[0] = MAX16(-10, st->features[0]+att_table[st->loss_count]); + fargan_synthesize_int(&st->fargan, pcm, &st->features[0]); + queue_features(st, st->features); + if (st->analysis_pos - FRAME_SIZE >= 0) st->analysis_pos -= FRAME_SIZE; + else st->analysis_gap = 1; + st->predict_pos = PLC_BUF_SIZE; + OPUS_MOVE(st->pcm, &st->pcm[FRAME_SIZE], PLC_BUF_SIZE-FRAME_SIZE); + for (i=0;ipcm[PLC_BUF_SIZE-FRAME_SIZE+i] = (1.f/32768.f)*pcm[i]; + st->blend = 1; + return 0; +} diff --git a/managed_components/78__esp-opus/dnn/lpcnet_private.h b/managed_components/78__esp-opus/dnn/lpcnet_private.h new file mode 100644 index 0000000..09db584 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/lpcnet_private.h @@ -0,0 +1,90 @@ +#ifndef LPCNET_PRIVATE_H +#define LPCNET_PRIVATE_H + +#include +#include "freq.h" +#include "lpcnet.h" +#include "plc_data.h" +#include "pitchdnn.h" +#include "fargan.h" + + +#define PITCH_FRAME_SIZE 320 +#define PITCH_BUF_SIZE (PITCH_MAX_PERIOD+PITCH_FRAME_SIZE) + +#define PLC_MAX_FEC 100 +#define MAX_FEATURE_BUFFER_SIZE 4 + +#define PITCH_IF_MAX_FREQ 30 +#define PITCH_IF_FEATURES (3*PITCH_IF_MAX_FREQ - 2) + +#define CONT_VECTORS 5 + +#define FEATURES_DELAY 1 + +struct LPCNetEncState{ + PitchDNNState pitchdnn; + float analysis_mem[OVERLAP_SIZE]; + float mem_preemph; + kiss_fft_cpx prev_if[PITCH_IF_MAX_FREQ]; + float if_features[PITCH_IF_FEATURES]; + float xcorr_features[PITCH_MAX_PERIOD - PITCH_MIN_PERIOD]; + float dnn_pitch; + float pitch_mem[LPC_ORDER]; + float pitch_filt; + float exc_buf[PITCH_BUF_SIZE]; + float lp_buf[PITCH_BUF_SIZE]; + float lp_mem[4]; + float lpc[LPC_ORDER]; + float features[NB_TOTAL_FEATURES]; + float sig_mem[LPC_ORDER]; + float burg_cepstrum[2*NB_BANDS]; +}; + +typedef struct { + float gru1_state[PLC_GRU1_STATE_SIZE]; + float gru2_state[PLC_GRU2_STATE_SIZE]; +} PLCNetState; + +#define PLC_BUF_SIZE ((CONT_VECTORS+10)*FRAME_SIZE) +struct LPCNetPLCState { + PLCModel model; + FARGANState fargan; + LPCNetEncState enc; + int loaded; + int arch; + +#define LPCNET_PLC_RESET_START fec + float fec[PLC_MAX_FEC][NB_FEATURES]; + int analysis_gap; + int fec_read_pos; + int fec_fill_pos; + int fec_skip; + int analysis_pos; + int predict_pos; + float pcm[PLC_BUF_SIZE]; + int blend; + float features[NB_TOTAL_FEATURES]; + float cont_features[CONT_VECTORS*NB_FEATURES]; + int loss_count; + PLCNetState plc_net; + PLCNetState plc_bak[2]; +}; + +void preemphasis(float *y, float *mem, const float *x, float coef, int N); + +void compute_frame_features(LPCNetEncState *st, const float *in, int arch); + +void lpcnet_reset_signal(LPCNetState *lpcnet); +void run_frame_network(LPCNetState *lpcnet, float *gru_a_condition, float *gru_b_condition, float *lpc, const float *features); +void run_frame_network_deferred(LPCNetState *lpcnet, const float *features); +void run_frame_network_flush(LPCNetState *lpcnet); + + +void lpcnet_synthesize_tail_impl(LPCNetState *lpcnet, opus_int16 *output, int N, int preload); +void lpcnet_synthesize_impl(LPCNetState *lpcnet, const float *features, opus_int16 *output, int N, int preload); +void lpcnet_synthesize_blend_impl(LPCNetState *lpcnet, const opus_int16 *pcm_in, opus_int16 *output, int N); + +void run_frame_network(LPCNetState *lpcnet, float *gru_a_condition, float *gru_b_condition, float *lpc, const float *features); + +#endif diff --git a/managed_components/78__esp-opus/dnn/lpcnet_tables.c b/managed_components/78__esp-opus/dnn/lpcnet_tables.c new file mode 100644 index 0000000..467cb47 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/lpcnet_tables.c @@ -0,0 +1,307 @@ +/* The contents of this file was automatically generated by dump_lpcnet_tables.c*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif +#include "kiss_fft.h" + +static const arch_fft_state arch_fft = {0, NULL}; + +static const opus_int16 fft_bitrev[320] = { +0, 64, 128, 192, 256, 16, 80, 144, 208, 272, 32, 96, 160, 224, 288, +48, 112, 176, 240, 304, 4, 68, 132, 196, 260, 20, 84, 148, 212, 276, +36, 100, 164, 228, 292, 52, 116, 180, 244, 308, 8, 72, 136, 200, 264, +24, 88, 152, 216, 280, 40, 104, 168, 232, 296, 56, 120, 184, 248, 312, +12, 76, 140, 204, 268, 28, 92, 156, 220, 284, 44, 108, 172, 236, 300, +60, 124, 188, 252, 316, 1, 65, 129, 193, 257, 17, 81, 145, 209, 273, +33, 97, 161, 225, 289, 49, 113, 177, 241, 305, 5, 69, 133, 197, 261, +21, 85, 149, 213, 277, 37, 101, 165, 229, 293, 53, 117, 181, 245, 309, +9, 73, 137, 201, 265, 25, 89, 153, 217, 281, 41, 105, 169, 233, 297, +57, 121, 185, 249, 313, 13, 77, 141, 205, 269, 29, 93, 157, 221, 285, +45, 109, 173, 237, 301, 61, 125, 189, 253, 317, 2, 66, 130, 194, 258, +18, 82, 146, 210, 274, 34, 98, 162, 226, 290, 50, 114, 178, 242, 306, +6, 70, 134, 198, 262, 22, 86, 150, 214, 278, 38, 102, 166, 230, 294, +54, 118, 182, 246, 310, 10, 74, 138, 202, 266, 26, 90, 154, 218, 282, +42, 106, 170, 234, 298, 58, 122, 186, 250, 314, 14, 78, 142, 206, 270, +30, 94, 158, 222, 286, 46, 110, 174, 238, 302, 62, 126, 190, 254, 318, +3, 67, 131, 195, 259, 19, 83, 147, 211, 275, 35, 99, 163, 227, 291, +51, 115, 179, 243, 307, 7, 71, 135, 199, 263, 23, 87, 151, 215, 279, +39, 103, 167, 231, 295, 55, 119, 183, 247, 311, 11, 75, 139, 203, 267, +27, 91, 155, 219, 283, 43, 107, 171, 235, 299, 59, 123, 187, 251, 315, +15, 79, 143, 207, 271, 31, 95, 159, 223, 287, 47, 111, 175, 239, 303, +63, 127, 191, 255, 319, }; + +static const kiss_twiddle_cpx fft_twiddles[320] = { +{1.00000000f, -0.00000000f}, {0.999807239f, -0.0196336918f}, +{0.999229014f, -0.0392598175f}, {0.998265624f, -0.0588708036f}, +{0.996917307f, -0.0784590989f}, {0.995184720f, -0.0980171412f}, +{0.993068457f, -0.117537394f}, {0.990569353f, -0.137012348f}, +{0.987688363f, -0.156434461f}, {0.984426558f, -0.175796285f}, +{0.980785251f, -0.195090324f}, {0.976765871f, -0.214309156f}, +{0.972369909f, -0.233445361f}, {0.967599094f, -0.252491564f}, +{0.962455213f, -0.271440446f}, {0.956940353f, -0.290284663f}, +{0.951056540f, -0.309017003f}, {0.944806039f, -0.327630192f}, +{0.938191354f, -0.346117049f}, {0.931214929f, -0.364470512f}, +{0.923879504f, -0.382683426f}, {0.916187942f, -0.400748819f}, +{0.908143163f, -0.418659747f}, {0.899748266f, -0.436409235f}, +{0.891006529f, -0.453990489f}, {0.881921291f, -0.471396744f}, +{0.872496009f, -0.488621235f}, {0.862734377f, -0.505657375f}, +{0.852640152f, -0.522498548f}, {0.842217207f, -0.539138317f}, +{0.831469595f, -0.555570245f}, {0.820401430f, -0.571787953f}, +{0.809017003f, -0.587785244f}, {0.797320664f, -0.603555918f}, +{0.785316944f, -0.619093955f}, {0.773010433f, -0.634393275f}, +{0.760405958f, -0.649448037f}, {0.747508347f, -0.664252460f}, +{0.734322488f, -0.678800762f}, {0.720853567f, -0.693087339f}, +{0.707106769f, -0.707106769f}, {0.693087339f, -0.720853567f}, +{0.678800762f, -0.734322488f}, {0.664252460f, -0.747508347f}, +{0.649448037f, -0.760405958f}, {0.634393275f, -0.773010433f}, +{0.619093955f, -0.785316944f}, {0.603555918f, -0.797320664f}, +{0.587785244f, -0.809017003f}, {0.571787953f, -0.820401430f}, +{0.555570245f, -0.831469595f}, {0.539138317f, -0.842217207f}, +{0.522498548f, -0.852640152f}, {0.505657375f, -0.862734377f}, +{0.488621235f, -0.872496009f}, {0.471396744f, -0.881921291f}, +{0.453990489f, -0.891006529f}, {0.436409235f, -0.899748266f}, +{0.418659747f, -0.908143163f}, {0.400748819f, -0.916187942f}, +{0.382683426f, -0.923879504f}, {0.364470512f, -0.931214929f}, +{0.346117049f, -0.938191354f}, {0.327630192f, -0.944806039f}, +{0.309017003f, -0.951056540f}, {0.290284663f, -0.956940353f}, +{0.271440446f, -0.962455213f}, {0.252491564f, -0.967599094f}, +{0.233445361f, -0.972369909f}, {0.214309156f, -0.976765871f}, +{0.195090324f, -0.980785251f}, {0.175796285f, -0.984426558f}, +{0.156434461f, -0.987688363f}, {0.137012348f, -0.990569353f}, +{0.117537394f, -0.993068457f}, {0.0980171412f, -0.995184720f}, +{0.0784590989f, -0.996917307f}, {0.0588708036f, -0.998265624f}, +{0.0392598175f, -0.999229014f}, {0.0196336918f, -0.999807239f}, +{6.12323426e-17f, -1.00000000f}, {-0.0196336918f, -0.999807239f}, +{-0.0392598175f, -0.999229014f}, {-0.0588708036f, -0.998265624f}, +{-0.0784590989f, -0.996917307f}, {-0.0980171412f, -0.995184720f}, +{-0.117537394f, -0.993068457f}, {-0.137012348f, -0.990569353f}, +{-0.156434461f, -0.987688363f}, {-0.175796285f, -0.984426558f}, +{-0.195090324f, -0.980785251f}, {-0.214309156f, -0.976765871f}, +{-0.233445361f, -0.972369909f}, {-0.252491564f, -0.967599094f}, +{-0.271440446f, -0.962455213f}, {-0.290284663f, -0.956940353f}, +{-0.309017003f, -0.951056540f}, {-0.327630192f, -0.944806039f}, +{-0.346117049f, -0.938191354f}, {-0.364470512f, -0.931214929f}, +{-0.382683426f, -0.923879504f}, {-0.400748819f, -0.916187942f}, +{-0.418659747f, -0.908143163f}, {-0.436409235f, -0.899748266f}, +{-0.453990489f, -0.891006529f}, {-0.471396744f, -0.881921291f}, +{-0.488621235f, -0.872496009f}, {-0.505657375f, -0.862734377f}, +{-0.522498548f, -0.852640152f}, {-0.539138317f, -0.842217207f}, +{-0.555570245f, -0.831469595f}, {-0.571787953f, -0.820401430f}, +{-0.587785244f, -0.809017003f}, {-0.603555918f, -0.797320664f}, +{-0.619093955f, -0.785316944f}, {-0.634393275f, -0.773010433f}, +{-0.649448037f, -0.760405958f}, {-0.664252460f, -0.747508347f}, +{-0.678800762f, -0.734322488f}, {-0.693087339f, -0.720853567f}, +{-0.707106769f, -0.707106769f}, {-0.720853567f, -0.693087339f}, +{-0.734322488f, -0.678800762f}, {-0.747508347f, -0.664252460f}, +{-0.760405958f, -0.649448037f}, {-0.773010433f, -0.634393275f}, +{-0.785316944f, -0.619093955f}, {-0.797320664f, -0.603555918f}, +{-0.809017003f, -0.587785244f}, {-0.820401430f, -0.571787953f}, +{-0.831469595f, -0.555570245f}, {-0.842217207f, -0.539138317f}, +{-0.852640152f, -0.522498548f}, {-0.862734377f, -0.505657375f}, +{-0.872496009f, -0.488621235f}, {-0.881921291f, -0.471396744f}, +{-0.891006529f, -0.453990489f}, {-0.899748266f, -0.436409235f}, +{-0.908143163f, -0.418659747f}, {-0.916187942f, -0.400748819f}, +{-0.923879504f, -0.382683426f}, {-0.931214929f, -0.364470512f}, +{-0.938191354f, -0.346117049f}, {-0.944806039f, -0.327630192f}, +{-0.951056540f, -0.309017003f}, {-0.956940353f, -0.290284663f}, +{-0.962455213f, -0.271440446f}, {-0.967599094f, -0.252491564f}, +{-0.972369909f, -0.233445361f}, {-0.976765871f, -0.214309156f}, +{-0.980785251f, -0.195090324f}, {-0.984426558f, -0.175796285f}, +{-0.987688363f, -0.156434461f}, {-0.990569353f, -0.137012348f}, +{-0.993068457f, -0.117537394f}, {-0.995184720f, -0.0980171412f}, +{-0.996917307f, -0.0784590989f}, {-0.998265624f, -0.0588708036f}, +{-0.999229014f, -0.0392598175f}, {-0.999807239f, -0.0196336918f}, +{-1.00000000f, -1.22464685e-16f}, {-0.999807239f, 0.0196336918f}, +{-0.999229014f, 0.0392598175f}, {-0.998265624f, 0.0588708036f}, +{-0.996917307f, 0.0784590989f}, {-0.995184720f, 0.0980171412f}, +{-0.993068457f, 0.117537394f}, {-0.990569353f, 0.137012348f}, +{-0.987688363f, 0.156434461f}, {-0.984426558f, 0.175796285f}, +{-0.980785251f, 0.195090324f}, {-0.976765871f, 0.214309156f}, +{-0.972369909f, 0.233445361f}, {-0.967599094f, 0.252491564f}, +{-0.962455213f, 0.271440446f}, {-0.956940353f, 0.290284663f}, +{-0.951056540f, 0.309017003f}, {-0.944806039f, 0.327630192f}, +{-0.938191354f, 0.346117049f}, {-0.931214929f, 0.364470512f}, +{-0.923879504f, 0.382683426f}, {-0.916187942f, 0.400748819f}, +{-0.908143163f, 0.418659747f}, {-0.899748266f, 0.436409235f}, +{-0.891006529f, 0.453990489f}, {-0.881921291f, 0.471396744f}, +{-0.872496009f, 0.488621235f}, {-0.862734377f, 0.505657375f}, +{-0.852640152f, 0.522498548f}, {-0.842217207f, 0.539138317f}, +{-0.831469595f, 0.555570245f}, {-0.820401430f, 0.571787953f}, +{-0.809017003f, 0.587785244f}, {-0.797320664f, 0.603555918f}, +{-0.785316944f, 0.619093955f}, {-0.773010433f, 0.634393275f}, +{-0.760405958f, 0.649448037f}, {-0.747508347f, 0.664252460f}, +{-0.734322488f, 0.678800762f}, {-0.720853567f, 0.693087339f}, +{-0.707106769f, 0.707106769f}, {-0.693087339f, 0.720853567f}, +{-0.678800762f, 0.734322488f}, {-0.664252460f, 0.747508347f}, +{-0.649448037f, 0.760405958f}, {-0.634393275f, 0.773010433f}, +{-0.619093955f, 0.785316944f}, {-0.603555918f, 0.797320664f}, +{-0.587785244f, 0.809017003f}, {-0.571787953f, 0.820401430f}, +{-0.555570245f, 0.831469595f}, {-0.539138317f, 0.842217207f}, +{-0.522498548f, 0.852640152f}, {-0.505657375f, 0.862734377f}, +{-0.488621235f, 0.872496009f}, {-0.471396744f, 0.881921291f}, +{-0.453990489f, 0.891006529f}, {-0.436409235f, 0.899748266f}, +{-0.418659747f, 0.908143163f}, {-0.400748819f, 0.916187942f}, +{-0.382683426f, 0.923879504f}, {-0.364470512f, 0.931214929f}, +{-0.346117049f, 0.938191354f}, {-0.327630192f, 0.944806039f}, +{-0.309017003f, 0.951056540f}, {-0.290284663f, 0.956940353f}, +{-0.271440446f, 0.962455213f}, {-0.252491564f, 0.967599094f}, +{-0.233445361f, 0.972369909f}, {-0.214309156f, 0.976765871f}, +{-0.195090324f, 0.980785251f}, {-0.175796285f, 0.984426558f}, +{-0.156434461f, 0.987688363f}, {-0.137012348f, 0.990569353f}, +{-0.117537394f, 0.993068457f}, {-0.0980171412f, 0.995184720f}, +{-0.0784590989f, 0.996917307f}, {-0.0588708036f, 0.998265624f}, +{-0.0392598175f, 0.999229014f}, {-0.0196336918f, 0.999807239f}, +{-1.83697015e-16f, 1.00000000f}, {0.0196336918f, 0.999807239f}, +{0.0392598175f, 0.999229014f}, {0.0588708036f, 0.998265624f}, +{0.0784590989f, 0.996917307f}, {0.0980171412f, 0.995184720f}, +{0.117537394f, 0.993068457f}, {0.137012348f, 0.990569353f}, +{0.156434461f, 0.987688363f}, {0.175796285f, 0.984426558f}, +{0.195090324f, 0.980785251f}, {0.214309156f, 0.976765871f}, +{0.233445361f, 0.972369909f}, {0.252491564f, 0.967599094f}, +{0.271440446f, 0.962455213f}, {0.290284663f, 0.956940353f}, +{0.309017003f, 0.951056540f}, {0.327630192f, 0.944806039f}, +{0.346117049f, 0.938191354f}, {0.364470512f, 0.931214929f}, +{0.382683426f, 0.923879504f}, {0.400748819f, 0.916187942f}, +{0.418659747f, 0.908143163f}, {0.436409235f, 0.899748266f}, +{0.453990489f, 0.891006529f}, {0.471396744f, 0.881921291f}, +{0.488621235f, 0.872496009f}, {0.505657375f, 0.862734377f}, +{0.522498548f, 0.852640152f}, {0.539138317f, 0.842217207f}, +{0.555570245f, 0.831469595f}, {0.571787953f, 0.820401430f}, +{0.587785244f, 0.809017003f}, {0.603555918f, 0.797320664f}, +{0.619093955f, 0.785316944f}, {0.634393275f, 0.773010433f}, +{0.649448037f, 0.760405958f}, {0.664252460f, 0.747508347f}, +{0.678800762f, 0.734322488f}, {0.693087339f, 0.720853567f}, +{0.707106769f, 0.707106769f}, {0.720853567f, 0.693087339f}, +{0.734322488f, 0.678800762f}, {0.747508347f, 0.664252460f}, +{0.760405958f, 0.649448037f}, {0.773010433f, 0.634393275f}, +{0.785316944f, 0.619093955f}, {0.797320664f, 0.603555918f}, +{0.809017003f, 0.587785244f}, {0.820401430f, 0.571787953f}, +{0.831469595f, 0.555570245f}, {0.842217207f, 0.539138317f}, +{0.852640152f, 0.522498548f}, {0.862734377f, 0.505657375f}, +{0.872496009f, 0.488621235f}, {0.881921291f, 0.471396744f}, +{0.891006529f, 0.453990489f}, {0.899748266f, 0.436409235f}, +{0.908143163f, 0.418659747f}, {0.916187942f, 0.400748819f}, +{0.923879504f, 0.382683426f}, {0.931214929f, 0.364470512f}, +{0.938191354f, 0.346117049f}, {0.944806039f, 0.327630192f}, +{0.951056540f, 0.309017003f}, {0.956940353f, 0.290284663f}, +{0.962455213f, 0.271440446f}, {0.967599094f, 0.252491564f}, +{0.972369909f, 0.233445361f}, {0.976765871f, 0.214309156f}, +{0.980785251f, 0.195090324f}, {0.984426558f, 0.175796285f}, +{0.987688363f, 0.156434461f}, {0.990569353f, 0.137012348f}, +{0.993068457f, 0.117537394f}, {0.995184720f, 0.0980171412f}, +{0.996917307f, 0.0784590989f}, {0.998265624f, 0.0588708036f}, +{0.999229014f, 0.0392598175f}, {0.999807239f, 0.0196336918f}, +}; + +const kiss_fft_state kfft = { +320, /* nfft */ +0.0031250000f, /* scale */ +-1, /* shift */ +{5, 64, 4, 16, 4, 4, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, /* factors */ +fft_bitrev, /* bitrev*/ +fft_twiddles, /* twiddles*/ +(arch_fft_state *)&arch_fft, /* arch_fft*/ +}; + +const float half_window[] = { +3.78491532e-05f, 0.000340620492f, 0.000946046319f, 0.00185389258f, 0.00306380726f, +0.00457531959f, 0.00638783723f, 0.00850064680f, 0.0109129101f, 0.0136236614f, +0.0166318044f, 0.0199361145f, 0.0235352255f, 0.0274276342f, 0.0316116922f, +0.0360856056f, 0.0408474281f, 0.0458950549f, 0.0512262285f, 0.0568385124f, +0.0627293140f, 0.0688958541f, 0.0753351897f, 0.0820441842f, 0.0890194997f, +0.0962576419f, 0.103754878f, 0.111507311f, 0.119510807f, 0.127761051f, +0.136253506f, 0.144983411f, 0.153945804f, 0.163135484f, 0.172547072f, +0.182174906f, 0.192013159f, 0.202055752f, 0.212296382f, 0.222728521f, +0.233345464f, 0.244140238f, 0.255105674f, 0.266234398f, 0.277518868f, +0.288951218f, 0.300523549f, 0.312227666f, 0.324055225f, 0.335997701f, +0.348046392f, 0.360192508f, 0.372427016f, 0.384740859f, 0.397124738f, +0.409569323f, 0.422065198f, 0.434602767f, 0.447172493f, 0.459764689f, +0.472369671f, 0.484977663f, 0.497579008f, 0.510163903f, 0.522722721f, +0.535245717f, 0.547723293f, 0.560145974f, 0.572504222f, 0.584788740f, +0.596990347f, 0.609099925f, 0.621108532f, 0.633007407f, 0.644788086f, +0.656442165f, 0.667961538f, 0.679338276f, 0.690564752f, 0.701633692f, +0.712537885f, 0.723270535f, 0.733825266f, 0.744195819f, 0.754376352f, +0.764361382f, 0.774145722f, 0.783724606f, 0.793093503f, 0.802248418f, +0.811185598f, 0.819901764f, 0.828393936f, 0.836659551f, 0.844696403f, +0.852502763f, 0.860077202f, 0.867418647f, 0.874526560f, 0.881400526f, +0.888040781f, 0.894447744f, 0.900622249f, 0.906565487f, 0.912279010f, +0.917764664f, 0.923024654f, 0.928061485f, 0.932878017f, 0.937477291f, +0.941862822f, 0.946038187f, 0.950007319f, 0.953774393f, 0.957343817f, +0.960720181f, 0.963908315f, 0.966913164f, 0.969739914f, 0.972393870f, +0.974880517f, 0.977205336f, 0.979374051f, 0.981392324f, 0.983266115f, +0.985001266f, 0.986603677f, 0.988079309f, 0.989434063f, 0.990674019f, +0.991804957f, 0.992832899f, 0.993763626f, 0.994602919f, 0.995356441f, +0.996029854f, 0.996628702f, 0.997158289f, 0.997623861f, 0.998030603f, +0.998383403f, 0.998687088f, 0.998946249f, 0.999165416f, 0.999348700f, +0.999500215f, 0.999623775f, 0.999723017f, 0.999801278f, 0.999861658f, +0.999907196f, 0.999940455f, 0.999963880f, 0.999979615f, 0.999989510f, +0.999995291f, 0.999998271f, 0.999999523f, 0.999999940f, 1.00000000f, +}; + +const float dct_table[] = { +0.707106769f, 0.996194720f, 0.984807730f, 0.965925813f, 0.939692616f, +0.906307817f, 0.866025388f, 0.819152057f, 0.766044438f, 0.707106769f, +0.642787635f, 0.573576450f, 0.500000000f, 0.422618270f, 0.342020154f, +0.258819044f, 0.173648179f, 0.0871557444f, 0.707106769f, 0.965925813f, +0.866025388f, 0.707106769f, 0.500000000f, 0.258819044f, 6.12323426e-17f, +-0.258819044f, -0.500000000f, -0.707106769f, -0.866025388f, -0.965925813f, +-1.00000000f, -0.965925813f, -0.866025388f, -0.707106769f, -0.500000000f, +-0.258819044f, 0.707106769f, 0.906307817f, 0.642787635f, 0.258819044f, +-0.173648179f, -0.573576450f, -0.866025388f, -0.996194720f, -0.939692616f, +-0.707106769f, -0.342020154f, 0.0871557444f, 0.500000000f, 0.819152057f, +0.984807730f, 0.965925813f, 0.766044438f, 0.422618270f, 0.707106769f, +0.819152057f, 0.342020154f, -0.258819044f, -0.766044438f, -0.996194720f, +-0.866025388f, -0.422618270f, 0.173648179f, 0.707106769f, 0.984807730f, +0.906307817f, 0.500000000f, -0.0871557444f, -0.642787635f, -0.965925813f, +-0.939692616f, -0.573576450f, 0.707106769f, 0.707106769f, 6.12323426e-17f, +-0.707106769f, -1.00000000f, -0.707106769f, -1.83697015e-16f, 0.707106769f, +1.00000000f, 0.707106769f, 3.06161700e-16f, -0.707106769f, -1.00000000f, +-0.707106769f, -4.28626385e-16f, 0.707106769f, 1.00000000f, 0.707106769f, +0.707106769f, 0.573576450f, -0.342020154f, -0.965925813f, -0.766044438f, +0.0871557444f, 0.866025388f, 0.906307817f, 0.173648179f, -0.707106769f, +-0.984807730f, -0.422618270f, 0.500000000f, 0.996194720f, 0.642787635f, +-0.258819044f, -0.939692616f, -0.819152057f, 0.707106769f, 0.422618270f, +-0.642787635f, -0.965925813f, -0.173648179f, 0.819152057f, 0.866025388f, +-0.0871557444f, -0.939692616f, -0.707106769f, 0.342020154f, 0.996194720f, +0.500000000f, -0.573576450f, -0.984807730f, -0.258819044f, 0.766044438f, +0.906307817f, 0.707106769f, 0.258819044f, -0.866025388f, -0.707106769f, +0.500000000f, 0.965925813f, 3.06161700e-16f, -0.965925813f, -0.500000000f, +0.707106769f, 0.866025388f, -0.258819044f, -1.00000000f, -0.258819044f, +0.866025388f, 0.707106769f, -0.500000000f, -0.965925813f, 0.707106769f, +0.0871557444f, -0.984807730f, -0.258819044f, 0.939692616f, 0.422618270f, +-0.866025388f, -0.573576450f, 0.766044438f, 0.707106769f, -0.642787635f, +-0.819152057f, 0.500000000f, 0.906307817f, -0.342020154f, -0.965925813f, +0.173648179f, 0.996194720f, 0.707106769f, -0.0871557444f, -0.984807730f, +0.258819044f, 0.939692616f, -0.422618270f, -0.866025388f, 0.573576450f, +0.766044438f, -0.707106769f, -0.642787635f, 0.819152057f, 0.500000000f, +-0.906307817f, -0.342020154f, 0.965925813f, 0.173648179f, -0.996194720f, +0.707106769f, -0.258819044f, -0.866025388f, 0.707106769f, 0.500000000f, +-0.965925813f, -4.28626385e-16f, 0.965925813f, -0.500000000f, -0.707106769f, +0.866025388f, 0.258819044f, -1.00000000f, 0.258819044f, 0.866025388f, +-0.707106769f, -0.500000000f, 0.965925813f, 0.707106769f, -0.422618270f, +-0.642787635f, 0.965925813f, -0.173648179f, -0.819152057f, 0.866025388f, +0.0871557444f, -0.939692616f, 0.707106769f, 0.342020154f, -0.996194720f, +0.500000000f, 0.573576450f, -0.984807730f, 0.258819044f, 0.766044438f, +-0.906307817f, 0.707106769f, -0.573576450f, -0.342020154f, 0.965925813f, +-0.766044438f, -0.0871557444f, 0.866025388f, -0.906307817f, 0.173648179f, +0.707106769f, -0.984807730f, 0.422618270f, 0.500000000f, -0.996194720f, +0.642787635f, 0.258819044f, -0.939692616f, 0.819152057f, 0.707106769f, +-0.707106769f, -1.83697015e-16f, 0.707106769f, -1.00000000f, 0.707106769f, +5.51091070e-16f, -0.707106769f, 1.00000000f, -0.707106769f, -2.69484189e-15f, +0.707106769f, -1.00000000f, 0.707106769f, -4.90477710e-16f, -0.707106769f, +1.00000000f, -0.707106769f, 0.707106769f, -0.819152057f, 0.342020154f, +0.258819044f, -0.766044438f, 0.996194720f, -0.866025388f, 0.422618270f, +0.173648179f, -0.707106769f, 0.984807730f, -0.906307817f, 0.500000000f, +0.0871557444f, -0.642787635f, 0.965925813f, -0.939692616f, 0.573576450f, +0.707106769f, -0.906307817f, 0.642787635f, -0.258819044f, -0.173648179f, +0.573576450f, -0.866025388f, 0.996194720f, -0.939692616f, 0.707106769f, +-0.342020154f, -0.0871557444f, 0.500000000f, -0.819152057f, 0.984807730f, +-0.965925813f, 0.766044438f, -0.422618270f, 0.707106769f, -0.965925813f, +0.866025388f, -0.707106769f, 0.500000000f, -0.258819044f, 1.10280111e-15f, +0.258819044f, -0.500000000f, 0.707106769f, -0.866025388f, 0.965925813f, +-1.00000000f, 0.965925813f, -0.866025388f, 0.707106769f, -0.500000000f, +0.258819044f, 0.707106769f, -0.996194720f, 0.984807730f, -0.965925813f, +0.939692616f, -0.906307817f, 0.866025388f, -0.819152057f, 0.766044438f, +-0.707106769f, 0.642787635f, -0.573576450f, 0.500000000f, -0.422618270f, +0.342020154f, -0.258819044f, 0.173648179f, -0.0871557444f, }; diff --git a/managed_components/78__esp-opus/dnn/meson.build b/managed_components/78__esp-opus/dnn/meson.build new file mode 100644 index 0000000..7b32401 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/meson.build @@ -0,0 +1,64 @@ +dnn_sources = sources['DEEP_PLC_SOURCES'] + +dred_sources = sources['DRED_SOURCES'] +if opt_dred.enabled() + dnn_sources += dred_sources +endif + +osce_sources = sources['OSCE_SOURCES'] +if opt_osce.enabled() + dnn_sources += osce_sources +endif + +dnn_sources_sse2 = sources['DNN_SOURCES_SSE2'] +dnn_sources_sse4_1 = sources['DNN_SOURCES_SSE4_1'] +dnn_sources_avx2 = sources['DNN_SOURCES_AVX2'] + +dnn_sources_neon_intr = sources['DNN_SOURCES_NEON'] +dnn_sources_dotprod_intr = sources['DNN_SOURCES_DOTPROD'] + +dnn_includes = [opus_includes] +dnn_static_libs = [] + +if host_cpu_family in ['x86', 'x86_64'] and opus_conf.has('OPUS_HAVE_RTCD') + dnn_sources += sources['DNN_SOURCES_X86_RTCD'] +endif + +if host_cpu_family in ['arm', 'aarch64'] and have_arm_intrinsics_or_asm + if opus_conf.has('OPUS_HAVE_RTCD') + dnn_sources += sources['DNN_SOURCES_ARM_RTCD'] + endif +endif + +foreach intr_name : ['sse2', 'sse4_1', 'avx2', 'neon_intr', 'dotprod_intr'] + have_intr = get_variable('have_' + intr_name) + if not have_intr + continue + endif + + intr_sources = get_variable('dnn_sources_' + intr_name) + + intr_args = get_variable('opus_@0@_args'.format(intr_name), []) + dnn_static_libs += static_library('dnn_' + intr_name, intr_sources, + c_args: intr_args, + include_directories: dnn_includes, + install: false) +endforeach + +dnn_c_args = [] +if host_machine.system() == 'windows' + dnn_c_args += ['-DDLL_EXPORT'] +endif + + +if opt_deep_plc.enabled() + dnn_lib = static_library('opus-dnn', + dnn_sources, + c_args: dnn_c_args, + include_directories: dnn_includes, + link_whole: [dnn_static_libs], + dependencies: libm, + install: false) +else + dnn_lib = [] +endif diff --git a/managed_components/78__esp-opus/dnn/nndsp.c b/managed_components/78__esp-opus/dnn/nndsp.c new file mode 100644 index 0000000..caa7703 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nndsp.c @@ -0,0 +1,416 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + + +#include "nndsp.h" +#include "arch.h" +#include "nnet.h" +#include "os_support.h" +#include "pitch.h" + +#include + +#ifndef M_PI +#define M_PI 3.141592653589793f +#endif + +#define KERNEL_INDEX(i_out_channels, i_in_channels, i_kernel) ((((i_out_channels) * in_channels) + (i_in_channels)) * kernel_size + (i_kernel)) + +void init_adaconv_state(AdaConvState *hAdaConv) +{ + OPUS_CLEAR(hAdaConv, 1); +} + +void init_adacomb_state(AdaCombState *hAdaComb) +{ + OPUS_CLEAR(hAdaComb, 1); +} + +void init_adashape_state(AdaShapeState *hAdaShape) +{ + OPUS_CLEAR(hAdaShape, 1); +} + +void compute_overlap_window(float *window, int overlap_size) +{ + int i_sample; + for (i_sample=0; i_sample < overlap_size; i_sample++) + { + window[i_sample] = 0.5f + 0.5f * cos(M_PI * (i_sample + 0.5f) / overlap_size); + } +} + +#ifdef DEBUG_NNDSP +void print_float_vector(const char* name, const float *vec, int length) +{ + for (int i = 0; i < length; i ++) + { + printf("%s[%d]: %f\n", name, i, vec[i]); + } +} +#endif + +static void scale_kernel( + float *kernel, + int in_channels, + int out_channels, + int kernel_size, + float *gain +) +/* normalizes (p-norm) kernel over input channel and kernel dimension */ +{ + float norm; + int i_in_channels, i_out_channels, i_kernel; + + for (i_out_channels = 0; i_out_channels < out_channels; i_out_channels++) + { + norm = 0; + for (i_in_channels = 0; i_in_channels < in_channels; i_in_channels ++) + { + for (i_kernel = 0; i_kernel < kernel_size; i_kernel++) + { + norm += kernel[KERNEL_INDEX(i_out_channels, i_in_channels, i_kernel)] * kernel[KERNEL_INDEX(i_out_channels, i_in_channels, i_kernel)]; + } + } +#ifdef DEBUG_NNDSP + printf("kernel norm: %f, %f\n", norm, sqrt(norm)); +#endif + norm = 1.f / (1e-6f + sqrt(norm)); + for (i_in_channels = 0; i_in_channels < in_channels; i_in_channels++) + { + for (i_kernel = 0; i_kernel < kernel_size; i_kernel++) + { + + kernel[KERNEL_INDEX(i_out_channels, i_in_channels, i_kernel)] *= norm * gain[i_out_channels]; + } + } + } +} + +static void transform_gains( + float *gains, + int num_gains, + float filter_gain_a, + float filter_gain_b +) +{ + int i; + for (i = 0; i < num_gains; i++) + { + gains[i] = exp(filter_gain_a * gains[i] + filter_gain_b); + } +} + +void adaconv_process_frame( + AdaConvState* hAdaConv, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *kernel_layer, + const LinearLayer *gain_layer, + int feature_dim, + int frame_size, + int overlap_size, + int in_channels, + int out_channels, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float shape_gain, + float *window, + int arch +) +{ + float output_buffer[ADACONV_MAX_FRAME_SIZE * ADACONV_MAX_OUTPUT_CHANNELS]; + float kernel_buffer[ADACONV_MAX_KERNEL_SIZE * ADACONV_MAX_INPUT_CHANNELS * ADACONV_MAX_OUTPUT_CHANNELS]; + float input_buffer[ADACONV_MAX_INPUT_CHANNELS * (ADACONV_MAX_FRAME_SIZE + ADACONV_MAX_KERNEL_SIZE)]; + float kernel0[ADACONV_MAX_KERNEL_SIZE]; + float kernel1[ADACONV_MAX_KERNEL_SIZE]; + float channel_buffer0[ADACONV_MAX_OVERLAP_SIZE]; + float channel_buffer1[ADACONV_MAX_FRAME_SIZE]; + float gain_buffer[ADACONV_MAX_OUTPUT_CHANNELS]; + float *p_input; + int i_in_channels, i_out_channels, i_sample; + + (void) feature_dim; /* ToDo: figure out whether we might need this information */ + + celt_assert(shape_gain == 1); + celt_assert(left_padding == kernel_size - 1); /* currently only supports causal version. Non-causal version not difficult to implement but will require third loop */ + celt_assert(kernel_size < frame_size); + + OPUS_CLEAR(output_buffer, ADACONV_MAX_FRAME_SIZE * ADACONV_MAX_OUTPUT_CHANNELS); + OPUS_CLEAR(kernel_buffer, ADACONV_MAX_KERNEL_SIZE * ADACONV_MAX_INPUT_CHANNELS * ADACONV_MAX_OUTPUT_CHANNELS); + OPUS_CLEAR(input_buffer, ADACONV_MAX_INPUT_CHANNELS * (ADACONV_MAX_FRAME_SIZE + ADACONV_MAX_KERNEL_SIZE)); + +#ifdef DEBUG_NNDSP + print_float_vector("x_in", x_in, in_channels * frame_size); +#endif + + /* prepare input */ + for (i_in_channels=0; i_in_channels < in_channels; i_in_channels ++) + { + OPUS_COPY(input_buffer + i_in_channels * (kernel_size + frame_size), hAdaConv->history + i_in_channels * kernel_size, kernel_size); + OPUS_COPY(input_buffer + kernel_size + i_in_channels * (kernel_size + frame_size), x_in + frame_size * i_in_channels, frame_size); + } + p_input = input_buffer + kernel_size; + + + /* calculate new kernel and new gain */ + compute_generic_dense(kernel_layer, kernel_buffer, features, ACTIVATION_LINEAR, arch); + compute_generic_dense(gain_layer, gain_buffer, features, ACTIVATION_TANH, arch); +#ifdef DEBUG_NNDSP + print_float_vector("features", features, feature_dim); + print_float_vector("adaconv_kernel_raw", kernel_buffer, in_channels * out_channels * kernel_size); + print_float_vector("adaconv_gain_raw", gain_buffer, out_channels); +#endif + transform_gains(gain_buffer, out_channels, filter_gain_a, filter_gain_b); + scale_kernel(kernel_buffer, in_channels, out_channels, kernel_size, gain_buffer); + +#ifdef DEBUG_NNDSP + print_float_vector("adaconv_kernel", kernel_buffer, in_channels * out_channels * kernel_size); + print_float_vector("adaconv_gain", gain_buffer, out_channels); +#endif + + /* calculate overlapping part using kernel from last frame */ + + for (i_out_channels = 0; i_out_channels < out_channels; i_out_channels++) + { + for (i_in_channels = 0; i_in_channels < in_channels; i_in_channels++) + { + OPUS_CLEAR(kernel0, ADACONV_MAX_KERNEL_SIZE); + OPUS_CLEAR(kernel1, ADACONV_MAX_KERNEL_SIZE); + + OPUS_COPY(kernel0, hAdaConv->last_kernel + KERNEL_INDEX(i_out_channels, i_in_channels, 0), kernel_size); + OPUS_COPY(kernel1, kernel_buffer + KERNEL_INDEX(i_out_channels, i_in_channels, 0), kernel_size); + celt_pitch_xcorr(kernel0, p_input + i_in_channels * (frame_size + kernel_size) - left_padding, channel_buffer0, ADACONV_MAX_KERNEL_SIZE, overlap_size, arch); + celt_pitch_xcorr(kernel1, p_input + i_in_channels * (frame_size + kernel_size) - left_padding, channel_buffer1, ADACONV_MAX_KERNEL_SIZE, frame_size, arch); + for (i_sample = 0; i_sample < overlap_size; i_sample++) + { + output_buffer[i_sample + i_out_channels * frame_size] += window[i_sample] * channel_buffer0[i_sample]; + output_buffer[i_sample + i_out_channels * frame_size] += (1.f - window[i_sample]) * channel_buffer1[i_sample]; + } + for (i_sample = overlap_size; i_sample < frame_size; i_sample++) + { + output_buffer[i_sample + i_out_channels * frame_size] += channel_buffer1[i_sample]; + } + } + } + + OPUS_COPY(x_out, output_buffer, out_channels * frame_size); + +#ifdef DEBUG_NNDSP + print_float_vector("x_out", x_out, out_channels * frame_size); +#endif + + /* buffer update */ + for (i_in_channels=0; i_in_channels < in_channels; i_in_channels ++) + { + OPUS_COPY(hAdaConv->history + i_in_channels * kernel_size, p_input + i_in_channels * (frame_size + kernel_size) + frame_size - kernel_size, kernel_size); + } + OPUS_COPY(hAdaConv->last_kernel, kernel_buffer, kernel_size * in_channels * out_channels); +} + +void adacomb_process_frame( + AdaCombState* hAdaComb, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *kernel_layer, + const LinearLayer *gain_layer, + const LinearLayer *global_gain_layer, + int pitch_lag, + int feature_dim, + int frame_size, + int overlap_size, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float log_gain_limit, + float *window, + int arch +) +{ + float output_buffer[ADACOMB_MAX_FRAME_SIZE]; + float output_buffer_last[ADACOMB_MAX_FRAME_SIZE]; + float kernel_buffer[ADACOMB_MAX_KERNEL_SIZE]; + float input_buffer[ADACOMB_MAX_FRAME_SIZE + ADACOMB_MAX_LAG + ADACOMB_MAX_KERNEL_SIZE]; + float gain, global_gain; + float *p_input; + int i_sample; + float kernel[16]; + float last_kernel[16]; + + (void) feature_dim; /* ToDo: figure out whether we might need this information */ + + OPUS_CLEAR(output_buffer, ADACOMB_MAX_FRAME_SIZE); + OPUS_CLEAR(kernel_buffer, ADACOMB_MAX_KERNEL_SIZE); + OPUS_CLEAR(input_buffer, ADACOMB_MAX_FRAME_SIZE + ADACOMB_MAX_LAG + ADACOMB_MAX_KERNEL_SIZE); + + OPUS_COPY(input_buffer, hAdaComb->history, kernel_size + ADACOMB_MAX_LAG); + OPUS_COPY(input_buffer + kernel_size + ADACOMB_MAX_LAG, x_in, frame_size); + p_input = input_buffer + kernel_size + ADACOMB_MAX_LAG; + + /* calculate new kernel and new gain */ + compute_generic_dense(kernel_layer, kernel_buffer, features, ACTIVATION_LINEAR, arch); + compute_generic_dense(gain_layer, &gain, features, ACTIVATION_RELU, arch); + compute_generic_dense(global_gain_layer, &global_gain, features, ACTIVATION_TANH, arch); +#ifdef DEBUG_NNDSP + print_float_vector("features", features, feature_dim); + print_float_vector("adacomb_kernel_raw", kernel_buffer, kernel_size); + print_float_vector("adacomb_gain_raw", &gain, 1); + print_float_vector("adacomb_global_gain_raw", &global_gain, 1); +#endif + gain = exp(log_gain_limit - gain); + global_gain = exp(filter_gain_a * global_gain + filter_gain_b); + scale_kernel(kernel_buffer, 1, 1, kernel_size, &gain); + +#ifdef DEBUG_NNDSP + print_float_vector("adacomb_kernel", kernel_buffer, kernel_size); + print_float_vector("adacomb_gain", &gain, 1); +#endif + + OPUS_CLEAR(kernel, ADACOMB_MAX_KERNEL_SIZE); + OPUS_CLEAR(last_kernel, ADACOMB_MAX_KERNEL_SIZE); + OPUS_COPY(kernel, kernel_buffer, kernel_size); + OPUS_COPY(last_kernel, hAdaComb->last_kernel, kernel_size); + + celt_pitch_xcorr(last_kernel, &p_input[- left_padding - hAdaComb->last_pitch_lag], output_buffer_last, ADACOMB_MAX_KERNEL_SIZE, overlap_size, arch); + + celt_pitch_xcorr(kernel, &p_input[- left_padding - pitch_lag], output_buffer, ADACOMB_MAX_KERNEL_SIZE, frame_size, arch); + for (i_sample = 0; i_sample < overlap_size; i_sample++) + { + output_buffer[i_sample] = hAdaComb->last_global_gain * window[i_sample] * output_buffer_last[i_sample] + global_gain * (1.f - window[i_sample]) * output_buffer[i_sample]; + } + + for (i_sample = 0; i_sample < overlap_size; i_sample++) + { + output_buffer[i_sample] += (window[i_sample] * hAdaComb->last_global_gain + (1.f - window[i_sample]) * global_gain) * p_input[i_sample]; + } + + for (i_sample = overlap_size; i_sample < frame_size; i_sample++) + { + output_buffer[i_sample] = global_gain * (output_buffer[i_sample] + p_input[i_sample]); + } + OPUS_COPY(x_out, output_buffer, frame_size); + +#ifdef DEBUG_NNDSP + print_float_vector("x_out", x_out, frame_size); +#endif + + /* buffer update */ + OPUS_COPY(hAdaComb->last_kernel, kernel_buffer, kernel_size); + OPUS_COPY(hAdaComb->history, p_input + frame_size - kernel_size - ADACOMB_MAX_LAG, kernel_size + ADACOMB_MAX_LAG); + hAdaComb->last_pitch_lag = pitch_lag; + hAdaComb->last_global_gain = global_gain; +} + + +void adashape_process_frame( + AdaShapeState *hAdaShape, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *alpha1f, + const LinearLayer *alpha1t, + const LinearLayer *alpha2, + int feature_dim, + int frame_size, + int avg_pool_k, + int arch +) +{ + float in_buffer[ADASHAPE_MAX_INPUT_DIM + ADASHAPE_MAX_FRAME_SIZE]; + float out_buffer[ADASHAPE_MAX_FRAME_SIZE]; + float tmp_buffer[ADASHAPE_MAX_FRAME_SIZE]; + int i, k; + int tenv_size; + float mean; + float *tenv; + + celt_assert(frame_size % avg_pool_k == 0); + celt_assert(feature_dim + frame_size / avg_pool_k + 1 < ADASHAPE_MAX_INPUT_DIM); + + tenv_size = frame_size / avg_pool_k; + tenv = in_buffer + feature_dim; + OPUS_CLEAR(tenv, tenv_size + 1); + + OPUS_COPY(in_buffer, features, feature_dim); + + /* calculate temporal envelope */ + mean = 0; + for (i = 0; i < tenv_size; i++) + { + for (k = 0; k < avg_pool_k; k++) + { + tenv[i] += fabs(x_in[i * avg_pool_k + k]); + } + tenv[i] = log(tenv[i] / avg_pool_k + 1.52587890625e-05f); + mean += tenv[i]; + } + mean /= tenv_size; + for (i = 0; i < tenv_size; i++) + { + tenv[i] -= mean; + } + tenv[tenv_size] = mean; +#ifdef DEBUG_NNDSP + print_float_vector("tenv", tenv, tenv_size + 1); +#endif + + /* calculate temporal weights */ +#ifdef DEBUG_NNDSP + print_float_vector("alpha1_in", in_buffer, feature_dim + tenv_size + 1); +#endif + compute_generic_conv1d(alpha1f, out_buffer, hAdaShape->conv_alpha1f_state, in_buffer, feature_dim, ACTIVATION_LINEAR, arch); + compute_generic_conv1d(alpha1t, tmp_buffer, hAdaShape->conv_alpha1t_state, tenv, tenv_size + 1, ACTIVATION_LINEAR, arch); +#ifdef DEBUG_NNDSP + print_float_vector("alpha1_out", out_buffer, frame_size); +#endif + /* compute leaky ReLU by hand. ToDo: try tanh activation */ + for (i = 0; i < frame_size; i ++) + { + float tmp = out_buffer[i] + tmp_buffer[i]; + in_buffer[i] = tmp >= 0 ? tmp : 0.2 * tmp; + } +#ifdef DEBUG_NNDSP + print_float_vector("post_alpha1", in_buffer, frame_size); +#endif + compute_generic_conv1d(alpha2, out_buffer, hAdaShape->conv_alpha2_state, in_buffer, frame_size, ACTIVATION_LINEAR, arch); + + /* shape signal */ + for (i = 0; i < frame_size; i ++) + { + x_out[i] = exp(out_buffer[i]) * x_in[i]; + } + +} diff --git a/managed_components/78__esp-opus/dnn/nndsp.h b/managed_components/78__esp-opus/dnn/nndsp.h new file mode 100644 index 0000000..6021250 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nndsp.h @@ -0,0 +1,143 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef NNDSP_H +#define NNDSP_H + +#include "opus_types.h" +#include "nnet.h" +#include + + +#define ADACONV_MAX_KERNEL_SIZE 16 +#define ADACONV_MAX_INPUT_CHANNELS 2 +#define ADACONV_MAX_OUTPUT_CHANNELS 2 +#define ADACONV_MAX_FRAME_SIZE 80 +#define ADACONV_MAX_OVERLAP_SIZE 40 + +#define ADACOMB_MAX_LAG 300 +#define ADACOMB_MAX_KERNEL_SIZE 16 +#define ADACOMB_MAX_FRAME_SIZE 80 +#define ADACOMB_MAX_OVERLAP_SIZE 40 + +#define ADASHAPE_MAX_INPUT_DIM 512 +#define ADASHAPE_MAX_FRAME_SIZE 160 + +/*#define DEBUG_NNDSP*/ +#ifdef DEBUG_NNDSP +#include +#endif + + +void print_float_vector(const char* name, const float *vec, int length); + +typedef struct { + float history[ADACONV_MAX_KERNEL_SIZE * ADACONV_MAX_INPUT_CHANNELS]; + float last_kernel[ADACONV_MAX_KERNEL_SIZE * ADACONV_MAX_INPUT_CHANNELS * ADACONV_MAX_OUTPUT_CHANNELS]; + float last_gain; +} AdaConvState; + + +typedef struct { + float history[ADACOMB_MAX_KERNEL_SIZE + ADACOMB_MAX_LAG]; + float last_kernel[ADACOMB_MAX_KERNEL_SIZE]; + float last_global_gain; + int last_pitch_lag; +} AdaCombState; + + +typedef struct { + float conv_alpha1f_state[ADASHAPE_MAX_INPUT_DIM]; + float conv_alpha1t_state[ADASHAPE_MAX_INPUT_DIM]; + float conv_alpha2_state[ADASHAPE_MAX_FRAME_SIZE]; +} AdaShapeState; + +void init_adaconv_state(AdaConvState *hAdaConv); + +void init_adacomb_state(AdaCombState *hAdaComb); + +void init_adashape_state(AdaShapeState *hAdaShape); + +void compute_overlap_window(float *window, int overlap_size); + +void adaconv_process_frame( + AdaConvState* hAdaConv, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *kernel_layer, + const LinearLayer *gain_layer, + int feature_dim, /* not strictly necessary */ + int frame_size, + int overlap_size, + int in_channels, + int out_channels, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float shape_gain, + float *window, + int arch +); + +void adacomb_process_frame( + AdaCombState* hAdaComb, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *kernel_layer, + const LinearLayer *gain_layer, + const LinearLayer *global_gain_layer, + int pitch_lag, + int feature_dim, + int frame_size, + int overlap_size, + int kernel_size, + int left_padding, + float filter_gain_a, + float filter_gain_b, + float log_gain_limit, + float *window, + int arch +); + +void adashape_process_frame( + AdaShapeState *hAdaShape, + float *x_out, + const float *x_in, + const float *features, + const LinearLayer *alpha1f, + const LinearLayer *alpha1t, + const LinearLayer *alpha2, + int feature_dim, + int frame_size, + int avg_pool_k, + int arch +); + +#endif diff --git a/managed_components/78__esp-opus/dnn/nnet.c b/managed_components/78__esp-opus/dnn/nnet.c new file mode 100644 index 0000000..5e87f20 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nnet.c @@ -0,0 +1,149 @@ +/* Copyright (c) 2018 Mozilla + 2008-2011 Octasic Inc. + 2012-2017 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include "opus_types.h" +#include "arch.h" +#include "nnet.h" +#include "dred_rdovae_constants.h" +#include "plc_data.h" +#include "fargan.h" +#include "os_support.h" +#include "vec.h" + +#ifdef ENABLE_OSCE +#include "osce.h" +#endif + +#ifdef NO_OPTIMIZATIONS +#if defined(_MSC_VER) +#pragma message ("Compiling without any vectorization. This code will be very slow") +#else +#warning Compiling without any vectorization. This code will be very slow +#endif +#endif + + +#define SOFTMAX_HACK + + +void compute_generic_dense(const LinearLayer *layer, float *output, const float *input, int activation, int arch) +{ + compute_linear(layer, output, input, arch); + compute_activation(output, output, layer->nb_outputs, activation, arch); +} + +#ifdef ENABLE_OSCE +#define MAX_RNN_NEURONS_ALL IMAX(IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS), OSCE_MAX_RNN_NEURONS) +#else +#define MAX_RNN_NEURONS_ALL IMAX(IMAX(FARGAN_MAX_RNN_NEURONS, PLC_MAX_RNN_UNITS), DRED_MAX_RNN_NEURONS) +#endif + +void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch) +{ + int i; + int N; + float zrh[3*MAX_RNN_NEURONS_ALL]; + float recur[3*MAX_RNN_NEURONS_ALL]; + float *z; + float *r; + float *h; + celt_assert(3*recurrent_weights->nb_inputs == recurrent_weights->nb_outputs); + celt_assert(input_weights->nb_outputs == recurrent_weights->nb_outputs); + N = recurrent_weights->nb_inputs; + z = zrh; + r = &zrh[N]; + h = &zrh[2*N]; + celt_assert(recurrent_weights->nb_outputs <= 3*MAX_RNN_NEURONS_ALL); + celt_assert(in != state); + compute_linear(input_weights, zrh, in, arch); + compute_linear(recurrent_weights, recur, state, arch); + for (i=0;i<2*N;i++) + zrh[i] += recur[i]; + compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID, arch); + for (i=0;inb_inputs == layer->nb_outputs); + compute_linear(layer, act2, input, arch); + compute_activation(act2, act2, layer->nb_outputs, ACTIVATION_SIGMOID, arch); + if (input == output) { + /* Give a vectorization hint to the compiler for the in-place case. */ + for (i=0;inb_outputs;i++) output[i] = output[i]*act2[i]; + } else { + for (i=0;inb_outputs;i++) output[i] = input[i]*act2[i]; + } +} + +#define MAX_CONV_INPUTS_ALL DRED_MAX_CONV_INPUTS + +void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int activation, int arch) +{ + float tmp[MAX_CONV_INPUTS_ALL]; + celt_assert(input != output); + celt_assert(layer->nb_inputs <= MAX_CONV_INPUTS_ALL); + if (layer->nb_inputs!=input_size) OPUS_COPY(tmp, mem, layer->nb_inputs-input_size); + OPUS_COPY(&tmp[layer->nb_inputs-input_size], input, input_size); + compute_linear(layer, output, tmp, arch); + compute_activation(output, output, layer->nb_outputs, activation, arch); + if (layer->nb_inputs!=input_size) OPUS_COPY(mem, &tmp[input_size], layer->nb_inputs-input_size); +} + +void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int dilation, int activation, int arch) +{ + float tmp[MAX_CONV_INPUTS_ALL]; + int ksize = layer->nb_inputs/input_size; + int i; + celt_assert(input != output); + celt_assert(layer->nb_inputs <= MAX_CONV_INPUTS_ALL); + if (dilation==1) OPUS_COPY(tmp, mem, layer->nb_inputs-input_size); + else for (i=0;inb_inputs-input_size], input, input_size); + compute_linear(layer, output, tmp, arch); + compute_activation(output, output, layer->nb_outputs, activation, arch); + if (dilation==1) OPUS_COPY(mem, &tmp[input_size], layer->nb_inputs-input_size); + else { + OPUS_COPY(mem, &mem[input_size], input_size*dilation*(ksize-1)-input_size); + OPUS_COPY(&mem[input_size*dilation*(ksize-1)-input_size], input, input_size); + } +} diff --git a/managed_components/78__esp-opus/dnn/nnet.h b/managed_components/78__esp-opus/dnn/nnet.h new file mode 100644 index 0000000..7bc6133 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nnet.h @@ -0,0 +1,163 @@ +/* Copyright (c) 2018 Mozilla + Copyright (c) 2017 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef NNET_H_ +#define NNET_H_ + +#include +#include "opus_types.h" + +#define ACTIVATION_LINEAR 0 +#define ACTIVATION_SIGMOID 1 +#define ACTIVATION_TANH 2 +#define ACTIVATION_RELU 3 +#define ACTIVATION_SOFTMAX 4 +#define ACTIVATION_SWISH 5 + +#define WEIGHT_BLOB_VERSION 0 +#define WEIGHT_BLOCK_SIZE 64 +typedef struct { + const char *name; + int type; + int size; + const void *data; +} WeightArray; + +#define WEIGHT_TYPE_float 0 +#define WEIGHT_TYPE_int 1 +#define WEIGHT_TYPE_qweight 2 +#define WEIGHT_TYPE_int8 3 + +typedef struct { + char head[4]; + int version; + int type; + int size; + int block_size; + char name[44]; +} WeightHead; + +/* Generic sparse affine transformation. */ +typedef struct { + const float *bias; + const float *subias; + const opus_int8 *weights; + const float *float_weights; + const int *weights_idx; + const float *diag; + const float *scale; + int nb_inputs; + int nb_outputs; +} LinearLayer; + +/* Generic sparse affine transformation. */ +typedef struct { + const float *bias; + const float *float_weights; + int in_channels; + int out_channels; + int ktime; + int kheight; +} Conv2dLayer; + + +void compute_generic_dense(const LinearLayer *layer, float *output, const float *input, int activation, int arch); +void compute_generic_gru(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch); +void compute_generic_conv1d(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int activation, int arch); +void compute_generic_conv1d_dilation(const LinearLayer *layer, float *output, float *mem, const float *input, int input_size, int dilation, int activation, int arch); +void compute_glu(const LinearLayer *layer, float *output, const float *input, int arch); +void compute_gated_activation(const LinearLayer *layer, float *output, const float *input, int activation, int arch); + + +int parse_weights(WeightArray **list, const void *data, int len); + + +extern const WeightArray lpcnet_arrays[]; +extern const WeightArray plcmodel_arrays[]; +extern const WeightArray rdovaeenc_arrays[]; +extern const WeightArray rdovaedec_arrays[]; +extern const WeightArray fwgan_arrays[]; +extern const WeightArray fargan_arrays[]; +extern const WeightArray pitchdnn_arrays[]; +extern const WeightArray lossgen_arrays[]; + +int linear_init(LinearLayer *layer, const WeightArray *arrays, + const char *bias, + const char *subias, + const char *weights, + const char *float_weights, + const char *weights_idx, + const char *diag, + const char *scale, + int nb_inputs, + int nb_outputs); + +int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays, + const char *bias, + const char *float_weights, + int in_channels, + int out_channels, + int ktime, + int kheight); + + +void compute_linear_c(const LinearLayer *linear, float *out, const float *in); +void compute_activation_c(float *output, const float *input, int N, int activation); +void compute_conv2d_c(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation); + + +#if defined(OPUS_ARM_MAY_HAVE_DOTPROD) || defined(OPUS_ARM_MAY_HAVE_NEON_INTR) +#include "arm/dnn_arm.h" +#endif + +#if defined(OPUS_X86_MAY_HAVE_SSE2) +#include "x86/dnn_x86.h" +#endif + +#ifndef OVERRIDE_COMPUTE_LINEAR +#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_c(linear, out, in)) +#endif + +#ifndef OVERRIDE_COMPUTE_ACTIVATION +#define compute_activation(output, input, N, activation, arch) ((void)(arch),compute_activation_c(output, input, N, activation)) +#endif + +#ifndef OVERRIDE_COMPUTE_CONV2D +#define compute_conv2d(conv, out, mem, in, height, hstride, activation, arch) ((void)(arch),compute_conv2d_c(conv, out, mem, in, height, hstride, activation)) +#endif + +#if defined(__x86_64__) && !defined(OPUS_X86_MAY_HAVE_SSE4_1) && !defined(OPUS_X86_MAY_HAVE_AVX2) +#if defined(_MSC_VER) +#pragma message ("Only SSE and SSE2 are available. On newer machines, enable SSSE3/AVX/AVX2 to get better performance") +#else +#warning "Only SSE and SSE2 are available. On newer machines, enable SSSE3/AVX/AVX2 using -march= to get better performance" +#endif +#endif + + + +#endif /* NNET_H_ */ diff --git a/managed_components/78__esp-opus/dnn/nnet_arch.h b/managed_components/78__esp-opus/dnn/nnet_arch.h new file mode 100644 index 0000000..694a360 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nnet_arch.h @@ -0,0 +1,247 @@ +/* Copyright (c) 2018-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef NNET_ARCH_H +#define NNET_ARCH_H + +#include "nnet.h" +#include "arch.h" +#include "os_support.h" +#include "vec.h" + +#define CAT_SUFFIX2(a,b) a ## b +#define CAT_SUFFIX(a,b) CAT_SUFFIX2(a, b) + +#define RTCD_SUF(name) CAT_SUFFIX(name, RTCD_ARCH) + +/* Force vectorization on for DNN code because some of the loops rely on + compiler vectorization rather than explicitly using intrinsics. */ +#if OPUS_GNUC_PREREQ(5,1) +#define GCC_POP_OPTIONS +#pragma GCC push_options +#pragma GCC optimize("tree-vectorize") +#endif + + +#define MAX_ACTIVATIONS (4096) + +static OPUS_INLINE void vec_swish(float *y, const float *x, int N) +{ + int i; + float tmp[MAX_ACTIVATIONS]; + celt_assert(N <= MAX_ACTIVATIONS); + vec_sigmoid(tmp, x, N); + for (i=0;ibias; + M = linear->nb_inputs; + N = linear->nb_outputs; + if (linear->float_weights != NULL) { + if (linear->weights_idx != NULL) sparse_sgemv8x4(out, linear->float_weights, linear->weights_idx, N, in); + else sgemv(out, linear->float_weights, N, M, N, in); + } else if (linear->weights != NULL) { + if (linear->weights_idx != NULL) sparse_cgemv8x4(out, linear->weights, linear->weights_idx, linear->scale, N, M, in); + else cgemv8x4(out, linear->weights, linear->scale, N, M, in); + /* Only use SU biases on for integer matrices on SU archs. */ +#ifdef USE_SU_BIAS + bias = linear->subias; +#endif + } + else OPUS_CLEAR(out, N); + if (bias != NULL) { + for (i=0;idiag) { + /* Diag is only used for GRU recurrent weights. */ + celt_assert(3*M == N); + for (i=0;idiag[i]*in[i]; + out[i+M] += linear->diag[i+M]*in[i]; + out[i+2*M] += linear->diag[i+2*M]*in[i]; + } + } +} + +/* Computes non-padded convolution for input [ ksize1 x in_channels x (len2+ksize2) ], + kernel [ out_channels x in_channels x ksize1 x ksize2 ], + storing the output as [ out_channels x len2 ]. + We assume that the output dimension along the ksize1 axis is 1, + i.e. processing one frame at a time. */ +static void conv2d_float(float *out, const float *weights, int in_channels, int out_channels, int ktime, int kheight, const float *in, int height, int hstride) +{ + int i; + int in_stride; + in_stride = height+kheight-1; + for (i=0;iin_channels*(height+conv->kheight-1); + celt_assert(conv->ktime*time_stride <= MAX_CONV2D_INPUTS); + OPUS_COPY(in_buf, mem, (conv->ktime-1)*time_stride); + OPUS_COPY(&in_buf[(conv->ktime-1)*time_stride], in, time_stride); + OPUS_COPY(mem, &in_buf[time_stride], (conv->ktime-1)*time_stride); + bias = conv->bias; + if (conv->kheight == 3 && conv->ktime == 3) + conv2d_3x3_float(out, conv->float_weights, conv->in_channels, conv->out_channels, in_buf, height, hstride); + else + conv2d_float(out, conv->float_weights, conv->in_channels, conv->out_channels, conv->ktime, conv->kheight, in_buf, height, hstride); + if (bias != NULL) { + for (i=0;iout_channels;i++) { + int j; + for (j=0;jout_channels;i++) { + RTCD_SUF(compute_activation_)(&out[i*hstride], &out[i*hstride], height, activation); + } +} + +#ifdef GCC_POP_OPTIONS +#pragma GCC pop_options +#endif + +#endif diff --git a/managed_components/78__esp-opus/dnn/nnet_default.c b/managed_components/78__esp-opus/dnn/nnet_default.c new file mode 100644 index 0000000..4316f0f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/nnet_default.c @@ -0,0 +1,35 @@ +/* Copyright (c) 2018-2019 Mozilla + 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + + +#define RTCD_ARCH c + +#include "nnet_arch.h" diff --git a/managed_components/78__esp-opus/dnn/osce.c b/managed_components/78__esp-opus/dnn/osce.c new file mode 100644 index 0000000..e6fb5ce --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce.c @@ -0,0 +1,1419 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + + +#include +#include "osce.h" +#include "osce_features.h" +#include "os_support.h" +#include "nndsp.h" +#include "float_cast.h" +#include "arch.h" + +#ifdef OSCE_DEBUG +#include +/*#define WRITE_FEATURES*/ +/*#define DEBUG_LACE*/ +/*#define DEBUG_NOLACE*/ +#define FINIT(fid, name, mode) do{if (fid == NULL) {fid = fopen(name, mode);}} while(0) +#endif + +#ifdef ENABLE_OSCE_TRAINING_DATA +#include +#endif + +#define CLIP(a, min, max) (((a) < (min) ? (min) : (a)) > (max) ? (max) : (a)) + +extern const WeightArray lacelayers_arrays[]; +extern const WeightArray nolacelayers_arrays[]; + +/* LACE */ + +#ifndef DISABLE_LACE + +static void compute_lace_numbits_embedding(float *emb, float numbits, int dim, float min_val, float max_val, int logscale) +{ + float x; + (void) dim; + + numbits = logscale ? log(numbits) : numbits; + x = CLIP(numbits, min_val, max_val) - (max_val + min_val) / 2; + + emb[0] = sin(x * LACE_NUMBITS_SCALE_0 - 0.5f); + emb[1] = sin(x * LACE_NUMBITS_SCALE_1 - 0.5f); + emb[2] = sin(x * LACE_NUMBITS_SCALE_2 - 0.5f); + emb[3] = sin(x * LACE_NUMBITS_SCALE_3 - 0.5f); + emb[4] = sin(x * LACE_NUMBITS_SCALE_4 - 0.5f); + emb[5] = sin(x * LACE_NUMBITS_SCALE_5 - 0.5f); + emb[6] = sin(x * LACE_NUMBITS_SCALE_6 - 0.5f); + emb[7] = sin(x * LACE_NUMBITS_SCALE_7 - 0.5f); +} + + +static int init_lace(LACE *hLACE, const WeightArray *weights) +{ + int ret = 0; + OPUS_CLEAR(hLACE, 1); + celt_assert(weights != NULL); + + ret = init_lacelayers(&hLACE->layers, weights); + + compute_overlap_window(hLACE->window, LACE_OVERLAP_SIZE); + + return ret; +} + +static void reset_lace_state(LACEState *state) +{ + OPUS_CLEAR(state, 1); + + init_adacomb_state(&state->cf1_state); + init_adacomb_state(&state->cf2_state); + init_adaconv_state(&state->af1_state); +} + +static void lace_feature_net( + LACE *hLACE, + LACEState *state, + float *output, + const float *features, + const float *numbits, + const int *periods, + int arch +) +{ + float input_buffer[IMAX(4 * IMAX(LACE_COND_DIM, LACE_HIDDEN_FEATURE_DIM), LACE_NUM_FEATURES + LACE_PITCH_EMBEDDING_DIM + 2*LACE_NUMBITS_EMBEDDING_DIM)]; + float output_buffer[4 * IMAX(LACE_COND_DIM, LACE_HIDDEN_FEATURE_DIM)]; + float numbits_embedded[2 * LACE_NUMBITS_EMBEDDING_DIM]; + int i_subframe; + + compute_lace_numbits_embedding(numbits_embedded, numbits[0], LACE_NUMBITS_EMBEDDING_DIM, + log(LACE_NUMBITS_RANGE_LOW), log(LACE_NUMBITS_RANGE_HIGH), 1); + compute_lace_numbits_embedding(numbits_embedded + LACE_NUMBITS_EMBEDDING_DIM, numbits[1], LACE_NUMBITS_EMBEDDING_DIM, + log(LACE_NUMBITS_RANGE_LOW), log(LACE_NUMBITS_RANGE_HIGH), 1); + + /* scaling and dimensionality reduction */ + for (i_subframe = 0; i_subframe < 4; i_subframe ++) + { + OPUS_COPY(input_buffer, features + i_subframe * LACE_NUM_FEATURES, LACE_NUM_FEATURES); + OPUS_COPY(input_buffer + LACE_NUM_FEATURES, hLACE->layers.lace_pitch_embedding.float_weights + periods[i_subframe] * LACE_PITCH_EMBEDDING_DIM, LACE_PITCH_EMBEDDING_DIM); + OPUS_COPY(input_buffer + LACE_NUM_FEATURES + LACE_PITCH_EMBEDDING_DIM, numbits_embedded, 2 * LACE_NUMBITS_EMBEDDING_DIM); + + compute_generic_conv1d( + &hLACE->layers.lace_fnet_conv1, + output_buffer + i_subframe * LACE_HIDDEN_FEATURE_DIM, + NULL, + input_buffer, + LACE_NUM_FEATURES + LACE_PITCH_EMBEDDING_DIM + 2 * LACE_NUMBITS_EMBEDDING_DIM, + ACTIVATION_TANH, + arch); + } + + /* subframe accumulation */ + OPUS_COPY(input_buffer, output_buffer, 4 * LACE_HIDDEN_FEATURE_DIM); + compute_generic_conv1d( + &hLACE->layers.lace_fnet_conv2, + output_buffer, + state->feature_net_conv2_state, + input_buffer, + 4 * LACE_HIDDEN_FEATURE_DIM, + ACTIVATION_TANH, + arch + ); + + /* tconv upsampling */ + OPUS_COPY(input_buffer, output_buffer, 4 * LACE_COND_DIM); + compute_generic_dense( + &hLACE->layers.lace_fnet_tconv, + output_buffer, + input_buffer, + ACTIVATION_TANH, + arch + ); + + /* GRU */ + OPUS_COPY(input_buffer, output_buffer, 4 * LACE_COND_DIM); + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + compute_generic_gru( + &hLACE->layers.lace_fnet_gru_input, + &hLACE->layers.lace_fnet_gru_recurrent, + state->feature_net_gru_state, + input_buffer + i_subframe * LACE_COND_DIM, + arch + ); + OPUS_COPY(output + i_subframe * LACE_COND_DIM, state->feature_net_gru_state, LACE_COND_DIM); + } +} + + +static void lace_process_20ms_frame( + LACE* hLACE, + LACEState *state, + float *x_out, + const float *x_in, + const float *features, + const float *numbits, + const int *periods, + int arch +) +{ + float feature_buffer[4 * LACE_COND_DIM]; + float output_buffer[4 * LACE_FRAME_SIZE]; + int i_subframe, i_sample; + +#ifdef DEBUG_LACE + static FILE *f_features=NULL, *f_encfeatures=NULL, *f_xin=NULL, *f_xpreemph=NULL, *f_postcf1=NULL; + static FILE *f_postcf2=NULL, *f_postaf1=NULL, *f_xdeemph, *f_numbits, *f_periods; + + + FINIT(f_features, "debug/c_features.f32", "wb"); + FINIT(f_encfeatures, "debug/c_encoded_features.f32", "wb"); + FINIT(f_xin, "debug/c_x_in.f32", "wb"); + FINIT(f_xpreemph, "debug/c_xpreemph.f32", "wb"); + FINIT(f_xdeemph, "debug/c_xdeemph.f32", "wb"); + FINIT(f_postcf1, "debug/c_post_cf1.f32", "wb"); + FINIT(f_postcf2, "debug/c_post_cf2.f32", "wb"); + FINIT(f_postaf1, "debug/c_post_af1.f32", "wb"); + FINIT(f_numbits, "debug/c_numbits.f32", "wb"); + FINIT(f_periods, "debug/c_periods.s32", "wb"); + + fwrite(x_in, sizeof(*x_in), 4 * LACE_FRAME_SIZE, f_xin); + fwrite(numbits, sizeof(*numbits), 2, f_numbits); + fwrite(periods, sizeof(*periods), 4, f_periods); +#endif + + /* pre-emphasis */ + for (i_sample = 0; i_sample < 4 * LACE_FRAME_SIZE; i_sample ++) + { + output_buffer[i_sample] = x_in[i_sample] - LACE_PREEMPH * state->preemph_mem; + state->preemph_mem = x_in[i_sample]; + } + + /* run feature encoder */ + lace_feature_net(hLACE, state, feature_buffer, features, numbits, periods, arch); +#ifdef DEBUG_LACE + fwrite(features, sizeof(*features), 4 * LACE_NUM_FEATURES, f_features); + fwrite(feature_buffer, sizeof(*feature_buffer), 4 * LACE_COND_DIM, f_encfeatures); + fwrite(output_buffer, sizeof(float), 4 * LACE_FRAME_SIZE, f_xpreemph); +#endif + + /* 1st comb filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + adacomb_process_frame( + &state->cf1_state, + output_buffer + i_subframe * LACE_FRAME_SIZE, + output_buffer + i_subframe * LACE_FRAME_SIZE, + feature_buffer + i_subframe * LACE_COND_DIM, + &hLACE->layers.lace_cf1_kernel, + &hLACE->layers.lace_cf1_gain, + &hLACE->layers.lace_cf1_global_gain, + periods[i_subframe], + LACE_COND_DIM, + LACE_FRAME_SIZE, + LACE_OVERLAP_SIZE, + LACE_CF1_KERNEL_SIZE, + LACE_CF1_LEFT_PADDING, + LACE_CF1_FILTER_GAIN_A, + LACE_CF1_FILTER_GAIN_B, + LACE_CF1_LOG_GAIN_LIMIT, + hLACE->window, + arch); + } + +#ifdef DEBUG_LACE + fwrite(output_buffer, sizeof(float), 4 * LACE_FRAME_SIZE, f_postcf1); +#endif + + /* 2nd comb filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + adacomb_process_frame( + &state->cf2_state, + output_buffer + i_subframe * LACE_FRAME_SIZE, + output_buffer + i_subframe * LACE_FRAME_SIZE, + feature_buffer + i_subframe * LACE_COND_DIM, + &hLACE->layers.lace_cf2_kernel, + &hLACE->layers.lace_cf2_gain, + &hLACE->layers.lace_cf2_global_gain, + periods[i_subframe], + LACE_COND_DIM, + LACE_FRAME_SIZE, + LACE_OVERLAP_SIZE, + LACE_CF2_KERNEL_SIZE, + LACE_CF2_LEFT_PADDING, + LACE_CF2_FILTER_GAIN_A, + LACE_CF2_FILTER_GAIN_B, + LACE_CF2_LOG_GAIN_LIMIT, + hLACE->window, + arch); + } +#ifdef DEBUG_LACE + fwrite(output_buffer, sizeof(float), 4 * LACE_FRAME_SIZE, f_postcf2); +#endif + + /* final adaptive filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + adaconv_process_frame( + &state->af1_state, + output_buffer + i_subframe * LACE_FRAME_SIZE, + output_buffer + i_subframe * LACE_FRAME_SIZE, + feature_buffer + i_subframe * LACE_COND_DIM, + &hLACE->layers.lace_af1_kernel, + &hLACE->layers.lace_af1_gain, + LACE_COND_DIM, + LACE_FRAME_SIZE, + LACE_OVERLAP_SIZE, + LACE_AF1_IN_CHANNELS, + LACE_AF1_OUT_CHANNELS, + LACE_AF1_KERNEL_SIZE, + LACE_AF1_LEFT_PADDING, + LACE_AF1_FILTER_GAIN_A, + LACE_AF1_FILTER_GAIN_B, + LACE_AF1_SHAPE_GAIN, + hLACE->window, + arch); + } +#ifdef DEBUG_LACE + fwrite(output_buffer, sizeof(float), 4 * LACE_FRAME_SIZE, f_postaf1); +#endif + + /* de-emphasis */ + for (i_sample = 0; i_sample < 4 * LACE_FRAME_SIZE; i_sample ++) + { + x_out[i_sample] = output_buffer[i_sample] + LACE_PREEMPH * state->deemph_mem; + state->deemph_mem = x_out[i_sample]; + } +#ifdef DEBUG_LACE + fwrite(x_out, sizeof(float), 4 * LACE_FRAME_SIZE, f_xdeemph); +#endif +} + +#endif /* #ifndef DISABLE_LACE */ + + +/* NoLACE */ +#ifndef DISABLE_NOLACE + +static void compute_nolace_numbits_embedding(float *emb, float numbits, int dim, float min_val, float max_val, int logscale) +{ + float x; + (void) dim; + + numbits = logscale ? log(numbits) : numbits; + x = CLIP(numbits, min_val, max_val) - (max_val + min_val) / 2; + + emb[0] = sin(x * NOLACE_NUMBITS_SCALE_0 - 0.5f); + emb[1] = sin(x * NOLACE_NUMBITS_SCALE_1 - 0.5f); + emb[2] = sin(x * NOLACE_NUMBITS_SCALE_2 - 0.5f); + emb[3] = sin(x * NOLACE_NUMBITS_SCALE_3 - 0.5f); + emb[4] = sin(x * NOLACE_NUMBITS_SCALE_4 - 0.5f); + emb[5] = sin(x * NOLACE_NUMBITS_SCALE_5 - 0.5f); + emb[6] = sin(x * NOLACE_NUMBITS_SCALE_6 - 0.5f); + emb[7] = sin(x * NOLACE_NUMBITS_SCALE_7 - 0.5f); +} + +static int init_nolace(NoLACE *hNoLACE, const WeightArray *weights) +{ + int ret = 0; + OPUS_CLEAR(hNoLACE, 1); + celt_assert(weights != NULL); + + ret = init_nolacelayers(&hNoLACE->layers, weights); + + compute_overlap_window(hNoLACE->window, NOLACE_OVERLAP_SIZE); + + return ret; +} + +static void reset_nolace_state(NoLACEState *state) +{ + OPUS_CLEAR(state, 1); + + init_adacomb_state(&state->cf1_state); + init_adacomb_state(&state->cf2_state); + init_adaconv_state(&state->af1_state); + init_adaconv_state(&state->af2_state); + init_adaconv_state(&state->af3_state); + init_adaconv_state(&state->af4_state); + init_adashape_state(&state->tdshape1_state); + init_adashape_state(&state->tdshape2_state); + init_adashape_state(&state->tdshape3_state); +} + +static void nolace_feature_net( + NoLACE *hNoLACE, + NoLACEState *state, + float *output, + const float *features, + const float *numbits, + const int *periods, + int arch +) +{ + float input_buffer[4 * IMAX(NOLACE_COND_DIM, NOLACE_HIDDEN_FEATURE_DIM)]; + float output_buffer[4 * IMAX(NOLACE_COND_DIM, NOLACE_HIDDEN_FEATURE_DIM)]; + float numbits_embedded[2 * NOLACE_NUMBITS_EMBEDDING_DIM]; + int i_subframe; + + compute_nolace_numbits_embedding(numbits_embedded, numbits[0], NOLACE_NUMBITS_EMBEDDING_DIM, + log(NOLACE_NUMBITS_RANGE_LOW), log(NOLACE_NUMBITS_RANGE_HIGH), 1); + compute_nolace_numbits_embedding(numbits_embedded + NOLACE_NUMBITS_EMBEDDING_DIM, numbits[1], NOLACE_NUMBITS_EMBEDDING_DIM, + log(NOLACE_NUMBITS_RANGE_LOW), log(NOLACE_NUMBITS_RANGE_HIGH), 1); + + /* scaling and dimensionality reduction */ + for (i_subframe = 0; i_subframe < 4; i_subframe ++) + { + OPUS_COPY(input_buffer, features + i_subframe * NOLACE_NUM_FEATURES, NOLACE_NUM_FEATURES); + OPUS_COPY(input_buffer + NOLACE_NUM_FEATURES, hNoLACE->layers.nolace_pitch_embedding.float_weights + periods[i_subframe] * NOLACE_PITCH_EMBEDDING_DIM, NOLACE_PITCH_EMBEDDING_DIM); + OPUS_COPY(input_buffer + NOLACE_NUM_FEATURES + NOLACE_PITCH_EMBEDDING_DIM, numbits_embedded, 2 * NOLACE_NUMBITS_EMBEDDING_DIM); + + compute_generic_conv1d( + &hNoLACE->layers.nolace_fnet_conv1, + output_buffer + i_subframe * NOLACE_HIDDEN_FEATURE_DIM, + NULL, + input_buffer, + NOLACE_NUM_FEATURES + NOLACE_PITCH_EMBEDDING_DIM + 2 * NOLACE_NUMBITS_EMBEDDING_DIM, + ACTIVATION_TANH, + arch); + } + + /* subframe accumulation */ + OPUS_COPY(input_buffer, output_buffer, 4 * NOLACE_HIDDEN_FEATURE_DIM); + compute_generic_conv1d( + &hNoLACE->layers.nolace_fnet_conv2, + output_buffer, + state->feature_net_conv2_state, + input_buffer, + 4 * NOLACE_HIDDEN_FEATURE_DIM, + ACTIVATION_TANH, + arch + ); + + /* tconv upsampling */ + OPUS_COPY(input_buffer, output_buffer, 4 * NOLACE_COND_DIM); + compute_generic_dense( + &hNoLACE->layers.nolace_fnet_tconv, + output_buffer, + input_buffer, + ACTIVATION_TANH, + arch + ); + + /* GRU */ + OPUS_COPY(input_buffer, output_buffer, 4 * NOLACE_COND_DIM); + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + compute_generic_gru( + &hNoLACE->layers.nolace_fnet_gru_input, + &hNoLACE->layers.nolace_fnet_gru_recurrent, + state->feature_net_gru_state, + input_buffer + i_subframe * NOLACE_COND_DIM, + arch + ); + OPUS_COPY(output + i_subframe * NOLACE_COND_DIM, state->feature_net_gru_state, NOLACE_COND_DIM); + } +} + + +static void nolace_process_20ms_frame( + NoLACE* hNoLACE, + NoLACEState *state, + float *x_out, + const float *x_in, + const float *features, + const float *numbits, + const int *periods, + int arch +) +{ + float feature_buffer[4 * NOLACE_COND_DIM]; + float feature_transform_buffer[4 * NOLACE_COND_DIM]; + float x_buffer1[8 * NOLACE_FRAME_SIZE]; + float x_buffer2[8 * NOLACE_FRAME_SIZE]; + int i_subframe, i_sample; + NOLACELayers *layers = &hNoLACE->layers; + +#ifdef DEBUG_NOLACE + static FILE *f_features=NULL, *f_encfeatures=NULL, *f_xin=NULL, *f_xpreemph=NULL, *f_postcf1=NULL; + static FILE *f_postcf2=NULL, *f_postaf1=NULL, *f_xdeemph, *f_numbits, *f_periods; + static FILE *f_ffpostcf1, *f_fpostcf2, *f_fpostaf1; + + + FINIT(f_features, "debug/c_features.f32", "wb"); + FINIT(f_encfeatures, "debug/c_encoded_features.f32", "wb"); + FINIT(f_xin, "debug/c_x_in.f32", "wb"); + FINIT(f_xpreemph, "debug/c_xpreemph.f32", "wb"); + FINIT(f_xdeemph, "debug/c_xdeemph.f32", "wb"); + FINIT(f_postcf1, "debug/c_post_cf1.f32", "wb"); + FINIT(f_postcf2, "debug/c_post_cf2.f32", "wb"); + FINIT(f_postaf1, "debug/c_post_af1.f32", "wb"); + FINIT(f_numbits, "debug/c_numbits.f32", "wb"); + FINIT(f_periods, "debug/c_periods.s32", "wb"); + + fwrite(x_in, sizeof(*x_in), 4 * NOLACE_FRAME_SIZE, f_xin); + fwrite(numbits, sizeof(*numbits), 2, f_numbits); + fwrite(periods, sizeof(*periods), 4, f_periods); +#endif + + /* pre-emphasis */ + for (i_sample = 0; i_sample < 4 * NOLACE_FRAME_SIZE; i_sample ++) + { + x_buffer1[i_sample] = x_in[i_sample] - NOLACE_PREEMPH * state->preemph_mem; + state->preemph_mem = x_in[i_sample]; + } + + /* run feature encoder */ + nolace_feature_net(hNoLACE, state, feature_buffer, features, numbits, periods, arch); +#ifdef DEBUG_NOLACE + fwrite(features, sizeof(*features), 4 * NOLACE_NUM_FEATURES, f_features); + fwrite(feature_buffer, sizeof(*feature_buffer), 4 * NOLACE_COND_DIM, f_encfeatures); + fwrite(output_buffer, sizeof(float), 4 * NOLACE_FRAME_SIZE, f_xpreemph); +#endif + + /* 1st comb filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + /* modifies signal in place */ + adacomb_process_frame( + &state->cf1_state, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_cf1_kernel, + &hNoLACE->layers.nolace_cf1_gain, + &hNoLACE->layers.nolace_cf1_global_gain, + periods[i_subframe], + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_CF1_KERNEL_SIZE, + NOLACE_CF1_LEFT_PADDING, + NOLACE_CF1_FILTER_GAIN_A, + NOLACE_CF1_FILTER_GAIN_B, + NOLACE_CF1_LOG_GAIN_LIMIT, + hNoLACE->window, + arch); + + compute_generic_conv1d( + &layers->nolace_post_cf1, + feature_transform_buffer + i_subframe * NOLACE_COND_DIM, + state->post_cf1_state, + feature_buffer + i_subframe * NOLACE_COND_DIM, + NOLACE_COND_DIM, + ACTIVATION_TANH, + arch); + } + + /* update feature buffer */ + OPUS_COPY(feature_buffer, feature_transform_buffer, 4 * NOLACE_COND_DIM); + +#ifdef DEBUG_NOLACE + fwrite(x_buffer1, sizeof(float), 4 * NOLACE_FRAME_SIZE, f_postcf1); +#endif + + /* 2nd comb filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + /* modifies signal in place */ + adacomb_process_frame( + &state->cf2_state, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_cf2_kernel, + &hNoLACE->layers.nolace_cf2_gain, + &hNoLACE->layers.nolace_cf2_global_gain, + periods[i_subframe], + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_CF2_KERNEL_SIZE, + NOLACE_CF2_LEFT_PADDING, + NOLACE_CF2_FILTER_GAIN_A, + NOLACE_CF2_FILTER_GAIN_B, + NOLACE_CF2_LOG_GAIN_LIMIT, + hNoLACE->window, + arch); + + compute_generic_conv1d( + &layers->nolace_post_cf2, + feature_transform_buffer + i_subframe * NOLACE_COND_DIM, + state->post_cf2_state, + feature_buffer + i_subframe * NOLACE_COND_DIM, + NOLACE_COND_DIM, + ACTIVATION_TANH, + arch); + } + + /* update feature buffer */ + OPUS_COPY(feature_buffer, feature_transform_buffer, 4 * NOLACE_COND_DIM); + +#ifdef DEBUG_NOLACE + fwrite(x_buffer1, sizeof(float), 4 * NOLACE_FRAME_SIZE, f_postcf2); +#endif + + /* final adaptive filtering stage */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + adaconv_process_frame( + &state->af1_state, + x_buffer2 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF1_OUT_CHANNELS, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_af1_kernel, + &hNoLACE->layers.nolace_af1_gain, + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_AF1_IN_CHANNELS, + NOLACE_AF1_OUT_CHANNELS, + NOLACE_AF1_KERNEL_SIZE, + NOLACE_AF1_LEFT_PADDING, + NOLACE_AF1_FILTER_GAIN_A, + NOLACE_AF1_FILTER_GAIN_B, + NOLACE_AF1_SHAPE_GAIN, + hNoLACE->window, + arch); + + compute_generic_conv1d( + &layers->nolace_post_af1, + feature_transform_buffer + i_subframe * NOLACE_COND_DIM, + state->post_af1_state, + feature_buffer + i_subframe * NOLACE_COND_DIM, + NOLACE_COND_DIM, + ACTIVATION_TANH, + arch); + } + + /* update feature buffer */ + OPUS_COPY(feature_buffer, feature_transform_buffer, 4 * NOLACE_COND_DIM); + +#ifdef DEBUG_NOLACE + fwrite(x_buffer2, sizeof(float), 4 * NOLACE_FRAME_SIZE * NOLACE_AF1_OUT_CHANNELS, f_postaf1); +#endif + + /* first shape-mix round */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + celt_assert(NOLACE_AF1_OUT_CHANNELS == 2); + /* modifies second channel in place */ + adashape_process_frame( + &state->tdshape1_state, + x_buffer2 + i_subframe * NOLACE_AF1_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + x_buffer2 + i_subframe * NOLACE_AF1_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &layers->nolace_tdshape1_alpha1_f, + &layers->nolace_tdshape1_alpha1_t, + &layers->nolace_tdshape1_alpha2, + NOLACE_TDSHAPE1_FEATURE_DIM, + NOLACE_TDSHAPE1_FRAME_SIZE, + NOLACE_TDSHAPE1_AVG_POOL_K, + arch + ); + + adaconv_process_frame( + &state->af2_state, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF2_OUT_CHANNELS, + x_buffer2 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF2_IN_CHANNELS, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_af2_kernel, + &hNoLACE->layers.nolace_af2_gain, + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_AF2_IN_CHANNELS, + NOLACE_AF2_OUT_CHANNELS, + NOLACE_AF2_KERNEL_SIZE, + NOLACE_AF2_LEFT_PADDING, + NOLACE_AF2_FILTER_GAIN_A, + NOLACE_AF2_FILTER_GAIN_B, + NOLACE_AF2_SHAPE_GAIN, + hNoLACE->window, + arch); + + compute_generic_conv1d( + &layers->nolace_post_af2, + feature_transform_buffer + i_subframe * NOLACE_COND_DIM, + state->post_af2_state, + feature_buffer + i_subframe * NOLACE_COND_DIM, + NOLACE_COND_DIM, + ACTIVATION_TANH, + arch); + } + + /* update feature buffer */ + OPUS_COPY(feature_buffer, feature_transform_buffer, 4 * NOLACE_COND_DIM); + +#ifdef DEBUG_NOLACE + fwrite(x_buffer1, sizeof(float), 4 * NOLACE_FRAME_SIZE * NOLACE_AF2_OUT_CHANNELS, f_postaf2); +#endif + + /* second shape-mix round */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + celt_assert(NOLACE_AF2_OUT_CHANNELS == 2); + /* modifies second channel in place */ + adashape_process_frame( + &state->tdshape2_state, + x_buffer1 + i_subframe * NOLACE_AF2_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + x_buffer1 + i_subframe * NOLACE_AF2_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &layers->nolace_tdshape2_alpha1_f, + &layers->nolace_tdshape2_alpha1_t, + &layers->nolace_tdshape2_alpha2, + NOLACE_TDSHAPE2_FEATURE_DIM, + NOLACE_TDSHAPE2_FRAME_SIZE, + NOLACE_TDSHAPE2_AVG_POOL_K, + arch + ); + + adaconv_process_frame( + &state->af3_state, + x_buffer2 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF3_OUT_CHANNELS, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF3_IN_CHANNELS, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_af3_kernel, + &hNoLACE->layers.nolace_af3_gain, + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_AF3_IN_CHANNELS, + NOLACE_AF3_OUT_CHANNELS, + NOLACE_AF3_KERNEL_SIZE, + NOLACE_AF3_LEFT_PADDING, + NOLACE_AF3_FILTER_GAIN_A, + NOLACE_AF3_FILTER_GAIN_B, + NOLACE_AF3_SHAPE_GAIN, + hNoLACE->window, + arch); + + compute_generic_conv1d( + &layers->nolace_post_af3, + feature_transform_buffer + i_subframe * NOLACE_COND_DIM, + state->post_af3_state, + feature_buffer + i_subframe * NOLACE_COND_DIM, + NOLACE_COND_DIM, + ACTIVATION_TANH, + arch); + } + + /* update feature buffer */ + OPUS_COPY(feature_buffer, feature_transform_buffer, 4 * NOLACE_COND_DIM); + + /* third shape-mix round */ + for (i_subframe = 0; i_subframe < 4; i_subframe++) + { + celt_assert(NOLACE_AF3_OUT_CHANNELS == 2); + /* modifies second channel in place */ + adashape_process_frame( + &state->tdshape3_state, + x_buffer2 + i_subframe * NOLACE_AF3_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + x_buffer2 + i_subframe * NOLACE_AF3_OUT_CHANNELS * NOLACE_FRAME_SIZE + NOLACE_FRAME_SIZE, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &layers->nolace_tdshape3_alpha1_f, + &layers->nolace_tdshape3_alpha1_t, + &layers->nolace_tdshape3_alpha2, + NOLACE_TDSHAPE3_FEATURE_DIM, + NOLACE_TDSHAPE3_FRAME_SIZE, + NOLACE_TDSHAPE3_AVG_POOL_K, + arch + ); + + adaconv_process_frame( + &state->af4_state, + x_buffer1 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF4_OUT_CHANNELS, + x_buffer2 + i_subframe * NOLACE_FRAME_SIZE * NOLACE_AF4_IN_CHANNELS, + feature_buffer + i_subframe * NOLACE_COND_DIM, + &hNoLACE->layers.nolace_af4_kernel, + &hNoLACE->layers.nolace_af4_gain, + NOLACE_COND_DIM, + NOLACE_FRAME_SIZE, + NOLACE_OVERLAP_SIZE, + NOLACE_AF4_IN_CHANNELS, + NOLACE_AF4_OUT_CHANNELS, + NOLACE_AF4_KERNEL_SIZE, + NOLACE_AF4_LEFT_PADDING, + NOLACE_AF4_FILTER_GAIN_A, + NOLACE_AF4_FILTER_GAIN_B, + NOLACE_AF4_SHAPE_GAIN, + hNoLACE->window, + arch); + + } + + + /* de-emphasis */ + for (i_sample = 0; i_sample < 4 * NOLACE_FRAME_SIZE; i_sample ++) + { + x_out[i_sample] = x_buffer1[i_sample] + NOLACE_PREEMPH * state->deemph_mem; + state->deemph_mem = x_out[i_sample]; + } +#ifdef DEBUG_NOLACE + fwrite(x_out, sizeof(float), 4 * NOLACE_FRAME_SIZE, f_xdeemph); +#endif +} + +#endif /* #ifndef DISABLE_NOLACE */ + +/* API */ + +void osce_reset(silk_OSCE_struct *hOSCE, int method) +{ + OSCEState *state = &hOSCE->state; + + OPUS_CLEAR(&hOSCE->features, 1); + + switch(method) + { + case OSCE_METHOD_NONE: + break; +#ifndef DISABLE_LACE + case OSCE_METHOD_LACE: + reset_lace_state(&state->lace); + break; +#endif +#ifndef DISABLE_NOLACE + case OSCE_METHOD_NOLACE: + reset_nolace_state(&state->nolace); + break; +#endif + default: + celt_assert(0 && "method not defined"); /* Question: return error code? */ + } + hOSCE->method = method; + hOSCE->features.reset = 2; +} + + +#if 0 +#include +static void print_float_array(FILE *fid, const char *name, const float *array, int n) +{ + int i; + for (i = 0; i < n; i++) + { + fprintf(fid, "%s[%d]: %f\n", name, i, array[i]); + } +} + +static void print_int_array(FILE *fid, const char *name, const int *array, int n) +{ + int i; + for (i = 0; i < n; i++) + { + fprintf(fid, "%s[%d]: %d\n", name, i, array[i]); + } +} + +static void print_int8_array(FILE *fid, const char *name, const opus_int8 *array, int n) +{ + int i; + for (i = 0; i < n; i++) + { + fprintf(fid, "%s[%d]: %d\n", name, i, array[i]); + } +} + +static void print_linear_layer(FILE *fid, const char *name, LinearLayer *layer) +{ + int i, n_in, n_out, n_total; + char tmp[256]; + + n_in = layer->nb_inputs; + n_out = layer->nb_outputs; + n_total = n_in * n_out; + + fprintf(fid, "\nprinting layer %s...\n", name); + fprintf(fid, "%s.nb_inputs: %d\n%s.nb_outputs: %d\n", name, n_in, name, n_out); + + if (layer->bias !=NULL){} + if (layer->subias !=NULL){} + if (layer->weights !=NULL){} + if (layer->float_weights !=NULL){} + + if (layer->bias != NULL) {sprintf(tmp, "%s.bias", name); print_float_array(fid, tmp, layer->bias, n_out);} + if (layer->subias != NULL) {sprintf(tmp, "%s.subias", name); print_float_array(fid, tmp, layer->subias, n_out);} + if (layer->weights != NULL) {sprintf(tmp, "%s.weights", name); print_int8_array(fid, tmp, layer->weights, n_total);} + if (layer->float_weights != NULL) {sprintf(tmp, "%s.float_weights", name); print_float_array(fid, tmp, layer->float_weights, n_total);} + //if (layer->weights_idx != NULL) {sprintf(tmp, "%s.weights_idx", name); print_float_array(fid, tmp, layer->weights_idx, n_total);} + if (layer->diag != NULL) {sprintf(tmp, "%s.diag", name); print_float_array(fid, tmp, layer->diag, n_in);} + if (layer->scale != NULL) {sprintf(tmp, "%s.scale", name); print_float_array(fid, tmp, layer->scale, n_out);} + +} +#endif + +int osce_load_models(OSCEModel *model, const void *data, int len) +{ + int ret = 0; + WeightArray *list; + + if (data != NULL && len) + { + /* init from buffer */ + parse_weights(&list, data, len); + +#ifndef DISABLE_LACE + if (ret == 0) {ret = init_lace(&model->lace, list);} +#endif + +#ifndef DISABLE_NOLACE + if (ret == 0) {ret = init_nolace(&model->nolace, list);} +#endif + + free(list); + } else + { +#ifdef USE_WEIGHTS_FILE + return -1; +#else +#ifndef DISABLE_LACE + if (ret == 0) {ret = init_lace(&model->lace, lacelayers_arrays);} +#endif + +#ifndef DISABLE_NOLACE + if (ret == 0) {ret = init_nolace(&model->nolace, nolacelayers_arrays);} +#endif + +#endif /* USE_WEIGHTS_FILE */ + } + + ret = ret ? -1 : 0; + return ret; +} + +void osce_enhance_frame( + OSCEModel *model, /* I OSCE model struct */ + silk_decoder_state *psDec, /* I/O Decoder state */ + silk_decoder_control *psDecCtrl, /* I Decoder control */ + opus_int16 xq[], /* I/O Decoded speech */ + opus_int32 num_bits, /* I Size of SILK payload in bits */ + int arch /* I Run-time architecture */ +) +{ + float in_buffer[320]; + float out_buffer[320]; + float features[4 * OSCE_FEATURE_DIM]; + float numbits[2]; + int periods[4]; + int i; + int method; + + /* enhancement only implemented for 20 ms frame at 16kHz */ + if (psDec->fs_kHz != 16 || psDec->nb_subfr != 4) + { + osce_reset(&psDec->osce, psDec->osce.method); + return; + } + + osce_calculate_features(psDec, psDecCtrl, features, numbits, periods, xq, num_bits); + + /* scale input */ + for (i = 0; i < 320; i++) + { + in_buffer[i] = ((float) xq[i]) * (1.f/32768.f); + } + + if (model->loaded) + method = psDec->osce.method; + else + method = OSCE_METHOD_NONE; + switch(method) + { + case OSCE_METHOD_NONE: + OPUS_COPY(out_buffer, in_buffer, 320); + break; +#ifndef DISABLE_LACE + case OSCE_METHOD_LACE: + lace_process_20ms_frame(&model->lace, &psDec->osce.state.lace, out_buffer, in_buffer, features, numbits, periods, arch); + break; +#endif +#ifndef DISABLE_NOLACE + case OSCE_METHOD_NOLACE: + nolace_process_20ms_frame(&model->nolace, &psDec->osce.state.nolace, out_buffer, in_buffer, features, numbits, periods, arch); + break; +#endif + default: + celt_assert(0 && "method not defined"); + } + +#ifdef ENABLE_OSCE_TRAINING_DATA + int k; + + static FILE *flpc = NULL; + static FILE *fgain = NULL; + static FILE *fltp = NULL; + static FILE *fperiod = NULL; + static FILE *fnoisy16k = NULL; + static FILE* f_numbits = NULL; + static FILE* f_numbits_smooth = NULL; + + if (flpc == NULL) {flpc = fopen("features_lpc.f32", "wb");} + if (fgain == NULL) {fgain = fopen("features_gain.f32", "wb");} + if (fltp == NULL) {fltp = fopen("features_ltp.f32", "wb");} + if (fperiod == NULL) {fperiod = fopen("features_period.s16", "wb");} + if (fnoisy16k == NULL) {fnoisy16k = fopen("noisy_16k.s16", "wb");} + if(f_numbits == NULL) {f_numbits = fopen("features_num_bits.s32", "wb");} + if (f_numbits_smooth == NULL) {f_numbits_smooth = fopen("features_num_bits_smooth.f32", "wb");} + + fwrite(&num_bits, sizeof(num_bits), 1, f_numbits); + fwrite(&(psDec->osce.features.numbits_smooth), sizeof(psDec->osce.features.numbits_smooth), 1, f_numbits_smooth); + + for (k = 0; k < psDec->nb_subfr; k++) + { + float tmp; + int16_t itmp; + float lpc_buffer[16] = {0}; + opus_int16 *A_Q12, *B_Q14; + + (void) num_bits; + (void) arch; + + /* gain */ + tmp = (float) psDecCtrl->Gains_Q16[k] / (1UL << 16); + fwrite(&tmp, sizeof(tmp), 1, fgain); + + /* LPC */ + A_Q12 = psDecCtrl->PredCoef_Q12[ k >> 1 ]; + for (i = 0; i < psDec->LPC_order; i++) + { + lpc_buffer[i] = (float) A_Q12[i] / (1U << 12); + } + fwrite(lpc_buffer, sizeof(lpc_buffer[0]), 16, flpc); + + /* LTP */ + B_Q14 = &psDecCtrl->LTPCoef_Q14[ k * LTP_ORDER ]; + for (i = 0; i < 5; i++) + { + tmp = (float) B_Q14[i] / (1U << 14); + fwrite(&tmp, sizeof(tmp), 1, fltp); + } + + /* periods */ + itmp = psDec->indices.signalType == TYPE_VOICED ? psDecCtrl->pitchL[ k ] : 0; + fwrite(&itmp, sizeof(itmp), 1, fperiod); + } + + fwrite(xq, psDec->nb_subfr * psDec->subfr_length, sizeof(xq[0]), fnoisy16k); +#endif + + if (psDec->osce.features.reset > 1) + { + OPUS_COPY(out_buffer, in_buffer, 320); + psDec->osce.features.reset --; + } + else if (psDec->osce.features.reset) + { + osce_cross_fade_10ms(out_buffer, in_buffer, 320); + psDec->osce.features.reset = 0; + } + + /* scale output */ + for (i = 0; i < 320; i++) + { + float tmp = 32768.f * out_buffer[i]; + if (tmp > 32767.f) tmp = 32767.f; + if (tmp < -32767.f) tmp = -32767.f; + xq[i] = float2int(tmp); + } + +} + + +#if 0 + +#include + +void lace_feature_net_compare( + const char * prefix, + int num_frames, + LACE* hLACE +) +{ + char in_feature_file[256]; + char out_feature_file[256]; + char numbits_file[256]; + char periods_file[256]; + char message[512]; + int i_frame, i_feature; + float mse; + float in_features[4 * LACE_NUM_FEATURES]; + float out_features[4 * LACE_COND_DIM]; + float out_features2[4 * LACE_COND_DIM]; + float numbits[2]; + int periods[4]; + + init_lace(hLACE); + + FILE *f_in_features, *f_out_features, *f_numbits, *f_periods; + + strcpy(in_feature_file, prefix); + strcat(in_feature_file, "_in_features.f32"); + f_in_features = fopen(in_feature_file, "rb"); + if (f_in_features == NULL) + { + sprintf(message, "could not open file %s", in_feature_file); + perror(message); + exit(1); + } + + strcpy(out_feature_file, prefix); + strcat(out_feature_file, "_out_features.f32"); + f_out_features = fopen(out_feature_file, "rb"); + if (f_out_features == NULL) + { + sprintf(message, "could not open file %s", out_feature_file); + perror(message); + exit(1); + } + + strcpy(periods_file, prefix); + strcat(periods_file, "_periods.s32"); + f_periods = fopen(periods_file, "rb"); + if (f_periods == NULL) + { + sprintf(message, "could not open file %s", periods_file); + perror(message); + exit(1); + } + + strcpy(numbits_file, prefix); + strcat(numbits_file, "_numbits.f32"); + f_numbits = fopen(numbits_file, "rb"); + if (f_numbits == NULL) + { + sprintf(message, "could not open file %s", numbits_file); + perror(message); + exit(1); + } + + for (i_frame = 0; i_frame < num_frames; i_frame ++) + { + if(fread(in_features, sizeof(float), 4 * LACE_NUM_FEATURES, f_in_features) != 4 * LACE_NUM_FEATURES) + { + fprintf(stderr, "could not read frame %d from in_features\n", i_frame); + exit(1); + } + if(fread(out_features, sizeof(float), 4 * LACE_COND_DIM, f_out_features) != 4 * LACE_COND_DIM) + { + fprintf(stderr, "could not read frame %d from out_features\n", i_frame); + exit(1); + } + if(fread(periods, sizeof(int), 4, f_periods) != 4) + { + fprintf(stderr, "could not read frame %d from periods\n", i_frame); + exit(1); + } + if(fread(numbits, sizeof(float), 2, f_numbits) != 2) + { + fprintf(stderr, "could not read frame %d from numbits\n", i_frame); + exit(1); + } + + + lace_feature_net(hLACE, out_features2, in_features, numbits, periods); + + float mse = 0; + for (int i = 0; i < 4 * LACE_COND_DIM; i ++) + { + mse += pow(out_features[i] - out_features2[i], 2); + } + mse /= (4 * LACE_COND_DIM); + printf("rmse: %f\n", sqrt(mse)); + + } + + fclose(f_in_features); + fclose(f_out_features); + fclose(f_numbits); + fclose(f_periods); +} + + +void lace_demo( + char *prefix, + char *output +) +{ + char feature_file[256]; + char numbits_file[256]; + char periods_file[256]; + char x_in_file[256]; + char message[512]; + int i_frame; + float mse; + float features[4 * LACE_NUM_FEATURES]; + float numbits[2]; + int periods[4]; + float x_in[4 * LACE_FRAME_SIZE]; + int16_t x_out[4 * LACE_FRAME_SIZE]; + float buffer[4 * LACE_FRAME_SIZE]; + LACE hLACE; + int frame_counter = 0; + FILE *f_features, *f_numbits, *f_periods, *f_x_in, *f_x_out; + + init_lace(&hLACE); + + strcpy(feature_file, prefix); + strcat(feature_file, "_features.f32"); + f_features = fopen(feature_file, "rb"); + if (f_features == NULL) + { + sprintf(message, "could not open file %s", feature_file); + perror(message); + exit(1); + } + + strcpy(x_in_file, prefix); + strcat(x_in_file, "_x_in.f32"); + f_x_in = fopen(x_in_file, "rb"); + if (f_x_in == NULL) + { + sprintf(message, "could not open file %s", x_in_file); + perror(message); + exit(1); + } + + f_x_out = fopen(output, "wb"); + if (f_x_out == NULL) + { + sprintf(message, "could not open file %s", output); + perror(message); + exit(1); + } + + strcpy(periods_file, prefix); + strcat(periods_file, "_periods.s32"); + f_periods = fopen(periods_file, "rb"); + if (f_periods == NULL) + { + sprintf(message, "could not open file %s", periods_file); + perror(message); + exit(1); + } + + strcpy(numbits_file, prefix); + strcat(numbits_file, "_numbits.f32"); + f_numbits = fopen(numbits_file, "rb"); + if (f_numbits == NULL) + { + sprintf(message, "could not open file %s", numbits_file); + perror(message); + exit(1); + } + + printf("processing %s\n", prefix); + + while (fread(x_in, sizeof(float), 4 * LACE_FRAME_SIZE, f_x_in) == 4 * LACE_FRAME_SIZE) + { + printf("\rframe: %d", frame_counter++); + if(fread(features, sizeof(float), 4 * LACE_NUM_FEATURES, f_features) != 4 * LACE_NUM_FEATURES) + { + fprintf(stderr, "could not read frame %d from features\n", i_frame); + exit(1); + } + if(fread(periods, sizeof(int), 4, f_periods) != 4) + { + fprintf(stderr, "could not read frame %d from periods\n", i_frame); + exit(1); + } + if(fread(numbits, sizeof(float), 2, f_numbits) != 2) + { + fprintf(stderr, "could not read frame %d from numbits\n", i_frame); + exit(1); + } + + lace_process_20ms_frame( + &hLACE, + buffer, + x_in, + features, + numbits, + periods + ); + + for (int n=0; n < 4 * LACE_FRAME_SIZE; n ++) + { + float tmp = (1UL<<15) * buffer[n]; + tmp = CLIP(tmp, -32768, 32767); + x_out[n] = (int16_t) round(tmp); + } + + fwrite(x_out, sizeof(int16_t), 4 * LACE_FRAME_SIZE, f_x_out); + } + printf("\ndone!\n"); + + fclose(f_features); + fclose(f_numbits); + fclose(f_periods); + fclose(f_x_in); + fclose(f_x_out); +} + +void nolace_demo( + char *prefix, + char *output +) +{ + char feature_file[256]; + char numbits_file[256]; + char periods_file[256]; + char x_in_file[256]; + char message[512]; + int i_frame; + float mse; + float features[4 * LACE_NUM_FEATURES]; + float numbits[2]; + int periods[4]; + float x_in[4 * LACE_FRAME_SIZE]; + int16_t x_out[4 * LACE_FRAME_SIZE]; + float buffer[4 * LACE_FRAME_SIZE]; + NoLACE hNoLACE; + int frame_counter = 0; + FILE *f_features, *f_numbits, *f_periods, *f_x_in, *f_x_out; + + init_nolace(&hNoLACE); + + strcpy(feature_file, prefix); + strcat(feature_file, "_features.f32"); + f_features = fopen(feature_file, "rb"); + if (f_features == NULL) + { + sprintf(message, "could not open file %s", feature_file); + perror(message); + exit(1); + } + + strcpy(x_in_file, prefix); + strcat(x_in_file, "_x_in.f32"); + f_x_in = fopen(x_in_file, "rb"); + if (f_x_in == NULL) + { + sprintf(message, "could not open file %s", x_in_file); + perror(message); + exit(1); + } + + f_x_out = fopen(output, "wb"); + if (f_x_out == NULL) + { + sprintf(message, "could not open file %s", output); + perror(message); + exit(1); + } + + strcpy(periods_file, prefix); + strcat(periods_file, "_periods.s32"); + f_periods = fopen(periods_file, "rb"); + if (f_periods == NULL) + { + sprintf(message, "could not open file %s", periods_file); + perror(message); + exit(1); + } + + strcpy(numbits_file, prefix); + strcat(numbits_file, "_numbits.f32"); + f_numbits = fopen(numbits_file, "rb"); + if (f_numbits == NULL) + { + sprintf(message, "could not open file %s", numbits_file); + perror(message); + exit(1); + } + + printf("processing %s\n", prefix); + + while (fread(x_in, sizeof(float), 4 * LACE_FRAME_SIZE, f_x_in) == 4 * LACE_FRAME_SIZE) + { + printf("\rframe: %d", frame_counter++); + if(fread(features, sizeof(float), 4 * LACE_NUM_FEATURES, f_features) != 4 * LACE_NUM_FEATURES) + { + fprintf(stderr, "could not read frame %d from features\n", i_frame); + exit(1); + } + if(fread(periods, sizeof(int), 4, f_periods) != 4) + { + fprintf(stderr, "could not read frame %d from periods\n", i_frame); + exit(1); + } + if(fread(numbits, sizeof(float), 2, f_numbits) != 2) + { + fprintf(stderr, "could not read frame %d from numbits\n", i_frame); + exit(1); + } + + nolace_process_20ms_frame( + &hNoLACE, + buffer, + x_in, + features, + numbits, + periods + ); + + for (int n=0; n < 4 * LACE_FRAME_SIZE; n ++) + { + float tmp = (1UL<<15) * buffer[n]; + tmp = CLIP(tmp, -32768, 32767); + x_out[n] = (int16_t) round(tmp); + } + + fwrite(x_out, sizeof(int16_t), 4 * LACE_FRAME_SIZE, f_x_out); + } + printf("\ndone!\n"); + + fclose(f_features); + fclose(f_numbits); + fclose(f_periods); + fclose(f_x_in); + fclose(f_x_out); +} + + +int main() +{ +#if 0 + LACE hLACE; + + lace_feature_net_compare("testvec2/lace", 5, &hLACE); + + lace_demo("testdata/test9", "out_lace_c_9kbps.pcm"); + lace_demo("testdata/test6", "out_lace_c_6kbps.pcm"); +#endif + nolace_demo("testdata/test9", "out_nolace_c_9kbps.pcm"); + +} +#endif + +/*gcc -I ../include -I . -I ../silk -I ../celt osce.c nndsp.c lace_data.c nolace_data.c nnet.c parse_lpcnet_weights.c -lm -o lacetest*/ diff --git a/managed_components/78__esp-opus/dnn/osce.h b/managed_components/78__esp-opus/dnn/osce.h new file mode 100644 index 0000000..1aadfad --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce.h @@ -0,0 +1,84 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef OSCE_H +#define OSCE_H + + +#include "opus_types.h" +/*#include "osce_config.h"*/ +#ifndef DISABLE_LACE +#include "lace_data.h" +#endif +#ifndef DISABLE_NOLACE +#include "nolace_data.h" +#endif +#include "nndsp.h" +#include "nnet.h" +#include "osce_structs.h" +#include "structs.h" + +#define OSCE_METHOD_NONE 0 +#ifndef DISABLE_LACE +#define OSCE_METHOD_LACE 1 +#endif +#ifndef DISABLE_NOLACE +#define OSCE_METHOD_NOLACE 2 +#endif + +#if !defined(DISABLE_NOLACE) +#define OSCE_DEFAULT_METHOD OSCE_METHOD_NOLACE +#define OSCE_MAX_RNN_NEURONS NOLACE_FNET_GRU_STATE_SIZE +#elif !defined(DISABLE_LACE) +#define OSCE_DEFAULT_METHOD OSCE_METHOD_LACE +#define OSCE_MAX_RNN_NEURONS LACE_FNET_GRU_STATE_SIZE +#else +#define OSCE_DEFAULT_METHOD OSCE_METHOD_NONE +#define OSCE_MAX_RNN_NEURONS 0 +#endif + + + + +/* API */ + + +void osce_enhance_frame( + OSCEModel *model, /* I OSCE model struct */ + silk_decoder_state *psDec, /* I/O Decoder state */ + silk_decoder_control *psDecCtrl, /* I Decoder control */ + opus_int16 xq[], /* I/O Decoded speech */ + opus_int32 num_bits, /* I Size of SILK payload in bits */ + int arch /* I Run-time architecture */ +); + + +int osce_load_models(OSCEModel *hModel, const void *data, int len); +void osce_reset(silk_OSCE_struct *hOSCE, int method); + + +#endif diff --git a/managed_components/78__esp-opus/dnn/osce_config.h b/managed_components/78__esp-opus/dnn/osce_config.h new file mode 100644 index 0000000..bc187bb --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce_config.h @@ -0,0 +1,60 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef OSCE_CONFIG +#define OSCE_CONFIG + +#define OSCE_FEATURES_MAX_HISTORY 350 +#define OSCE_FEATURE_DIM 93 +#define OSCE_MAX_FEATURE_FRAMES 4 + +#define OSCE_CLEAN_SPEC_NUM_BANDS 64 +#define OSCE_NOISY_SPEC_NUM_BANDS 18 + +#define OSCE_NO_PITCH_VALUE 7 + +#define OSCE_PREEMPH 0.85f + +#define OSCE_PITCH_HANGOVER 0 + +#define OSCE_CLEAN_SPEC_START 0 +#define OSCE_CLEAN_SPEC_LENGTH 64 + +#define OSCE_NOISY_CEPSTRUM_START 64 +#define OSCE_NOISY_CEPSTRUM_LENGTH 18 + +#define OSCE_ACORR_START 82 +#define OSCE_ACORR_LENGTH 5 + +#define OSCE_LTP_START 87 +#define OSCE_LTP_LENGTH 5 + +#define OSCE_LOG_GAIN_START 92 +#define OSCE_LOG_GAIN_LENGTH 1 + + +#endif diff --git a/managed_components/78__esp-opus/dnn/osce_features.c b/managed_components/78__esp-opus/dnn/osce_features.c new file mode 100644 index 0000000..bcd4801 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce_features.c @@ -0,0 +1,454 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#define OSCE_SPEC_WINDOW_SIZE 320 +#define OSCE_SPEC_NUM_FREQS 161 + + +/*DEBUG*/ +/*#define WRITE_FEATURES*/ +/*#define DEBUG_PRING*/ +/*******/ + +#include "stack_alloc.h" +#include "osce_features.h" +#include "kiss_fft.h" +#include "os_support.h" +#include "osce.h" +#include "freq.h" + + +#if defined(WRITE_FEATURES) || defined(DEBUG_PRING) +#include +#include +#endif + +static const int center_bins_clean[64] = { + 0, 2, 5, 8, 10, 12, 15, 18, + 20, 22, 25, 28, 30, 33, 35, 38, + 40, 42, 45, 48, 50, 52, 55, 58, + 60, 62, 65, 68, 70, 73, 75, 78, + 80, 82, 85, 88, 90, 92, 95, 98, + 100, 102, 105, 108, 110, 112, 115, 118, + 120, 122, 125, 128, 130, 132, 135, 138, + 140, 142, 145, 148, 150, 152, 155, 160 +}; + +static const int center_bins_noisy[18] = { + 0, 4, 8, 12, 16, 20, 24, 28, + 32, 40, 48, 56, 64, 80, 96, 112, + 136, 160 +}; + +static const float band_weights_clean[64] = { + 0.666666666667f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.400000000000f, 0.400000000000f, 0.400000000000f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.400000000000f, 0.400000000000f, 0.400000000000f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.333333333333f, 0.400000000000f, + 0.500000000000f, 0.400000000000f, 0.250000000000f, 0.333333333333f +}; + +static const float band_weights_noisy[18] = { + 0.400000000000f, 0.250000000000f, 0.250000000000f, 0.250000000000f, + 0.250000000000f, 0.250000000000f, 0.250000000000f, 0.250000000000f, + 0.166666666667f, 0.125000000000f, 0.125000000000f, 0.125000000000f, + 0.083333333333f, 0.062500000000f, 0.062500000000f, 0.050000000000f, + 0.041666666667f, 0.080000000000f +}; + +static float osce_window[OSCE_SPEC_WINDOW_SIZE] = { + 0.004908718808f, 0.014725683311f, 0.024541228523f, 0.034354408400f, 0.044164277127f, + 0.053969889210f, 0.063770299562f, 0.073564563600f, 0.083351737332f, 0.093130877450f, + 0.102901041421f, 0.112661287575f, 0.122410675199f, 0.132148264628f, 0.141873117332f, + 0.151584296010f, 0.161280864678f, 0.170961888760f, 0.180626435180f, 0.190273572448f, + 0.199902370753f, 0.209511902052f, 0.219101240157f, 0.228669460829f, 0.238215641862f, + 0.247738863176f, 0.257238206902f, 0.266712757475f, 0.276161601717f, 0.285583828929f, + 0.294978530977f, 0.304344802381f, 0.313681740399f, 0.322988445118f, 0.332264019538f, + 0.341507569661f, 0.350718204573f, 0.359895036535f, 0.369037181064f, 0.378143757022f, + 0.387213886697f, 0.396246695891f, 0.405241314005f, 0.414196874117f, 0.423112513073f, + 0.431987371563f, 0.440820594212f, 0.449611329655f, 0.458358730621f, 0.467061954019f, + 0.475720161014f, 0.484332517110f, 0.492898192230f, 0.501416360796f, 0.509886201809f, + 0.518306898929f, 0.526677640552f, 0.534997619887f, 0.543266035038f, 0.551482089078f, + 0.559644990127f, 0.567753951426f, 0.575808191418f, 0.583806933818f, 0.591749407690f, + 0.599634847523f, 0.607462493302f, 0.615231590581f, 0.622941390558f, 0.630591150148f, + 0.638180132051f, 0.645707604824f, 0.653172842954f, 0.660575126926f, 0.667913743292f, + 0.675187984742f, 0.682397150168f, 0.689540544737f, 0.696617479953f, 0.703627273726f, + 0.710569250438f, 0.717442741007f, 0.724247082951f, 0.730981620454f, 0.737645704427f, + 0.744238692572f, 0.750759949443f, 0.757208846506f, 0.763584762206f, 0.769887082016f, + 0.776115198508f, 0.782268511401f, 0.788346427627f, 0.794348361383f, 0.800273734191f, + 0.806121974951f, 0.811892519997f, 0.817584813152f, 0.823198305781f, 0.828732456844f, + 0.834186732948f, 0.839560608398f, 0.844853565250f, 0.850065093356f, 0.855194690420f, + 0.860241862039f, 0.865206121757f, 0.870086991109f, 0.874883999665f, 0.879596685080f, + 0.884224593137f, 0.888767277786f, 0.893224301196f, 0.897595233788f, 0.901879654283f, + 0.906077149740f, 0.910187315596f, 0.914209755704f, 0.918144082372f, 0.921989916403f, + 0.925746887127f, 0.929414632439f, 0.932992798835f, 0.936481041442f, 0.939879024058f, + 0.943186419177f, 0.946402908026f, 0.949528180593f, 0.952561935658f, 0.955503880820f, + 0.958353732530f, 0.961111216112f, 0.963776065795f, 0.966348024735f, 0.968826845041f, + 0.971212287799f, 0.973504123096f, 0.975702130039f, 0.977806096779f, 0.979815820533f, + 0.981731107599f, 0.983551773378f, 0.985277642389f, 0.986908548290f, 0.988444333892f, + 0.989884851171f, 0.991229961288f, 0.992479534599f, 0.993633450666f, 0.994691598273f, + 0.995653875433f, 0.996520189401f, 0.997290456679f, 0.997964603026f, 0.998542563469f, + 0.999024282300f, 0.999409713092f, 0.999698818696f, 0.999891571247f, 0.999987952167f, + 0.999987952167f, 0.999891571247f, 0.999698818696f, 0.999409713092f, 0.999024282300f, + 0.998542563469f, 0.997964603026f, 0.997290456679f, 0.996520189401f, 0.995653875433f, + 0.994691598273f, 0.993633450666f, 0.992479534599f, 0.991229961288f, 0.989884851171f, + 0.988444333892f, 0.986908548290f, 0.985277642389f, 0.983551773378f, 0.981731107599f, + 0.979815820533f, 0.977806096779f, 0.975702130039f, 0.973504123096f, 0.971212287799f, + 0.968826845041f, 0.966348024735f, 0.963776065795f, 0.961111216112f, 0.958353732530f, + 0.955503880820f, 0.952561935658f, 0.949528180593f, 0.946402908026f, 0.943186419177f, + 0.939879024058f, 0.936481041442f, 0.932992798835f, 0.929414632439f, 0.925746887127f, + 0.921989916403f, 0.918144082372f, 0.914209755704f, 0.910187315596f, 0.906077149740f, + 0.901879654283f, 0.897595233788f, 0.893224301196f, 0.888767277786f, 0.884224593137f, + 0.879596685080f, 0.874883999665f, 0.870086991109f, 0.865206121757f, 0.860241862039f, + 0.855194690420f, 0.850065093356f, 0.844853565250f, 0.839560608398f, 0.834186732948f, + 0.828732456844f, 0.823198305781f, 0.817584813152f, 0.811892519997f, 0.806121974951f, + 0.800273734191f, 0.794348361383f, 0.788346427627f, 0.782268511401f, 0.776115198508f, + 0.769887082016f, 0.763584762206f, 0.757208846506f, 0.750759949443f, 0.744238692572f, + 0.737645704427f, 0.730981620454f, 0.724247082951f, 0.717442741007f, 0.710569250438f, + 0.703627273726f, 0.696617479953f, 0.689540544737f, 0.682397150168f, 0.675187984742f, + 0.667913743292f, 0.660575126926f, 0.653172842954f, 0.645707604824f, 0.638180132051f, + 0.630591150148f, 0.622941390558f, 0.615231590581f, 0.607462493302f, 0.599634847523f, + 0.591749407690f, 0.583806933818f, 0.575808191418f, 0.567753951426f, 0.559644990127f, + 0.551482089078f, 0.543266035038f, 0.534997619887f, 0.526677640552f, 0.518306898929f, + 0.509886201809f, 0.501416360796f, 0.492898192230f, 0.484332517110f, 0.475720161014f, + 0.467061954019f, 0.458358730621f, 0.449611329655f, 0.440820594212f, 0.431987371563f, + 0.423112513073f, 0.414196874117f, 0.405241314005f, 0.396246695891f, 0.387213886697f, + 0.378143757022f, 0.369037181064f, 0.359895036535f, 0.350718204573f, 0.341507569661f, + 0.332264019538f, 0.322988445118f, 0.313681740399f, 0.304344802381f, 0.294978530977f, + 0.285583828929f, 0.276161601717f, 0.266712757475f, 0.257238206902f, 0.247738863176f, + 0.238215641862f, 0.228669460829f, 0.219101240157f, 0.209511902052f, 0.199902370753f, + 0.190273572448f, 0.180626435180f, 0.170961888760f, 0.161280864678f, 0.151584296010f, + 0.141873117332f, 0.132148264628f, 0.122410675199f, 0.112661287575f, 0.102901041421f, + 0.093130877450f, 0.083351737332f, 0.073564563600f, 0.063770299562f, 0.053969889210f, + 0.044164277127f, 0.034354408400f, 0.024541228523f, 0.014725683311f, 0.004908718808f +}; + +static void apply_filterbank(float *x_out, float *x_in, const int *center_bins, const float* band_weights, int num_bands) +{ + int b, i; + float frac; + + celt_assert(x_in != x_out) + + x_out[0] = 0; + for (b = 0; b < num_bands - 1; b++) + { + x_out[b+1] = 0; + for (i = center_bins[b]; i < center_bins[b+1]; i++) + { + frac = (float) (center_bins[b+1] - i) / (center_bins[b+1] - center_bins[b]); + x_out[b] += band_weights[b] * frac * x_in[i]; + x_out[b+1] += band_weights[b+1] * (1 - frac) * x_in[i]; + + } + } + x_out[num_bands - 1] += band_weights[num_bands - 1] * x_in[center_bins[num_bands - 1]]; +#ifdef DEBUG_PRINT + for (b = 0; b < num_bands; b++) + { + printf("band[%d]: %f\n", b, x_out[b]); + } +#endif +} + + +static void mag_spec_320_onesided(float *out, float *in) +{ + celt_assert(OSCE_SPEC_WINDOW_SIZE == 320); + kiss_fft_cpx buffer[OSCE_SPEC_WINDOW_SIZE]; + int k; + forward_transform(buffer, in); + + for (k = 0; k < OSCE_SPEC_NUM_FREQS; k++) + { + out[k] = OSCE_SPEC_WINDOW_SIZE * sqrt(buffer[k].r * buffer[k].r + buffer[k].i * buffer[k].i); +#ifdef DEBUG_PRINT + printf("magspec[%d]: %f\n", k, out[k]); +#endif + } +} + + +static void calculate_log_spectrum_from_lpc(float *spec, opus_int16 *a_q12, int lpc_order) +{ + float buffer[OSCE_SPEC_WINDOW_SIZE] = {0}; + int i; + + /* zero expansion */ + buffer[0] = 1; + for (i = 0; i < lpc_order; i++) + { + buffer[i+1] = - (float)a_q12[i] / (1U << 12); + } + + /* calculate and invert magnitude spectrum */ + mag_spec_320_onesided(buffer, buffer); + + for (i = 0; i < OSCE_SPEC_NUM_FREQS; i++) + { + buffer[i] = 1.f / (buffer[i] + 1e-9f); + } + + /* apply filterbank */ + apply_filterbank(spec, buffer, center_bins_clean, band_weights_clean, OSCE_CLEAN_SPEC_NUM_BANDS); + + /* log and scaling */ + for (i = 0; i < OSCE_CLEAN_SPEC_NUM_BANDS; i++) + { + spec[i] = 0.3f * log(spec[i] + 1e-9f); + } +} + +static void calculate_cepstrum(float *cepstrum, float *signal) +{ + float buffer[OSCE_SPEC_WINDOW_SIZE]; + float *spec = &buffer[OSCE_SPEC_NUM_FREQS + 3]; + int n; + + celt_assert(cepstrum != signal) + + for (n = 0; n < OSCE_SPEC_WINDOW_SIZE; n++) + { + buffer[n] = osce_window[n] * signal[n]; + } + + /* calculate magnitude spectrum */ + mag_spec_320_onesided(buffer, buffer); + + /* accumulate bands */ + apply_filterbank(spec, buffer, center_bins_noisy, band_weights_noisy, OSCE_NOISY_SPEC_NUM_BANDS); + + /* log domain conversion */ + for (n = 0; n < OSCE_NOISY_SPEC_NUM_BANDS; n++) + { + spec[n] = log(spec[n] + 1e-9f); +#ifdef DEBUG_PRINT + printf("logspec[%d]: %f\n", n, spec[n]); +#endif + } + + /* DCT-II (orthonormal) */ + celt_assert(OSCE_NOISY_SPEC_NUM_BANDS == NB_BANDS); + dct(cepstrum, spec); +} + +static void calculate_acorr(float *acorr, float *signal, int lag) +{ + int n, k; + celt_assert(acorr != signal) + + for (k = -2; k <= 2; k++) + { + acorr[k+2] = 0; + float xx = 0; + float xy = 0; + float yy = 0; + for (n = 0; n < 80; n++) + { + /* obviously wasteful -> fix later */ + xx += signal[n] * signal[n]; + yy += signal[n - lag + k] * signal[n - lag + k]; + xy += signal[n] * signal[n - lag + k]; + } + acorr[k+2] = xy / sqrt(xx * yy + 1e-9f); + } +} + +static int pitch_postprocessing(OSCEFeatureState *psFeatures, int lag, int type) +{ + int new_lag; + int modulus; + +#ifdef OSCE_HANGOVER_BUGFIX +#define TESTBIT 1 +#else +#define TESTBIT 0 +#endif + + modulus = OSCE_PITCH_HANGOVER; + if (modulus == 0) modulus ++; + + /* hangover is currently disabled to reflect a bug in the python code. ToDo: re-evaluate hangover */ + if (type != TYPE_VOICED && psFeatures->last_type == TYPE_VOICED && TESTBIT) + /* enter hangover */ + { + new_lag = OSCE_NO_PITCH_VALUE; + if (psFeatures->pitch_hangover_count < OSCE_PITCH_HANGOVER) + { + new_lag = psFeatures->last_lag; + psFeatures->pitch_hangover_count = (psFeatures->pitch_hangover_count + 1) % modulus; + } + } + else if (type != TYPE_VOICED && psFeatures->pitch_hangover_count && TESTBIT) + /* continue hangover */ + { + new_lag = psFeatures->last_lag; + psFeatures->pitch_hangover_count = (psFeatures->pitch_hangover_count + 1) % modulus; + } + else if (type != TYPE_VOICED) + /* unvoiced frame after hangover */ + { + new_lag = OSCE_NO_PITCH_VALUE; + psFeatures->pitch_hangover_count = 0; + } + else + /* voiced frame: update last_lag */ + { + new_lag = lag; + psFeatures->last_lag = lag; + psFeatures->pitch_hangover_count = 0; + } + + /* buffer update */ + psFeatures->last_type = type; + + /* with the current setup this should never happen (but who knows...) */ + celt_assert(new_lag) + + return new_lag; +} + +void osce_calculate_features( + silk_decoder_state *psDec, /* I/O Decoder state */ + silk_decoder_control *psDecCtrl, /* I Decoder control */ + float *features, /* O input features */ + float *numbits, /* O numbits and smoothed numbits */ + int *periods, /* O pitch lags on subframe basis */ + const opus_int16 xq[], /* I Decoded speech */ + opus_int32 num_bits /* I Size of SILK payload in bits */ +) +{ + int num_subframes, num_samples; + float buffer[OSCE_FEATURES_MAX_HISTORY + OSCE_MAX_FEATURE_FRAMES * 80]; + float *frame, *pfeatures; + OSCEFeatureState *psFeatures; + int i, n, k; +#ifdef WRITE_FEATURES + static FILE *f_feat = NULL; + if (f_feat == NULL) + { + f_feat = fopen("assembled_features.f32", "wb"); + } +#endif + + /*OPUS_CLEAR(buffer, 1);*/ + memset(buffer, 0, sizeof(buffer)); + + num_subframes = psDec->nb_subfr; + num_samples = num_subframes * 80; + psFeatures = &psDec->osce.features; + + /* smooth bit count */ + psFeatures->numbits_smooth = 0.9f * psFeatures->numbits_smooth + 0.1f * num_bits; + numbits[0] = num_bits; + numbits[1] = psFeatures->numbits_smooth; + + for (n = 0; n < num_samples; n++) + { + buffer[OSCE_FEATURES_MAX_HISTORY + n] = (float) xq[n] / (1U<<15); + } + OPUS_COPY(buffer, psFeatures->signal_history, OSCE_FEATURES_MAX_HISTORY); + + for (k = 0; k < num_subframes; k++) + { + pfeatures = features + k * OSCE_FEATURE_DIM; + frame = &buffer[OSCE_FEATURES_MAX_HISTORY + k * 80]; + memset(pfeatures, 0, OSCE_FEATURE_DIM); /* precaution */ + + /* clean spectrum from lpcs (update every other frame) */ + if (k % 2 == 0) + { + calculate_log_spectrum_from_lpc(pfeatures + OSCE_CLEAN_SPEC_START, psDecCtrl->PredCoef_Q12[k >> 1], psDec->LPC_order); + } + else + { + OPUS_COPY(pfeatures + OSCE_CLEAN_SPEC_START, pfeatures + OSCE_CLEAN_SPEC_START - OSCE_FEATURE_DIM, OSCE_CLEAN_SPEC_LENGTH); + } + + /* noisy cepstrum from signal (update every other frame) */ + if (k % 2 == 0) + { + calculate_cepstrum(pfeatures + OSCE_NOISY_CEPSTRUM_START, frame - 160); + } + else + { + OPUS_COPY(pfeatures + OSCE_NOISY_CEPSTRUM_START, pfeatures + OSCE_NOISY_CEPSTRUM_START - OSCE_FEATURE_DIM, OSCE_NOISY_CEPSTRUM_LENGTH); + } + + /* pitch hangover and zero value replacement */ + periods[k] = pitch_postprocessing(psFeatures, psDecCtrl->pitchL[k], psDec->indices.signalType); + + /* auto-correlation around pitch lag */ + calculate_acorr(pfeatures + OSCE_ACORR_START, frame, periods[k]); + + /* ltp */ + celt_assert(OSCE_LTP_LENGTH == LTP_ORDER) + for (i = 0; i < OSCE_LTP_LENGTH; i++) + { + pfeatures[OSCE_LTP_START + i] = (float) psDecCtrl->LTPCoef_Q14[k * LTP_ORDER + i] / (1U << 14); + } + + /* frame gain */ + pfeatures[OSCE_LOG_GAIN_START] = log((float) psDecCtrl->Gains_Q16[k] / (1UL << 16) + 1e-9f); + +#ifdef WRITE_FEATURES + fwrite(pfeatures, sizeof(*pfeatures), 93, f_feat); +#endif + } + + /* buffer update */ + OPUS_COPY(psFeatures->signal_history, &buffer[num_samples], OSCE_FEATURES_MAX_HISTORY); +} + + +void osce_cross_fade_10ms(float *x_enhanced, float *x_in, int length) +{ + int i; + celt_assert(length >= 160); + + for (i = 0; i < 160; i++) + { + x_enhanced[i] = osce_window[i] * x_enhanced[i] + (1.f - osce_window[i]) * x_in[i]; + } + + +} diff --git a/managed_components/78__esp-opus/dnn/osce_features.h b/managed_components/78__esp-opus/dnn/osce_features.h new file mode 100644 index 0000000..1eeb567 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce_features.h @@ -0,0 +1,50 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef OSCE_FEATURES_H +#define OSCE_FEATURES_H + + +#include "structs.h" +#include "opus_types.h" + +#define OSCE_NUMBITS_BUGFIX + +void osce_calculate_features( + silk_decoder_state *psDec, /* I/O Decoder state */ + silk_decoder_control *psDecCtrl, /* I Decoder control */ + float *features, /* O input features */ + float *numbits, /* O numbits and smoothed numbits */ + int *periods, /* O pitch lags on subframe basis */ + const opus_int16 xq[], /* I Decoded speech */ + opus_int32 num_bits /* I Size of SILK payload in bits */ +); + + +void osce_cross_fade_10ms(float *x_enhanced, float *x_in, int length); + +#endif diff --git a/managed_components/78__esp-opus/dnn/osce_structs.h b/managed_components/78__esp-opus/dnn/osce_structs.h new file mode 100644 index 0000000..6358681 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/osce_structs.h @@ -0,0 +1,125 @@ +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef OSCE_STRUCTS_H +#define OSCE_STRUCTS_H + +#include "opus_types.h" +#include "osce_config.h" +#ifndef DISABLE_LACE +#include "lace_data.h" +#endif +#ifndef DISABLE_NOLACE +#include "nolace_data.h" +#endif +#include "nndsp.h" +#include "nnet.h" + +/* feature calculation */ + +typedef struct { + float numbits_smooth; + int pitch_hangover_count; + int last_lag; + int last_type; + float signal_history[OSCE_FEATURES_MAX_HISTORY]; + int reset; +} OSCEFeatureState; + + +#ifndef DISABLE_LACE +/* LACE */ +typedef struct { + float feature_net_conv2_state[LACE_FNET_CONV2_STATE_SIZE]; + float feature_net_gru_state[LACE_COND_DIM]; + AdaCombState cf1_state; + AdaCombState cf2_state; + AdaConvState af1_state; + float preemph_mem; + float deemph_mem; +} LACEState; + +typedef struct +{ + LACELayers layers; + float window[LACE_OVERLAP_SIZE]; +} LACE; + +#endif /* #ifndef DISABLE_LACE */ + + +#ifndef DISABLE_NOLACE +/* NoLACE */ +typedef struct { + float feature_net_conv2_state[NOLACE_FNET_CONV2_STATE_SIZE]; + float feature_net_gru_state[NOLACE_COND_DIM]; + float post_cf1_state[NOLACE_COND_DIM]; + float post_cf2_state[NOLACE_COND_DIM]; + float post_af1_state[NOLACE_COND_DIM]; + float post_af2_state[NOLACE_COND_DIM]; + float post_af3_state[NOLACE_COND_DIM]; + AdaCombState cf1_state; + AdaCombState cf2_state; + AdaConvState af1_state; + AdaConvState af2_state; + AdaConvState af3_state; + AdaConvState af4_state; + AdaShapeState tdshape1_state; + AdaShapeState tdshape2_state; + AdaShapeState tdshape3_state; + float preemph_mem; + float deemph_mem; +} NoLACEState; + +typedef struct { + NOLACELayers layers; + float window[LACE_OVERLAP_SIZE]; +} NoLACE; + +#endif /* #ifndef DISABLE_NOLACE */ + +/* OSCEModel */ +typedef struct { + int loaded; +#ifndef DISABLE_LACE + LACE lace; +#endif +#ifndef DISABLE_NOLACE + NoLACE nolace; +#endif +} OSCEModel; + +typedef union { +#ifndef DISABLE_LACE + LACEState lace; +#endif +#ifndef DISABLE_NOLACE + NoLACEState nolace; +#endif +} OSCEState; + +#endif diff --git a/managed_components/78__esp-opus/dnn/parse_lpcnet_weights.c b/managed_components/78__esp-opus/dnn/parse_lpcnet_weights.c new file mode 100644 index 0000000..01ab7f8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/parse_lpcnet_weights.c @@ -0,0 +1,238 @@ +/* Copyright (c) 2023 Amazon */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include "nnet.h" +#include "os_support.h" + +#define SPARSE_BLOCK_SIZE 32 + +int parse_record(const void **data, int *len, WeightArray *array) { + WeightHead *h = (WeightHead *)*data; + if (*len < WEIGHT_BLOCK_SIZE) return -1; + if (h->block_size < h->size) return -1; + if (h->block_size > *len-WEIGHT_BLOCK_SIZE) return -1; + if (h->name[sizeof(h->name)-1] != 0) return -1; + if (h->size < 0) return -1; + array->name = h->name; + array->type = h->type; + array->size = h->size; + array->data = (void*)((unsigned char*)(*data)+WEIGHT_BLOCK_SIZE); + + *data = (void*)((unsigned char*)*data + h->block_size+WEIGHT_BLOCK_SIZE); + *len -= h->block_size+WEIGHT_BLOCK_SIZE; + return array->size; +} + +int parse_weights(WeightArray **list, const void *data, int len) +{ + int nb_arrays=0; + int capacity=20; + *list = opus_alloc(capacity*sizeof(WeightArray)); + while (len > 0) { + int ret; + WeightArray array = {NULL, 0, 0, 0}; + ret = parse_record(&data, &len, &array); + if (ret > 0) { + if (nb_arrays+1 >= capacity) { + /* Make sure there's room for the ending NULL element too. */ + capacity = capacity*3/2; + *list = opus_realloc(*list, capacity*sizeof(WeightArray)); + } + (*list)[nb_arrays++] = array; + } else { + opus_free(*list); + *list = NULL; + return -1; + } + } + (*list)[nb_arrays].name=NULL; + return nb_arrays; +} + +static const void *find_array_entry(const WeightArray *arrays, const char *name) { + while (arrays->name && strcmp(arrays->name, name) != 0) arrays++; + return arrays; +} + +static const void *find_array_check(const WeightArray *arrays, const char *name, int size) { + const WeightArray *a = find_array_entry(arrays, name); + if (a->name && a->size == size) return a->data; + else return NULL; +} + +static const void *opt_array_check(const WeightArray *arrays, const char *name, int size, int *error) { + const WeightArray *a = find_array_entry(arrays, name); + *error = (a->name != NULL && a->size != size); + if (a->name && a->size == size) return a->data; + else return NULL; +} + +static const void *find_idx_check(const WeightArray *arrays, const char *name, int nb_in, int nb_out, int *total_blocks) { + int remain; + const int *idx; + const WeightArray *a = find_array_entry(arrays, name); + *total_blocks = 0; + if (a == NULL) return NULL; + idx = a->data; + remain = a->size/sizeof(int); + while (remain > 0) { + int nb_blocks; + int i; + nb_blocks = *idx++; + if (remain < nb_blocks+1) return NULL; + for (i=0;i= nb_in || (pos&0x3)) return NULL; + } + nb_out -= 8; + remain -= nb_blocks+1; + *total_blocks += nb_blocks; + } + if (nb_out != 0) return NULL; + return a->data; +} + +int linear_init(LinearLayer *layer, const WeightArray *arrays, + const char *bias, + const char *subias, + const char *weights, + const char *float_weights, + const char *weights_idx, + const char *diag, + const char *scale, + int nb_inputs, + int nb_outputs) +{ + int err; + layer->bias = NULL; + layer->subias = NULL; + layer->weights = NULL; + layer->float_weights = NULL; + layer->weights_idx = NULL; + layer->diag = NULL; + layer->scale = NULL; + if (bias != NULL) { + if ((layer->bias = find_array_check(arrays, bias, nb_outputs*sizeof(layer->bias[0]))) == NULL) return 1; + } + if (subias != NULL) { + if ((layer->subias = find_array_check(arrays, subias, nb_outputs*sizeof(layer->subias[0]))) == NULL) return 1; + } + if (weights_idx != NULL) { + int total_blocks; + if ((layer->weights_idx = find_idx_check(arrays, weights_idx, nb_inputs, nb_outputs, &total_blocks)) == NULL) return 1; + if (weights != NULL) { + if ((layer->weights = find_array_check(arrays, weights, SPARSE_BLOCK_SIZE*total_blocks*sizeof(layer->weights[0]))) == NULL) return 1; + } + if (float_weights != NULL) { + layer->float_weights = opt_array_check(arrays, float_weights, SPARSE_BLOCK_SIZE*total_blocks*sizeof(layer->float_weights[0]), &err); + if (err) return 1; + } + } else { + if (weights != NULL) { + if ((layer->weights = find_array_check(arrays, weights, nb_inputs*nb_outputs*sizeof(layer->weights[0]))) == NULL) return 1; + } + if (float_weights != NULL) { + layer->float_weights = opt_array_check(arrays, float_weights, nb_inputs*nb_outputs*sizeof(layer->float_weights[0]), &err); + if (err) return 1; + } + } + if (diag != NULL) { + if ((layer->diag = find_array_check(arrays, diag, nb_outputs*sizeof(layer->diag[0]))) == NULL) return 1; + } + if (weights != NULL) { + if ((layer->scale = find_array_check(arrays, scale, nb_outputs*sizeof(layer->scale[0]))) == NULL) return 1; + } + layer->nb_inputs = nb_inputs; + layer->nb_outputs = nb_outputs; + return 0; +} + +int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays, + const char *bias, + const char *float_weights, + int in_channels, + int out_channels, + int ktime, + int kheight) +{ + int err; + layer->bias = NULL; + layer->float_weights = NULL; + if (bias != NULL) { + if ((layer->bias = find_array_check(arrays, bias, out_channels*sizeof(layer->bias[0]))) == NULL) return 1; + } + if (float_weights != NULL) { + layer->float_weights = opt_array_check(arrays, float_weights, in_channels*out_channels*ktime*kheight*sizeof(layer->float_weights[0]), &err); + if (err) return 1; + } + layer->in_channels = in_channels; + layer->out_channels = out_channels; + layer->ktime = ktime; + layer->kheight = kheight; + return 0; +} + + + +#if 0 +#include +#include +#include +#include +#include + +int main() +{ + int fd; + void *data; + int len; + int nb_arrays; + int i; + WeightArray *list; + struct stat st; + const char *filename = "weights_blob.bin"; + stat(filename, &st); + len = st.st_size; + fd = open(filename, O_RDONLY); + data = mmap(NULL, len, PROT_READ, MAP_SHARED, fd, 0); + printf("size is %d\n", len); + nb_arrays = parse_weights(&list, data, len); + for (i=0;i +#include "pitchdnn.h" +#include "os_support.h" +#include "nnet.h" +#include "lpcnet_private.h" + + +float compute_pitchdnn( + PitchDNNState *st, + const float *if_features, + const float *xcorr_features, + int arch + ) +{ + float if1_out[DENSE_IF_UPSAMPLER_1_OUT_SIZE]; + float downsampler_in[NB_XCORR_FEATURES + DENSE_IF_UPSAMPLER_2_OUT_SIZE]; + float downsampler_out[DENSE_DOWNSAMPLER_OUT_SIZE]; + float conv1_tmp1[(NB_XCORR_FEATURES + 2)*8] = {0}; + float conv1_tmp2[(NB_XCORR_FEATURES + 2)*8] = {0}; + float output[DENSE_FINAL_UPSAMPLER_OUT_SIZE]; + int i; + int pos=0; + float maxval=-1; + float sum=0; + float count=0; + PitchDNN *model = &st->model; + /* IF */ + compute_generic_dense(&model->dense_if_upsampler_1, if1_out, if_features, ACTIVATION_TANH, arch); + compute_generic_dense(&model->dense_if_upsampler_2, &downsampler_in[NB_XCORR_FEATURES], if1_out, ACTIVATION_TANH, arch); + /* xcorr*/ + OPUS_COPY(&conv1_tmp1[1], xcorr_features, NB_XCORR_FEATURES); + compute_conv2d(&model->conv2d_1, &conv1_tmp2[1], st->xcorr_mem1, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH, arch); + compute_conv2d(&model->conv2d_2, downsampler_in, st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, NB_XCORR_FEATURES, ACTIVATION_TANH, arch); + + compute_generic_dense(&model->dense_downsampler, downsampler_out, downsampler_in, ACTIVATION_TANH, arch); + compute_generic_gru(&model->gru_1_input, &model->gru_1_recurrent, st->gru_state, downsampler_out, arch); + compute_generic_dense(&model->dense_final_upsampler, output, st->gru_state, ACTIVATION_LINEAR, arch); + for (i=0;i<180;i++) { + if (output[i] > maxval) { + pos = i; + maxval = output[i]; + } + } + for (i=IMAX(0, pos-2); i<=IMIN(179, pos+2); i++) { + float p = exp(output[i]); + sum += p*i; + count += p; + } + /*printf("%d %f\n", pos, sum/count);*/ + return (1.f/60.f)*(sum/count) - 1.5; + /*return 256.f/pow(2.f, (1.f/60.f)*i);*/ +} + + +void pitchdnn_init(PitchDNNState *st) +{ + int ret; + OPUS_CLEAR(st, 1); +#ifndef USE_WEIGHTS_FILE + ret = init_pitchdnn(&st->model, pitchdnn_arrays); +#else + ret = 0; +#endif + celt_assert(ret == 0); +} + +int pitchdnn_load_model(PitchDNNState *st, const void *data, int len) { + WeightArray *list; + int ret; + parse_weights(&list, data, len); + ret = init_pitchdnn(&st->model, list); + opus_free(list); + if (ret == 0) return 0; + else return -1; +} diff --git a/managed_components/78__esp-opus/dnn/pitchdnn.h b/managed_components/78__esp-opus/dnn/pitchdnn.h new file mode 100644 index 0000000..25fa3a4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/pitchdnn.h @@ -0,0 +1,34 @@ +#ifndef PITCHDNN_H +#define PITCHDNN_H + + +typedef struct PitchDNN PitchDNN; + +#include "pitchdnn_data.h" + +#define PITCH_MIN_PERIOD 32 +#define PITCH_MAX_PERIOD 256 + +#define NB_XCORR_FEATURES (PITCH_MAX_PERIOD-PITCH_MIN_PERIOD) + + +typedef struct { + PitchDNN model; + float gru_state[GRU_1_STATE_SIZE]; + float xcorr_mem1[(NB_XCORR_FEATURES + 2)*2]; + float xcorr_mem2[(NB_XCORR_FEATURES + 2)*2*8]; + float xcorr_mem3[(NB_XCORR_FEATURES + 2)*2*8]; +} PitchDNNState; + + +void pitchdnn_init(PitchDNNState *st); +int pitchdnn_load_model(PitchDNNState *st, const void *data, int len); + +float compute_pitchdnn( + PitchDNNState *st, + const float *if_features, + const float *xcorr_features, + int arch + ); + +#endif diff --git a/managed_components/78__esp-opus/dnn/tansig_table.h b/managed_components/78__esp-opus/dnn/tansig_table.h new file mode 100644 index 0000000..ebec7e3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/tansig_table.h @@ -0,0 +1,50 @@ +/* This file is auto-generated by gen_tables */ + +#ifndef TANSIG_TABLE_H +#define TANSIG_TABLE_H + +static const float tansig_table[201] = { +0.000000f, 0.039979f, 0.079830f, 0.119427f, 0.158649f, +0.197375f, 0.235496f, 0.272905f, 0.309507f, 0.345214f, +0.379949f, 0.413644f, 0.446244f, 0.477700f, 0.507977f, +0.537050f, 0.564900f, 0.591519f, 0.616909f, 0.641077f, +0.664037f, 0.685809f, 0.706419f, 0.725897f, 0.744277f, +0.761594f, 0.777888f, 0.793199f, 0.807569f, 0.821040f, +0.833655f, 0.845456f, 0.856485f, 0.866784f, 0.876393f, +0.885352f, 0.893698f, 0.901468f, 0.908698f, 0.915420f, +0.921669f, 0.927473f, 0.932862f, 0.937863f, 0.942503f, +0.946806f, 0.950795f, 0.954492f, 0.957917f, 0.961090f, +0.964028f, 0.966747f, 0.969265f, 0.971594f, 0.973749f, +0.975743f, 0.977587f, 0.979293f, 0.980869f, 0.982327f, +0.983675f, 0.984921f, 0.986072f, 0.987136f, 0.988119f, +0.989027f, 0.989867f, 0.990642f, 0.991359f, 0.992020f, +0.992631f, 0.993196f, 0.993718f, 0.994199f, 0.994644f, +0.995055f, 0.995434f, 0.995784f, 0.996108f, 0.996407f, +0.996682f, 0.996937f, 0.997172f, 0.997389f, 0.997590f, +0.997775f, 0.997946f, 0.998104f, 0.998249f, 0.998384f, +0.998508f, 0.998623f, 0.998728f, 0.998826f, 0.998916f, +0.999000f, 0.999076f, 0.999147f, 0.999213f, 0.999273f, +0.999329f, 0.999381f, 0.999428f, 0.999472f, 0.999513f, +0.999550f, 0.999585f, 0.999617f, 0.999646f, 0.999673f, +0.999699f, 0.999722f, 0.999743f, 0.999763f, 0.999781f, +0.999798f, 0.999813f, 0.999828f, 0.999841f, 0.999853f, +0.999865f, 0.999875f, 0.999885f, 0.999893f, 0.999902f, +0.999909f, 0.999916f, 0.999923f, 0.999929f, 0.999934f, +0.999939f, 0.999944f, 0.999948f, 0.999952f, 0.999956f, +0.999959f, 0.999962f, 0.999965f, 0.999968f, 0.999970f, +0.999973f, 0.999975f, 0.999977f, 0.999978f, 0.999980f, +0.999982f, 0.999983f, 0.999984f, 0.999986f, 0.999987f, +0.999988f, 0.999989f, 0.999990f, 0.999990f, 0.999991f, +0.999992f, 0.999992f, 0.999993f, 0.999994f, 0.999994f, +0.999994f, 0.999995f, 0.999995f, 0.999996f, 0.999996f, +0.999996f, 0.999997f, 0.999997f, 0.999997f, 0.999997f, +0.999997f, 0.999998f, 0.999998f, 0.999998f, 0.999998f, +0.999998f, 0.999998f, 0.999999f, 0.999999f, 0.999999f, +0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f, +0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f, +1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f, +1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f, +1.000000f, +}; + +#endif /*TANSIG_TABLE_H*/ diff --git a/managed_components/78__esp-opus/dnn/test_vec.c b/managed_components/78__esp-opus/dnn/test_vec.c new file mode 100644 index 0000000..d14d250 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/test_vec.c @@ -0,0 +1,128 @@ +#include +#include +#include "opus_types.h" +#include "arch.h" +#include "common.h" +#include "tansig_table.h" + +#define LPCNET_TEST + +// we need to call two versions of each functions that have the same +// name, so use #defines to temp rename them + +#define lpcnet_exp2 lpcnet_exp2_fast +#define tansig_approx tansig_approx_fast +#define sigmoid_approx sigmoid_approx_fast +#define softmax softmax_fast +#define vec_tanh vec_tanh_fast +#define vec_sigmoid vec_sigmoid_fast +#define sgemv_accum16 sgemv_accum16_fast +#define sparse_sgemv_accum16 sparse_sgemv_accum16_fast + +#ifdef __AVX__ +#include "vec_avx.h" +#ifdef __AVX2__ +const char simd[]="AVX2"; +#else +const char simd[]="AVX"; +#endif +#elif __ARM_NEON__ +#include "vec_neon.h" +const char simd[]="NEON"; +#else +const char simd[]="none"; + +#endif + +#undef lpcnet_exp2 +#undef tansig_approx +#undef sigmoid_approx +#undef softmax +#undef vec_tanh +#undef vec_sigmoid +#undef sgemv_accum16 +#undef sparse_sgemv_accum16 +#include "vec.h" + +#define ROW_STEP 16 +#define ROWS ROW_STEP*10 +#define COLS 2 +#define ENTRIES 2 + +int test_sgemv_accum16() { + float weights[ROWS*COLS]; + float x[COLS]; + float out[ROWS], out_fast[ROWS]; + int i; + + printf("sgemv_accum16.....................: "); + for(i=0; i None: + self.names = names + self.quantization_noise = None + self.scale = scale + + def __call__(self, module, inputs, *args, before=True): + if not module.training: return + + if before: + self.quantization_noise = dict() + for name in self.names: + weight = getattr(module, name) + if self.scale is None: + self.quantization_noise[name] = q_scaled_noise(module, weight) + else: + self.quantization_noise[name] = \ + self.scale * (torch.rand_like(weight) - 0.5) + with torch.no_grad(): + weight.data[:] = weight + self.quantization_noise[name] + else: + for name in self.names: + weight = getattr(module, name) + with torch.no_grad(): + weight.data[:] = weight - self.quantization_noise[name] + self.quantization_noise = None + + def apply(module, names=['weight'], scale=None): + fn = SoftQuant(names, scale) + + for name in names: + if not hasattr(module, name): + raise ValueError("") + + fn_before = lambda *x : fn(*x, before=True) + fn_after = lambda *x : fn(*x, before=False) + setattr(fn_before, 'sqm', fn) + setattr(fn_after, 'sqm', fn) + + + module.register_forward_pre_hook(fn_before) + module.register_forward_hook(fn_after) + + module + + return fn + + +def soft_quant(module, names=['weight'], scale=None): + fn = SoftQuant.apply(module, names, scale) + return module + +def remove_soft_quant(module, names=['weight']): + for k, hook in module._forward_pre_hooks.items(): + if hasattr(hook, 'sqm'): + if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: + del module._forward_pre_hooks[k] + for k, hook in module._forward_hooks.items(): + if hasattr(hook, 'sqm'): + if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: + del module._forward_hooks[k] + + return module \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/__init__.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/__init__.py new file mode 100644 index 0000000..cee0143 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/__init__.py @@ -0,0 +1,2 @@ +from .relegance import relegance_gradient_weighting, relegance_create_tconv_kernel, relegance_map_relevance_to_input_domain, relegance_resize_relevance_to_input_size +from .meta_critic import MetaCritic \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py new file mode 100644 index 0000000..1af0f8f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py @@ -0,0 +1,85 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +class MetaCritic(): + def __init__(self, normalize=False, gamma=0.9, beta=0.0, joint_stats=False): + """ Class for assessing relevance of discriminator scores + + Args: + gamma (float, optional): update rate for tracking discriminator stats. Defaults to 0.9. + beta (float, optional): Miminum confidence related threshold. Defaults to 0.0. + """ + self.normalize = normalize + self.gamma = gamma + self.beta = beta + self.joint_stats = joint_stats + + self.disc_stats = dict() + + def __call__(self, disc_id, real_scores, generated_scores): + """ calculates relevance from normalized scores + + Args: + disc_id (any valid key): id for tracking discriminator statistics + real_scores (torch.tensor): scores for real data + generated_scores (torch.tensor): scores for generated data; expecting device to match real_scores.device + + Returns: + torch.tensor: output-domain relevance + """ + + if self.normalize: + real_std = torch.std(real_scores.detach()).cpu().item() + gen_std = torch.std(generated_scores.detach()).cpu().item() + std = (real_std**2 + gen_std**2) ** .5 + mean = torch.mean(real_scores.detach()).cpu().item() - torch.mean(generated_scores.detach()).cpu().item() + + key = 0 if self.joint_stats else disc_id + + if key in self.disc_stats: + self.disc_stats[key]['std'] = self.gamma * self.disc_stats[key]['std'] + (1 - self.gamma) * std + self.disc_stats[key]['mean'] = self.gamma * self.disc_stats[key]['mean'] + (1 - self.gamma) * mean + else: + self.disc_stats[key] = { + 'std': std + 1e-5, + 'mean': mean + } + + std = self.disc_stats[key]['std'] + mean = self.disc_stats[key]['mean'] + else: + mean, std = 0, 1 + + relevance = torch.relu((real_scores - generated_scores - mean) / std + mean - self.beta) + + if False: print(f"relevance({disc_id}): {relevance.min()=} {relevance.max()=} {relevance.mean()=}") + + return relevance \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/relegance.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/relegance.py new file mode 100644 index 0000000..29c5be2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/relegance/relegance.py @@ -0,0 +1,449 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +import torch.nn.functional as F + + +def view_one_hot(index, length): + vec = length * [1] + vec[index] = -1 + return vec + +def create_smoothing_kernel(widths, gamma=1.5): + """ creates a truncated gaussian smoothing kernel for the given widths + + Parameters: + ----------- + widths: list[Int] or torch.LongTensor + specifies the shape of the smoothing kernel, entries must be > 0. + + gamma: float, optional + decay factor for gaussian relative to kernel size + + Returns: + -------- + kernel: torch.FloatTensor + """ + + widths = torch.LongTensor(widths) + num_dims = len(widths) + + assert(widths.min() > 0) + + centers = widths.float() / 2 - 0.5 + sigmas = gamma * (centers + 1) + + vals = [] + + vals= [((torch.arange(widths[i]) - centers[i]) / sigmas[i]) ** 2 for i in range(num_dims)] + vals = sum([vals[i].view(view_one_hot(i, num_dims)) for i in range(num_dims)]) + + kernel = torch.exp(- vals) + kernel = kernel / kernel.sum() + + return kernel + + +def create_partition_kernel(widths, strides): + """ creates a partition kernel for mapping a convolutional network output back to the input domain + + Given a fully convolutional network with receptive field of shape widths and the given strides, this + function construncts an intorpolation kernel whose tranlations by multiples of the given strides form + a partition of one on the input domain. + + Parameter: + ---------- + widths: list[Int] or torch.LongTensor + shape of receptive field + + strides: list[Int] or torch.LongTensor + total strides of convolutional network + + Returns: + kernel: torch.FloatTensor + """ + + num_dims = len(widths) + assert num_dims == len(strides) and num_dims in {1, 2, 3} + + convs = {1 : F.conv1d, 2 : F.conv2d, 3 : F.conv3d} + + widths = torch.LongTensor(widths) + strides = torch.LongTensor(strides) + + proto_kernel = torch.ones(torch.minimum(strides, widths).tolist()) + + # create interpolation kernel eta + eta_widths = widths - strides + 1 + if eta_widths.min() <= 0: + print("[create_partition_kernel] warning: receptive field does not cover input domain") + eta_widths = torch.maximum(eta_widths, torch.ones_like(eta_widths)) + + + eta = create_smoothing_kernel(eta_widths).view(1, 1, *eta_widths.tolist()) + + padding = torch.repeat_interleave(eta_widths - 1, 2, 0).tolist()[::-1] # ordering of dimensions for padding and convolution functions is reversed in torch + padded_proto_kernel = F.pad(proto_kernel, padding) + padded_proto_kernel = padded_proto_kernel.view(1, 1, *padded_proto_kernel.shape) + kernel = convs[num_dims](padded_proto_kernel, eta) + + return kernel + + +def receptive_field(conv_model, input_shape, output_position): + """ estimates boundaries of receptive field connected to output_position via autograd + + Parameters: + ----------- + conv_model: nn.Module or autograd function + function or model implementing fully convolutional model + + input_shape: List[Int] + input shape ignoring batch dimension, i.e. [num_channels, dim1, dim2, ...] + + output_position: List[Int] + output position for which the receptive field is determined; the function raises an exception + if output_position is out of bounds for the given input_shape. + + Returns: + -------- + low: List[Int] + start indices of receptive field + + high: List[Int] + stop indices of receptive field + + """ + + x = torch.randn((1,) + tuple(input_shape), requires_grad=True) + y = conv_model(x) + + # collapse channels and remove batch dimension + y = torch.sum(y, 1)[0] + + # create mask + mask = torch.zeros_like(y) + index = [torch.tensor(i) for i in output_position] + try: + mask.index_put_(index, torch.tensor(1, dtype=mask.dtype)) + except IndexError: + raise ValueError('output_position out of bounds') + + (mask * y).sum().backward() + + # sum over channels and remove batch dimension + grad = torch.sum(x.grad, dim=1)[0] + tmp = torch.nonzero(grad, as_tuple=True) + low = [t.min().item() for t in tmp] + high = [t.max().item() for t in tmp] + + return low, high + +def estimate_conv_parameters(model, num_channels, num_dims, width, max_stride=10): + """ attempts to estimate receptive field size, strides and left paddings for given model + + + Parameters: + ----------- + model: nn.Module or autograd function + fully convolutional model for which parameters are estimated + + num_channels: Int + number of input channels for model + + num_dims: Int + number of input dimensions for model (without channel dimension) + + width: Int + width of the input tensor (a hyper-square) on which the receptive fields are derived via autograd + + max_stride: Int, optional + assumed maximal stride of the model for any dimension, when set too low the function may fail for + any value of width + + Returns: + -------- + receptive_field_size: List[Int] + receptive field size in all dimension + + strides: List[Int] + stride in all dimensions + + left_paddings: List[Int] + left padding in all dimensions; this is relevant for aligning the receptive field on the input plane + + Raises: + ------- + ValueError, KeyError + + """ + + input_shape = [num_channels] + num_dims * [width] + output_position1 = num_dims * [width // (2 * max_stride)] + output_position2 = num_dims * [width // (2 * max_stride) + 1] + + low1, high1 = receptive_field(model, input_shape, output_position1) + low2, high2 = receptive_field(model, input_shape, output_position2) + + widths1 = [h - l + 1 for l, h in zip(low1, high1)] + widths2 = [h - l + 1 for l, h in zip(low2, high2)] + + if not all([w1 - w2 == 0 for w1, w2 in zip(widths1, widths2)]) or not all([l1 != l2 for l1, l2 in zip(low1, low2)]): + raise ValueError("[estimate_strides]: widths to small to determine strides") + + receptive_field_size = widths1 + strides = [l2 - l1 for l1, l2 in zip(low1, low2)] + left_paddings = [s * p - l for l, s, p in zip(low1, strides, output_position1)] + + return receptive_field_size, strides, left_paddings + +def inspect_conv_model(model, num_channels, num_dims, max_width=10000, width_hint=None, stride_hint=None, verbose=False): + """ determines size of receptive field, strides and padding probabilistically + + + Parameters: + ----------- + model: nn.Module or autograd function + fully convolutional model for which parameters are estimated + + num_channels: Int + number of input channels for model + + num_dims: Int + number of input dimensions for model (without channel dimension) + + max_width: Int + maximum width of the input tensor (a hyper-square) on which the receptive fields are derived via autograd + + verbose: bool, optional + if true, the function prints parameters for individual trials + + Returns: + -------- + receptive_field_size: List[Int] + receptive field size in all dimension + + strides: List[Int] + stride in all dimensions + + left_paddings: List[Int] + left padding in all dimensions; this is relevant for aligning the receptive field on the input plane + + Raises: + ------- + ValueError + + """ + + max_stride = max_width // 2 + stride = max_stride // 100 + width = max_width // 100 + + if width_hint is not None: width = 2 * width_hint + if stride_hint is not None: stride = stride_hint + + did_it = False + while width < max_width and stride < max_stride: + try: + if verbose: print(f"[inspect_conv_model] trying parameters {width=}, {stride=}") + receptive_field_size, strides, left_paddings = estimate_conv_parameters(model, num_channels, num_dims, width, stride) + did_it = True + except: + pass + + if did_it: break + + width *= 2 + if width >= max_width and stride < max_stride: + stride *= 2 + width = 2 * stride + + if not did_it: + raise ValueError(f'could not determine conv parameter with given max_width={max_width}') + + return receptive_field_size, strides, left_paddings + + +class GradWeight(torch.autograd.Function): + def __init__(self): + super().__init__() + + @staticmethod + def forward(ctx, x, weight): + ctx.save_for_backward(weight) + return x.clone() + + @staticmethod + def backward(ctx, grad_output): + weight, = ctx.saved_tensors + + grad_input = grad_output * weight + + return grad_input, None + + +# API + +def relegance_gradient_weighting(x, weight): + """ + + Args: + x (torch.tensor): input tensor + weight (torch.tensor or None): weight tensor for gradients of x; if None, no gradient weighting will be applied in backward pass + + Returns: + torch.tensor: the unmodified input tensor x + + Raises: + RuntimeError: if estimation of parameters fails due to exceeded compute budget + """ + if weight is None: + return x + else: + return GradWeight.apply(x, weight) + + + +def relegance_create_tconv_kernel(model, num_channels, num_dims, width_hint=None, stride_hint=None, verbose=False): + """ creates parameters for mapping back output domain relevance to input tomain + + Args: + model (nn.Module or autograd.Function): fully convolutional model + num_channels (int): number of input channels to model + num_dims (int): number of input dimensions of model (without channel and batch dimension) + width_hint(int or None): optional hint at maximal width of receptive field + stride_hint(int or None): optional hint at maximal stride + + Returns: + dict: contains kernel, kernel dimensions, strides and left paddings for transposed convolution + """ + + max_width = int(100000 / (10 ** num_dims)) + + did_it = False + try: + receptive_field_size, strides, left_paddings = inspect_conv_model(model, num_channels, num_dims, max_width=max_width, width_hint=width_hint, stride_hint=stride_hint, verbose=verbose) + did_it = True + except: + # try once again with larger max_width + max_width *= 10 + + # crash if exception is raised + try: + if not did_it: receptive_field_size, strides, left_paddings = inspect_conv_model(model, num_channels, num_dims, max_width=max_width, width_hint=width_hint, stride_hint=stride_hint, verbose=verbose) + except: + raise RuntimeError("could not determine parameters within given compute budget") + + partition_kernel = create_partition_kernel(receptive_field_size, strides) + partition_kernel = torch.repeat_interleave(partition_kernel, num_channels, 1) + + tconv_parameters = { + 'kernel': partition_kernel, + 'receptive_field_shape': receptive_field_size, + 'stride': strides, + 'left_padding': left_paddings, + 'num_dims': num_dims + } + + return tconv_parameters + + + +def relegance_map_relevance_to_input_domain(od_relevance, tconv_parameters): + """ maps output-domain relevance to input-domain relevance via transpose convolution + + Args: + od_relevance (torch.tensor): output-domain relevance + tconv_parameters (dict): parameter dict as created by relegance_create_tconv_kernel + + Returns: + torch.tensor: input-domain relevance. The tensor is left aligned, i.e. the all-zero index of the output corresponds to the all-zero index of the discriminator input. + Otherwise, the size of the output tensor does not need to match the size of the discriminator input. Use relegance_resize_relevance_to_input_size for a + convenient way to adjust the output to the correct size. + + Raises: + ValueError: if number of dimensions is not supported + """ + + kernel = tconv_parameters['kernel'].to(od_relevance.device) + rf_shape = tconv_parameters['receptive_field_shape'] + stride = tconv_parameters['stride'] + left_padding = tconv_parameters['left_padding'] + + num_dims = len(kernel.shape) - 2 + + # repeat boundary values + od_padding = [rf_shape[i//2] // stride[i//2] + 1 for i in range(2 * num_dims)] + padded_od_relevance = F.pad(od_relevance, od_padding[::-1], mode='replicate') + od_padding = od_padding[::2] + + # apply mapping and left trimming + if num_dims == 1: + id_relevance = F.conv_transpose1d(padded_od_relevance, kernel, stride=stride) + id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :] + elif num_dims == 2: + id_relevance = F.conv_transpose2d(padded_od_relevance, kernel, stride=stride) + id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :, left_padding[1] + stride[1] * od_padding[1]:] + elif num_dims == 3: + id_relevance = F.conv_transpose2d(padded_od_relevance, kernel, stride=stride) + id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :, left_padding[1] + stride[1] * od_padding[1]:, left_padding[2] + stride[2] * od_padding[2] :] + else: + raise ValueError(f'[relegance_map_to_input_domain] error: num_dims = {num_dims} not supported') + + return id_relevance + + +def relegance_resize_relevance_to_input_size(reference_input, relevance): + """ adjusts size of relevance tensor to reference input size + + Args: + reference_input (torch.tensor): discriminator input tensor for reference + relevance (torch.tensor): input-domain relevance corresponding to input tensor reference_input + + Returns: + torch.tensor: resized relevance + + Raises: + ValueError: if number of dimensions is not supported + """ + resized_relevance = torch.zeros_like(reference_input) + + num_dims = len(reference_input.shape) - 2 + with torch.no_grad(): + if num_dims == 1: + resized_relevance[:] = relevance[..., : min(reference_input.size(-1), relevance.size(-1))] + elif num_dims == 2: + resized_relevance[:] = relevance[..., : min(reference_input.size(-2), relevance.size(-2)), : min(reference_input.size(-1), relevance.size(-1))] + elif num_dims == 3: + resized_relevance[:] = relevance[..., : min(reference_input.size(-3), relevance.size(-3)), : min(reference_input.size(-2), relevance.size(-2)), : min(reference_input.size(-1), relevance.size(-1))] + else: + raise ValueError(f'[relegance_map_to_input_domain] error: num_dims = {num_dims} not supported') + + return resized_relevance \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py new file mode 100644 index 0000000..fcc9174 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py @@ -0,0 +1,6 @@ +from .gru_sparsifier import GRUSparsifier +from .conv1d_sparsifier import Conv1dSparsifier +from .conv_transpose1d_sparsifier import ConvTranspose1dSparsifier +from .linear_sparsifier import LinearSparsifier +from .common import sparsify_matrix, calculate_gru_flops_per_step +from .utils import mark_for_sparsification, create_sparsifier \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py new file mode 100644 index 0000000..dd62f40 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py @@ -0,0 +1,58 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +class BaseSparsifier: + def __init__(self, task_list, start, stop, interval, exponent=3): + + # just copying parameters... + self.start = start + self.stop = stop + self.interval = interval + self.exponent = exponent + self.task_list = task_list + + # ... and setting counter to 0 + self.step_counter = 0 + + def step(self, verbose=False): + # compute current interpolation factor + self.step_counter += 1 + + if self.step_counter < self.start: + return + elif self.step_counter < self.stop: + # update only every self.interval-th interval + if self.step_counter % self.interval: + return + + alpha = ((self.stop - self.step_counter) / (self.stop - self.start)) ** self.exponent + else: + alpha = 0 + + self.sparsify(alpha, verbose=verbose) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/common.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/common.py new file mode 100644 index 0000000..4718180 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/common.py @@ -0,0 +1,123 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +debug=True + +def sparsify_matrix(matrix : torch.tensor, density : float, block_size, keep_diagonal : bool=False, return_mask : bool=False): + """ sparsifies matrix with specified block size + + Parameters: + ----------- + matrix : torch.tensor + matrix to sparsify + density : int + target density + block_size : [int, int] + block size dimensions + keep_diagonal : bool + If true, the diagonal will be kept. This option requires block_size[0] == block_size[1] and defaults to False + """ + + m, n = matrix.shape + m1, n1 = block_size + + if m % m1 or n % n1: + raise ValueError(f"block size {(m1, n1)} does not divide matrix size {(m, n)}") + + # extract diagonal if keep_diagonal = True + if keep_diagonal: + if m != n: + raise ValueError("Attempting to sparsify non-square matrix with keep_diagonal=True") + + to_spare = torch.diag(torch.diag(matrix)) + matrix = matrix - to_spare + else: + to_spare = torch.zeros_like(matrix) + + # calculate energy in sub-blocks + x = torch.reshape(matrix, (m // m1, m1, n // n1, n1)) + x = x ** 2 + block_energies = torch.sum(torch.sum(x, dim=3), dim=1) + + number_of_blocks = (m * n) // (m1 * n1) + number_of_survivors = round(number_of_blocks * density) + + # masking threshold + if number_of_survivors == 0: + threshold = 0 + else: + threshold = torch.sort(torch.flatten(block_energies)).values[-number_of_survivors] + + # create mask + mask = torch.ones_like(block_energies) + mask[block_energies < threshold] = 0 + mask = torch.repeat_interleave(mask, m1, dim=0) + mask = torch.repeat_interleave(mask, n1, dim=1) + + # perform masking + masked_matrix = mask * matrix + to_spare + + if return_mask: + return masked_matrix, mask + else: + return masked_matrix + +def calculate_gru_flops_per_step(gru, sparsification_dict=dict(), drop_input=False): + input_size = gru.input_size + hidden_size = gru.hidden_size + flops = 0 + + input_density = ( + sparsification_dict.get('W_ir', [1])[0] + + sparsification_dict.get('W_in', [1])[0] + + sparsification_dict.get('W_iz', [1])[0] + ) / 3 + + recurrent_density = ( + sparsification_dict.get('W_hr', [1])[0] + + sparsification_dict.get('W_hn', [1])[0] + + sparsification_dict.get('W_hz', [1])[0] + ) / 3 + + # input matrix vector multiplications + if not drop_input: + flops += 2 * 3 * input_size * hidden_size * input_density + + # recurrent matrix vector multiplications + flops += 2 * 3 * hidden_size * hidden_size * recurrent_density + + # biases + flops += 6 * hidden_size + + # activations estimated by 10 flops per activation + flops += 30 * hidden_size + + return flops diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py new file mode 100644 index 0000000..1ac51d0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py @@ -0,0 +1,133 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +from .base_sparsifier import BaseSparsifier +from .common import sparsify_matrix, debug + + +class Conv1dSparsifier(BaseSparsifier): + def __init__(self, task_list, start, stop, interval, exponent=3): + """ Sparsifier for torch.nn.GRUs + + Parameters: + ----------- + task_list : list + task_list contains a list of tuples (conv1d, params), where conv1d is an instance + of torch.nn.Conv1d and params is a tuple (density, [m, n]), + where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which + sparsification is applied. + + start : int + training step after which sparsification will be started. + + stop : int + training step after which sparsification will be completed. + + interval : int + sparsification interval for steps between start and stop. After stop sparsification will be + carried out after every call to GRUSparsifier.step() + + exponent : float + Interpolation exponent for sparsification interval. In step i sparsification will be carried out + with density (alpha + target_density * (1 * alpha)), where + alpha = ((stop - i) / (start - stop)) ** exponent + + Example: + -------- + >>> import torch + >>> conv = torch.nn.Conv1d(8, 16, 8) + >>> params = (0.2, [8, 4]) + >>> sparsifier = Conv1dSparsifier([(conv, params)], 0, 100, 50) + >>> for i in range(100): + ... sparsifier.step() + """ + super().__init__(task_list, start, stop, interval, exponent=3) + + self.last_mask = None + + + def sparsify(self, alpha, verbose=False): + """ carries out sparsification step + + Call this function after optimizer.step in your + training loop. + + Parameters: + ---------- + alpha : float + density interpolation parameter (1: dense, 0: target density) + verbose : bool + if true, densities are printed out + + Returns: + -------- + None + + """ + + with torch.no_grad(): + for conv, params in self.task_list: + # reshape weight + if hasattr(conv, 'weight_v'): + weight = conv.weight_v + else: + weight = conv.weight + i, o, k = weight.shape + w = weight.permute(0, 2, 1).flatten(1) + target_density, block_size = params + density = alpha + (1 - alpha) * target_density + w, new_mask = sparsify_matrix(w, density, block_size, return_mask=True) + w = w.reshape(i, k, o).permute(0, 2, 1) + weight[:] = w + + if self.last_mask is not None: + if not torch.all(self.last_mask * new_mask == new_mask) and debug: + print("weight resurrection in conv.weight") + + self.last_mask = new_mask + + if verbose: + print(f"conv1d_sparsier[{self.step_counter}]: {density=}") + + +if __name__ == "__main__": + print("Testing sparsifier") + + import torch + conv = torch.nn.Conv1d(8, 16, 8) + params = (0.2, [8, 4]) + + sparsifier = Conv1dSparsifier([(conv, params)], 0, 100, 5) + + for i in range(100): + sparsifier.step(verbose=True) + + print(conv.weight) diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py new file mode 100644 index 0000000..6d9398f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py @@ -0,0 +1,134 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + + +from .base_sparsifier import BaseSparsifier +from .common import sparsify_matrix, debug + + +class ConvTranspose1dSparsifier(BaseSparsifier): + def __init__(self, task_list, start, stop, interval, exponent=3): + """ Sparsifier for torch.nn.GRUs + + Parameters: + ----------- + task_list : list + task_list contains a list of tuples (conv1d, params), where conv1d is an instance + of torch.nn.Conv1d and params is a tuple (density, [m, n]), + where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which + sparsification is applied. + + start : int + training step after which sparsification will be started. + + stop : int + training step after which sparsification will be completed. + + interval : int + sparsification interval for steps between start and stop. After stop sparsification will be + carried out after every call to GRUSparsifier.step() + + exponent : float + Interpolation exponent for sparsification interval. In step i sparsification will be carried out + with density (alpha + target_density * (1 * alpha)), where + alpha = ((stop - i) / (start - stop)) ** exponent + + Example: + -------- + >>> import torch + >>> conv = torch.nn.ConvTranspose1d(8, 16, 8) + >>> params = (0.2, [8, 4]) + >>> sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 50) + >>> for i in range(100): + ... sparsifier.step() + """ + + super().__init__(task_list, start, stop, interval, exponent=3) + + self.last_mask = None + + def sparsify(self, alpha, verbose=False): + """ carries out sparsification step + + Call this function after optimizer.step in your + training loop. + + Parameters: + ---------- + alpha : float + density interpolation parameter (1: dense, 0: target density) + verbose : bool + if true, densities are printed out + + Returns: + -------- + None + + """ + + with torch.no_grad(): + for conv, params in self.task_list: + # reshape weight + if hasattr(conv, 'weight_v'): + weight = conv.weight_v + else: + weight = conv.weight + i, o, k = weight.shape + w = weight.permute(2, 1, 0).reshape(k * o, i) + target_density, block_size = params + density = alpha + (1 - alpha) * target_density + w, new_mask = sparsify_matrix(w, density, block_size, return_mask=True) + w = w.reshape(k, o, i).permute(2, 1, 0) + weight[:] = w + + if self.last_mask is not None: + if not torch.all(self.last_mask * new_mask == new_mask) and debug: + print("weight resurrection in conv.weight") + + self.last_mask = new_mask + + if verbose: + print(f"convtrans1d_sparsier[{self.step_counter}]: {density=}") + + +if __name__ == "__main__": + print("Testing sparsifier") + + import torch + conv = torch.nn.ConvTranspose1d(8, 16, 4, 4) + params = (0.2, [8, 4]) + + sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 5) + + for i in range(100): + sparsifier.step(verbose=True) + + print(conv.weight) diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py new file mode 100644 index 0000000..417b04b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py @@ -0,0 +1,178 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +from .base_sparsifier import BaseSparsifier +from .common import sparsify_matrix, debug + + +class GRUSparsifier(BaseSparsifier): + def __init__(self, task_list, start, stop, interval, exponent=3): + """ Sparsifier for torch.nn.GRUs + + Parameters: + ----------- + task_list : list + task_list contains a list of tuples (gru, sparsify_dict), where gru is an instance + of torch.nn.GRU and sparsify_dic is a dictionary with keys in {'W_ir', 'W_iz', 'W_in', + 'W_hr', 'W_hz', 'W_hn'} corresponding to the input and recurrent weights for the reset, + update, and new gate. The values of sparsify_dict are tuples (density, [m, n], keep_diagonal), + where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which + sparsification is applied and keep_diagonal is a bool variable indicating whether the diagonal + should be kept. + + start : int + training step after which sparsification will be started. + + stop : int + training step after which sparsification will be completed. + + interval : int + sparsification interval for steps between start and stop. After stop sparsification will be + carried out after every call to GRUSparsifier.step() + + exponent : float + Interpolation exponent for sparsification interval. In step i sparsification will be carried out + with density (alpha + target_density * (1 * alpha)), where + alpha = ((stop - i) / (start - stop)) ** exponent + + Example: + -------- + >>> import torch + >>> gru = torch.nn.GRU(10, 20) + >>> sparsify_dict = { + ... 'W_ir' : (0.5, [2, 2], False), + ... 'W_iz' : (0.6, [2, 2], False), + ... 'W_in' : (0.7, [2, 2], False), + ... 'W_hr' : (0.1, [4, 4], True), + ... 'W_hz' : (0.2, [4, 4], True), + ... 'W_hn' : (0.3, [4, 4], True), + ... } + >>> sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 50) + >>> for i in range(100): + ... sparsifier.step() + """ + super().__init__(task_list, start, stop, interval, exponent=3) + + self.last_masks = {key : None for key in ['W_ir', 'W_in', 'W_iz', 'W_hr', 'W_hn', 'W_hz']} + + def sparsify(self, alpha, verbose=False): + """ carries out sparsification step + + Call this function after optimizer.step in your + training loop. + + Parameters: + ---------- + alpha : float + density interpolation parameter (1: dense, 0: target density) + verbose : bool + if true, densities are printed out + + Returns: + -------- + None + + """ + + with torch.no_grad(): + for gru, params in self.task_list: + hidden_size = gru.hidden_size + + # input weights + for i, key in enumerate(['W_ir', 'W_iz', 'W_in']): + if key in params: + if hasattr(gru, 'weight_ih_l0_v'): + weight = gru.weight_ih_l0_v + else: + weight = gru.weight_ih_l0 + density = alpha + (1 - alpha) * params[key][0] + if verbose: + print(f"[{self.step_counter}]: {key} density: {density}") + + weight[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( + weight[i * hidden_size : (i + 1) * hidden_size, : ], + density, # density + params[key][1], # block_size + params[key][2], # keep_diagonal (might want to set this to False) + return_mask=True + ) + + if type(self.last_masks[key]) != type(None): + if not torch.all(self.last_masks[key] * new_mask == new_mask) and debug: + print("weight resurrection in weight_ih_l0_v") + + self.last_masks[key] = new_mask + + # recurrent weights + for i, key in enumerate(['W_hr', 'W_hz', 'W_hn']): + if key in params: + if hasattr(gru, 'weight_hh_l0_v'): + weight = gru.weight_hh_l0_v + else: + weight = gru.weight_hh_l0 + density = alpha + (1 - alpha) * params[key][0] + if verbose: + print(f"[{self.step_counter}]: {key} density: {density}") + weight[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( + weight[i * hidden_size : (i + 1) * hidden_size, : ], + density, + params[key][1], # block_size + params[key][2], # keep_diagonal (might want to set this to False) + return_mask=True + ) + + if type(self.last_masks[key]) != type(None): + if not torch.all(self.last_masks[key] * new_mask == new_mask) and True: + print("weight resurrection in weight_hh_l0_v") + + self.last_masks[key] = new_mask + + + +if __name__ == "__main__": + print("Testing sparsifier") + + gru = torch.nn.GRU(10, 20) + sparsify_dict = { + 'W_ir' : (0.5, [2, 2], False), + 'W_iz' : (0.6, [2, 2], False), + 'W_in' : (0.7, [2, 2], False), + 'W_hr' : (0.1, [4, 4], True), + 'W_hz' : (0.2, [4, 4], True), + 'W_hn' : (0.3, [4, 4], True), + } + + sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 10) + + for i in range(100): + sparsifier.step(verbose=True) + + print(gru.weight_hh_l0) diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py new file mode 100644 index 0000000..59251dd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py @@ -0,0 +1,128 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +from .base_sparsifier import BaseSparsifier +from .common import sparsify_matrix + + +class LinearSparsifier(BaseSparsifier): + def __init__(self, task_list, start, stop, interval, exponent=3): + """ Sparsifier for torch.nn.GRUs + + Parameters: + ----------- + task_list : list + task_list contains a list of tuples (linear, params), where linear is an instance + of torch.nn.Linear and params is a tuple (density, [m, n]), + where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which + sparsification is applied. + + start : int + training step after which sparsification will be started. + + stop : int + training step after which sparsification will be completed. + + interval : int + sparsification interval for steps between start and stop. After stop sparsification will be + carried out after every call to GRUSparsifier.step() + + exponent : float + Interpolation exponent for sparsification interval. In step i sparsification will be carried out + with density (alpha + target_density * (1 * alpha)), where + alpha = ((stop - i) / (start - stop)) ** exponent + + Example: + -------- + >>> import torch + >>> linear = torch.nn.Linear(8, 16) + >>> params = (0.2, [8, 4]) + >>> sparsifier = LinearSparsifier([(linear, params)], 0, 100, 50) + >>> for i in range(100): + ... sparsifier.step() + """ + + super().__init__(task_list, start, stop, interval, exponent=3) + + self.last_mask = None + + def sparsify(self, alpha, verbose=False): + """ carries out sparsification step + + Call this function after optimizer.step in your + training loop. + + Parameters: + ---------- + alpha : float + density interpolation parameter (1: dense, 0: target density) + verbose : bool + if true, densities are printed out + + Returns: + -------- + None + + """ + + with torch.no_grad(): + for linear, params in self.task_list: + if hasattr(linear, 'weight_v'): + weight = linear.weight_v + else: + weight = linear.weight + target_density, block_size = params + density = alpha + (1 - alpha) * target_density + weight[:], new_mask = sparsify_matrix(weight, density, block_size, return_mask=True) + + if self.last_mask is not None: + if not torch.all(self.last_mask * new_mask == new_mask) and debug: + print("weight resurrection in conv.weight") + + self.last_mask = new_mask + + if verbose: + print(f"linear_sparsifier[{self.step_counter}]: {density=}") + + +if __name__ == "__main__": + print("Testing sparsifier") + + import torch + linear = torch.nn.Linear(8, 16) + params = (0.2, [4, 2]) + + sparsifier = LinearSparsifier([(linear, params)], 0, 100, 5) + + for i in range(100): + sparsifier.step(verbose=True) + + print(linear.weight) diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/utils.py b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/utils.py new file mode 100644 index 0000000..42f2235 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/dnntools/sparsification/utils.py @@ -0,0 +1,64 @@ +import torch + +from dnntools.sparsification import GRUSparsifier, LinearSparsifier, Conv1dSparsifier, ConvTranspose1dSparsifier + +def mark_for_sparsification(module, params): + setattr(module, 'sparsify', True) + setattr(module, 'sparsification_params', params) + return module + +def create_sparsifier(module, start, stop, interval): + sparsifier_list = [] + for m in module.modules(): + if hasattr(m, 'sparsify'): + if isinstance(m, torch.nn.GRU): + sparsifier_list.append( + GRUSparsifier([(m, m.sparsification_params)], start, stop, interval) + ) + elif isinstance(m, torch.nn.Linear): + sparsifier_list.append( + LinearSparsifier([(m, m.sparsification_params)], start, stop, interval) + ) + elif isinstance(m, torch.nn.Conv1d): + sparsifier_list.append( + Conv1dSparsifier([(m, m.sparsification_params)], start, stop, interval) + ) + elif isinstance(m, torch.nn.ConvTranspose1d): + sparsifier_list.append( + ConvTranspose1dSparsifier([(m, m.sparsification_params)], start, stop, interval) + ) + else: + print(f"[create_sparsifier] warning: module {m} marked for sparsification but no suitable sparsifier exists.") + + def sparsify(verbose=False): + for sparsifier in sparsifier_list: + sparsifier.step(verbose) + + return sparsify + + +def count_parameters(model, verbose=False): + total = 0 + for name, p in model.named_parameters(): + count = torch.ones_like(p).sum().item() + + if verbose: + print(f"{name}: {count} parameters") + + total += count + + return total + +def estimate_nonzero_parameters(module): + num_zero_parameters = 0 + if hasattr(module, 'sparsify'): + params = module.sparsification_params + if isinstance(module, torch.nn.Conv1d) or isinstance(module, torch.nn.ConvTranspose1d): + num_zero_parameters = torch.ones_like(module.weight).sum().item() * (1 - params[0]) + elif isinstance(module, torch.nn.GRU): + num_zero_parameters = module.input_size * module.hidden_size * (3 - params['W_ir'][0] - params['W_iz'][0] - params['W_in'][0]) + num_zero_parameters += module.hidden_size * module.hidden_size * (3 - params['W_hr'][0] - params['W_hz'][0] - params['W_hn'][0]) + elif isinstance(module, torch.nn.Linear): + num_zero_parameters = module.in_features * module.out_features * params[0] + else: + raise ValueError(f'unknown sparsification method for module of type {type(module)}') diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/requirements.txt b/managed_components/78__esp-opus/dnn/torch/dnntools/requirements.txt new file mode 100644 index 0000000..08ed5ee --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/requirements.txt @@ -0,0 +1 @@ +torch \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/dnntools/setup.py b/managed_components/78__esp-opus/dnn/torch/dnntools/setup.py new file mode 100644 index 0000000..bc4ef3f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/dnntools/setup.py @@ -0,0 +1,48 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +#!/usr/bin/env/python +import os +from setuptools import setup + +lib_folder = os.path.dirname(os.path.realpath(__file__)) + +with open(os.path.join(lib_folder, 'requirements.txt'), 'r') as f: + install_requires = list(f.read().splitlines()) + +print(install_requires) + +setup(name='dnntools', + version='1.0', + author='Jan Buethe', + author_email='jbuethe@amazon.de', + description='Non-Standard tools for deep neural network training with PyTorch', + packages=['dnntools', 'dnntools.sparsification', 'dnntools.quantization'], + install_requires=install_requires + ) diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/README.md b/managed_components/78__esp-opus/dnn/torch/fargan/README.md new file mode 100644 index 0000000..b986e38 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/README.md @@ -0,0 +1,54 @@ +# Framewise Auto-Regressive GAN (FARGAN) + +Implementation of FARGAN, a low-complexity neural vocoder. Pre-trained models +are provided as C code in the dnn/ directory with the corresponding model in +dnn/models/ directory (name starts with fargan_). If you don't want to train +a new FARGAN model, you can skip straight to the Inference section. + +## Data preparation + +For data preparation you need to build Opus as detailed in the top-level README. +You will need to use the --enable-deep-plc configure option. +The build will produce an executable named "dump_data". +To prepare the training data, run: +``` +./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm +``` +Where the in_speech.pcm speech file is a raw 16-bit PCM file sampled at 16 kHz. +The speech data used for training the model can be found at: +https://media.xiph.org/lpcnet/speech/tts_speech_negative_16k.sw + +## Training + +To perform pre-training, run the following command: +``` +python ./train_fargan.py out_features.f32 out_speech.pcm output_dir --epochs 400 --batch-size 4096 --lr 0.002 --cuda-visible-devices 0 +``` +Once pre-training is complete, run adversarial training using: +``` +python adv_train_fargan.py out_features.f32 out_speech.pcm output_dir --lr 0.000002 --reg-weight 5 --batch-size 160 --cuda-visible-devices 0 --initial-checkpoint output_dir/checkpoints/fargan_400.pth +``` +The final model will be in output_dir/checkpoints/fargan_adv_50.pth. + +The model can optionally be converted to C using: +``` +python dump_fargan_weights.py output_dir/checkpoints/fargan_adv_50.pth fargan_c_dir +``` +which will create a fargan_data.c and a fargan_data.h file in the fargan_c_dir directory. +Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus. + +## Inference + +To run the inference, start by generating the features from the audio using: +``` +./fargan_demo -features test_speech.pcm test_features.f32 +``` +Synthesis can be achieved either using the PyTorch code or the C code. +To synthesize from PyTorch, run: +``` +python test_fargan.py output_dir/checkpoints/fargan_adv_50.pth test_features.f32 output_speech.pcm +``` +To synthesize from the C code, run: +``` +./fargan_demo -fargan-synthesis test_features.f32 output_speech.pcm +``` diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/adv_train_fargan.py b/managed_components/78__esp-opus/dnn/torch/fargan/adv_train_fargan.py new file mode 100644 index 0000000..37816c3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/adv_train_fargan.py @@ -0,0 +1,278 @@ +import os +import argparse +import random +import numpy as np +import sys +import math as m + +import torch +from torch import nn +import torch.nn.functional as F +import tqdm + +import fargan +from dataset import FARGANDataset +from stft_loss import * + +source_dir = os.path.split(os.path.abspath(__file__))[0] +sys.path.append(os.path.join(source_dir, "../osce/")) + +import models as osce_models + + +def fmap_loss(scores_real, scores_gen): + num_discs = len(scores_real) + loss_feat = 0 + for k in range(num_discs): + num_layers = len(scores_gen[k]) - 1 + f = 4 / num_discs / num_layers + for l in range(num_layers): + loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) + + return loss_feat + +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='path to feature file in .f32 format') +parser.add_argument('signal', type=str, help='path to signal file in .s16 format') +parser.add_argument('output', type=str, help='path to output folder') + +parser.add_argument('--suffix', type=str, help="model name suffix", default="") +parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) + + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) +model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9) +model_group.add_argument('--softquant', action="store_true", help="enables soft quantization during training") + +training_group = parser.add_argument_group(title="training parameters") +training_group.add_argument('--batch-size', type=int, help="batch size, default: 128", default=128) +training_group.add_argument('--lr', type=float, help='learning rate, default: 5e-4', default=5e-4) +training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 50', default=50) +training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 60', default=60) +training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 0.0', default=0.0) +training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) +training_group.add_argument('--reg-weight', type=float, help='regression loss weight, default: 1.0', default=1.0) +training_group.add_argument('--fmap-weight', type=float, help='feature matchin loss weight, default: 1.0', default=1.) + +args = parser.parse_args() + +if args.cuda_visible_devices != None: + os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices + +# checkpoints +checkpoint_dir = os.path.join(args.output, 'checkpoints') +checkpoint = dict() +os.makedirs(checkpoint_dir, exist_ok=True) + + +# training parameters +batch_size = args.batch_size +lr = args.lr +epochs = args.epochs +sequence_length = args.sequence_length +lr_decay = args.lr_decay + +adam_betas = [0.8, 0.99] +adam_eps = 1e-8 +features_file = args.features +signal_file = args.signal + +# model parameters +cond_size = args.cond_size + + +checkpoint['batch_size'] = batch_size +checkpoint['lr'] = lr +checkpoint['lr_decay'] = lr_decay +checkpoint['epochs'] = epochs +checkpoint['sequence_length'] = sequence_length +checkpoint['adam_betas'] = adam_betas + + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +checkpoint['model_args'] = () +checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gamma': args.gamma, 'softquant': args.softquant} +print(checkpoint['model_kwargs']) +model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) + + +#discriminator +disc_name = 'fdmresdisc' +disc = osce_models.model_dict[disc_name]( + architecture='free', + design='f_down', + fft_sizes_16k=[2**n for n in range(6, 12)], + freq_roi=[0, 7400], + max_channels=256, + noise_gain=0.0 +) + +if type(args.initial_checkpoint) != type(None): + checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(checkpoint['state_dict'], strict=False) + +checkpoint['state_dict'] = model.state_dict() + + +dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length) +dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) + + +optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) +optimizer_disc = torch.optim.AdamW([p for p in disc.parameters() if p.requires_grad], lr=lr, betas=adam_betas, eps=adam_eps) + + +# learning rate scheduler +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) +scheduler_disc = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer_disc, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) + +states = None + +spect_loss = MultiResolutionSTFTLoss(device).to(device) + +for param in model.parameters(): + param.requires_grad = False + +batch_count = 0 +if __name__ == '__main__': + model.to(device) + disc.to(device) + + for epoch in range(1, epochs + 1): + + m_r = 0 + m_f = 0 + s_r = 1 + s_f = 1 + + running_cont_loss = 0 + running_disc_loss = 0 + running_gen_loss = 0 + running_fmap_loss = 0 + running_reg_loss = 0 + running_wc = 0 + + print(f"training epoch {epoch}...") + with tqdm.tqdm(dataloader, unit='batch') as tepoch: + for i, (features, periods, target, lpc) in enumerate(tepoch): + if epoch == 1 and i == 400: + for param in model.parameters(): + param.requires_grad = True + for param in model.cond_net.parameters(): + param.requires_grad = False + for param in model.sig_net.cond_gain_dense.parameters(): + param.requires_grad = False + + optimizer.zero_grad() + features = features.to(device) + #lpc = lpc.to(device) + #lpc = lpc*(args.gamma**torch.arange(1,17, device=device)) + #lpc = fargan.interp_lpc(lpc, 4) + periods = periods.to(device) + if True: + target = target[:, :sequence_length*160] + #lpc = lpc[:,:sequence_length*4,:] + features = features[:,:sequence_length+4,:] + periods = periods[:,:sequence_length+4] + else: + target=target[::2, :] + #lpc=lpc[::2,:] + features=features[::2,:] + periods=periods[::2,:] + target = target.to(device) + #target = fargan.analysis_filter(target, lpc[:,:,:], nb_subframes=1, gamma=args.gamma) + + #nb_pre = random.randrange(1, 6) + nb_pre = 2 + pre = target[:, :nb_pre*160] + output, _ = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None) + output = torch.cat([pre, output], -1) + + + # discriminator update + scores_gen = disc(output.detach().unsqueeze(1)) + scores_real = disc(target.unsqueeze(1)) + + disc_loss = 0 + for scale in scores_gen: + disc_loss += ((scale[-1]) ** 2).mean() + m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item() + s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item() + + for scale in scores_real: + disc_loss += ((1 - scale[-1]) ** 2).mean() + m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item() + s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item() + + disc_loss = 0.5 * disc_loss / len(scores_gen) + winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) + running_wc += winning_chance + + disc.zero_grad() + disc_loss.backward() + optimizer_disc.step() + + # model update + scores_gen = disc(output.unsqueeze(1)) + if False: # todo: check whether that makes a difference + with torch.no_grad(): + scores_real = disc(target.unsqueeze(1)) + + cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+80], output[:, nb_pre*160:nb_pre*160+80]) + specc_loss = spect_loss(output, target.detach()) + reg_loss = (.00*cont_loss + specc_loss) + + loss_gen = 0 + for scale in scores_gen: + loss_gen += ((1 - scale[-1]) ** 2).mean() / len(scores_gen) + + feat_loss = args.fmap_weight * fmap_loss(scores_real, scores_gen) + + reg_weight = args.reg_weight# + 15./(1 + (batch_count/7600.)) + gen_loss = reg_weight * reg_loss + feat_loss + loss_gen + + model.zero_grad() + + + gen_loss.backward() + optimizer.step() + + #model.clip_weights() + + scheduler.step() + scheduler_disc.step() + + running_cont_loss += cont_loss.detach().cpu().item() + running_gen_loss += loss_gen.detach().cpu().item() + running_disc_loss += disc_loss.detach().cpu().item() + running_fmap_loss += feat_loss.detach().cpu().item() + running_reg_loss += reg_loss.detach().cpu().item() + + + + tepoch.set_postfix(cont_loss=f"{running_cont_loss/(i+1):8.5f}", + reg_weight=f"{reg_weight:8.5f}", + gen_loss=f"{running_gen_loss/(i+1):8.5f}", + disc_loss=f"{running_disc_loss/(i+1):8.5f}", + fmap_loss=f"{running_fmap_loss/(i+1):8.5f}", + reg_loss=f"{running_reg_loss/(i+1):8.5f}", + wc = f"{running_wc/(i+1):8.5f}", + ) + batch_count = batch_count + 1 + + # save checkpoint + checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_adv_{epoch}.pth') + checkpoint['state_dict'] = model.state_dict() + checkpoint['disc_sate_dict'] = disc.state_dict() + checkpoint['loss'] = { + 'cont': running_cont_loss / len(dataloader), + 'gen': running_gen_loss / len(dataloader), + 'disc': running_disc_loss / len(dataloader), + 'fmap': running_fmap_loss / len(dataloader), + 'reg': running_reg_loss / len(dataloader) + } + checkpoint['epoch'] = epoch + torch.save(checkpoint, checkpoint_path) diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/dataset.py b/managed_components/78__esp-opus/dnn/torch/fargan/dataset.py new file mode 100644 index 0000000..2dfbb0b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/dataset.py @@ -0,0 +1,61 @@ +import torch +import numpy as np +import fargan + +class FARGANDataset(torch.utils.data.Dataset): + def __init__(self, + feature_file, + signal_file, + frame_size=160, + sequence_length=15, + lookahead=1, + nb_used_features=20, + nb_features=36): + + self.frame_size = frame_size + self.sequence_length = sequence_length + self.lookahead = lookahead + self.nb_features = nb_features + self.nb_used_features = nb_used_features + pcm_chunk_size = self.frame_size*self.sequence_length + + self.data = np.memmap(signal_file, dtype='int16', mode='r') + #self.data = self.data[1::2] + self.nb_sequences = len(self.data)//(pcm_chunk_size)-4 + self.data = self.data[(4-self.lookahead)*self.frame_size:] + self.data = self.data[:self.nb_sequences*pcm_chunk_size] + + + #self.data = np.reshape(self.data, (self.nb_sequences, pcm_chunk_size)) + sizeof = self.data.strides[-1] + self.data = np.lib.stride_tricks.as_strided(self.data, shape=(self.nb_sequences, pcm_chunk_size*2), + strides=(pcm_chunk_size*sizeof, sizeof)) + + self.features = np.reshape(np.memmap(feature_file, dtype='float32', mode='r'), (-1, nb_features)) + sizeof = self.features.strides[-1] + self.features = np.lib.stride_tricks.as_strided(self.features, shape=(self.nb_sequences, self.sequence_length*2+4, nb_features), + strides=(self.sequence_length*self.nb_features*sizeof, self.nb_features*sizeof, sizeof)) + #self.periods = np.round(50*self.features[:,:,self.nb_used_features-2]+100).astype('int') + self.periods = np.round(np.clip(256./2**(self.features[:,:,self.nb_used_features-2]+1.5), 32, 255)).astype('int') + + self.lpc = self.features[:, :, self.nb_used_features:] + self.features = self.features[:, :, :self.nb_used_features] + print("lpc_size:", self.lpc.shape) + + def __len__(self): + return self.nb_sequences + + def __getitem__(self, index): + features = self.features[index, :, :].copy() + if self.lookahead != 0: + lpc = self.lpc[index, 4-self.lookahead:-self.lookahead, :].copy() + else: + lpc = self.lpc[index, 4:, :].copy() + data = self.data[index, :].copy().astype(np.float32) / 2**15 + periods = self.periods[index, :].copy() + #lpc = lpc*(self.gamma**np.arange(1,17)) + #lpc=lpc[None,:,:] + #lpc = fargan.interp_lpc(lpc, 4) + #lpc=lpc[0,:,:] + + return features, periods, data, lpc diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/dump_fargan_weights.py b/managed_components/78__esp-opus/dnn/torch/fargan/dump_fargan_weights.py new file mode 100644 index 0000000..516e8af --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/dump_fargan_weights.py @@ -0,0 +1,112 @@ +import os +import sys +import argparse + +import torch +from torch import nn + + +sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange')) +import wexchange.torch + +import fargan +#from models import model_dict + +unquantized = [ 'cond_net.pembed', 'cond_net.fdense1', 'sig_net.cond_gain_dense', 'sig_net.gain_dense_out' ] + +unquantized2 = [ + 'cond_net.pembed', + 'cond_net.fdense1', + 'cond_net.fconv1', + 'cond_net.fconv2', + 'cont_net.0', + 'sig_net.cond_gain_dense', + 'sig_net.fwc0.conv', + 'sig_net.fwc0.glu.gate', + 'sig_net.dense1_glu.gate', + 'sig_net.gru1_glu.gate', + 'sig_net.gru2_glu.gate', + 'sig_net.gru3_glu.gate', + 'sig_net.skip_glu.gate', + 'sig_net.skip_dense', + 'sig_net.sig_dense_out', + 'sig_net.gain_dense_out' +] + +description=f""" +This is an unsafe dumping script for FARGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer +and will fail to export any other weights. + +Furthermore, the quanitze option relies on the following explicit list of layers to be excluded: +{unquantized}. + +Modify this script manually if adjustments are needed. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('weightfile', type=str, help='weight file path') +parser.add_argument('export_folder', type=str) +parser.add_argument('--export-filename', type=str, default='fargan_data', help='filename for source and header file (.c and .h will be added), defaults to fargan_data') +parser.add_argument('--struct-name', type=str, default='FARGAN', help='name for C struct, defaults to FARGAN') +parser.add_argument('--quantize', action='store_true', help='apply quantization') + +if __name__ == "__main__": + args = parser.parse_args() + + print(f"loading weights from {args.weightfile}...") + saved_gen= torch.load(args.weightfile, map_location='cpu') + saved_gen['model_args'] = () + saved_gen['model_kwargs'] = {'cond_size': 256, 'gamma': 0.9} + + model = fargan.FARGAN(*saved_gen['model_args'], **saved_gen['model_kwargs']) + model.load_state_dict(saved_gen['state_dict'], strict=False) + def _remove_weight_norm(m): + try: + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + model.apply(_remove_weight_norm) + + + print("dumping model...") + quantize_model=args.quantize + + output_folder = args.export_folder + os.makedirs(output_folder, exist_ok=True) + + writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name, add_typedef=True) + + for name, module in model.named_modules(): + + if quantize_model: + quantize=name not in unquantized + scale = None if quantize else 1/128 + else: + quantize=False + scale=1/128 + + if isinstance(module, nn.Linear): + print(f"dumping linear layer {name}...") + wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) + + elif isinstance(module, nn.Conv1d): + print(f"dumping conv1d layer {name}...") + wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) + + elif isinstance(module, nn.GRU): + print(f"dumping GRU layer {name}...") + wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) + + elif isinstance(module, nn.GRUCell): + print(f"dumping GRUCell layer {name}...") + wexchange.torch.dump_torch_grucell_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) + + elif isinstance(module, nn.Embedding): + print(f"dumping Embedding layer {name}...") + wexchange.torch.dump_torch_embedding_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) + #wexchange.torch.dump_torch_embedding_weights(writer, module) + + else: + print(f"Ignoring layer {name}...") + + writer.close() diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/fargan.py b/managed_components/78__esp-opus/dnn/torch/fargan/fargan.py new file mode 100644 index 0000000..4a5a41d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/fargan.py @@ -0,0 +1,346 @@ +import os +import sys + +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +import filters +from torch.nn.utils import weight_norm +#from convert_lsp import lpc_to_lsp, lsp_to_lpc +from rc import lpc2rc, rc2lpc + +source_dir = os.path.split(os.path.abspath(__file__))[0] +sys.path.append(os.path.join(source_dir, "../dnntools")) +from dnntools.quantization import soft_quant + + +Fs = 16000 + +fid_dict = {} +def dump_signal(x, filename): + return + if filename in fid_dict: + fid = fid_dict[filename] + else: + fid = open(filename, "w") + fid_dict[filename] = fid + x = x.detach().numpy().astype('float32') + x.tofile(fid) + + +def sig_l1(y_true, y_pred): + return torch.mean(abs(y_true-y_pred))/torch.mean(abs(y_true)) + +def sig_loss(y_true, y_pred): + t = y_true/(1e-15+torch.norm(y_true, dim=-1, p=2, keepdim=True)) + p = y_pred/(1e-15+torch.norm(y_pred, dim=-1, p=2, keepdim=True)) + return torch.mean(1.-torch.sum(p*t, dim=-1)) + +def interp_lpc(lpc, factor): + #print(lpc.shape) + #f = (np.arange(factor)+.5*((factor+1)%2))/factor + lsp = torch.atanh(lpc2rc(lpc)) + #print("lsp0:") + #print(lsp) + shape = lsp.shape + #print("shape is", shape) + shape = (shape[0], shape[1]*factor, shape[2]) + interp_lsp = torch.zeros(shape, device=lpc.device) + for k in range(factor): + f = (k+.5*((factor+1)%2))/factor + interp = (1-f)*lsp[:,:-1,:] + f*lsp[:,1:,:] + interp_lsp[:,factor//2+k:-(factor//2):factor,:] = interp + for k in range(factor//2): + interp_lsp[:,k,:] = interp_lsp[:,factor//2,:] + for k in range((factor+1)//2): + interp_lsp[:,-k-1,:] = interp_lsp[:,-(factor+3)//2,:] + #print("lsp:") + #print(interp_lsp) + return rc2lpc(torch.tanh(interp_lsp)) + +def analysis_filter(x, lpc, nb_subframes=4, subframe_size=40, gamma=.9): + device = x.device + batch_size = lpc.size(0) + + nb_frames = lpc.shape[1] + + + sig = torch.zeros(batch_size, subframe_size+16, device=device) + x = torch.reshape(x, (batch_size, nb_frames*nb_subframes, subframe_size)) + out = torch.zeros((batch_size, 0), device=device) + + #if gamma is not None: + # bw = gamma**(torch.arange(1, 17, device=device)) + # lpc = lpc*bw[None,None,:] + ones = torch.ones((*(lpc.shape[:-1]), 1), device=device) + zeros = torch.zeros((*(lpc.shape[:-1]), subframe_size-1), device=device) + a = torch.cat([ones, lpc], -1) + a_big = torch.cat([a, zeros], -1) + fir_mat_big = filters.toeplitz_from_filter(a_big) + + #print(a_big[:,0,:]) + for n in range(nb_frames): + for k in range(nb_subframes): + + sig = torch.cat([sig[:,subframe_size:], x[:,n*nb_subframes + k, :]], 1) + exc = torch.bmm(fir_mat_big[:,n,:,:], sig[:,:,None]) + out = torch.cat([out, exc[:,-subframe_size:,0]], 1) + + return out + + +# weight initialization and clipping +def init_weights(module): + if isinstance(module, nn.GRU): + for p in module.named_parameters(): + if p[0].startswith('weight_hh_'): + nn.init.orthogonal_(p[1]) + +def gen_phase_embedding(periods, frame_size): + device = periods.device + batch_size = periods.size(0) + nb_frames = periods.size(1) + w0 = 2*torch.pi/periods + w0_shift = torch.cat([2*torch.pi*torch.rand((batch_size, 1), device=device)/frame_size, w0[:,:-1]], 1) + cum_phase = frame_size*torch.cumsum(w0_shift, 1) + fine_phase = w0[:,:,None]*torch.broadcast_to(torch.arange(frame_size, device=device), (batch_size, nb_frames, frame_size)) + embed = torch.unsqueeze(cum_phase, 2) + fine_phase + embed = torch.reshape(embed, (batch_size, -1)) + return torch.cos(embed), torch.sin(embed) + +class GLU(nn.Module): + def __init__(self, feat_size, softquant=False): + super(GLU, self).__init__() + + torch.manual_seed(5) + + self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) + + if softquant: + self.gate = soft_quant(self.gate) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + out = x * torch.sigmoid(self.gate(x)) + + return out + +class FWConv(nn.Module): + def __init__(self, in_size, out_size, kernel_size=2, softquant=False): + super(FWConv, self).__init__() + + torch.manual_seed(5) + + self.in_size = in_size + self.kernel_size = kernel_size + self.conv = weight_norm(nn.Linear(in_size*self.kernel_size, out_size, bias=False)) + self.glu = GLU(out_size, softquant=softquant) + + if softquant: + self.conv = soft_quant(self.conv) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, state): + xcat = torch.cat((state, x), -1) + #print(x.shape, state.shape, xcat.shape, self.in_size, self.kernel_size) + out = self.glu(torch.tanh(self.conv(xcat))) + return out, xcat[:,self.in_size:] + +def n(x): + return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) + +class FARGANCond(nn.Module): + def __init__(self, feature_dim=20, cond_size=256, pembed_dims=12, softquant=False): + super(FARGANCond, self).__init__() + + self.feature_dim = feature_dim + self.cond_size = cond_size + + self.pembed = nn.Embedding(224, pembed_dims) + self.fdense1 = nn.Linear(self.feature_dim + pembed_dims, 64, bias=False) + self.fconv1 = nn.Conv1d(64, 128, kernel_size=3, padding='valid', bias=False) + self.fdense2 = nn.Linear(128, 80*4, bias=False) + + if softquant: + self.fconv1 = soft_quant(self.fconv1) + self.fdense2 = soft_quant(self.fdense2) + + self.apply(init_weights) + nb_params = sum(p.numel() for p in self.parameters()) + print(f"cond model: {nb_params} weights") + + def forward(self, features, period): + features = features[:,2:,:] + period = period[:,2:] + p = self.pembed(period-32) + features = torch.cat((features, p), -1) + tmp = torch.tanh(self.fdense1(features)) + tmp = tmp.permute(0, 2, 1) + tmp = torch.tanh(self.fconv1(tmp)) + tmp = tmp.permute(0, 2, 1) + tmp = torch.tanh(self.fdense2(tmp)) + #tmp = torch.tanh(self.fdense2(tmp)) + return tmp + +class FARGANSub(nn.Module): + def __init__(self, subframe_size=40, nb_subframes=4, cond_size=256, softquant=False): + super(FARGANSub, self).__init__() + + self.subframe_size = subframe_size + self.nb_subframes = nb_subframes + self.cond_size = cond_size + self.cond_gain_dense = nn.Linear(80, 1) + + #self.sig_dense1 = nn.Linear(4*self.subframe_size+self.passthrough_size+self.cond_size, self.cond_size, bias=False) + self.fwc0 = FWConv(2*self.subframe_size+80+4, 192, softquant=softquant) + self.gru1 = nn.GRUCell(192+2*self.subframe_size, 160, bias=False) + self.gru2 = nn.GRUCell(160+2*self.subframe_size, 128, bias=False) + self.gru3 = nn.GRUCell(128+2*self.subframe_size, 128, bias=False) + + self.gru1_glu = GLU(160, softquant=softquant) + self.gru2_glu = GLU(128, softquant=softquant) + self.gru3_glu = GLU(128, softquant=softquant) + self.skip_glu = GLU(128, softquant=softquant) + #self.ptaps_dense = nn.Linear(4*self.cond_size, 5) + + self.skip_dense = nn.Linear(192+160+2*128+2*self.subframe_size, 128, bias=False) + self.sig_dense_out = nn.Linear(128, self.subframe_size, bias=False) + self.gain_dense_out = nn.Linear(192, 4) + + if softquant: + self.gru1 = soft_quant(self.gru1, names=['weight_hh', 'weight_ih']) + self.gru2 = soft_quant(self.gru2, names=['weight_hh', 'weight_ih']) + self.gru3 = soft_quant(self.gru3, names=['weight_hh', 'weight_ih']) + self.skip_dense = soft_quant(self.skip_dense) + self.sig_dense_out = soft_quant(self.sig_dense_out) + + self.apply(init_weights) + nb_params = sum(p.numel() for p in self.parameters()) + print(f"subframe model: {nb_params} weights") + + def forward(self, cond, prev_pred, exc_mem, period, states, gain=None): + device = exc_mem.device + #print(cond.shape, prev.shape) + + cond = n(cond) + dump_signal(gain, 'gain0.f32') + gain = torch.exp(self.cond_gain_dense(cond)) + dump_signal(gain, 'gain1.f32') + idx = 256-period[:,None] + rng = torch.arange(self.subframe_size+4, device=device) + idx = idx + rng[None,:] - 2 + mask = idx >= 256 + idx = idx - mask*period[:,None] + pred = torch.gather(exc_mem, 1, idx) + pred = n(pred/(1e-5+gain)) + + prev = exc_mem[:,-self.subframe_size:] + dump_signal(prev, 'prev_in.f32') + prev = n(prev/(1e-5+gain)) + dump_signal(prev, 'pitch_exc.f32') + dump_signal(exc_mem, 'exc_mem.f32') + + tmp = torch.cat((cond, pred, prev), 1) + #fpitch = taps[:,0:1]*pred[:,:-4] + taps[:,1:2]*pred[:,1:-3] + taps[:,2:3]*pred[:,2:-2] + taps[:,3:4]*pred[:,3:-1] + taps[:,4:]*pred[:,4:] + fpitch = pred[:,2:-2] + + #tmp = self.dense1_glu(torch.tanh(self.sig_dense1(tmp))) + fwc0_out, fwc0_state = self.fwc0(tmp, states[3]) + fwc0_out = n(fwc0_out) + pitch_gain = torch.sigmoid(self.gain_dense_out(fwc0_out)) + + gru1_state = self.gru1(torch.cat([fwc0_out, pitch_gain[:,0:1]*fpitch, prev], 1), states[0]) + gru1_out = self.gru1_glu(n(gru1_state)) + gru1_out = n(gru1_out) + gru2_state = self.gru2(torch.cat([gru1_out, pitch_gain[:,1:2]*fpitch, prev], 1), states[1]) + gru2_out = self.gru2_glu(n(gru2_state)) + gru2_out = n(gru2_out) + gru3_state = self.gru3(torch.cat([gru2_out, pitch_gain[:,2:3]*fpitch, prev], 1), states[2]) + gru3_out = self.gru3_glu(n(gru3_state)) + gru3_out = n(gru3_out) + gru3_out = torch.cat([gru1_out, gru2_out, gru3_out, fwc0_out], 1) + skip_out = torch.tanh(self.skip_dense(torch.cat([gru3_out, pitch_gain[:,3:4]*fpitch, prev], 1))) + skip_out = self.skip_glu(n(skip_out)) + sig_out = torch.tanh(self.sig_dense_out(skip_out)) + dump_signal(sig_out, 'exc_out.f32') + #taps = self.ptaps_dense(gru3_out) + #taps = .2*taps + torch.exp(taps) + #taps = taps / (1e-2 + torch.sum(torch.abs(taps), dim=-1, keepdim=True)) + #dump_signal(taps, 'taps.f32') + + dump_signal(pitch_gain, 'pgain.f32') + #sig_out = (sig_out + pitch_gain*fpitch) * gain + sig_out = sig_out * gain + exc_mem = torch.cat([exc_mem[:,self.subframe_size:], sig_out], 1) + prev_pred = torch.cat([prev_pred[:,self.subframe_size:], fpitch], 1) + dump_signal(sig_out, 'sig_out.f32') + return sig_out, exc_mem, prev_pred, (gru1_state, gru2_state, gru3_state, fwc0_state) + +class FARGAN(nn.Module): + def __init__(self, subframe_size=40, nb_subframes=4, feature_dim=20, cond_size=256, passthrough_size=0, has_gain=False, gamma=None, softquant=False): + super(FARGAN, self).__init__() + + self.subframe_size = subframe_size + self.nb_subframes = nb_subframes + self.frame_size = self.subframe_size*self.nb_subframes + self.feature_dim = feature_dim + self.cond_size = cond_size + + self.cond_net = FARGANCond(feature_dim=feature_dim, cond_size=cond_size, softquant=softquant) + self.sig_net = FARGANSub(subframe_size=subframe_size, nb_subframes=nb_subframes, cond_size=cond_size, softquant=softquant) + + def forward(self, features, period, nb_frames, pre=None, states=None): + device = features.device + batch_size = features.size(0) + + prev = torch.zeros(batch_size, 256, device=device) + exc_mem = torch.zeros(batch_size, 256, device=device) + nb_pre_frames = pre.size(1)//self.frame_size if pre is not None else 0 + + states = ( + torch.zeros(batch_size, 160, device=device), + torch.zeros(batch_size, 128, device=device), + torch.zeros(batch_size, 128, device=device), + torch.zeros(batch_size, (2*self.subframe_size+80+4)*1, device=device) + ) + + sig = torch.zeros((batch_size, 0), device=device) + cond = self.cond_net(features, period) + if pre is not None: + exc_mem[:,-self.frame_size:] = pre[:, :self.frame_size] + start = 1 if nb_pre_frames>0 else 0 + for n in range(start, nb_frames+nb_pre_frames): + for k in range(self.nb_subframes): + pos = n*self.frame_size + k*self.subframe_size + #print("now: ", preal.shape, prev.shape, sig_in.shape) + pitch = period[:, 3+n] + gain = .03*10**(0.5*features[:, 3+n, 0:1]/np.sqrt(18.0)) + #gain = gain[:,:,None] + out, exc_mem, prev, states = self.sig_net(cond[:, n, k*80:(k+1)*80], prev, exc_mem, pitch, states, gain=gain) + + if n < nb_pre_frames: + out = pre[:, pos:pos+self.subframe_size] + exc_mem[:,-self.subframe_size:] = out + else: + sig = torch.cat([sig, out], 1) + + states = [s.detach() for s in states] + return sig, states diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/filters.py b/managed_components/78__esp-opus/dnn/torch/fargan/filters.py new file mode 100644 index 0000000..8ec97ea --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/filters.py @@ -0,0 +1,46 @@ +import torch +from torch import nn +import torch.nn.functional as F +import math + +def toeplitz_from_filter(a): + device = a.device + L = a.size(-1) + size0 = (*(a.shape[:-1]), L, L+1) + size = (*(a.shape[:-1]), L, L) + rnge = torch.arange(0, L, dtype=torch.int64, device=device) + z = torch.tensor(0, device=device) + idx = torch.maximum(rnge[:,None] - rnge[None,:] + 1, z) + a = torch.cat([a[...,:1]*0, a], -1) + #print(a) + a = a[...,None,:] + #print(idx) + a = torch.broadcast_to(a, size0) + idx = torch.broadcast_to(idx, size) + #print(idx) + return torch.gather(a, -1, idx) + +def filter_iir_response(a, N): + device = a.device + L = a.size(-1) + ar = a.flip(dims=(2,)) + size = (*(a.shape[:-1]), N) + R = torch.zeros(size, device=device) + R[:,:,0] = torch.ones((a.shape[:-1]), device=device) + for i in range(1, L): + R[:,:,i] = - torch.sum(ar[:,:,L-i-1:-1] * R[:,:,:i], axis=-1) + #R[:,:,i] = - torch.einsum('ijk,ijk->ij', ar[:,:,L-i-1:-1], R[:,:,:i]) + for i in range(L, N): + R[:,:,i] = - torch.sum(ar[:,:,:-1] * R[:,:,i-L+1:i], axis=-1) + #R[:,:,i] = - torch.einsum('ijk,ijk->ij', ar[:,:,:-1], R[:,:,i-L+1:i]) + return R + +if __name__ == '__main__': + #a = torch.tensor([ [[1, -.9, 0.02], [1, -.8, .01]], [[1, .9, 0], [1, .8, 0]]]) + a = torch.tensor([ [[1, -.9, 0.02], [1, -.8, .01]]]) + A = toeplitz_from_filter(a) + #print(A) + R = filter_iir_response(a, 5) + + RA = toeplitz_from_filter(R) + print(RA) diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/rc.py b/managed_components/78__esp-opus/dnn/torch/fargan/rc.py new file mode 100644 index 0000000..7f67016 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/rc.py @@ -0,0 +1,29 @@ +import torch + + + +def rc2lpc(rc): + order = rc.shape[-1] + lpc=rc[...,0:1] + for i in range(1, order): + lpc = torch.cat([lpc + rc[...,i:i+1]*torch.flip(lpc,dims=(-1,)), rc[...,i:i+1]], -1) + #print("to:", lpc) + return lpc + +def lpc2rc(lpc): + order = lpc.shape[-1] + rc = lpc[...,-1:] + for i in range(order-1, 0, -1): + ki = lpc[...,-1:] + lpc = lpc[...,:-1] + lpc = (lpc - ki*torch.flip(lpc,dims=(-1,)))/(1 - ki*ki) + rc = torch.cat([lpc[...,-1:] , rc], -1) + return rc + +if __name__ == "__main__": + rc = torch.tensor([[.5, -.5, .6, -.6]]) + print(rc) + lpc = rc2lpc(rc) + print(lpc) + rc2 = lpc2rc(lpc) + print(rc2) diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/stft_loss.py b/managed_components/78__esp-opus/dnn/torch/fargan/stft_loss.py new file mode 100644 index 0000000..8c90405 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/stft_loss.py @@ -0,0 +1,186 @@ +"""STFT-based Loss modules.""" + +import torch +import torch.nn.functional as F +import numpy as np +import torchaudio + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + """ + + #x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=False) + #real = x_stft[..., 0] + #imag = x_stft[..., 1] + + # (kan-bayashi): clamp is needed to avoid nan or inf + #return torchaudio.functional.amplitude_to_DB(torch.abs(x_stft),db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) + #return torch.clamp(torch.abs(x_stft), min=1e-7) + + x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True) + return torch.clamp(torch.abs(x_stft), min=1e-7) + +class SpectralConvergenceLoss(torch.nn.Module): + """Spectral convergence loss module.""" + + def __init__(self): + """Initilize spectral convergence loss module.""" + super(SpectralConvergenceLoss, self).__init__() + + def forward(self, x_mag, y_mag): + """Calculate forward propagation. + Args: + x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). + y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). + Returns: + Tensor: Spectral convergence loss value. + """ + x_mag = torch.sqrt(x_mag) + y_mag = torch.sqrt(y_mag) + return torch.norm(y_mag - x_mag, p=1) / torch.norm(y_mag, p=1) + +class LogSTFTMagnitudeLoss(torch.nn.Module): + """Log STFT magnitude loss module.""" + + def __init__(self): + """Initilize los STFT magnitude loss module.""" + super(LogSTFTMagnitudeLoss, self).__init__() + + def forward(self, x, y): + """Calculate forward propagation. + Args: + x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). + y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). + Returns: + Tensor: Log STFT magnitude loss value. + """ + #F.l1_loss(torch.sqrt(y_mag), torch.sqrt(x_mag)) + + #F.l1_loss(torchaudio.functional.amplitude_to_DB(y_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80),\ + #torchaudio.functional.amplitude_to_DB(x_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80)) + + #y_mag[:,:y_mag.size(1)//2,:] = y_mag[:,:y_mag.size(1)//2,:] *0.0 + + #return F.l1_loss(torch.log(y_mag) + torch.sqrt(y_mag), torch.log(x_mag) + torch.sqrt(x_mag)) + + #return F.l1_loss(y_mag, x_mag) + + error_loss = F.l1_loss(y, x) #+ F.l1_loss(torch.sqrt(y), torch.sqrt(x))#F.l1_loss(torch.log(y), torch.log(x))# + + #x = torch.log(x) + #y = torch.log(y) + #x = x.permute(0,2,1).contiguous() + #y = y.permute(0,2,1).contiguous() + + '''mean_x = torch.mean(x, dim=1, keepdim=True) + mean_y = torch.mean(y, dim=1, keepdim=True) + + var_x = torch.var(x, dim=1, keepdim=True) + var_y = torch.var(y, dim=1, keepdim=True) + + std_x = torch.std(x, dim=1, keepdim=True) + std_y = torch.std(y, dim=1, keepdim=True) + + x_minus_mean = x - mean_x + y_minus_mean = y - mean_y + + pearson_corr = torch.sum(x_minus_mean * y_minus_mean, dim=1, keepdim=True) / \ + (torch.sqrt(torch.sum(x_minus_mean ** 2, dim=1, keepdim=True) + 1e-7) * \ + torch.sqrt(torch.sum(y_minus_mean ** 2, dim=1, keepdim=True) + 1e-7)) + + numerator = 2.0 * pearson_corr * std_x * std_y + denominator = var_x + var_y + (mean_y - mean_x)**2 + + ccc = numerator/denominator + + ccc_loss = F.l1_loss(1.0 - ccc, torch.zeros_like(ccc))''' + + return error_loss #+ ccc_loss#+ ccc_loss + + +class STFTLoss(torch.nn.Module): + """STFT loss module.""" + + def __init__(self, device, fft_size=1024, shift_size=120, win_length=600, window="hann_window"): + """Initialize STFT loss module.""" + super(STFTLoss, self).__init__() + self.fft_size = fft_size + self.shift_size = shift_size + self.win_length = win_length + self.window = getattr(torch, window)(win_length).to(device) + self.spectral_convergenge_loss = SpectralConvergenceLoss() + self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() + + def forward(self, x, y): + """Calculate forward propagation. + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + Returns: + Tensor: Spectral convergence loss value. + Tensor: Log STFT magnitude loss value. + """ + x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) + y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) + sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) + mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) + + return sc_loss, mag_loss + + +class MultiResolutionSTFTLoss(torch.nn.Module): + + '''def __init__(self, + device, + fft_sizes=[2048, 1024, 512, 256, 128, 64], + hop_sizes=[512, 256, 128, 64, 32, 16], + win_lengths=[2048, 1024, 512, 256, 128, 64], + window="hann_window"):''' + + '''def __init__(self, + device, + fft_sizes=[2048, 1024, 512, 256, 128, 64], + hop_sizes=[256, 128, 64, 32, 16, 8], + win_lengths=[1024, 512, 256, 128, 64, 32], + window="hann_window"):''' + + def __init__(self, + device, + fft_sizes=[2560, 1280, 640, 320, 160, 80], + hop_sizes=[640, 320, 160, 80, 40, 20], + win_lengths=[2560, 1280, 640, 320, 160, 80], + window="hann_window"): + + super(MultiResolutionSTFTLoss, self).__init__() + assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) + self.stft_losses = torch.nn.ModuleList() + for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): + self.stft_losses += [STFTLoss(device, fs, ss, wl, window)] + + def forward(self, x, y): + """Calculate forward propagation. + Args: + x (Tensor): Predicted signal (B, T). + y (Tensor): Groundtruth signal (B, T). + Returns: + Tensor: Multi resolution spectral convergence loss value. + Tensor: Multi resolution log STFT magnitude loss value. + """ + sc_loss = 0.0 + mag_loss = 0.0 + for f in self.stft_losses: + sc_l, mag_l = f(x, y) + sc_loss += sc_l + #mag_loss += mag_l + sc_loss /= len(self.stft_losses) + mag_loss /= len(self.stft_losses) + + return sc_loss #mag_loss #+ diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/test_fargan.py b/managed_components/78__esp-opus/dnn/torch/fargan/test_fargan.py new file mode 100644 index 0000000..d3aeb61 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/test_fargan.py @@ -0,0 +1,128 @@ +import os +import argparse +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F +import tqdm + +import fargan +from dataset import FARGANDataset + +nb_features = 36 +nb_used_features = 20 + +parser = argparse.ArgumentParser() + +parser.add_argument('model', type=str, help='CELPNet model') +parser.add_argument('features', type=str, help='path to feature file in .f32 format') +parser.add_argument('output', type=str, help='path to output file (16-bit PCM)') + +parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) + + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) + +args = parser.parse_args() + +if args.cuda_visible_devices != None: + os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices + + +features_file = args.features +signal_file = args.output + + + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +checkpoint = torch.load(args.model, map_location='cpu') + +model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) + + +model.load_state_dict(checkpoint['state_dict'], strict=False) + +features = np.reshape(np.memmap(features_file, dtype='float32', mode='r'), (1, -1, nb_features)) +lpc = features[:,4-1:-1,nb_used_features:] +features = features[:, :, :nb_used_features] +#periods = np.round(50*features[:,:,nb_used_features-2]+100).astype('int') +periods = np.round(np.clip(256./2**(features[:,:,nb_used_features-2]+1.5), 32, 255)).astype('int') + + +nb_frames = features.shape[1] +#nb_frames = 1000 +gamma = checkpoint['model_kwargs']['gamma'] + +def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)): + + out = np.zeros_like(frame) + filt = np.flip(filt) + + inp = frame[:] + + + for i in range(0, inp.shape[0]): + + s = inp[i] - np.dot(buffer*weighting_vector, filt) + + buffer[0] = s + + buffer = np.roll(buffer, -1) + + out[i] = s + + return out + +def inverse_perceptual_weighting (pw_signal, filters, weighting_vector): + + #inverse perceptual weighting= H_preemph / W(z/gamma) + + signal = np.zeros_like(pw_signal) + buffer = np.zeros(16) + num_frames = pw_signal.shape[0] //160 + assert num_frames == filters.shape[0] + for frame_idx in range(0, num_frames): + + in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:] + out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector) + signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:] + buffer[:] = out_sig_frame[-16:] + return signal + +def inverse_perceptual_weighting40 (pw_signal, filters): + + #inverse perceptual weighting= H_preemph / W(z/gamma) + + signal = np.zeros_like(pw_signal) + buffer = np.zeros(16) + num_frames = pw_signal.shape[0] //40 + assert num_frames == filters.shape[0] + for frame_idx in range(0, num_frames): + in_frame = pw_signal[frame_idx*40: (frame_idx+1)*40][:] + out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer) + signal[frame_idx*40: (frame_idx+1)*40] = out_sig_frame[:] + buffer[:] = out_sig_frame[-16:] + return signal + +from scipy.signal import lfilter + +if __name__ == '__main__': + model.to(device) + features = torch.tensor(features).to(device) + #lpc = torch.tensor(lpc).to(device) + periods = torch.tensor(periods).to(device) + weighting = gamma**np.arange(1, 17) + lpc = lpc*weighting + lpc = fargan.interp_lpc(torch.tensor(lpc), 4).numpy() + + sig, _ = model(features, periods, nb_frames - 4) + #weighting_vector = np.array([gamma**i for i in range(16,0,-1)]) + sig = sig.detach().numpy().flatten() + sig = lfilter(np.array([1.]), np.array([1., -.85]), sig) + #sig = inverse_perceptual_weighting40(sig, lpc[0,:,:]) + + pcm = np.round(32768*np.clip(sig, a_max=.99, a_min=-.99)).astype('int16') + pcm.tofile(signal_file) diff --git a/managed_components/78__esp-opus/dnn/torch/fargan/train_fargan.py b/managed_components/78__esp-opus/dnn/torch/fargan/train_fargan.py new file mode 100644 index 0000000..4846f99 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fargan/train_fargan.py @@ -0,0 +1,169 @@ +import os +import argparse +import random +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F +import tqdm + +import fargan +from dataset import FARGANDataset +from stft_loss import * + +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='path to feature file in .f32 format') +parser.add_argument('signal', type=str, help='path to signal file in .s16 format') +parser.add_argument('output', type=str, help='path to output folder') + +parser.add_argument('--suffix', type=str, help="model name suffix", default="") +parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) + + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) +model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9) +model_group.add_argument('--softquant', action="store_true", help="enables soft quantization during training") + +training_group = parser.add_argument_group(title="training parameters") +training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512) +training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3) +training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20) +training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15) +training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4) +training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) + +args = parser.parse_args() + +if args.cuda_visible_devices != None: + os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices + +# checkpoints +checkpoint_dir = os.path.join(args.output, 'checkpoints') +checkpoint = dict() +os.makedirs(checkpoint_dir, exist_ok=True) + + +# training parameters +batch_size = args.batch_size +lr = args.lr +epochs = args.epochs +sequence_length = args.sequence_length +lr_decay = args.lr_decay + +adam_betas = [0.8, 0.95] +adam_eps = 1e-8 +features_file = args.features +signal_file = args.signal + +# model parameters +cond_size = args.cond_size + + +checkpoint['batch_size'] = batch_size +checkpoint['lr'] = lr +checkpoint['lr_decay'] = lr_decay +checkpoint['epochs'] = epochs +checkpoint['sequence_length'] = sequence_length +checkpoint['adam_betas'] = adam_betas + + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +checkpoint['model_args'] = () +checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gamma': args.gamma, 'softquant': args.softquant} +print(checkpoint['model_kwargs']) +model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) + +#model = fargan.FARGAN() +#model = nn.DataParallel(model) + +if type(args.initial_checkpoint) != type(None): + checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(checkpoint['state_dict'], strict=False) + +checkpoint['state_dict'] = model.state_dict() + + +dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length) +dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) + + +optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) + + +# learning rate scheduler +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) + +states = None + +spect_loss = MultiResolutionSTFTLoss(device).to(device) + +if __name__ == '__main__': + model.to(device) + + for epoch in range(1, epochs + 1): + + running_specc = 0 + running_cont_loss = 0 + running_loss = 0 + + print(f"training epoch {epoch}...") + with tqdm.tqdm(dataloader, unit='batch') as tepoch: + for i, (features, periods, target, lpc) in enumerate(tepoch): + optimizer.zero_grad() + features = features.to(device) + #lpc = torch.tensor(fargan.interp_lpc(lpc.numpy(), 4)) + #print("interp size", lpc.shape) + #lpc = lpc.to(device) + #lpc = lpc*(args.gamma**torch.arange(1,17, device=device)) + #lpc = fargan.interp_lpc(lpc, 4) + periods = periods.to(device) + if (np.random.rand() > 0.1): + target = target[:, :sequence_length*160] + #lpc = lpc[:,:sequence_length*4,:] + features = features[:,:sequence_length+4,:] + periods = periods[:,:sequence_length+4] + else: + target=target[::2, :] + #lpc=lpc[::2,:] + features=features[::2,:] + periods=periods[::2,:] + target = target.to(device) + #print(target.shape, lpc.shape) + #target = fargan.analysis_filter(target, lpc[:,:,:], nb_subframes=1, gamma=args.gamma) + + #nb_pre = random.randrange(1, 6) + nb_pre = 2 + pre = target[:, :nb_pre*160] + sig, states = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None) + sig = torch.cat([pre, sig], -1) + + cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+160], sig[:, nb_pre*160:nb_pre*160+160]) + specc_loss = spect_loss(sig, target.detach()) + loss = .03*cont_loss + specc_loss + + loss.backward() + optimizer.step() + + #model.clip_weights() + + scheduler.step() + + running_specc += specc_loss.detach().cpu().item() + running_cont_loss += cont_loss.detach().cpu().item() + + running_loss += loss.detach().cpu().item() + tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", + cont_loss=f"{running_cont_loss/(i+1):8.5f}", + specc=f"{running_specc/(i+1):8.5f}", + ) + + # save checkpoint + checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_{epoch}.pth') + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = running_loss / len(dataloader) + checkpoint['epoch'] = epoch + torch.save(checkpoint, checkpoint_path) diff --git a/managed_components/78__esp-opus/dnn/torch/fwgan/dump_model_weights.py b/managed_components/78__esp-opus/dnn/torch/fwgan/dump_model_weights.py new file mode 100644 index 0000000..64f955a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fwgan/dump_model_weights.py @@ -0,0 +1,88 @@ +import os +import sys +import argparse + +import torch +from torch import nn + + +sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange')) +import wexchange.torch + +from models import model_dict + +unquantized = [ + 'bfcc_with_corr_upsampler.fc', + 'cont_net.0', + 'fwc6.cont_fc.0', + 'fwc6.fc.0', + 'fwc6.fc.1.gate', + 'fwc7.cont_fc.0', + 'fwc7.fc.0', + 'fwc7.fc.1.gate' +] + +description=f""" +This is an unsafe dumping script for FWGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer +and will fail to export any other weights. + +Furthermore, the quanitze option relies on the following explicit list of layers to be excluded: +{unquantized}. + +Modify this script manually if adjustments are needed. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name') +parser.add_argument('weightfile', type=str, help='weight file path') +parser.add_argument('export_folder', type=str) +parser.add_argument('--export-filename', type=str, default='fwgan_data', help='filename for source and header file (.c and .h will be added), defaults to fwgan_data') +parser.add_argument('--struct-name', type=str, default='FWGAN', help='name for C struct, defaults to FWGAN') +parser.add_argument('--quantize', action='store_true', help='apply quantization') + +if __name__ == "__main__": + args = parser.parse_args() + + model = model_dict[args.model]() + + print(f"loading weights from {args.weightfile}...") + saved_gen= torch.load(args.weightfile, map_location='cpu') + model.load_state_dict(saved_gen) + def _remove_weight_norm(m): + try: + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + model.apply(_remove_weight_norm) + + + print("dumping model...") + quantize_model=args.quantize + + output_folder = args.export_folder + os.makedirs(output_folder, exist_ok=True) + + writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name) + + for name, module in model.named_modules(): + + if quantize_model: + quantize=name not in unquantized + scale = None if quantize else 1/128 + else: + quantize=False + scale=1/128 + + if isinstance(module, nn.Linear): + print(f"dumping linear layer {name}...") + wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) + + if isinstance(module, nn.Conv1d): + print(f"dumping conv1d layer {name}...") + wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) + + if isinstance(module, nn.GRU): + print(f"dumping GRU layer {name}...") + wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) + + writer.close() diff --git a/managed_components/78__esp-opus/dnn/torch/fwgan/inference.py b/managed_components/78__esp-opus/dnn/torch/fwgan/inference.py new file mode 100644 index 0000000..c06b68b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fwgan/inference.py @@ -0,0 +1,141 @@ +import os +import time +import torch +import numpy as np +from scipy import signal as si +from scipy.io import wavfile +import argparse + +from models import model_dict + +parser = argparse.ArgumentParser() +parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name') +parser.add_argument('weightfile', type=str, help='weight file') +parser.add_argument('input', type=str, help='input: feature file or folder with feature files') +parser.add_argument('output', type=str, help='output: wav file name or folder name, depending on input') + + +########################### Signal Processing Layers ########################### + +def preemphasis(x, coef= -0.85): + + return si.lfilter(np.array([1.0, coef]), np.array([1.0]), x).astype('float32') + +def deemphasis(x, coef= -0.85): + + return si.lfilter(np.array([1.0]), np.array([1.0, coef]), x).astype('float32') + +gamma = 0.92 +weighting_vector = np.array([gamma**i for i in range(16,0,-1)]) + + +def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)): + + out = np.zeros_like(frame) + + filt = np.flip(filt) + + inp = frame[:] + + + for i in range(0, inp.shape[0]): + + s = inp[i] - np.dot(buffer*weighting_vector, filt) + + buffer[0] = s + + buffer = np.roll(buffer, -1) + + out[i] = s + + return out + +def inverse_perceptual_weighting (pw_signal, filters, weighting_vector): + + #inverse perceptual weighting= H_preemph / W(z/gamma) + + pw_signal = preemphasis(pw_signal) + + signal = np.zeros_like(pw_signal) + buffer = np.zeros(16) + num_frames = pw_signal.shape[0] //160 + assert num_frames == filters.shape[0] + + for frame_idx in range(0, num_frames): + + in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:] + out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector) + signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:] + buffer[:] = out_sig_frame[-16:] + + return signal + + +def process_item(generator, feature_filename, output_filename, verbose=False): + + feat = np.memmap(feature_filename, dtype='float32', mode='r') + + num_feat_frames = len(feat) // 36 + feat = np.reshape(feat, (num_feat_frames, 36)) + + bfcc = np.copy(feat[:, :18]) + corr = np.copy(feat[:, 19:20]) + 0.5 + bfcc_with_corr = torch.from_numpy(np.hstack((bfcc, corr))).type(torch.FloatTensor).unsqueeze(0)#.to(device) + + period = torch.from_numpy((0.1 + 50 * np.copy(feat[:, 18:19]) + 100)\ + .astype('int32')).type(torch.long).view(1,-1)#.to(device) + + lpc_filters = np.copy(feat[:, -16:]) + + start_time = time.time() + x1 = generator(period, bfcc_with_corr, torch.zeros(1,320)) #this means the vocoder runs in complete synthesis mode with zero history audio frames + end_time = time.time() + total_time = end_time - start_time + x1 = x1.squeeze(1).squeeze(0).detach().cpu().numpy() + gen_seconds = len(x1)/16000 + out = deemphasis(inverse_perceptual_weighting(x1, lpc_filters, weighting_vector)) + if verbose: + print(f"Took {total_time:.3f}s to generate {len(x1)} samples ({gen_seconds}s) -> {gen_seconds/total_time:.2f}x real time") + + out = np.clip(np.round(2**15 * out), -2**15, 2**15 -1).astype(np.int16) + wavfile.write(output_filename, 16000, out) + + +########################### The inference loop over folder containing lpcnet feature files ################################# +if __name__ == "__main__": + + args = parser.parse_args() + + generator = model_dict[args.model]() + + + #Load the FWGAN500Hz Checkpoint + saved_gen= torch.load(args.weightfile, map_location='cpu') + generator.load_state_dict(saved_gen) + + #this is just to remove the weight_norm from the model layers as it's no longer needed + def _remove_weight_norm(m): + try: + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + generator.apply(_remove_weight_norm) + + #enable inference mode + generator = generator.eval() + + print('Successfully loaded the generator model ... start generation:') + + if os.path.isdir(args.input): + + os.makedirs(args.output, exist_ok=True) + + for fn in os.listdir(args.input): + print(f"processing input {fn}...") + feature_filename = os.path.join(args.input, fn) + output_filename = os.path.join(args.output, os.path.splitext(fn)[0] + f"_{args.model}.wav") + process_item(generator, feature_filename, output_filename) + else: + process_item(generator, args.input, args.output) + + print("Finished!") \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/fwgan/models/__init__.py b/managed_components/78__esp-opus/dnn/torch/fwgan/models/__init__.py new file mode 100644 index 0000000..d52a6eb --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fwgan/models/__init__.py @@ -0,0 +1,7 @@ +from .fwgan400 import FWGAN400ContLarge +from .fwgan500 import FWGAN500Cont + +model_dict = { + 'fwgan400': FWGAN400ContLarge, + 'fwgan500': FWGAN500Cont +} \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan400.py b/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan400.py new file mode 100644 index 0000000..84d9849 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan400.py @@ -0,0 +1,308 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm +import numpy as np + +which_norm = weight_norm + +#################### Definition of basic model components #################### + +#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet) +class ConvLookahead(nn.Module): + def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False): + super(ConvLookahead, self).__init__() + torch.manual_seed(5) + + self.padding_left = (kernel_size - 2) * dilation + self.padding_right = 1 * dilation + + self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias)) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + x = F.pad(x,(self.padding_left, self.padding_right)) + conv_out = self.conv(x) + return conv_out + +#(modified) GLU Activation layer definition +class GLU(nn.Module): + def __init__(self, feat_size): + super(GLU, self).__init__() + + torch.manual_seed(5) + + self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False)) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + out = torch.tanh(x) * torch.sigmoid(self.gate(x)) + + return out + +#GRU layer definition +class ContForwardGRU(nn.Module): + def __init__(self, input_size, hidden_size, num_layers=1): + super(ContForwardGRU, self).__init__() + + torch.manual_seed(5) + + self.hidden_size = hidden_size + + self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.hidden_size, bias=False)), + nn.Tanh()) + + self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\ + bias=False) + + self.nl = GLU(self.hidden_size) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, x0): + + self.gru.flatten_parameters() + + h0 = self.cont_fc(x0).unsqueeze(0) + + output, h0 = self.gru(x, h0) + + return self.nl(output) + +# Framewise convolution layer definition +class ContFramewiseConv(torch.nn.Module): + + def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True): + + super(ContFramewiseConv, self).__init__() + torch.manual_seed(5) + + self.frame_kernel_size = frame_kernel_size + self.frame_len = frame_len + + if (causal == True) or (self.frame_kernel_size == 2): + + self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len + self.required_pad_right = 0 + + self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.required_pad_left, bias=False)), + nn.Tanh() + ) + + else: + + self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len + self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len + + self.fc_input_dim = self.frame_kernel_size * self.frame_len + self.fc_out_dim = out_dim + + if act=='glu': + self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), + GLU(self.fc_out_dim) + ) + if act=='tanh': + self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), + nn.Tanh() + ) + + self.init_weights() + + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ + isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, x0): + + if self.frame_kernel_size == 1: + return self.fc(x) + + x_flat = x.reshape(x.size(0),1,-1) + pad = self.cont_fc(x0).view(x0.size(0),1,-1) + x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2) + + x_flat_padded_unfolded = F.unfold(x_flat_padded,\ + kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous() + + out = self.fc(x_flat_padded_unfolded) + return out + +# A fully-connected based upsampling layer definition +class UpsampleFC(nn.Module): + def __init__(self, in_ch, out_ch, upsample_factor): + super(UpsampleFC, self).__init__() + torch.manual_seed(5) + + self.in_ch = in_ch + self.out_ch = out_ch + self.upsample_factor = upsample_factor + self.fc = nn.Linear(in_ch, out_ch * upsample_factor, bias=False) + self.nl = nn.Tanh() + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or\ + isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + batch_size = x.size(0) + x = x.permute(0, 2, 1) + x = self.nl(self.fc(x)) + x = x.reshape((batch_size, -1, self.out_ch)) + x = x.permute(0, 2, 1) + return x + +########################### The complete model definition ################################# + +class FWGAN400ContLarge(nn.Module): + def __init__(self): + super().__init__() + torch.manual_seed(5) + + self.bfcc_with_corr_upsampler = UpsampleFC(19,80,4) + + self.feat_in_conv1 = ConvLookahead(160,256,kernel_size=5) + self.feat_in_nl1 = GLU(256) + + self.cont_net = nn.Sequential(which_norm(nn.Linear(321, 160, bias=False)), + nn.Tanh(), + which_norm(nn.Linear(160, 160, bias=False)), + nn.Tanh(), + which_norm(nn.Linear(160, 80, bias=False)), + nn.Tanh(), + which_norm(nn.Linear(80, 80, bias=False)), + nn.Tanh(), + which_norm(nn.Linear(80, 64, bias=False)), + nn.Tanh(), + which_norm(nn.Linear(64, 64, bias=False)), + nn.Tanh()) + + self.rnn = ContForwardGRU(256,256) + + self.fwc1 = ContFramewiseConv(256, 256) + self.fwc2 = ContFramewiseConv(256, 128) + self.fwc3 = ContFramewiseConv(128, 128) + self.fwc4 = ContFramewiseConv(128, 64) + self.fwc5 = ContFramewiseConv(64, 64) + self.fwc6 = ContFramewiseConv(64, 40) + self.fwc7 = ContFramewiseConv(40, 40) + + self.init_weights() + self.count_parameters() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ + isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def count_parameters(self): + num_params = sum(p.numel() for p in self.parameters() if p.requires_grad) + print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n") + + def create_phase_signals(self, periods): + + batch_size = periods.size(0) + progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) + progression = torch.repeat_interleave(progression, batch_size, 0) + + phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) + chunks = [] + for sframe in range(periods.size(1)): + f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) + + chunk_sin = torch.sin(f * progression + phase0) + chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,40) + + chunk_cos = torch.cos(f * progression + phase0) + chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,40) + + chunk = torch.cat((chunk_sin, chunk_cos), dim = -1) + + phase0 = phase0 + 160 * f + + chunks.append(chunk) + + phase_signals = torch.cat(chunks, dim=1) + + return phase_signals + + + def gain_multiply(self, x, c0): + + gain = 10**(0.5*c0/np.sqrt(18.0)) + gain = torch.repeat_interleave(gain, 160, dim=-1) + gain = gain.reshape(gain.size(0),1,-1).squeeze(1) + + return x * gain + + def forward(self, pitch_period, bfcc_with_corr, x0): + + norm_x0 = torch.norm(x0,2, dim=-1, keepdim=True) + x0 = x0 / torch.sqrt((1e-8) + norm_x0**2) + x0 = torch.cat((torch.log(norm_x0 + 1e-7), x0), dim=-1) + + p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous() + + envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous()) + + feat_in = torch.cat((p_embed , envelope), dim=1) + + wav_latent1 = self.feat_in_nl1(self.feat_in_conv1(feat_in).permute(0,2,1).contiguous()) + + cont_latent = self.cont_net(x0) + + rnn_out = self.rnn(wav_latent1, cont_latent) + + fwc1_out = self.fwc1(rnn_out, cont_latent) + + fwc2_out = self.fwc2(fwc1_out, cont_latent) + + fwc3_out = self.fwc3(fwc2_out, cont_latent) + + fwc4_out = self.fwc4(fwc3_out, cont_latent) + + fwc5_out = self.fwc5(fwc4_out, cont_latent) + + fwc6_out = self.fwc6(fwc5_out, cont_latent) + + fwc7_out = self.fwc7(fwc6_out, cont_latent) + + waveform = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1) + + waveform = self.gain_multiply(waveform,bfcc_with_corr[:,:,:1]) + + return waveform \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan500.py b/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan500.py new file mode 100644 index 0000000..2c6dea5 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/fwgan/models/fwgan500.py @@ -0,0 +1,260 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm +import numpy as np + + +which_norm = weight_norm + +#################### Definition of basic model components #################### + +#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet) +class ConvLookahead(nn.Module): + def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False): + super(ConvLookahead, self).__init__() + torch.manual_seed(5) + + self.padding_left = (kernel_size - 2) * dilation + self.padding_right = 1 * dilation + + self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias)) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + x = F.pad(x,(self.padding_left, self.padding_right)) + conv_out = self.conv(x) + return conv_out + +#(modified) GLU Activation layer definition +class GLU(nn.Module): + def __init__(self, feat_size): + super(GLU, self).__init__() + + torch.manual_seed(5) + + self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False)) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + out = torch.tanh(x) * torch.sigmoid(self.gate(x)) + + return out + +#GRU layer definition +class ContForwardGRU(nn.Module): + def __init__(self, input_size, hidden_size, num_layers=1): + super(ContForwardGRU, self).__init__() + + torch.manual_seed(5) + + self.hidden_size = hidden_size + + #This is to initialize the layer with history audio samples for continuation. + self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.hidden_size, bias=False)), + nn.Tanh()) + + self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\ + bias=False) + + self.nl = GLU(self.hidden_size) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, x0): + + self.gru.flatten_parameters() + + h0 = self.cont_fc(x0).unsqueeze(0) + + output, h0 = self.gru(x, h0) + + return self.nl(output) + +# Framewise convolution layer definition +class ContFramewiseConv(torch.nn.Module): + + def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True): + + super(ContFramewiseConv, self).__init__() + torch.manual_seed(5) + + self.frame_kernel_size = frame_kernel_size + self.frame_len = frame_len + + if (causal == True) or (self.frame_kernel_size == 2): + + self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len + self.required_pad_right = 0 + + #This is to initialize the layer with history audio samples for continuation. + self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.required_pad_left, bias=False)), + nn.Tanh() + ) + + else: + #This means non-causal frame-wise convolution. We don't use it at the moment + self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len + self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len + + self.fc_input_dim = self.frame_kernel_size * self.frame_len + self.fc_out_dim = out_dim + + if act=='glu': + self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), + GLU(self.fc_out_dim) + ) + if act=='tanh': + self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), + nn.Tanh() + ) + + self.init_weights() + + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ + isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, x0): + + if self.frame_kernel_size == 1: + return self.fc(x) + + x_flat = x.reshape(x.size(0),1,-1) + pad = self.cont_fc(x0).view(x0.size(0),1,-1) + x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2) + + x_flat_padded_unfolded = F.unfold(x_flat_padded,\ + kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous() + + out = self.fc(x_flat_padded_unfolded) + return out + +########################### The complete model definition ################################# + +class FWGAN500Cont(nn.Module): + def __init__(self): + super().__init__() + torch.manual_seed(5) + + #PrecondNet: + self.bfcc_with_corr_upsampler = nn.Sequential(nn.ConvTranspose1d(19,64,kernel_size=5,stride=5,padding=0,\ + bias=False), + nn.Tanh()) + + self.feat_in_conv = ConvLookahead(128,256,kernel_size=5) + self.feat_in_nl = GLU(256) + + #GRU: + self.rnn = ContForwardGRU(256,256) + + #Frame-wise convolution stack: + self.fwc1 = ContFramewiseConv(256, 256) + self.fwc2 = ContFramewiseConv(256, 128) + self.fwc3 = ContFramewiseConv(128, 128) + self.fwc4 = ContFramewiseConv(128, 64) + self.fwc5 = ContFramewiseConv(64, 64) + self.fwc6 = ContFramewiseConv(64, 32) + self.fwc7 = ContFramewiseConv(32, 32, act='tanh') + + self.init_weights() + self.count_parameters() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ + isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def count_parameters(self): + num_params = sum(p.numel() for p in self.parameters() if p.requires_grad) + print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n") + + def create_phase_signals(self, periods): + + batch_size = periods.size(0) + progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) + progression = torch.repeat_interleave(progression, batch_size, 0) + + phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) + chunks = [] + for sframe in range(periods.size(1)): + f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) + + chunk_sin = torch.sin(f * progression + phase0) + chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,32) + + chunk_cos = torch.cos(f * progression + phase0) + chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,32) + + chunk = torch.cat((chunk_sin, chunk_cos), dim = -1) + + phase0 = phase0 + 160 * f + + chunks.append(chunk) + + phase_signals = torch.cat(chunks, dim=1) + + return phase_signals + + + def gain_multiply(self, x, c0): + + gain = 10**(0.5*c0/np.sqrt(18.0)) + gain = torch.repeat_interleave(gain, 160, dim=-1) + gain = gain.reshape(gain.size(0),1,-1).squeeze(1) + + return x * gain + + def forward(self, pitch_period, bfcc_with_corr, x0): + + #This should create a latent representation of shape [Batch_dim, 500 frames, 256 elemets per frame] + p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous() + envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous()) + feat_in = torch.cat((p_embed , envelope), dim=1) + wav_latent = self.feat_in_nl(self.feat_in_conv(feat_in).permute(0,2,1).contiguous()) + + #Generation with continuation using history samples x0 starts from here: + + rnn_out = self.rnn(wav_latent, x0) + + fwc1_out = self.fwc1(rnn_out, x0) + fwc2_out = self.fwc2(fwc1_out, x0) + fwc3_out = self.fwc3(fwc2_out, x0) + fwc4_out = self.fwc4(fwc3_out, x0) + fwc5_out = self.fwc5(fwc4_out, x0) + fwc6_out = self.fwc6(fwc5_out, x0) + fwc7_out = self.fwc7(fwc6_out, x0) + + waveform_unscaled = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1) + waveform = self.gain_multiply(waveform_unscaled,bfcc_with_corr[:,:,:1]) + + return waveform diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/README.md b/managed_components/78__esp-opus/dnn/torch/lossgen/README.md new file mode 100644 index 0000000..55c1b44 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/README.md @@ -0,0 +1,27 @@ +#Packet loss simulator + +This code is an attempt at simulating better packet loss scenarios. The most common way of simulating +packet loss is to use a random sequence where each packet loss event is uncorrelated with previous events. +That is a simplistic model since we know that losses often occur in bursts. This model uses real data +to build a generative model for packet loss. + +We use the training data provided for the Audio Deep Packet Loss Concealment Challenge, which is available at: + +http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test_train.tar.gz + +To create the training data, run: + +`./process_data.sh //test_train/train/lossy_signals/` + +That will create an ascii loss\_sorted.txt file with all loss data sorted in increasing packet loss +percentage. Then just run: + +`python ./train_lossgen.py` + +to train a model + +To generate a sequence, run + +`python3 ./test_lossgen.py output.txt --length 10000` + +where is the .pth model file and is the amount of loss (e.g. 0.2 for 20% loss). diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/export_lossgen.py b/managed_components/78__esp-opus/dnn/torch/lossgen/export_lossgen.py new file mode 100644 index 0000000..15a65c3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/export_lossgen.py @@ -0,0 +1,101 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) + + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint', type=str, help='model checkpoint') +parser.add_argument('output_dir', type=str, help='output folder') + +args = parser.parse_args() + +import torch +import numpy as np + +import lossgen +from wexchange.torch import dump_torch_weights +from wexchange.c_export import CWriter, print_vector + +def c_export(args, model): + + message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" + + writer = CWriter(os.path.join(args.output_dir, "lossgen_data"), message=message, model_struct_name='LossGen', enable_binary_blob=False, add_typedef=True) + writer.header.write( +f""" +#include "opus_types.h" +""" + ) + + dense_layers = [ + ('dense_in', "lossgen_dense_in"), + ('dense_out', "lossgen_dense_out") + ] + + + for name, export_name in dense_layers: + layer = model.get_submodule(name) + dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None) + + + gru_layers = [ + ("gru1", "lossgen_gru1"), + ("gru2", "lossgen_gru2"), + ] + + max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) + for name, export_name in gru_layers]) + + writer.header.write( +f""" + +#define LOSSGEN_MAX_RNN_UNITS {max_rnn_units} + +""" + ) + + writer.close() + + +if __name__ == "__main__": + + os.makedirs(args.output_dir, exist_ok=True) + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) + model.load_state_dict(checkpoint['state_dict'], strict=False) + #model = LossGen() + #checkpoint = torch.load(args.checkpoint, map_location='cpu') + #model.load_state_dict(checkpoint['state_dict']) + c_export(args, model) diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/lossgen.py b/managed_components/78__esp-opus/dnn/torch/lossgen/lossgen.py new file mode 100644 index 0000000..9025165 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/lossgen.py @@ -0,0 +1,29 @@ +import torch +from torch import nn +import torch.nn.functional as F + +class LossGen(nn.Module): + def __init__(self, gru1_size=16, gru2_size=16): + super(LossGen, self).__init__() + + self.gru1_size = gru1_size + self.gru2_size = gru2_size + self.dense_in = nn.Linear(2, 8) + self.gru1 = nn.GRU(8, self.gru1_size, batch_first=True) + self.gru2 = nn.GRU(self.gru1_size, self.gru2_size, batch_first=True) + self.dense_out = nn.Linear(self.gru2_size, 1) + + def forward(self, loss, perc, states=None): + #print(states) + device = loss.device + batch_size = loss.size(0) + if states is None: + gru1_state = torch.zeros((1, batch_size, self.gru1_size), device=device) + gru2_state = torch.zeros((1, batch_size, self.gru2_size), device=device) + else: + gru1_state = states[0] + gru2_state = states[1] + x = torch.tanh(self.dense_in(torch.cat([loss, perc], dim=-1))) + gru1_out, gru1_state = self.gru1(x, gru1_state) + gru2_out, gru2_state = self.gru2(gru1_out, gru2_state) + return self.dense_out(gru2_out), [gru1_state, gru2_state] diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/process_data.sh b/managed_components/78__esp-opus/dnn/torch/lossgen/process_data.sh new file mode 100644 index 0000000..308fd0a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/process_data.sh @@ -0,0 +1,17 @@ +#!/bin/sh + +#directory containing the loss files +datadir=$1 + +for i in $datadir/*_is_lost.txt +do + perc=`cat $i | awk '{a+=$1}END{print a/NR}'` + echo $perc $i +done > percentage_list.txt + +sort -n percentage_list.txt | awk '{print $2}' > percentage_sorted.txt + +for i in `cat percentage_sorted.txt` +do + cat $i +done > loss_sorted.txt diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/test_lossgen.py b/managed_components/78__esp-opus/dnn/torch/lossgen/test_lossgen.py new file mode 100644 index 0000000..95659b1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/test_lossgen.py @@ -0,0 +1,42 @@ +import lossgen +import os +import argparse +import torch +import numpy as np + + +parser = argparse.ArgumentParser() + +parser.add_argument('model', type=str, help='CELPNet model') +parser.add_argument('percentage', type=float, help='percentage loss') +parser.add_argument('output', type=str, help='path to output file (ascii)') + +parser.add_argument('--length', type=int, help="length of sequence to generate", default=500) + +args = parser.parse_args() + + + +checkpoint = torch.load(args.model, map_location='cpu') +model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) +model.load_state_dict(checkpoint['state_dict'], strict=False) + +states=None +last = torch.zeros((1,1,1)) +perc = torch.tensor((args.percentage,))[None,None,:] +seq = torch.zeros((0,1,1)) + +one = torch.ones((1,1,1)) +zero = torch.zeros((1,1,1)) + +if __name__ == '__main__': + for i in range(args.length): + prob, states = model(last, perc, states=states) + prob = torch.sigmoid(prob) + states[0] = states[0].detach() + states[1] = states[1].detach() + loss = one if np.random.rand() < prob else zero + last = loss + seq = torch.cat([seq, loss]) + +np.savetxt(args.output, seq[:,:,0].numpy().astype('int'), fmt='%d') diff --git a/managed_components/78__esp-opus/dnn/torch/lossgen/train_lossgen.py b/managed_components/78__esp-opus/dnn/torch/lossgen/train_lossgen.py new file mode 100644 index 0000000..0bbe279 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lossgen/train_lossgen.py @@ -0,0 +1,99 @@ +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +import tqdm +from scipy.signal import lfilter +import os +import lossgen + +class LossDataset(torch.utils.data.Dataset): + def __init__(self, + loss_file, + sequence_length=997): + + self.sequence_length = sequence_length + + self.loss = np.loadtxt(loss_file, dtype='float32') + + self.nb_sequences = self.loss.shape[0]//self.sequence_length + self.loss = self.loss[:self.nb_sequences*self.sequence_length] + self.perc = lfilter(np.array([.001], dtype='float32'), np.array([1., -.999], dtype='float32'), self.loss) + + self.loss = np.reshape(self.loss, (self.nb_sequences, self.sequence_length, 1)) + self.perc = np.reshape(self.perc, (self.nb_sequences, self.sequence_length, 1)) + + def __len__(self): + return self.nb_sequences + + def __getitem__(self, index): + r0 = np.random.normal(scale=.1, size=(1,1)).astype('float32') + r1 = np.random.normal(scale=.1, size=(self.sequence_length,1)).astype('float32') + perc = self.perc[index, :, :] + perc = perc + (r0+r1)*perc*(1-perc) + return [self.loss[index, :, :], perc] + + +adam_betas = [0.8, 0.98] +adam_eps = 1e-8 +batch_size=256 +lr_decay = 0.001 +lr = 0.003 +epsilon = 1e-5 +epochs = 2000 +checkpoint_dir='checkpoint' +os.makedirs(checkpoint_dir, exist_ok=True) +checkpoint = dict() + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +checkpoint['model_args'] = () +checkpoint['model_kwargs'] = {'gru1_size': 16, 'gru2_size': 32} +model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) +dataset = LossDataset('loss_sorted.txt') +dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) + + +optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) + + +# learning rate scheduler +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) + + +if __name__ == '__main__': + model.to(device) + states = None + for epoch in range(1, epochs + 1): + + running_loss = 0 + + print(f"training epoch {epoch}...") + with tqdm.tqdm(dataloader, unit='batch') as tepoch: + for i, (loss, perc) in enumerate(tepoch): + optimizer.zero_grad() + loss = loss.to(device) + perc = perc.to(device) + + out, states = model(loss, perc, states=states) + states = [state.detach() for state in states] + out = torch.sigmoid(out[:,:-1,:]) + target = loss[:,1:,:] + + loss = torch.mean(-target*torch.log(out+epsilon) - (1-target)*torch.log(1-out+epsilon)) + + loss.backward() + optimizer.step() + + scheduler.step() + + running_loss += loss.detach().cpu().item() + tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", + ) + + # save checkpoint + checkpoint_path = os.path.join(checkpoint_dir, f'lossgen_{epoch}.pth') + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = running_loss / len(dataloader) + checkpoint['epoch'] = epoch + torch.save(checkpoint, checkpoint_path) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/README.md b/managed_components/78__esp-opus/dnn/torch/lpcnet/README.md new file mode 100644 index 0000000..26d9ea1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/README.md @@ -0,0 +1,27 @@ +# LPCNet + +Incomplete pytorch implementation of LPCNet + +## Data preparation +For data preparation use dump_data in github.com/xiph/LPCNet. To turn this into +a training dataset, copy data and feature file to a folder and run + +python add_dataset_config.py my_dataset_folder + + +## Training +To train a model, create and adjust a setup file, e.g. with + +python make_default_setup.py my_setup.yml --path2dataset my_dataset_folder + +Then simply run + +python train_lpcnet.py my_setup.yml my_output + +## Inference +Create feature file with dump_data from github.com/xiph/LPCNet. Then run e.g. + +python test_lpcnet.py features.f32 my_output/checkpoints/checkpoint_ep_10.pth out.wav + +Inference runs on CPU and takes usually between 3 and 20 seconds per generated second of audio, +depending on the CPU. diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/add_dataset_config.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/add_dataset_config.py new file mode 100644 index 0000000..1b6b4e8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/add_dataset_config.py @@ -0,0 +1,77 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse +import os + +import yaml + + +from utils.templates import dataset_template_v1, dataset_template_v2 + + + + +parser = argparse.ArgumentParser("add_dataset_config.py") + +parser.add_argument('path', type=str, help='path to folder containing feature and data file') +parser.add_argument('--version', type=int, help="dataset version, 1 for classic LPCNet with 55 feature slots, 2 for new format with 36 feature slots.", default=2) +parser.add_argument('--description', type=str, help='brief dataset description', default="I will add a description later") +args = parser.parse_args() + + +if args.version == 1: + template = dataset_template_v1 + data_extension = '.u8' +elif args.version == 2: + template = dataset_template_v2 + data_extension = '.s16' +else: + raise ValueError(f"unknown dataset version {args.version}") + +# get folder content +content = os.listdir(args.path) + +features = [c for c in content if c.endswith('.f32')] + +if len(features) != 1: + print("could not determine feature file") +else: + template['feature_file'] = features[0] + +data = [c for c in content if c.endswith(data_extension)] +if len(data) != 1: + print("could not determine data file") +else: + template['signal_file'] = data[0] + +template['description'] = args.description + +with open(os.path.join(args.path, 'info.yml'), 'w') as f: + yaml.dump(template, f) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/data/__init__.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/data/__init__.py new file mode 100644 index 0000000..50bad87 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/data/__init__.py @@ -0,0 +1 @@ +from .lpcnet_dataset import LPCNetDataset \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/data/lpcnet_dataset.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/data/lpcnet_dataset.py new file mode 100644 index 0000000..dbefc57 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/data/lpcnet_dataset.py @@ -0,0 +1,227 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" Dataset for LPCNet training """ +import os + +import yaml +import torch +import numpy as np +from torch.utils.data import Dataset + + +scale = 255.0/32768.0 +scale_1 = 32768.0/255.0 +def ulaw2lin(u): + u = u - 128 + s = np.sign(u) + u = np.abs(u) + return s*scale_1*(np.exp(u/128.*np.log(256))-1) + + +def lin2ulaw(x): + s = np.sign(x) + x = np.abs(x) + u = (s*(128*np.log(1+scale*x)/np.log(256))) + u = np.clip(128 + np.round(u), 0, 255) + return u + + +def run_lpc(signal, lpcs, frame_length=160): + num_frames, lpc_order = lpcs.shape + + prediction = np.concatenate( + [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] + ) + error = signal[lpc_order :] - prediction + + return prediction, error + +class LPCNetDataset(Dataset): + def __init__(self, + path_to_dataset, + features=['cepstrum', 'periods', 'pitch_corr'], + input_signals=['last_signal', 'prediction', 'last_error'], + target='error', + frames_per_sample=15, + feature_history=2, + feature_lookahead=2, + lpc_gamma=1): + + super(LPCNetDataset, self).__init__() + + # load dataset info + self.path_to_dataset = path_to_dataset + with open(os.path.join(path_to_dataset, 'info.yml'), 'r') as f: + dataset = yaml.load(f, yaml.FullLoader) + + # dataset version + self.version = dataset['version'] + if self.version == 1: + self.getitem = self.getitem_v1 + elif self.version == 2: + self.getitem = self.getitem_v2 + else: + raise ValueError(f"dataset version {self.version} unknown") + + # features + self.feature_history = feature_history + self.feature_lookahead = feature_lookahead + self.frame_offset = 1 + self.feature_history + self.frames_per_sample = frames_per_sample + self.input_features = features + self.feature_frame_layout = dataset['feature_frame_layout'] + self.lpc_gamma = lpc_gamma + + # load feature file + self.feature_file = os.path.join(path_to_dataset, dataset['feature_file']) + self.features = np.memmap(self.feature_file, dtype=dataset['feature_dtype']) + self.feature_frame_length = dataset['feature_frame_length'] + + assert len(self.features) % self.feature_frame_length == 0 + self.features = self.features.reshape((-1, self.feature_frame_length)) + + # derive number of samples is dataset + self.dataset_length = (len(self.features) - self.frame_offset - self.feature_lookahead - 1) // self.frames_per_sample + + # signals + self.frame_length = dataset['frame_length'] + self.signal_frame_layout = dataset['signal_frame_layout'] + self.input_signals = input_signals + self.target = target + + # load signals + self.signal_file = os.path.join(path_to_dataset, dataset['signal_file']) + self.signals = np.memmap(self.signal_file, dtype=dataset['signal_dtype']) + self.signal_frame_length = dataset['signal_frame_length'] + self.signals = self.signals.reshape((-1, self.signal_frame_length)) + assert len(self.signals) == len(self.features) * self.frame_length + + def __getitem__(self, index): + return self.getitem(index) + + def getitem_v2(self, index): + sample = dict() + + # extract features + frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history + frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead + + for feature in self.input_features: + feature_start, feature_stop = self.feature_frame_layout[feature] + sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] + + # convert periods + if 'periods' in self.input_features: + sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') + + signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length + signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length + + # last_signal and signal are always expected to be there + sample['last_signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] + sample['signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['signal']] + + # calculate prediction and error if lpc coefficients present and prediction not given + if 'lpc' in self.feature_frame_layout and 'prediction' not in self.signal_frame_layout: + # lpc coefficients with one frame lookahead + # frame positions (start one frame early for past excitation) + frame_start = self.frame_offset + self.frames_per_sample * index - 1 + frame_stop = self.frame_offset + self.frames_per_sample * (index + 1) + + # feature positions + lpc_start, lpc_stop = self.feature_frame_layout['lpc'] + lpc_order = lpc_stop - lpc_start + lpcs = self.features[frame_start : frame_stop, lpc_start : lpc_stop] + + # LPC weighting + lpc_order = lpc_stop - lpc_start + weights = np.array([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]) + lpcs = lpcs * weights + + # signal position (lpc_order samples as history) + signal_start = frame_start * self.frame_length - lpc_order + 1 + signal_stop = frame_stop * self.frame_length + 1 + noisy_signal = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] + clean_signal = self.signals[signal_start - 1 : signal_stop - 1, self.signal_frame_layout['signal']] + + noisy_prediction, noisy_error = run_lpc(noisy_signal, lpcs, frame_length=self.frame_length) + + # extract signals + offset = self.frame_length + sample['prediction'] = noisy_prediction[offset : offset + self.frame_length * self.frames_per_sample] + sample['last_error'] = noisy_error[offset - 1 : offset - 1 + self.frame_length * self.frames_per_sample] + # calculate error between real signal and noisy prediction + + + sample['error'] = sample['signal'] - sample['prediction'] + + + # concatenate features + feature_keys = [key for key in self.input_features if not key.startswith("periods")] + features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) + signals = torch.cat([torch.LongTensor(lin2ulaw(sample[key])).unsqueeze(-1) for key in self.input_signals], dim=-1) + target = torch.LongTensor(lin2ulaw(sample[self.target])) + periods = torch.LongTensor(sample['periods']) + + return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} + + def getitem_v1(self, index): + sample = dict() + + # extract features + frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history + frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead + + for feature in self.input_features: + feature_start, feature_stop = self.feature_frame_layout[feature] + sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] + + # convert periods + if 'periods' in self.input_features: + sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') + + signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length + signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length + + # last_signal and signal are always expected to be there + for signal_name, index in self.signal_frame_layout.items(): + sample[signal_name] = self.signals[signal_start : signal_stop, index] + + # concatenate features + feature_keys = [key for key in self.input_features if not key.startswith("periods")] + features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) + signals = torch.cat([torch.LongTensor(sample[key]).unsqueeze(-1) for key in self.input_signals], dim=-1) + target = torch.LongTensor(sample[self.target]) + periods = torch.LongTensor(sample['periods']) + + return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} + + def __len__(self): + return self.dataset_length diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/engine/lpcnet_engine.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/engine/lpcnet_engine.py new file mode 100644 index 0000000..c964bdd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/engine/lpcnet_engine.py @@ -0,0 +1,141 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from tqdm import tqdm +import sys + +def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): + + model.to(device) + model.train() + + running_loss = 0 + previous_running_loss = 0 + + # gru states + gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) + gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) + gru_states = [gru_a_state, gru_b_state] + + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + # set gradients to zero + optimizer.zero_grad() + + # zero out initial gru states + gru_a_state.zero_() + gru_b_state.zero_() + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['features'], batch['periods'], batch['signals'], gru_states) + + # calculate loss + loss = criterion(output.permute(0, 2, 1), target) + + # calculate gradients + loss.backward() + + # update weights + optimizer.step() + + # update learning rate + scheduler.step() + + # call sparsifier + model.sparsify() + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss + +def evaluate(model, criterion, dataloader, device, log_interval=10): + + model.to(device) + model.eval() + + running_loss = 0 + previous_running_loss = 0 + + # gru states + gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) + gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) + gru_states = [gru_a_state, gru_b_state] + + with torch.no_grad(): + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + + # zero out initial gru states + gru_a_state.zero_() + gru_b_state.zero_() + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['features'], batch['periods'], batch['signals'], gru_states) + + # calculate loss + loss = criterion(output.permute(0, 2, 1), target) + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/make_default_setup.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/make_default_setup.py new file mode 100644 index 0000000..17031d2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/make_default_setup.py @@ -0,0 +1,56 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse + +import yaml + +from utils.templates import setup_dict + +parser = argparse.ArgumentParser() + +parser.add_argument('name', type=str, help='name of default setup file') +parser.add_argument('--model', choices=['lpcnet', 'multi_rate'], help='LPCNet model name', default='lpcnet') +parser.add_argument('--path2dataset', type=str, help='dataset path', default=None) + +args = parser.parse_args() + +setup = setup_dict[args.model] + +# update dataset if given +if type(args.path2dataset) != type(None): + setup['dataset'] = args.path2dataset + +name = args.name +if not name.endswith('.yml'): + name += '.yml' + +if __name__ == '__main__': + with open(name, 'w') as f: + f.write(yaml.dump(setup)) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/make_test_config.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/make_test_config.py new file mode 100644 index 0000000..7c414e0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/make_test_config.py @@ -0,0 +1,78 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse +import os +import sys + +parser = argparse.ArgumentParser() +parser.add_argument("config_name", type=str, help="name of config file (.yml will be appended)") +parser.add_argument("test_name", type=str, help="name for test result display") +parser.add_argument("checkpoint", type=str, help="checkpoint to test") +parser.add_argument("--lpcnet-demo", type=str, help="path to lpcnet_demo binary, default: /local/code/LPCNet/lpcnet_demo", default="/local/code/LPCNet/lpcnet_demo") +parser.add_argument("--lpcnext-path", type=str, help="path to lpcnext folder, defalut: dirname(__file__)", default=os.path.dirname(__file__)) +parser.add_argument("--python-exe", type=str, help='python executable path, default: sys.executable', default=sys.executable) +parser.add_argument("--pad", type=str, help="left pad of output in seconds, default: 0.015", default="0.015") +parser.add_argument("--trim", type=str, help="left trim of output in seconds, default: 0", default="0") + + + +template=''' +test: "{NAME}" +processing: + - "sox {{INPUT}} {{INPUT}}.raw" + - "{LPCNET_DEMO} -features {{INPUT}}.raw {{INPUT}}.features.f32" + - "{PYTHON} {WORKING}/test_lpcnet.py {{INPUT}}.features.f32 {CHECKPOINT} {{OUTPUT}}.ua.wav" + - "sox {{OUTPUT}}.ua.wav {{OUTPUT}}.uap.wav pad {PAD}" + - "sox {{OUTPUT}}.uap.wav {{OUTPUT}} trim {TRIM}" + - "rm {{INPUT}}.raw {{OUTPUT}}.uap.wav {{OUTPUT}}.ua.wav {{INPUT}}.features.f32" +''' + +if __name__ == "__main__": + args = parser.parse_args() + + + file_content = template.format( + NAME=args.test_name, + LPCNET_DEMO=os.path.abspath(args.lpcnet_demo), + PYTHON=os.path.abspath(args.python_exe), + PAD=args.pad, + TRIM=args.trim, + WORKING=os.path.abspath(args.lpcnext_path), + CHECKPOINT=os.path.abspath(args.checkpoint) + ) + + print(file_content) + + filename = args.config_name + if not filename.endswith(".yml"): + filename += ".yml" + + with open(filename, "w") as f: + f.write(file_content) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/models/__init__.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/__init__.py new file mode 100644 index 0000000..a26bc1c --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/__init__.py @@ -0,0 +1,8 @@ +from .lpcnet import LPCNet +from .multi_rate_lpcnet import MultiRateLPCNet + + +model_dict = { + 'lpcnet' : LPCNet, + 'multi_rate' : MultiRateLPCNet +} \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/models/lpcnet.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/lpcnet.py new file mode 100644 index 0000000..e83ae90 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/lpcnet.py @@ -0,0 +1,303 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import numpy as np + +from utils.ulaw import lin2ulawq, ulaw2lin +from utils.sample import sample_excitation +from utils.pcm import clip_to_int16 +from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step +from utils.layers import DualFC +from utils.misc import get_pdf_from_tree + + +class LPCNet(nn.Module): + def __init__(self, config): + super(LPCNet, self).__init__() + + # + self.input_layout = config['input_layout'] + self.feature_history = config['feature_history'] + self.feature_lookahead = config['feature_lookahead'] + + # frame rate network parameters + self.feature_dimension = config['feature_dimension'] + self.period_embedding_dim = config['period_embedding_dim'] + self.period_levels = config['period_levels'] + self.feature_channels = self.feature_dimension + self.period_embedding_dim + self.feature_conditioning_dim = config['feature_conditioning_dim'] + self.feature_conv_kernel_size = config['feature_conv_kernel_size'] + + + # frame rate network layers + self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim) + self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') + self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') + self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim) + self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim])) + + # sample rate network parameters + self.frame_size = config['frame_size'] + self.signal_levels = config['signal_levels'] + self.signal_embedding_dim = config['signal_embedding_dim'] + self.gru_a_units = config['gru_a_units'] + self.gru_b_units = config['gru_b_units'] + self.output_levels = config['output_levels'] + self.hsampling = config.get('hsampling', False) + + self.gru_a_input_dim = len(self.input_layout['signals']) * self.signal_embedding_dim + self.feature_conditioning_dim + self.gru_b_input_dim = self.gru_a_units + self.feature_conditioning_dim + + # sample rate network layers + self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim) + self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True) + self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True) + self.dual_fc = DualFC(self.gru_b_units, self.output_levels) + + # sparsification + self.sparsifier = [] + + # GRU A + if 'gru_a' in config['sparsification']: + gru_config = config['sparsification']['gru_a'] + task_list = [(self.gru_a, gru_config['params'])] + self.sparsifier.append(GRUSparsifier(task_list, + gru_config['start'], + gru_config['stop'], + gru_config['interval'], + gru_config['exponent']) + ) + self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, + gru_config['params'], drop_input=True) + else: + self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True) + + # GRU B + if 'gru_b' in config['sparsification']: + gru_config = config['sparsification']['gru_b'] + task_list = [(self.gru_b, gru_config['params'])] + self.sparsifier.append(GRUSparsifier(task_list, + gru_config['start'], + gru_config['stop'], + gru_config['interval'], + gru_config['exponent']) + ) + self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b, + gru_config['params']) + else: + self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b) + + # inference parameters + self.lpc_gamma = config.get('lpc_gamma', 1) + + def sparsify(self): + for sparsifier in self.sparsifier: + sparsifier.step() + + def get_gflops(self, fs, verbose=False): + gflops = 0 + + # frame rate network + conditioning_dim = self.feature_conditioning_dim + feature_channels = self.feature_channels + frame_rate = fs / self.frame_size + frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate + if verbose: + print(f"frame rate network: {frame_rate_network_complexity} GFLOPS") + gflops += frame_rate_network_complexity + + # gru a + gru_a_rate = fs + gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step + if verbose: + print(f"gru A: {gru_a_complexity} GFLOPS") + gflops += gru_a_complexity + + # gru b + gru_b_rate = fs + gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step + if verbose: + print(f"gru B: {gru_b_complexity} GFLOPS") + gflops += gru_b_complexity + + + # dual fcs + fc = self.dual_fc + rate = fs + input_size = fc.dense1.in_features + output_size = fc.dense1.out_features + dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate + if self.hsampling: + dual_fc_complexity /= 8 + if verbose: + print(f"dual_fc: {dual_fc_complexity} GFLOPS") + gflops += dual_fc_complexity + + if verbose: + print(f'total: {gflops} GFLOPS') + + return gflops + + def frame_rate_network(self, features, periods): + + embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3) + features = torch.concat((features, embedded_periods), dim=-1) + + # convert to channels first and calculate conditioning vector + c = torch.permute(features, [0, 2, 1]) + + c = torch.tanh(self.feature_conv1(c)) + c = torch.tanh(self.feature_conv2(c)) + # back to channels last + c = torch.permute(c, [0, 2, 1]) + c = torch.tanh(self.feature_dense1(c)) + c = torch.tanh(self.feature_dense2(c)) + + return c + + def sample_rate_network(self, signals, c, gru_states): + embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) + c_upsampled = torch.repeat_interleave(c, self.frame_size, dim=1) + + y = torch.concat((embedded_signals, c_upsampled), dim=-1) + y, gru_a_state = self.gru_a(y, gru_states[0]) + y = torch.concat((y, c_upsampled), dim=-1) + y, gru_b_state = self.gru_b(y, gru_states[1]) + + y = self.dual_fc(y) + + if self.hsampling: + y = torch.sigmoid(y) + log_probs = torch.log(get_pdf_from_tree(y) + 1e-6) + else: + log_probs = torch.log_softmax(y, dim=-1) + + return log_probs, (gru_a_state, gru_b_state) + + def decoder(self, signals, c, gru_states): + embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) + + y = torch.concat((embedded_signals, c), dim=-1) + y, gru_a_state = self.gru_a(y, gru_states[0]) + y = torch.concat((y, c), dim=-1) + y, gru_b_state = self.gru_b(y, gru_states[1]) + + y = self.dual_fc(y) + + if self.hsampling: + y = torch.sigmoid(y) + probs = get_pdf_from_tree(y) + else: + probs = torch.softmax(y, dim=-1) + + return probs, (gru_a_state, gru_b_state) + + def forward(self, features, periods, signals, gru_states): + + c = self.frame_rate_network(features, periods) + log_probs, _ = self.sample_rate_network(signals, c, gru_states) + + return log_probs + + def generate(self, features, periods, lpcs): + + with torch.no_grad(): + device = self.parameters().__next__().device + + num_frames = features.shape[0] - self.feature_history - self.feature_lookahead + lpc_order = lpcs.shape[-1] + num_input_signals = len(self.input_layout['signals']) + pitch_corr_position = self.input_layout['features']['pitch_corr'][0] + + # signal buffers + pcm = torch.zeros((num_frames * self.frame_size + lpc_order)) + output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16) + mem = 0 + + # state buffers + gru_a_state = torch.zeros((1, 1, self.gru_a_units)) + gru_b_state = torch.zeros((1, 1, self.gru_b_units)) + gru_states = [gru_a_state, gru_b_state] + + input_signals = torch.zeros((1, 1, num_input_signals), dtype=torch.long) + 128 + + # push data to device + features = features.to(device) + periods = periods.to(device) + lpcs = lpcs.to(device) + + # lpc weighting + weights = torch.FloatTensor([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]).to(device) + lpcs = lpcs * weights + + # run feature encoding + c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0)) + + for frame_index in range(num_frames): + frame_start = frame_index * self.frame_size + pitch_corr = features[frame_index + self.feature_history, pitch_corr_position] + a = - torch.flip(lpcs[frame_index + self.feature_history], [0]) + current_c = c[:, frame_index : frame_index + 1, :] + + for i in range(self.frame_size): + pcm_position = frame_start + i + lpc_order + output_position = frame_start + i + + # prepare input + pred = torch.sum(pcm[pcm_position - lpc_order : pcm_position] * a) + if 'prediction' in self.input_layout['signals']: + input_signals[0, 0, self.input_layout['signals']['prediction']] = lin2ulawq(pred) + + # run single step of sample rate network + probs, gru_states = self.decoder( + input_signals, + current_c, + gru_states + ) + + # sample from output + exc_ulaw = sample_excitation(probs, pitch_corr) + + # signal generation + exc = ulaw2lin(exc_ulaw) + sig = exc + pred + pcm[pcm_position] = sig + mem = 0.85 * mem + float(sig) + output[output_position] = clip_to_int16(round(mem)) + + # buffer update + if 'last_signal' in self.input_layout['signals']: + input_signals[0, 0, self.input_layout['signals']['last_signal']] = lin2ulawq(sig) + + if 'last_error' in self.input_layout['signals']: + input_signals[0, 0, self.input_layout['signals']['last_error']] = lin2ulawq(exc) + + return output diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py new file mode 100644 index 0000000..e613e74 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py @@ -0,0 +1,437 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +from utils.layers.subconditioner import get_subconditioner +from utils.layers import DualFC + +from utils.ulaw import lin2ulawq, ulaw2lin +from utils.sample import sample_excitation +from utils.pcm import clip_to_int16 +from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step + +from utils.misc import interleave_tensors + + + + +# MultiRateLPCNet +class MultiRateLPCNet(nn.Module): + def __init__(self, config): + super(MultiRateLPCNet, self).__init__() + + # general parameters + self.input_layout = config['input_layout'] + self.feature_history = config['feature_history'] + self.feature_lookahead = config['feature_lookahead'] + self.signals = config['signals'] + + # frame rate network parameters + self.feature_dimension = config['feature_dimension'] + self.period_embedding_dim = config['period_embedding_dim'] + self.period_levels = config['period_levels'] + self.feature_channels = self.feature_dimension + self.period_embedding_dim + self.feature_conditioning_dim = config['feature_conditioning_dim'] + self.feature_conv_kernel_size = config['feature_conv_kernel_size'] + + # frame rate network layers + self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim) + self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') + self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') + self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim) + self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim])) + + # sample rate network parameters + self.frame_size = config['frame_size'] + self.signal_levels = config['signal_levels'] + self.signal_embedding_dim = config['signal_embedding_dim'] + self.gru_a_units = config['gru_a_units'] + self.gru_b_units = config['gru_b_units'] + self.output_levels = config['output_levels'] + + # subconditioning B + sub_config = config['subconditioning']['subconditioning_b'] + self.substeps_b = sub_config['number_of_subsamples'] + self.subcondition_signals_b = sub_config['signals'] + self.signals_idx_b = [self.input_layout['signals'][key] for key in sub_config['signals']] + method = sub_config['method'] + kwargs = sub_config['kwargs'] + if type(kwargs) == type(None): + kwargs = dict() + + state_size = self.gru_b_units + self.subconditioner_b = get_subconditioner(method, + sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], + state_size, self.signal_levels, len(sub_config['signals']), + **sub_config['kwargs']) + + # subconditioning A + sub_config = config['subconditioning']['subconditioning_a'] + self.substeps_a = sub_config['number_of_subsamples'] + self.subcondition_signals_a = sub_config['signals'] + self.signals_idx_a = [self.input_layout['signals'][key] for key in sub_config['signals']] + method = sub_config['method'] + kwargs = sub_config['kwargs'] + if type(kwargs) == type(None): + kwargs = dict() + + state_size = self.gru_a_units + self.subconditioner_a = get_subconditioner(method, + sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], + state_size, self.signal_levels, self.substeps_b * len(sub_config['signals']), + **sub_config['kwargs']) + + + # wrap up subconditioning, group_size_gru_a holds the number + # of timesteps that are grouped as sample input for GRU A + # input and group_size_subcondition_a holds the number of samples that are + # grouped as input to pre-GRU B subconditioning + self.group_size_gru_a = self.substeps_a * self.substeps_b + self.group_size_subcondition_a = self.substeps_b + self.gru_a_rate_divider = self.group_size_gru_a + self.gru_b_rate_divider = self.substeps_b + + # gru sizes + self.gru_a_input_dim = self.group_size_gru_a * len(self.signals) * self.signal_embedding_dim + self.feature_conditioning_dim + self.gru_b_input_dim = self.subconditioner_a.get_output_dim(0) + self.feature_conditioning_dim + self.signals_idx = [self.input_layout['signals'][key] for key in self.signals] + + # sample rate network layers + self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim) + self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True) + self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True) + + # sparsification + self.sparsifier = [] + + # GRU A + if 'gru_a' in config['sparsification']: + gru_config = config['sparsification']['gru_a'] + task_list = [(self.gru_a, gru_config['params'])] + self.sparsifier.append(GRUSparsifier(task_list, + gru_config['start'], + gru_config['stop'], + gru_config['interval'], + gru_config['exponent']) + ) + self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, + gru_config['params'], drop_input=True) + else: + self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True) + + # GRU B + if 'gru_b' in config['sparsification']: + gru_config = config['sparsification']['gru_b'] + task_list = [(self.gru_b, gru_config['params'])] + self.sparsifier.append(GRUSparsifier(task_list, + gru_config['start'], + gru_config['stop'], + gru_config['interval'], + gru_config['exponent']) + ) + self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b, + gru_config['params']) + else: + self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b) + + + + # dual FCs + self.dual_fc = [] + for i in range(self.substeps_b): + dim = self.subconditioner_b.get_output_dim(i) + self.dual_fc.append(DualFC(dim, self.output_levels)) + self.add_module(f"dual_fc_{i}", self.dual_fc[-1]) + + def get_gflops(self, fs, verbose=False, hierarchical_sampling=False): + gflops = 0 + + # frame rate network + conditioning_dim = self.feature_conditioning_dim + feature_channels = self.feature_channels + frame_rate = fs / self.frame_size + frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate + if verbose: + print(f"frame rate network: {frame_rate_network_complexity} GFLOPS") + gflops += frame_rate_network_complexity + + # gru a + gru_a_rate = fs / self.group_size_gru_a + gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step + if verbose: + print(f"gru A: {gru_a_complexity} GFLOPS") + gflops += gru_a_complexity + + # subconditioning a + subcond_a_rate = fs / self.substeps_b + subconditioning_a_complexity = 1e-9 * self.subconditioner_a.get_average_flops_per_step() * subcond_a_rate + if verbose: + print(f"subconditioning A: {subconditioning_a_complexity} GFLOPS") + gflops += subconditioning_a_complexity + + # gru b + gru_b_rate = fs / self.substeps_b + gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step + if verbose: + print(f"gru B: {gru_b_complexity} GFLOPS") + gflops += gru_b_complexity + + # subconditioning b + subcond_b_rate = fs + subconditioning_b_complexity = 1e-9 * self.subconditioner_b.get_average_flops_per_step() * subcond_b_rate + if verbose: + print(f"subconditioning B: {subconditioning_b_complexity} GFLOPS") + gflops += subconditioning_b_complexity + + # dual fcs + for i, fc in enumerate(self.dual_fc): + rate = fs / len(self.dual_fc) + input_size = fc.dense1.in_features + output_size = fc.dense1.out_features + dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate + if hierarchical_sampling: + dual_fc_complexity /= 8 + if verbose: + print(f"dual_fc_{i}: {dual_fc_complexity} GFLOPS") + gflops += dual_fc_complexity + + if verbose: + print(f'total: {gflops} GFLOPS') + + return gflops + + + + def sparsify(self): + for sparsifier in self.sparsifier: + sparsifier.step() + + def frame_rate_network(self, features, periods): + + embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3) + features = torch.concat((features, embedded_periods), dim=-1) + + # convert to channels first and calculate conditioning vector + c = torch.permute(features, [0, 2, 1]) + + c = torch.tanh(self.feature_conv1(c)) + c = torch.tanh(self.feature_conv2(c)) + # back to channels last + c = torch.permute(c, [0, 2, 1]) + c = torch.tanh(self.feature_dense1(c)) + c = torch.tanh(self.feature_dense2(c)) + + return c + + def prepare_signals(self, signals, group_size, signal_idx): + """ extracts, delays and groups signals """ + + batch_size, sequence_length, num_signals = signals.shape + + # extract signals according to position + signals = torch.cat([signals[:, :, i : i + 1] for i in signal_idx], + dim=-1) + + # roll back pcm to account for grouping + signals = torch.roll(signals, group_size - 1, -2) + + # reshape + signals = torch.reshape(signals, + (batch_size, sequence_length // group_size, group_size * len(signal_idx))) + + return signals + + + def sample_rate_network(self, signals, c, gru_states): + + signals_a = self.prepare_signals(signals, self.group_size_gru_a, self.signals_idx) + embedded_signals = torch.flatten(self.signal_embedding(signals_a), 2, 3) + # features at GRU A rate + c_upsampled_a = torch.repeat_interleave(c, self.frame_size // self.gru_a_rate_divider, dim=1) + # features at GRU B rate + c_upsampled_b = torch.repeat_interleave(c, self.frame_size // self.gru_b_rate_divider, dim=1) + + y = torch.concat((embedded_signals, c_upsampled_a), dim=-1) + y, gru_a_state = self.gru_a(y, gru_states[0]) + # first round of upsampling and subconditioning + c_signals_a = self.prepare_signals(signals, self.group_size_subcondition_a, self.signals_idx_a) + y = self.subconditioner_a(y, c_signals_a) + y = interleave_tensors(y) + + y = torch.concat((y, c_upsampled_b), dim=-1) + y, gru_b_state = self.gru_b(y, gru_states[1]) + c_signals_b = self.prepare_signals(signals, 1, self.signals_idx_b) + y = self.subconditioner_b(y, c_signals_b) + + y = [self.dual_fc[i](y[i]) for i in range(self.substeps_b)] + y = interleave_tensors(y) + + return y, (gru_a_state, gru_b_state) + + def decoder(self, signals, c, gru_states): + embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) + + y = torch.concat((embedded_signals, c), dim=-1) + y, gru_a_state = self.gru_a(y, gru_states[0]) + y = torch.concat((y, c), dim=-1) + y, gru_b_state = self.gru_b(y, gru_states[1]) + + y = self.dual_fc(y) + + return torch.softmax(y, dim=-1), (gru_a_state, gru_b_state) + + def forward(self, features, periods, signals, gru_states): + + c = self.frame_rate_network(features, periods) + y, _ = self.sample_rate_network(signals, c, gru_states) + log_probs = torch.log_softmax(y, dim=-1) + + return log_probs + + def generate(self, features, periods, lpcs): + + with torch.no_grad(): + device = self.parameters().__next__().device + + num_frames = features.shape[0] - self.feature_history - self.feature_lookahead + lpc_order = lpcs.shape[-1] + num_input_signals = len(self.signals) + pitch_corr_position = self.input_layout['features']['pitch_corr'][0] + + # signal buffers + last_signal = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) + prediction = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) + last_error = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) + output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16) + mem = 0 + + # state buffers + gru_a_state = torch.zeros((1, 1, self.gru_a_units)) + gru_b_state = torch.zeros((1, 1, self.gru_b_units)) + + input_signals = 128 + torch.zeros(self.group_size_gru_a * num_input_signals, dtype=torch.long) + # conditioning signals for subconditioner a + c_signals_a = 128 + torch.zeros(self.group_size_subcondition_a * len(self.signals_idx_a), dtype=torch.long) + # conditioning signals for subconditioner b + c_signals_b = 128 + torch.zeros(len(self.signals_idx_b), dtype=torch.long) + + # signal dict + signal_dict = { + 'prediction' : prediction, + 'last_error' : last_error, + 'last_signal' : last_signal + } + + # push data to device + features = features.to(device) + periods = periods.to(device) + lpcs = lpcs.to(device) + + # run feature encoding + c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0)) + + for frame_index in range(num_frames): + frame_start = frame_index * self.frame_size + pitch_corr = features[frame_index + self.feature_history, pitch_corr_position] + a = - torch.flip(lpcs[frame_index + self.feature_history], [0]) + current_c = c[:, frame_index : frame_index + 1, :] + + for i in range(0, self.frame_size, self.group_size_gru_a): + pcm_position = frame_start + i + lpc_order + output_position = frame_start + i + + # calculate newest prediction + prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) + + # prepare input + for slot in range(self.group_size_gru_a): + k = slot - self.group_size_gru_a + 1 + for idx, name in enumerate(self.signals): + input_signals[idx + slot * num_input_signals] = lin2ulawq( + signal_dict[name][pcm_position + k] + ) + + + # run GRU A + embed_signals = self.signal_embedding(input_signals.reshape((1, 1, -1))) + embed_signals = torch.flatten(embed_signals, 2) + y = torch.cat((embed_signals, current_c), dim=-1) + h_a, gru_a_state = self.gru_a(y, gru_a_state) + + # loop over substeps_a + for step_a in range(self.substeps_a): + # prepare conditioning input + for slot in range(self.group_size_subcondition_a): + k = slot - self.group_size_subcondition_a + 1 + for idx, name in enumerate(self.subcondition_signals_a): + c_signals_a[idx + slot * num_input_signals] = lin2ulawq( + signal_dict[name][pcm_position + k] + ) + + # subconditioning + h_a = self.subconditioner_a.single_step(step_a, h_a, c_signals_a.reshape((1, 1, -1))) + + # run GRU B + y = torch.cat((h_a, current_c), dim=-1) + h_b, gru_b_state = self.gru_b(y, gru_b_state) + + # loop over substeps b + for step_b in range(self.substeps_b): + # prepare subconditioning input + for idx, name in enumerate(self.subcondition_signals_b): + c_signals_b[idx] = lin2ulawq( + signal_dict[name][pcm_position] + ) + + # subcondition + h_b = self.subconditioner_b.single_step(step_b, h_b, c_signals_b.reshape((1, 1, -1))) + + # run dual FC + probs = torch.softmax(self.dual_fc[step_b](h_b), dim=-1) + + # sample + new_exc = ulaw2lin(sample_excitation(probs, pitch_corr)) + + # update signals + sig = new_exc + prediction[pcm_position] + last_error[pcm_position + 1] = new_exc + last_signal[pcm_position + 1] = sig + + mem = 0.85 * mem + float(sig) + output[output_position] = clip_to_int16(round(mem)) + + # increase positions + pcm_position += 1 + output_position += 1 + + # calculate next prediction + prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) + + return output diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/print_lpcnet_complexity.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/print_lpcnet_complexity.py new file mode 100644 index 0000000..d6072dc --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/print_lpcnet_complexity.py @@ -0,0 +1,64 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse + +import yaml + +from models import model_dict + + +debug = False +if debug: + args = type('dummy', (object,), + { + 'setup' : 'setups/lpcnet_m/setup_1_4_concatenative.yml', + 'hierarchical_sampling' : False + })() +else: + parser = argparse.ArgumentParser() + parser.add_argument('setup', type=str, help='setup yaml file') + parser.add_argument('--hierarchical-sampling', action="store_true", help='whether to assume hierarchical sampling (default=False)', default=False) + + args = parser.parse_args() + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +# check model +if not 'model' in setup['lpcnet']: + print(f'warning: did not find model entry in setup, using default lpcnet') + model_name = 'lpcnet' +else: + model_name = setup['lpcnet']['model'] + +# create model +model = model_dict[model_name](setup['lpcnet']['config']) + +gflops = model.get_gflops(16000, verbose=True, hierarchical_sampling=args.hierarchical_sampling) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py new file mode 100644 index 0000000..ae662a4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py @@ -0,0 +1,190 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse +import os +from uuid import UUID +from collections import OrderedDict +import pickle + + +import torch +import numpy as np + +import utils + + + +parser = argparse.ArgumentParser() +parser.add_argument("input", type=str, help="input folder containing multi-run output") +parser.add_argument("tag", type=str, help="tag for multi-run experiment") +parser.add_argument("csv", type=str, help="name for output csv") + + +def is_uuid(val): + try: + UUID(val) + return True + except: + return False + + +def collect_results(folder): + + training_folder = os.path.join(folder, 'training') + testing_folder = os.path.join(folder, 'testing') + + # validation loss + checkpoint = torch.load(os.path.join(training_folder, 'checkpoints', 'checkpoint_finalize_epoch_1.pth'), map_location='cpu') + validation_loss = checkpoint['validation_loss'] + + # eval_warpq + eval_warpq = utils.data.parse_warpq_scores(os.path.join(training_folder, 'out_finalize.txt'))[-1] + + # testing results + testing_results = utils.data.collect_test_stats(os.path.join(testing_folder, 'final')) + + results = OrderedDict() + results['eval_loss'] = validation_loss + results['eval_warpq'] = eval_warpq + results['pesq_mean'] = testing_results['pesq'][0] + results['warpq_mean'] = testing_results['warpq'][0] + results['pitch_error_mean'] = testing_results['pitch_error'][0] + results['voicing_error_mean'] = testing_results['voicing_error'][0] + + return results + +def print_csv(path, results, tag, ranks=None, header=True): + + metrics = next(iter(results.values())).keys() + if ranks is not None: + rank_keys = next(iter(ranks.values())).keys() + else: + rank_keys = [] + + with open(path, 'w') as f: + if header: + f.write("uuid, tag") + + for metric in metrics: + f.write(f", {metric}") + + for rank in rank_keys: + f.write(f", {rank}") + + f.write("\n") + + + for uuid, values in results.items(): + f.write(f"{uuid}, {tag}") + + for val in values.values(): + f.write(f", {val:10.8f}") + + for rank in rank_keys: + f.write(f", {ranks[uuid][rank]:4d}") + + f.write("\n") + +def get_ranks(results): + + metrics = list(next(iter(results.values())).keys()) + + positive = {'pesq_mean', 'mix'} + + ranks = OrderedDict() + for key in results.keys(): + ranks[key] = OrderedDict() + + for metric in metrics: + sign = -1 if metric in positive else 1 + + x = sorted([(key, value[metric]) for key, value in results.items()], key=lambda x: sign * x[1]) + x = [y[0] for y in x] + + for key in results.keys(): + ranks[key]['rank_' + metric] = x.index(key) + 1 + + return ranks + +def analyse_metrics(results): + metrics = ['eval_loss', 'pesq_mean', 'warpq_mean', 'pitch_error_mean', 'voicing_error_mean'] + + x = [] + for metric in metrics: + x.append([val[metric] for val in results.values()]) + + x = np.array(x) + + print(x) + +def add_mix_metric(results): + metrics = ['eval_loss', 'pesq_mean', 'warpq_mean', 'pitch_error_mean', 'voicing_error_mean'] + + x = [] + for metric in metrics: + x.append([val[metric] for val in results.values()]) + + x = np.array(x).transpose() * np.array([-1, 1, -1, -1, -1]) + + z = (x - np.mean(x, axis=0)) / np.std(x, axis=0) + + print(f"covariance matrix for normalized scores of {metrics}:") + print(np.cov(z.transpose())) + + score = np.mean(z, axis=1) + + for i, key in enumerate(results.keys()): + results[key]['mix'] = score[i].item() + +if __name__ == "__main__": + args = parser.parse_args() + + uuids = sorted([x for x in os.listdir(args.input) if os.path.isdir(os.path.join(args.input, x)) and is_uuid(x)]) + + + results = OrderedDict() + + for uuid in uuids: + results[uuid] = collect_results(os.path.join(args.input, uuid)) + + + add_mix_metric(results) + + ranks = get_ranks(results) + + + + csv = args.csv if args.csv.endswith('.csv') else args.csv + '.csv' + + print_csv(args.csv, results, args.tag, ranks=ranks) + + + with open(csv[:-4] + '.pickle', 'wb') as f: + pickle.dump(results, f, protocol=pickle.HIGHEST_PROTOCOL) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/loop_run.sh b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/loop_run.sh new file mode 100644 index 0000000..7250f63 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/loop_run.sh @@ -0,0 +1,52 @@ +#!/bin/bash + + +case $# in + 9) SETUP=$1; OUTDIR=$2; NAME=$3; DEVICE=$4; ROUNDS=$5; LPCNEXT=$6; LPCNET=$7; TESTSUITE=$8; TESTITEMS=$9;; + *) echo "loop_run.sh setup outdir name device rounds lpcnext_repo lpcnet_repo testsuite_repo testitems"; exit;; +esac + + +PYTHON="/home/ubuntu/opt/miniconda3/envs/torch/bin/python" +TESTFEATURES=${LPCNEXT}/testitems/features/all_0_orig_features.f32 +WARPQREFERENCE=${LPCNEXT}/testitems/wav/all_0_orig.wav +METRICS="warpq,pesq,pitch_error,voicing_error" +LPCNETDEMO=${LPCNET}/lpcnet_demo + +for ((round = 1; round <= $ROUNDS; round++)) +do + echo + echo round $round + + UUID=$(uuidgen) + TRAINOUT=${OUTDIR}/${UUID}/training + TESTOUT=${OUTDIR}/${UUID}/testing + CHECKPOINT=${TRAINOUT}/checkpoints/checkpoint_last.pth + FINALCHECKPOINT=${TRAINOUT}/checkpoints/checkpoint_finalize_last.pth + + # run training + echo "starting training..." + $PYTHON $LPCNEXT/train_lpcnet.py $SETUP $TRAINOUT --device $DEVICE --test-features $TESTFEATURES --warpq-reference $WARPQREFERENCE + + # run finalization + echo "starting finalization..." + $PYTHON $LPCNEXT/train_lpcnet.py $SETUP $TRAINOUT \ + --device $DEVICE --test-features $TESTFEATURES \ + --warpq-reference $WARPQREFERENCE \ + --finalize --initial-checkpoint $CHECKPOINT + + # create test configs + $PYTHON $LPCNEXT/make_test_config.py ${OUTDIR}/${UUID}/testconfig.yml "$NAME $UUID" $CHECKPOINT --lpcnet-demo $LPCNETDEMO + $PYTHON $LPCNEXT/make_test_config.py ${OUTDIR}/${UUID}/testconfig_finalize.yml "$NAME $UUID finalized" $FINALCHECKPOINT --lpcnet-demo $LPCNETDEMO + + # run tests + echo "starting test 1 (no finalization)..." + $PYTHON $TESTSUITE/run_test.py ${OUTDIR}/${UUID}/testconfig.yml \ + $TESTITEMS ${TESTOUT}/prefinal --num-workers 8 \ + --num-testitems 400 --metrics $METRICS + + echo "starting test 2 (after finalization)..." + $PYTHON $TESTSUITE/run_test.py ${OUTDIR}/${UUID}/testconfig_finalize.yml \ + $TESTITEMS ${TESTOUT}/final --num-workers 8 \ + --num-testitems 400 --metrics $METRICS +done diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/make_animation.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/make_animation.py new file mode 100644 index 0000000..57656ef --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/make_animation.py @@ -0,0 +1,67 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +""" script for creating animations from debug data + +""" + + +import argparse + + +import sys +sys.path.append('./') + +from utils.endoscopy import make_animation, read_data + +parser = argparse.ArgumentParser() + +parser.add_argument('folder', type=str, help='endoscopy folder with debug output') +parser.add_argument('output', type=str, help='output file (will be auto-extended with .mp4)') + +parser.add_argument('--start-index', type=int, help='index of first sample to be considered', default=0) +parser.add_argument('--stop-index', type=int, help='index of last sample to be considered', default=-1) +parser.add_argument('--interval', type=int, help='interval between frames in ms', default=20) +parser.add_argument('--half-window-length', type=int, help='half size of window for displaying signals', default=80) + + +if __name__ == "__main__": + args = parser.parse_args() + + filename = args.output if args.output.endswith('.mp4') else args.output + '.mp4' + data = read_data(args.folder) + + make_animation( + data, + filename, + start_index=args.start_index, + stop_index = args.stop_index, + half_signal_window_length=args.half_window_length + ) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py new file mode 100644 index 0000000..a70fe16 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py @@ -0,0 +1,17 @@ +import argparse + +import numpy as np + + +parser = argparse.ArgumentParser(description="sets s_t to augmented_s_t") + +parser.add_argument('datafile', type=str, help='data.s16 file path') + +args = parser.parse_args() + +data = np.memmap(args.datafile, dtype='int16', mode='readwrite') + +# signal is in data[1::2] +# last augmented signal is in data[0::2] + +data[1 : - 1 : 2] = data[2 : : 2] diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/multi_run.sh b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/multi_run.sh new file mode 100644 index 0000000..fb0fee1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/multi_run.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +case $# in + 9) SETUP=$1; OUTDIR=$2; NAME=$3; NUMDEVICES=$4; ROUNDS=$5; LPCNEXT=$6; LPCNET=$7; TESTSUITE=$8; TESTITEMS=$9;; + *) echo "multi_run.sh setup outdir name num_devices rounds_per_device lpcnext_repo lpcnet_repo testsuite_repo testitems"; exit;; +esac + + +LOOPRUN=${LPCNEXT}/loop_run.sh + +mkdir -p $OUTDIR + +for ((i = 0; i < $NUMDEVICES; i++)) +do + echo "launching job queue for device $i" + nohup bash $LOOPRUN $SETUP $OUTDIR "$NAME" "cuda:$i" $ROUNDS $LPCNEXT $LPCNET $TESTSUITE $TESTITEMS > $OUTDIR/job_${i}_out.txt & +done diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/run_inference_test.sh b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/run_inference_test.sh new file mode 100644 index 0000000..9f22b03 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/run_inference_test.sh @@ -0,0 +1,22 @@ +#!/bin/bash + + +case $# in + 3) FEATURES=$1; FOLDER=$2; PYTHON=$3;; + *) echo "run_inference_test.sh "; exit;; +esac + + +SCRIPTFOLDER=$(dirname "$0") + +mkdir -p $FOLDER/inference_test + +# update checkpoints +for fn in $(find $FOLDER -type f -name "checkpoint*.pth") +do + tmp=$(basename $fn) + tmp=${tmp%.pth} + epoch=${tmp#checkpoint_epoch_} + echo "running inference with checkpoint $fn..." + $PYTHON $SCRIPTFOLDER/../test_lpcnet.py $FEATURES $fn $FOLDER/inference_test/output_epoch_${epoch}.wav +done diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_checkpoints.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_checkpoints.py new file mode 100644 index 0000000..8f00a7e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_checkpoints.py @@ -0,0 +1,54 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" script for updating checkpoints with new setup entries + + Use this script to update older outputs with newly introduced + parameters. (Saves us the trouble of backward compatibility) +""" + + +import argparse + +import torch + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint_file', type=str, help='checkpoint to be updated') +parser.add_argument('--model', type=str, help='model update', default=None) + +args = parser.parse_args() + +checkpoint = torch.load(args.checkpoint_file, map_location='cpu') + +# update model entry +if type(args.model) != type(None): + checkpoint['setup']['lpcnet']['model'] = args.model + +torch.save(checkpoint, args.checkpoint_file) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_output_folder.sh b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_output_folder.sh new file mode 100644 index 0000000..487d4a2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_output_folder.sh @@ -0,0 +1,22 @@ +#!/bin/bash + + +case $# in + 3) FOLDER=$1; MODEL=$2; PYTHON=$3;; + *) echo "update_output_folder.sh folder model python"; exit;; +esac + + +SCRIPTFOLDER=$(dirname "$0") + + +# update setup +echo "updating $FOLDER/setup.py..." +$PYTHON $SCRIPTFOLDER/update_setups.py $FOLDER/setup.yml --model $MODEL + +# update checkpoints +for fn in $(find $FOLDER -type f -name "checkpoint*.pth") +do + echo "updating $fn..." + $PYTHON $SCRIPTFOLDER/update_checkpoints.py $fn --model $MODEL +done \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_setups.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_setups.py new file mode 100644 index 0000000..52f81cf --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/scripts/update_setups.py @@ -0,0 +1,57 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" script for updating setup files with new setup entries + + Use this script to update older outputs with newly introduced + parameters. (Saves us the trouble of backward compatibility) +""" + +import argparse + +import yaml + +parser = argparse.ArgumentParser() + +parser.add_argument('setup_file', type=str, help='setup to be updated') +parser.add_argument('--model', type=str, help='model update', default=None) + +args = parser.parse_args() + +# load setup +with open(args.setup_file, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +# update model entry +if type(args.model) != type(None): + setup['lpcnet']['model'] = args.model + +# dump result +with open(args.setup_file, 'w') as f: + yaml.dump(setup, f) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/test_lpcnet.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/test_lpcnet.py new file mode 100644 index 0000000..49db8b0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/test_lpcnet.py @@ -0,0 +1,89 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse + +import torch +import numpy as np + + +from models import model_dict +from utils.data import load_features +from utils.wav import wavwrite16 + +debug = False +if debug: + args = type('dummy', (object,), + { + 'features' : 'features.f32', + 'checkpoint' : 'checkpoint.pth', + 'output' : 'out.wav', + 'version' : 2 + })() +else: + parser = argparse.ArgumentParser() + + parser.add_argument('features', type=str, help='feature file') + parser.add_argument('checkpoint', type=str, help='checkpoint file') + parser.add_argument('output', type=str, help='output file') + parser.add_argument('--version', type=int, help='feature version', default=2) + + args = parser.parse_args() + + +torch.set_num_threads(2) + +version = args.version +feature_file = args.features +checkpoint_file = args.checkpoint + + + +output_file = args.output +if not output_file.endswith('.wav'): + output_file += '.wav' + +checkpoint = torch.load(checkpoint_file, map_location="cpu") + +# check model +if not 'model' in checkpoint['setup']['lpcnet']: + print(f'warning: did not find model entry in setup, using default lpcnet') + model_name = 'lpcnet' +else: + model_name = checkpoint['setup']['lpcnet']['model'] + +model = model_dict[model_name](checkpoint['setup']['lpcnet']['config']) + +model.load_state_dict(checkpoint['state_dict']) + +data = load_features(feature_file) + +output = model.generate(data['features'], data['periods'], data['lpcs']) + +wavwrite16(output_file, output.numpy(), 16000) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/train_lpcnet.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/train_lpcnet.py new file mode 100644 index 0000000..c6121c8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/train_lpcnet.py @@ -0,0 +1,272 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +try: + import git + has_git = True +except: + has_git = False + +import yaml + + +import torch +from torch.optim.lr_scheduler import LambdaLR + +from data import LPCNetDataset +from models import model_dict +from engine.lpcnet_engine import train_one_epoch, evaluate +from utils.data import load_features +from utils.wav import wavwrite16 + + +debug = False +if debug: + args = type('dummy', (object,), + { + 'setup' : 'setup.yml', + 'output' : 'testout', + 'device' : None, + 'test_features' : None, + 'finalize': False, + 'initial_checkpoint': None, + 'no-redirect': False + })() +else: + parser = argparse.ArgumentParser("train_lpcnet.py") + parser.add_argument('setup', type=str, help='setup yaml file') + parser.add_argument('output', type=str, help='output path') + parser.add_argument('--device', type=str, help='compute device', default=None) + parser.add_argument('--test-features', type=str, help='test feature file in v2 format', default=None) + parser.add_argument('--finalize', action='store_true', help='run single training round with lr=1e-5') + parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) + parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of output') + + args = parser.parse_args() + + +torch.set_num_threads(4) + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +if args.finalize: + if args.initial_checkpoint is None: + raise ValueError('finalization requires initial checkpoint') + + if 'sparsification' in setup['lpcnet']['config']: + for sp_job in setup['lpcnet']['config']['sparsification'].values(): + sp_job['start'], sp_job['stop'] = 0, 0 + + setup['training']['lr'] = 1.0e-5 + setup['training']['lr_decay_factor'] = 0.0 + setup['training']['epochs'] = 1 + + checkpoint_prefix = 'checkpoint_finalize' + output_prefix = 'output_finalize' + setup_name = 'setup_finalize.yml' + output_file='out_finalize.txt' +else: + checkpoint_prefix = 'checkpoint' + output_prefix = 'output' + setup_name = 'setup.yml' + output_file='out.txt' + + +# check model +if not 'model' in setup['lpcnet']: + print(f'warning: did not find model entry in setup, using default lpcnet') + model_name = 'lpcnet' +else: + model_name = setup['lpcnet']['model'] + +# prepare output folder +if os.path.exists(args.output) and not debug and not args.finalize: + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit() +else: + os.makedirs(args.output, exist_ok=True) + +checkpoint_dir = os.path.join(args.output, 'checkpoints') +os.makedirs(checkpoint_dir, exist_ok=True) + + +# add repo info to setup +if has_git: + working_dir = os.path.split(__file__)[0] + try: + repo = git.Repo(working_dir) + setup['repo'] = dict() + hash = repo.head.object.hexsha + urls = list(repo.remote().urls) + is_dirty = repo.is_dirty() + + if is_dirty: + print("warning: repo is dirty") + + setup['repo']['hash'] = hash + setup['repo']['urls'] = urls + setup['repo']['dirty'] = is_dirty + except: + has_git = False + +# dump setup +with open(os.path.join(args.output, setup_name), 'w') as f: + yaml.dump(setup, f) + +# prepare inference test if wanted +run_inference_test = False +if type(args.test_features) != type(None): + test_features = load_features(args.test_features) + inference_test_dir = os.path.join(args.output, 'inference_test') + os.makedirs(inference_test_dir, exist_ok=True) + run_inference_test = True + +# training parameters +batch_size = setup['training']['batch_size'] +epochs = setup['training']['epochs'] +lr = setup['training']['lr'] +lr_decay_factor = setup['training']['lr_decay_factor'] + +# load training dataset +lpcnet_config = setup['lpcnet']['config'] +data = LPCNetDataset( setup['dataset'], + features=lpcnet_config['features'], + input_signals=lpcnet_config['signals'], + target=lpcnet_config['target'], + frames_per_sample=setup['training']['frames_per_sample'], + feature_history=lpcnet_config['feature_history'], + feature_lookahead=lpcnet_config['feature_lookahead'], + lpc_gamma=lpcnet_config.get('lpc_gamma', 1)) + +# load validation dataset if given +if 'validation_dataset' in setup: + validation_data = LPCNetDataset( setup['validation_dataset'], + features=lpcnet_config['features'], + input_signals=lpcnet_config['signals'], + target=lpcnet_config['target'], + frames_per_sample=setup['training']['frames_per_sample'], + feature_history=lpcnet_config['feature_history'], + feature_lookahead=lpcnet_config['feature_lookahead'], + lpc_gamma=lpcnet_config.get('lpc_gamma', 1)) + + validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) + + run_validation = True +else: + run_validation = False + +# create model +model = model_dict[model_name](setup['lpcnet']['config']) + +if args.initial_checkpoint is not None: + print(f"loading state dict from {args.initial_checkpoint}...") + chkpt = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(chkpt['state_dict']) + +# set compute device +if type(args.device) == type(None): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +else: + device = torch.device(args.device) + +# push model to device +model.to(device) + +# dataloader +dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) + +# optimizer is introduced to trainable parameters +parameters = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(parameters, lr=lr) + +# learning rate scheduler +scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +# loss +criterion = torch.nn.NLLLoss() + +# model checkpoint +checkpoint = { + 'setup' : setup, + 'state_dict' : model.state_dict(), + 'loss' : -1 +} + +if not args.no_redirect: + print(f"re-directing output to {os.path.join(args.output, output_file)}") + sys.stdout = open(os.path.join(args.output, output_file), "w") + +best_loss = 1e9 + +for ep in range(1, epochs + 1): + print(f"training epoch {ep}...") + new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) + + + # save checkpoint + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = new_loss + + if run_validation: + print("running validation...") + validation_loss = evaluate(model, criterion, validation_dataloader, device) + checkpoint['validation_loss'] = validation_loss + + if validation_loss < best_loss: + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) + best_loss = validation_loss + + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) + + # run inference test + if run_inference_test: + model.to("cpu") + print("running inference test...") + + output = model.generate(test_features['features'], test_features['periods'], test_features['lpcs']) + + testfilename = os.path.join(inference_test_dir, output_prefix + f'_epoch_{ep}.wav') + + wavwrite16(testfilename, output.numpy(), 16000) + + model.to(device) + + print() diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/__init__.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/__init__.py new file mode 100644 index 0000000..edbbe02 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/__init__.py @@ -0,0 +1,4 @@ +from . import sparsification +from . import data +from . import pcm +from . import sample \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/data.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/data.py new file mode 100644 index 0000000..5d6659e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/data.py @@ -0,0 +1,141 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + +import torch +import numpy as np + +def load_features(feature_file, version=2): + if version == 2: + layout = { + 'cepstrum': [0,18], + 'periods': [18, 19], + 'pitch_corr': [19, 20], + 'lpc': [20, 36] + } + frame_length = 36 + + elif version == 1: + layout = { + 'cepstrum': [0,18], + 'periods': [36, 37], + 'pitch_corr': [37, 38], + 'lpc': [39, 55], + } + frame_length = 55 + else: + raise ValueError(f'unknown feature version: {version}') + + + raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32')) + raw_features = raw_features.reshape((-1, frame_length)) + + features = torch.cat( + [ + raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]], + raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]] + ], + dim=1 + ) + + lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]] + periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long() + + return {'features' : features, 'periods' : periods, 'lpcs' : lpcs} + + + +def create_new_data(signal_path, reference_data_path, new_data_path, offset=320, preemph_factor=0.85): + ref_data = np.memmap(reference_data_path, dtype=np.int16) + signal = np.memmap(signal_path, dtype=np.int16) + + signal_preemph_path = os.path.splitext(signal_path)[0] + '_preemph.raw' + signal_preemph = np.memmap(signal_preemph_path, dtype=np.int16, mode='write', shape=signal.shape) + + + assert len(signal) % 160 == 0 + num_frames = len(signal) // 160 + mem = np.zeros(1) + for fr in range(len(signal)//160): + signal_preemph[fr * 160 : (fr + 1) * 160] = np.convolve(np.concatenate((mem, signal[fr * 160 : (fr + 1) * 160])), [1, -preemph_factor], mode='valid') + mem = signal[(fr + 1) * 160 - 1 : (fr + 1) * 160] + + new_data = np.memmap(new_data_path, dtype=np.int16, mode='write', shape=ref_data.shape) + + new_data[:] = 0 + N = len(signal) - offset + new_data[1 : 2*N + 1: 2] = signal_preemph[offset:] + new_data[2 : 2*N + 2: 2] = signal_preemph[offset:] + + +def parse_warpq_scores(output_file): + """ extracts warpq scores from output file """ + + with open(output_file, "r") as f: + lines = f.readlines() + + scores = [float(line.split("WARP-Q score:")[-1]) for line in lines if line.startswith("WARP-Q score:")] + + return scores + + +def parse_stats_file(file): + + with open(file, "r") as f: + lines = f.readlines() + + mean = float(lines[0].split(":")[-1]) + bt_mean = float(lines[1].split(":")[-1]) + top_mean = float(lines[2].split(":")[-1]) + + return mean, bt_mean, top_mean + +def collect_test_stats(test_folder): + """ collects statistics for all discovered metrics from test folder """ + + metrics = {'pesq', 'warpq', 'pitch_error', 'voicing_error'} + + results = dict() + + content = os.listdir(test_folder) + + stats_files = [file for file in content if file.startswith('stats_')] + + for file in stats_files: + metric = file[len("stats_") : -len(".txt")] + + if metric not in metrics: + print(f"warning: unknown metric {metric}") + + mean, bt_mean, top_mean = parse_stats_file(os.path.join(test_folder, file)) + + results[metric] = [mean, bt_mean, top_mean] + + return results diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/endoscopy.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/endoscopy.py new file mode 100644 index 0000000..141447e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/endoscopy.py @@ -0,0 +1,234 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" module for inspecting models during inference """ + +import os + +import yaml +import matplotlib.pyplot as plt +import matplotlib.animation as animation + +import torch +import numpy as np + +# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} +_state = dict() +_folder = 'endoscopy' + +def get_gru_gates(gru, input, state): + hidden_size = gru.hidden_size + + direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) + recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) + + # reset gate + start, stop = 0 * hidden_size, 1 * hidden_size + reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # update gate + start, stop = 1 * hidden_size, 2 * hidden_size + update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # new gate + start, stop = 2 * hidden_size, 3 * hidden_size + new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) + + return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} + + +def init(folder='endoscopy'): + """ sets up output folder for endoscopy data """ + + global _folder + _folder = folder + + if not os.path.exists(folder): + os.makedirs(folder) + else: + print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") + +def write_data(key, data, fs): + """ appends data to previous data written under key """ + + global _state + + # convert to numpy if torch.Tensor is given + if isinstance(data, torch.Tensor): + data = data.detach().numpy() + + if not key in _state: + _state[key] = { + 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), + 'fs' : fs, + 'dim' : tuple(data.shape), + 'dtype' : str(data.dtype) + } + + with open(os.path.join(_folder, key + '.yml'), 'w') as f: + f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) + else: + if _state[key]['fs'] != fs: + raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") + if _state[key]['dtype'] != str(data.dtype): + raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") + if _state[key]['dim'] != tuple(data.shape): + raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") + + _state[key]['fid'].write(data.tobytes()) + +def close(folder='endoscopy'): + """ clean up """ + for key in _state.keys(): + _state[key]['fid'].close() + + +def read_data(folder='endoscopy'): + """ retrieves written data as numpy arrays """ + + + keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] + + return_dict = dict() + + for key in keys: + with open(os.path.join(folder, key + '.yml'), 'r') as f: + value = yaml.load(f.read(), yaml.FullLoader) + + with open(os.path.join(folder, key + '.bin'), 'rb') as f: + data = np.frombuffer(f.read(), dtype=value['dtype']) + + value['data'] = data.reshape((-1,) + value['dim']) + + return_dict[key] = value + + return return_dict + +def get_best_reshape(shape, target_ratio=1): + """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" + + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return (1,) + + num_columns = int((pixel_count / target_ratio)**.5) + + while (pixel_count % num_columns): + num_columns -= 1 + + num_rows = pixel_count // num_columns + + return (num_rows, num_columns) + +def get_type_and_shape(shape): + + # can happen if data is one dimensional + if len(shape) == 0: + shape = (1,) + + # calculate pixel count + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return 'plot', (1, ) + + # stay with shape if already 2-dimensional + if len(shape) == 2: + if (shape[0] != pixel_count) or (shape[1] != pixel_count): + return 'image', shape + + return 'image', get_best_reshape(shape) + +def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): + + # determine plot setup + num_keys = len(data.keys()) + + num_rows = int((num_keys * 3/4) ** .5) + + num_cols = (num_keys + num_rows - 1) // num_rows + + fig, axs = plt.subplots(num_rows, num_cols) + fig.set_size_inches(num_cols * 5, num_rows * 5) + + display = dict() + + fs_max = max([val['fs'] for val in data.values()]) + + num_samples = max([val['data'].shape[0] for val in data.values()]) + + keys = sorted(data.keys()) + + # inspect data + for i, key in enumerate(keys): + axs[i // num_cols, i % num_cols].title.set_text(key) + + display[key] = dict() + + display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) + display[key]['down_factor'] = data[key]['fs'] / fs_max + + start_index = max(start_index, half_signal_window_length) + while stop_index < 0: + stop_index += num_samples + + stop_index = min(stop_index, num_samples - half_signal_window_length) + + # actual plotting + frames = [] + for index in range(start_index, stop_index): + ims = [] + for i, key in enumerate(keys): + feature_index = int(round(index * display[key]['down_factor'])) + + if display[key]['type'] == 'plot': + ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) + + elif display[key]['type'] == 'image': + ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) + + frames.append(ims) + + ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) + + if not filename.endswith('.mp4'): + filename += '.mp4' + + ani.save(filename) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/__init__.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/__init__.py new file mode 100644 index 0000000..4a58f22 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/__init__.py @@ -0,0 +1,3 @@ +from .dual_fc import DualFC +from .subconditioner import AdditiveSubconditioner, ModulativeSubconditioner, ConcatenativeSubconditioner +from .pcm_embeddings import PCMEmbedding, DifferentiablePCMEmbedding \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/dual_fc.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/dual_fc.py new file mode 100644 index 0000000..25d9a5f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/dual_fc.py @@ -0,0 +1,44 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn + +class DualFC(nn.Module): + def __init__(self, input_dim, output_dim): + super(DualFC, self).__init__() + + self.dense1 = nn.Linear(input_dim, output_dim) + self.dense2 = nn.Linear(input_dim, output_dim) + + self.alpha = nn.Parameter(torch.tensor([0.5]), requires_grad=True) + self.beta = nn.Parameter(torch.tensor([0.5]), requires_grad=True) + + def forward(self, x): + return self.alpha * torch.tanh(self.dense1(x)) + self.beta * torch.tanh(self.dense2(x)) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py new file mode 100644 index 0000000..603a17a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py @@ -0,0 +1,71 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" module implementing PCM embeddings for LPCNet """ + +import math as m + +import torch +from torch import nn + + +class PCMEmbedding(nn.Module): + def __init__(self, embed_dim=128, num_levels=256): + super(PCMEmbedding, self).__init__() + + self.embed_dim = embed_dim + self.num_levels = num_levels + + self.embedding = nn.Embedding(self.num_levels, self.num_dim) + + # initialize + with torch.no_grad(): + num_rows, num_cols = self.num_levels, self.embed_dim + a = m.sqrt(12) * (torch.rand(num_rows, num_cols) - 0.5) + for i in range(num_rows): + a[i, :] += m.sqrt(12) * (i - num_rows / 2) + self.embedding.weight[:, :] = 0.1 * a + + def forward(self, x): + return self.embeddint(x) + + +class DifferentiablePCMEmbedding(PCMEmbedding): + def __init__(self, embed_dim, num_levels=256): + super(DifferentiablePCMEmbedding, self).__init__(embed_dim, num_levels) + + def forward(self, x): + x_int = (x - torch.floor(x)).detach().long() + x_frac = x - x_int + x_next = torch.minimum(x_int + 1, self.num_levels) + + embed_0 = self.embedding(x_int) + embed_1 = self.embedding(x_next) + + return (1 - x_frac) * embed_0 + x_frac * embed_1 diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/subconditioner.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/subconditioner.py new file mode 100644 index 0000000..691eb44 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/layers/subconditioner.py @@ -0,0 +1,497 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from re import sub +import torch +from torch import nn + + + + +def get_subconditioner( method, + number_of_subsamples, + pcm_embedding_size, + state_size, + pcm_levels, + number_of_signals, + **kwargs): + + subconditioner_dict = { + 'additive' : AdditiveSubconditioner, + 'concatenative' : ConcatenativeSubconditioner, + 'modulative' : ModulativeSubconditioner + } + + return subconditioner_dict[method](number_of_subsamples, + pcm_embedding_size, state_size, pcm_levels, number_of_signals, **kwargs) + + +class Subconditioner(nn.Module): + def __init__(self): + """ upsampling by subconditioning + + Upsamples a sequence of states conditioning on pcm signals and + optionally a feature vector. + """ + super(Subconditioner, self).__init__() + + def forward(self, states, signals, features=None): + raise Exception("Base class should not be called") + + def single_step(self, index, state, signals, features): + raise Exception("Base class should not be called") + + def get_output_dim(self, index): + raise Exception("Base class should not be called") + + +class AdditiveSubconditioner(Subconditioner): + def __init__(self, + number_of_subsamples, + pcm_embedding_size, + state_size, + pcm_levels, + number_of_signals, + **kwargs): + """ subconditioning by addition """ + + super(AdditiveSubconditioner, self).__init__() + + self.number_of_subsamples = number_of_subsamples + self.pcm_embedding_size = pcm_embedding_size + self.state_size = state_size + self.pcm_levels = pcm_levels + self.number_of_signals = number_of_signals + + if self.pcm_embedding_size != self.state_size: + raise ValueError('For additive subconditioning state and embedding ' + + f'sizes must match but but got {self.state_size} and {self.pcm_embedding_size}') + + self.embeddings = [None] + for i in range(1, self.number_of_subsamples): + embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) + self.add_module('pcm_embedding_' + str(i), embedding) + self.embeddings.append(embedding) + + def forward(self, states, signals): + """ creates list of subconditioned states + + Parameters: + ----------- + states : torch.tensor + states of shape (batch, seq_length // s, state_size) + signals : torch.tensor + signals of shape (batch, seq_length, number_of_signals) + + Returns: + -------- + c_states : list of torch.tensor + list of s subconditioned states + """ + + s = self.number_of_subsamples + + c_states = [states] + new_states = states + for i in range(1, self.number_of_subsamples): + embed = self.embeddings[i](signals[:, i::s]) + # reduce signal dimension + embed = torch.sum(embed, dim=2) + + new_states = new_states + embed + c_states.append(new_states) + + return c_states + + def single_step(self, index, state, signals): + """ carry out single step for inference + + Parameters: + ----------- + index : int + position in subconditioning batch + + state : torch.tensor + state to sub-condition + + signals : torch.tensor + signals for subconditioning, all but the last dimensions + must match those of state + + Returns: + c_state : torch.tensor + subconditioned state + """ + + if index == 0: + c_state = state + else: + embed_signals = self.embeddings[index](signals) + c = torch.sum(embed_signals, dim=-2) + c_state = state + c + + return c_state + + def get_output_dim(self, index): + return self.state_size + + def get_average_flops_per_step(self): + s = self.number_of_subsamples + flops = (s - 1) / s * self.number_of_signals * self.pcm_embedding_size + return flops + + +class ConcatenativeSubconditioner(Subconditioner): + def __init__(self, + number_of_subsamples, + pcm_embedding_size, + state_size, + pcm_levels, + number_of_signals, + recurrent=True, + **kwargs): + """ subconditioning by concatenation """ + + super(ConcatenativeSubconditioner, self).__init__() + + self.number_of_subsamples = number_of_subsamples + self.pcm_embedding_size = pcm_embedding_size + self.state_size = state_size + self.pcm_levels = pcm_levels + self.number_of_signals = number_of_signals + self.recurrent = recurrent + + self.embeddings = [] + start_index = 0 + if self.recurrent: + start_index = 1 + self.embeddings.append(None) + + for i in range(start_index, self.number_of_subsamples): + embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) + self.add_module('pcm_embedding_' + str(i), embedding) + self.embeddings.append(embedding) + + def forward(self, states, signals): + """ creates list of subconditioned states + + Parameters: + ----------- + states : torch.tensor + states of shape (batch, seq_length // s, state_size) + signals : torch.tensor + signals of shape (batch, seq_length, number_of_signals) + + Returns: + -------- + c_states : list of torch.tensor + list of s subconditioned states + """ + s = self.number_of_subsamples + + if self.recurrent: + c_states = [states] + start = 1 + else: + c_states = [] + start = 0 + + new_states = states + for i in range(start, self.number_of_subsamples): + embed = self.embeddings[i](signals[:, i::s]) + # reduce signal dimension + embed = torch.flatten(embed, -2) + + if self.recurrent: + new_states = torch.cat((new_states, embed), dim=-1) + else: + new_states = torch.cat((states, embed), dim=-1) + + c_states.append(new_states) + + return c_states + + def single_step(self, index, state, signals): + """ carry out single step for inference + + Parameters: + ----------- + index : int + position in subconditioning batch + + state : torch.tensor + state to sub-condition + + signals : torch.tensor + signals for subconditioning, all but the last dimensions + must match those of state + + Returns: + c_state : torch.tensor + subconditioned state + """ + + if index == 0 and self.recurrent: + c_state = state + else: + embed_signals = self.embeddings[index](signals) + c = torch.flatten(embed_signals, -2) + if not self.recurrent and index > 0: + # overwrite previous conditioning vector + c_state = torch.cat((state[...,:self.state_size], c), dim=-1) + else: + c_state = torch.cat((state, c), dim=-1) + return c_state + + return c_state + + def get_average_flops_per_step(self): + return 0 + + def get_output_dim(self, index): + if self.recurrent: + return self.state_size + index * self.pcm_embedding_size * self.number_of_signals + else: + return self.state_size + self.pcm_embedding_size * self.number_of_signals + +class ModulativeSubconditioner(Subconditioner): + def __init__(self, + number_of_subsamples, + pcm_embedding_size, + state_size, + pcm_levels, + number_of_signals, + state_recurrent=False, + **kwargs): + """ subconditioning by modulation """ + + super(ModulativeSubconditioner, self).__init__() + + self.number_of_subsamples = number_of_subsamples + self.pcm_embedding_size = pcm_embedding_size + self.state_size = state_size + self.pcm_levels = pcm_levels + self.number_of_signals = number_of_signals + self.state_recurrent = state_recurrent + + self.hidden_size = self.pcm_embedding_size * self.number_of_signals + + if self.state_recurrent: + self.hidden_size += self.pcm_embedding_size + self.state_transform = nn.Linear(self.state_size, self.pcm_embedding_size) + + self.embeddings = [None] + self.alphas = [None] + self.betas = [None] + + for i in range(1, self.number_of_subsamples): + embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) + self.add_module('pcm_embedding_' + str(i), embedding) + self.embeddings.append(embedding) + + self.alphas.append(nn.Linear(self.hidden_size, self.state_size)) + self.add_module('alpha_dense_' + str(i), self.alphas[-1]) + + self.betas.append(nn.Linear(self.hidden_size, self.state_size)) + self.add_module('beta_dense_' + str(i), self.betas[-1]) + + + + def forward(self, states, signals): + """ creates list of subconditioned states + + Parameters: + ----------- + states : torch.tensor + states of shape (batch, seq_length // s, state_size) + signals : torch.tensor + signals of shape (batch, seq_length, number_of_signals) + + Returns: + -------- + c_states : list of torch.tensor + list of s subconditioned states + """ + s = self.number_of_subsamples + + c_states = [states] + new_states = states + for i in range(1, self.number_of_subsamples): + embed = self.embeddings[i](signals[:, i::s]) + # reduce signal dimension + embed = torch.flatten(embed, -2) + + if self.state_recurrent: + comp_states = self.state_transform(new_states) + embed = torch.cat((embed, comp_states), dim=-1) + + alpha = torch.tanh(self.alphas[i](embed)) + beta = torch.tanh(self.betas[i](embed)) + + # new state obtained by modulating previous state + new_states = torch.tanh((1 + alpha) * new_states + beta) + + c_states.append(new_states) + + return c_states + + def single_step(self, index, state, signals): + """ carry out single step for inference + + Parameters: + ----------- + index : int + position in subconditioning batch + + state : torch.tensor + state to sub-condition + + signals : torch.tensor + signals for subconditioning, all but the last dimensions + must match those of state + + Returns: + c_state : torch.tensor + subconditioned state + """ + + if index == 0: + c_state = state + else: + embed_signals = self.embeddings[index](signals) + c = torch.flatten(embed_signals, -2) + if self.state_recurrent: + r_state = self.state_transform(state) + c = torch.cat((c, r_state), dim=-1) + alpha = torch.tanh(self.alphas[index](c)) + beta = torch.tanh(self.betas[index](c)) + c_state = torch.tanh((1 + alpha) * state + beta) + return c_state + + return c_state + + def get_output_dim(self, index): + return self.state_size + + def get_average_flops_per_step(self): + s = self.number_of_subsamples + + # estimate activation by 10 flops + # c_state = torch.tanh((1 + alpha) * state + beta) + flops = 13 * self.state_size + + # hidden size + hidden_size = self.number_of_signals * self.pcm_embedding_size + if self.state_recurrent: + hidden_size += self.pcm_embedding_size + + # counting 2 * A * B flops for Linear(A, B) + # alpha = torch.tanh(self.alphas[index](c)) + # beta = torch.tanh(self.betas[index](c)) + flops += 4 * hidden_size * self.state_size + 20 * self.state_size + + # r_state = self.state_transform(state) + if self.state_recurrent: + flops += 2 * self.state_size * self.pcm_embedding_size + + # average over steps + flops *= (s - 1) / s + + return flops + +class ComparitiveSubconditioner(Subconditioner): + def __init__(self, + number_of_subsamples, + pcm_embedding_size, + state_size, + pcm_levels, + number_of_signals, + error_index=-1, + apply_gate=True, + normalize=False): + """ subconditioning by comparison """ + + super(ComparitiveSubconditioner, self).__init__() + + self.comparison_size = self.pcm_embedding_size + self.error_position = error_index + self.apply_gate = apply_gate + self.normalize = normalize + + self.state_transform = nn.Linear(self.state_size, self.comparison_size) + + self.alpha_dense = nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size) + self.beta_dense = nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size) + + if self.apply_gate: + self.gate_dense = nn.Linear(self.pcm_embedding_size, self.state_size) + + # embeddings and state transforms + self.embeddings = [None] + self.alpha_denses = [None] + self.beta_denses = [None] + self.state_transforms = [nn.Linear(self.state_size, self.comparison_size)] + self.add_module('state_transform_0', self.state_transforms[0]) + + for i in range(1, self.number_of_subsamples): + embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) + self.add_module('pcm_embedding_' + str(i), embedding) + self.embeddings.append(embedding) + + state_transform = nn.Linear(self.state_size, self.comparison_size) + self.add_module('state_transform_' + str(i), state_transform) + self.state_transforms.append(state_transform) + + self.alpha_denses.append(nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size)) + self.add_module('alpha_dense_' + str(i), self.alpha_denses[-1]) + + self.beta_denses.append(nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size)) + self.add_module('beta_dense_' + str(i), self.beta_denses[-1]) + + def forward(self, states, signals): + s = self.number_of_subsamples + + c_states = [states] + new_states = states + for i in range(1, self.number_of_subsamples): + embed = self.embeddings[i](signals[:, i::s]) + # reduce signal dimension + embed = torch.flatten(embed, -2) + + comp_states = self.state_transforms[i](new_states) + + alpha = torch.tanh(self.alpha_dense(embed)) + beta = torch.tanh(self.beta_dense(embed)) + + # new state obtained by modulating previous state + new_states = torch.tanh((1 + alpha) * comp_states + beta) + + c_states.append(new_states) + + return c_states diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/misc.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/misc.py new file mode 100644 index 0000000..b295d6c --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/misc.py @@ -0,0 +1,65 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + + +def find(a, v): + try: + idx = a.index(v) + except: + idx = -1 + return idx + +def interleave_tensors(tensors, dim=-2): + """ interleave list of tensors along sequence dimension """ + + x = torch.cat([x.unsqueeze(dim) for x in tensors], dim=dim) + x = torch.flatten(x, dim - 1, dim) + + return x + +def _interleave(x, pcm_levels=256): + + repeats = pcm_levels // (2*x.size(-1)) + x = x.unsqueeze(-1) + p = torch.flatten(torch.repeat_interleave(torch.cat((x, 1 - x), dim=-1), repeats, dim=-1), -2) + + return p + +def get_pdf_from_tree(x): + pcm_levels = x.size(-1) + + p = _interleave(x[..., 1:2]) + n = 4 + while n <= pcm_levels: + p = p * _interleave(x[..., n//2:n]) + n *= 2 + + return p \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/pcm.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/pcm.py new file mode 100644 index 0000000..7e985b8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/pcm.py @@ -0,0 +1,35 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +def clip_to_int16(x): + int_min = -2**15 + int_max = 2**15 - 1 + x_clipped = max(int_min, min(x, int_max)) + return x_clipped diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sample.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sample.py new file mode 100644 index 0000000..63f9e52 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sample.py @@ -0,0 +1,44 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + + +def sample_excitation(probs, pitch_corr): + + norm = lambda x : x / (x.sum() + 1e-18) + + # lowering the temperature + probs = norm(probs ** (1 + max(0, 1.5 * pitch_corr - 0.5))) + # cut-off tails + probs = norm(torch.maximum(probs - 0.002 , torch.FloatTensor([0]))) + # sample + exc = torch.multinomial(probs.squeeze(), 1) + + return exc diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/__init__.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/__init__.py new file mode 100644 index 0000000..ebfa9d9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/__init__.py @@ -0,0 +1,2 @@ +from .gru_sparsifier import GRUSparsifier +from .common import sparsify_matrix, calculate_gru_flops_per_step \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/common.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/common.py new file mode 100644 index 0000000..2600cd0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/common.py @@ -0,0 +1,121 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +def sparsify_matrix(matrix : torch.tensor, density : float, block_size, keep_diagonal : bool=False, return_mask : bool=False): + """ sparsifies matrix with specified block size + + Parameters: + ----------- + matrix : torch.tensor + matrix to sparsify + density : int + target density + block_size : [int, int] + block size dimensions + keep_diagonal : bool + If true, the diagonal will be kept. This option requires block_size[0] == block_size[1] and defaults to False + """ + + m, n = matrix.shape + m1, n1 = block_size + + if m % m1 or n % n1: + raise ValueError(f"block size {(m1, n1)} does not divide matrix size {(m, n)}") + + # extract diagonal if keep_diagonal = True + if keep_diagonal: + if m != n: + raise ValueError("Attempting to sparsify non-square matrix with keep_diagonal=True") + + to_spare = torch.diag(torch.diag(matrix)) + matrix = matrix - to_spare + else: + to_spare = torch.zeros_like(matrix) + + # calculate energy in sub-blocks + x = torch.reshape(matrix, (m // m1, m1, n // n1, n1)) + x = x ** 2 + block_energies = torch.sum(torch.sum(x, dim=3), dim=1) + + number_of_blocks = (m * n) // (m1 * n1) + number_of_survivors = round(number_of_blocks * density) + + # masking threshold + if number_of_survivors == 0: + threshold = 0 + else: + threshold = torch.sort(torch.flatten(block_energies)).values[-number_of_survivors] + + # create mask + mask = torch.ones_like(block_energies) + mask[block_energies < threshold] = 0 + mask = torch.repeat_interleave(mask, m1, dim=0) + mask = torch.repeat_interleave(mask, n1, dim=1) + + # perform masking + masked_matrix = mask * matrix + to_spare + + if return_mask: + return masked_matrix, mask + else: + return masked_matrix + +def calculate_gru_flops_per_step(gru, sparsification_dict=dict(), drop_input=False): + input_size = gru.input_size + hidden_size = gru.hidden_size + flops = 0 + + input_density = ( + sparsification_dict.get('W_ir', [1])[0] + + sparsification_dict.get('W_in', [1])[0] + + sparsification_dict.get('W_iz', [1])[0] + ) / 3 + + recurrent_density = ( + sparsification_dict.get('W_hr', [1])[0] + + sparsification_dict.get('W_hn', [1])[0] + + sparsification_dict.get('W_hz', [1])[0] + ) / 3 + + # input matrix vector multiplications + if not drop_input: + flops += 2 * 3 * input_size * hidden_size * input_density + + # recurrent matrix vector multiplications + flops += 2 * 3 * hidden_size * hidden_size * recurrent_density + + # biases + flops += 6 * hidden_size + + # activations estimated by 10 flops per activation + flops += 30 * hidden_size + + return flops diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py new file mode 100644 index 0000000..4dfdaf0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py @@ -0,0 +1,187 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch + +from .common import sparsify_matrix + + +class GRUSparsifier: + def __init__(self, task_list, start, stop, interval, exponent=3): + """ Sparsifier for torch.nn.GRUs + + Parameters: + ----------- + task_list : list + task_list contains a list of tuples (gru, sparsify_dict), where gru is an instance + of torch.nn.GRU and sparsify_dic is a dictionary with keys in {'W_ir', 'W_iz', 'W_in', + 'W_hr', 'W_hz', 'W_hn'} corresponding to the input and recurrent weights for the reset, + update, and new gate. The values of sparsify_dict are tuples (density, [m, n], keep_diagonal), + where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which + sparsification is applied and keep_diagonal is a bool variable indicating whether the diagonal + should be kept. + + start : int + training step after which sparsification will be started. + + stop : int + training step after which sparsification will be completed. + + interval : int + sparsification interval for steps between start and stop. After stop sparsification will be + carried out after every call to GRUSparsifier.step() + + exponent : float + Interpolation exponent for sparsification interval. In step i sparsification will be carried out + with density (alpha + target_density * (1 * alpha)), where + alpha = ((stop - i) / (start - stop)) ** exponent + + Example: + -------- + >>> import torch + >>> gru = torch.nn.GRU(10, 20) + >>> sparsify_dict = { + ... 'W_ir' : (0.5, [2, 2], False), + ... 'W_iz' : (0.6, [2, 2], False), + ... 'W_in' : (0.7, [2, 2], False), + ... 'W_hr' : (0.1, [4, 4], True), + ... 'W_hz' : (0.2, [4, 4], True), + ... 'W_hn' : (0.3, [4, 4], True), + ... } + >>> sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 50) + >>> for i in range(100): + ... sparsifier.step() + """ + # just copying parameters... + self.start = start + self.stop = stop + self.interval = interval + self.exponent = exponent + self.task_list = task_list + + # ... and setting counter to 0 + self.step_counter = 0 + + self.last_masks = {key : None for key in ['W_ir', 'W_in', 'W_iz', 'W_hr', 'W_hn', 'W_hz']} + + def step(self, verbose=False): + """ carries out sparsification step + + Call this function after optimizer.step in your + training loop. + + Parameters: + ---------- + verbose : bool + if true, densities are printed out + + Returns: + -------- + None + + """ + # compute current interpolation factor + self.step_counter += 1 + + if self.step_counter < self.start: + return + elif self.step_counter < self.stop: + # update only every self.interval-th interval + if self.step_counter % self.interval: + return + + alpha = ((self.stop - self.step_counter) / (self.stop - self.start)) ** self.exponent + else: + alpha = 0 + + + with torch.no_grad(): + for gru, params in self.task_list: + hidden_size = gru.hidden_size + + # input weights + for i, key in enumerate(['W_ir', 'W_iz', 'W_in']): + if key in params: + density = alpha + (1 - alpha) * params[key][0] + if verbose: + print(f"[{self.step_counter}]: {key} density: {density}") + + gru.weight_ih_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( + gru.weight_ih_l0[i * hidden_size : (i + 1) * hidden_size, : ], + density, # density + params[key][1], # block_size + params[key][2], # keep_diagonal (might want to set this to False) + return_mask=True + ) + + if type(self.last_masks[key]) != type(None): + if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop: + print(f"sparsification mask {key} changed for gru {gru}") + + self.last_masks[key] = new_mask + + # recurrent weights + for i, key in enumerate(['W_hr', 'W_hz', 'W_hn']): + if key in params: + density = alpha + (1 - alpha) * params[key][0] + if verbose: + print(f"[{self.step_counter}]: {key} density: {density}") + gru.weight_hh_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( + gru.weight_hh_l0[i * hidden_size : (i + 1) * hidden_size, : ], + density, + params[key][1], # block_size + params[key][2], # keep_diagonal (might want to set this to False) + return_mask=True + ) + + if type(self.last_masks[key]) != type(None): + if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop: + print(f"sparsification mask {key} changed for gru {gru}") + + self.last_masks[key] = new_mask + + + +if __name__ == "__main__": + print("Testing sparsifier") + + gru = torch.nn.GRU(10, 20) + sparsify_dict = { + 'W_ir' : (0.5, [2, 2], False), + 'W_iz' : (0.6, [2, 2], False), + 'W_in' : (0.7, [2, 2], False), + 'W_hr' : (0.1, [4, 4], True), + 'W_hz' : (0.2, [4, 4], True), + 'W_hn' : (0.3, [4, 4], True), + } + + sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 10) + + for i in range(100): + sparsifier.step(verbose=True) diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/templates.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/templates.py new file mode 100644 index 0000000..8906856 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/templates.py @@ -0,0 +1,157 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from models import multi_rate_lpcnet +import copy + +setup_dict = dict() + +dataset_template_v2 = { + 'version' : 2, + 'feature_file' : 'features.f32', + 'signal_file' : 'data.s16', + 'frame_length' : 160, + 'feature_frame_length' : 36, + 'signal_frame_length' : 2, + 'feature_dtype' : 'float32', + 'signal_dtype' : 'int16', + 'feature_frame_layout' : {'cepstrum': [0,18], 'periods': [18, 19], 'pitch_corr': [19, 20], 'lpc': [20, 36]}, + 'signal_frame_layout' : {'last_signal' : 0, 'signal': 1} # signal, last_signal, error, prediction +} + +dataset_template_v1 = { + 'version' : 1, + 'feature_file' : 'features.f32', + 'signal_file' : 'data.u8', + 'frame_length' : 160, + 'feature_frame_length' : 55, + 'signal_frame_length' : 4, + 'feature_dtype' : 'float32', + 'signal_dtype' : 'uint8', + 'feature_frame_layout' : {'cepstrum': [0,18], 'periods': [36, 37], 'pitch_corr': [37, 38], 'lpc': [39, 55]}, + 'signal_frame_layout' : {'last_signal' : 0, 'prediction' : 1, 'last_error': 2, 'error': 3} # signal, last_signal, error, prediction +} + +# lpcnet + +lpcnet_config = { + 'frame_size' : 160, + 'gru_a_units' : 384, + 'gru_b_units' : 64, + 'feature_conditioning_dim' : 128, + 'feature_conv_kernel_size' : 3, + 'period_levels' : 257, + 'period_embedding_dim' : 64, + 'signal_embedding_dim' : 128, + 'signal_levels' : 256, + 'feature_dimension' : 19, + 'output_levels' : 256, + 'lpc_gamma' : 0.9, + 'features' : ['cepstrum', 'periods', 'pitch_corr'], + 'signals' : ['last_signal', 'prediction', 'last_error'], + 'input_layout' : { 'signals' : {'last_signal' : 0, 'prediction' : 1, 'last_error' : 2}, + 'features' : {'cepstrum' : [0, 18], 'pitch_corr' : [18, 19]} }, + 'target' : 'error', + 'feature_history' : 2, + 'feature_lookahead' : 2, + 'sparsification' : { + 'gru_a' : { + 'start' : 10000, + 'stop' : 30000, + 'interval' : 100, + 'exponent' : 3, + 'params' : { + 'W_hr' : (0.05, [4, 8], True), + 'W_hz' : (0.05, [4, 8], True), + 'W_hn' : (0.2, [4, 8], True) + }, + }, + 'gru_b' : { + 'start' : 10000, + 'stop' : 30000, + 'interval' : 100, + 'exponent' : 3, + 'params' : { + 'W_ir' : (0.5, [4, 8], False), + 'W_iz' : (0.5, [4, 8], False), + 'W_in' : (0.5, [4, 8], False) + }, + } + }, + 'add_reference_phase' : False, + 'reference_phase_dim' : 0 +} + + + +# multi rate +subconditioning = { + 'subconditioning_a' : { + 'number_of_subsamples' : 2, + 'method' : 'modulative', + 'signals' : ['last_signal', 'prediction', 'last_error'], + 'pcm_embedding_size' : 64, + 'kwargs' : dict() + + }, + 'subconditioning_b' : { + 'number_of_subsamples' : 2, + 'method' : 'modulative', + 'signals' : ['last_signal', 'prediction', 'last_error'], + 'pcm_embedding_size' : 64, + 'kwargs' : dict() + } +} + +multi_rate_lpcnet_config = lpcnet_config.copy() +multi_rate_lpcnet_config['subconditioning'] = subconditioning + +training_default = { + 'batch_size' : 256, + 'epochs' : 20, + 'lr' : 1e-3, + 'lr_decay_factor' : 2.5e-5, + 'adam_betas' : [0.9, 0.99], + 'frames_per_sample' : 15 +} + +lpcnet_setup = { + 'dataset' : '/local/datasets/lpcnet_training', + 'lpcnet' : {'config' : lpcnet_config, 'model': 'lpcnet'}, + 'training' : training_default +} + +multi_rate_lpcnet_setup = copy.deepcopy(lpcnet_setup) +multi_rate_lpcnet_setup['lpcnet']['config'] = multi_rate_lpcnet_config +multi_rate_lpcnet_setup['lpcnet']['model'] = 'multi_rate' + +setup_dict = { + 'lpcnet' : lpcnet_setup, + 'multi_rate' : multi_rate_lpcnet_setup +} diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/ulaw.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/ulaw.py new file mode 100644 index 0000000..05c395e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/ulaw.py @@ -0,0 +1,58 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import math as m + +import torch + + + +def ulaw2lin(u): + scale_1 = 32768.0 / 255.0 + u = u - 128 + s = torch.sign(u) + u = torch.abs(u) + return s * scale_1 * (torch.exp(u / 128. * m.log(256)) - 1) + + +def lin2ulawq(x): + scale = 255.0 / 32768.0 + s = torch.sign(x) + x = torch.abs(x) + u = s * (128 * torch.log(1 + scale * x) / m.log(256)) + u = torch.clip(128 + torch.round(u), 0, 255) + return u + +def lin2ulaw(x): + scale = 255.0 / 32768.0 + s = torch.sign(x) + x = torch.abs(x) + u = s * (128 * torch.log(1 + scale * x) / torch.log(256)) + u = torch.clip(128 + u, 0, 255) + return u \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/wav.py b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/wav.py new file mode 100644 index 0000000..d955c32 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/lpcnet/utils/wav.py @@ -0,0 +1,43 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import wave + +def wavwrite16(filename, x, fs): + """ writes x as int16 to file with name filename + + If x.dtype is int16 x is written as is. Otherwise, + it is scaled by 2**15 - 1 and converted to int16. + """ + if x.dtype != 'int16': + x = ((2**15 - 1) * x).astype('int16') + + with wave.open(filename, 'wb') as f: + f.setparams((1, 2, fs, len(x), 'NONE', "")) + f.writeframes(x.tobytes()) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/README.md b/managed_components/78__esp-opus/dnn/torch/neural-pitch/README.md new file mode 100644 index 0000000..6323ead --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/README.md @@ -0,0 +1,18 @@ +## Neural Pitch Estimation + +- Dataset Installation + 1. Download and unzip PTDB Dataset: + wget https://www2.spsc.tugraz.at/databases/PTDB-TUG/SPEECH_DATA_ZIPPED.zip + unzip SPEECH_DATA_ZIPPED.zip + + 2. Inside "SPEECH DATA" above, run ptdb_process.sh to combine male/female + + 3. To Download and combine demand, simply run download_demand.sh + +- LPCNet preparation + 1. To extract xcorr, add lpcnet_extractor.c and add relevant functions to lpcnet_enc.c, add source for headers/c files and Makefile.am, and compile to generate ./lpcnet_xcorr_extractor object + +- Dataset Augmentation and training (check out arguments to each of the following) + 1. Run data_augmentation.py + 2. Run training.py using augmented data + 3. Run experiments.py diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/data_augmentation.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/data_augmentation.py new file mode 100644 index 0000000..ee7a3ca --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/data_augmentation.py @@ -0,0 +1,149 @@ +""" +Perform Data Augmentation (Gain, Additive Noise, Random Filtering) on Input TTS Data +1. Read in chunks and compute clean pitch first +2. Then add in augmentation (Noise/Level/Response) + - Adds filtered noise from the "Demand" dataset, https://zenodo.org/record/1227121#.XRKKxYhKiUk + - When using the Demand Dataset, consider each channel as a possible noise input, and keep the first 4 minutes of noise for training +3. Use this "augmented" audio for feature computation, and compute pitch using CREPE on the clean input + +Notes: To ensure consistency with the discovered CREPE offset, we do the following +- We pad the input audio to the zero-centered CREPE estimator with 80 zeros +- We pad the input audio to our feature computation with 160 zeros to center them +""" + +import argparse +parser = argparse.ArgumentParser() + +parser.add_argument('data', type=str, help='input raw audio data') +parser.add_argument('output', type=str, help='output directory') +parser.add_argument('path_lpcnet_extractor', type=str, help='path to LPCNet extractor object file (generated on compilation)') +parser.add_argument('noise_dataset', type=str, help='Location of the Demand Datset') +parser.add_argument('--flag_xcorr', type=bool, help='Flag to additionally dump xcorr features',choices=[True,False],default = False,required = False) +parser.add_argument('--fraction_input_use', type=float, help='Fraction of input data to consider',default = 0.3,required = False) +parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) +parser.add_argument('--choice_augment', type=str, help='Choice of noise augmentation, either use additive synthetic noise or add noise from the demand dataset',choices = ['demand','synthetic'],default = "demand",required = False) +parser.add_argument('--fraction_clean', type=float, help='Fraction of data to keep clean (that is not augment with anything)',default = 0.2,required = False) +parser.add_argument('--chunk_size', type=int, help='Number of samples to augment with for each iteration',default = 80000,required = False) +parser.add_argument('--N', type=int, help='STFT window size',default = 320,required = False) +parser.add_argument('--H', type=int, help='STFT Hop size',default = 160,required = False) +parser.add_argument('--freq_keep', type=int, help='Number of Frequencies to keep',default = 30,required = False) + +args = parser.parse_args() + +import os +os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) + +from utils import stft, random_filter + +import numpy as np +import tqdm +import crepe +import random +import glob +import subprocess + +data_full = np.memmap(args.data, dtype=np.int16,mode = 'r') +data = data_full[:(int)(args.fraction_input_use*data_full.shape[0])] + +# list_features = [] +list_cents = [] +list_confidences = [] + +N = args.N +H = args.H +freq_keep = args.freq_keep +# Minimum/Maximum periods, decided by LPCNet +min_period = 32 +max_period = 256 +f_ref = 16000/max_period +chunk_size = args.chunk_size +num_frames_chunk = chunk_size//H +list_indices_keep = np.concatenate([np.arange(freq_keep), (N//2 + 1) + np.arange(freq_keep), 2*(N//2 + 1) + np.arange(freq_keep)]) + +output_IF = np.memmap(args.output + '_iffeat.f32', dtype=np.float32, shape=(((data.shape[0]//chunk_size - 1)//1)*num_frames_chunk,list_indices_keep.shape[0]), mode='w+') +if args.flag_xcorr: + output_xcorr = np.memmap(args.output + '_xcorr.f32', dtype=np.float32, shape=(((data.shape[0]//chunk_size - 1)//1)*num_frames_chunk,257), mode='w+') + +fraction_clean = args.fraction_clean + +noise_dataset = args.noise_dataset + +for i in tqdm.trange((data.shape[0]//chunk_size - 1)//1): + chunk = data[i*chunk_size:(i + 1)*chunk_size]/(2**15 - 1) + + # Clean Pitch/Confidence Estimate + # Padding input to CREPE by 80 samples to ensure it aligns + _, pitch, confidence, _ = crepe.predict(np.concatenate([np.zeros(80),chunk]), 16000, center=True, viterbi=True,verbose=0) + cent = 1200*np.log2(np.divide(pitch, f_ref, out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8) + + # Filter out of range pitches/confidences + confidence[pitch < 16000/max_period] = 0 + confidence[pitch > 16000/min_period] = 0 + + # Keep fraction of data clean, augment only 1 minus the fraction + if (np.random.rand() > fraction_clean): + # Response, generate controlled/random 2nd order IIR filter and filter chunk + chunk = random_filter(chunk) + + # Level/Gain response {scale by random gain between 1.0e-3 and 10} + # Generate random gain in dB and then convert to scale + g_dB = np.random.uniform(low = -60, high = 20, size = 1) + # g_dB = 0 + g = 10**(g_dB/20) + + # Noise Addition {Add random SNR 2nd order randomly colored noise} + # Generate noise SNR value and add corresponding noise + snr_dB = np.random.uniform(low = -20, high = 30, size = 1) + + if args.choice_augment == 'synthetic': + n = np.random.randn(chunk_size) + else: + list_noisefiles = noise_dataset + '*.wav' + noise_file = random.choice(glob.glob(list_noisefiles)) + n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) + rand_range = np.random.randint(low = 0, high = (n.shape[0] - 16000*60 - chunk.shape[0])) # 16000 is subtracted because we will use the last 1 minutes of noise for testing + n = n[rand_range:rand_range + chunk.shape[0]] + + # Randomly filter the sampled noise as well + n = random_filter(n) + # generate random prime number between 0,500 and make those samples of noise 0 (to prevent GRU from picking up temporal patterns) + Nprime = random.choice([2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541]) + n[chunk_size - Nprime:] = np.zeros(Nprime) + snr_multiplier = np.sqrt((np.sum(np.abs(chunk)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) + + chunk = g*(chunk + snr_multiplier*n) + + # Zero pad input audio by 160 to center the frames + spec = stft(x = np.concatenate([np.zeros(160),chunk]), w = 'boxcar', N = N, H = H).T + phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) + phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) + feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T + feature = feature[:,list_indices_keep] + + if args.flag_xcorr: + # Dump noisy audio into temp file + data_temp = np.memmap('./temp_augment.raw', dtype=np.int16, shape=(chunk.shape[0]), mode='w+') + # data_temp[:chunk.shape[0]] = (chunk/(np.max(np.abs(chunk)))*(2**15 - 1)).astype(np.int16) + data_temp[:chunk.shape[0]] = ((chunk)*(2**15 - 1)).astype(np.int16) + + subprocess.run([args.path_lpcnet_extractor, './temp_augment.raw', './temp_augment_xcorr.f32']) + feature_xcorr = np.flip(np.fromfile('./temp_augment_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) + ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) + feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) + + os.remove('./temp_augment.raw') + os.remove('./temp_augment_xcorr.f32') + num_frames = min(cent.shape[0],feature.shape[0],feature_xcorr.shape[0],num_frames_chunk) + feature = feature[:num_frames,:] + cent = cent[:num_frames] + confidence = confidence[:num_frames] + feature_xcorr = feature_xcorr[:num_frames] + output_IF[i*num_frames_chunk:(i + 1)*num_frames_chunk,:] = feature + output_xcorr[i*num_frames_chunk:(i + 1)*num_frames_chunk,:] = feature_xcorr + list_cents.append(cent) + list_confidences.append(confidence) + +list_cents = np.hstack(list_cents) +list_confidences = np.hstack(list_confidences) + +np.save(args.output + '_pitches',np.vstack([list_cents,list_confidences])) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/download_demand.sh b/managed_components/78__esp-opus/dnn/torch/neural-pitch/download_demand.sh new file mode 100644 index 0000000..0cff06a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/download_demand.sh @@ -0,0 +1,43 @@ +wget https://zenodo.org/record/1227121/files/DKITCHEN_16k.zip + +wget https://zenodo.org/record/1227121/files/DLIVING_16k.zip + +wget https://zenodo.org/record/1227121/files/DWASHING_16k.zip + +wget https://zenodo.org/record/1227121/files/NFIELD_16k.zip + +wget https://zenodo.org/record/1227121/files/NPARK_16k.zip + +wget https://zenodo.org/record/1227121/files/NRIVER_16k.zip + +wget https://zenodo.org/record/1227121/files/OHALLWAY_16k.zip + +wget https://zenodo.org/record/1227121/files/OMEETING_16k.zip + +wget https://zenodo.org/record/1227121/files/OOFFICE_16k.zip + +wget https://zenodo.org/record/1227121/files/PCAFETER_16k.zip + +wget https://zenodo.org/record/1227121/files/PRESTO_16k.zip + +wget https://zenodo.org/record/1227121/files/PSTATION_16k.zip + +wget https://zenodo.org/record/1227121/files/TMETRO_16k.zip + +wget https://zenodo.org/record/1227121/files/TCAR_16k.zip + +wget https://zenodo.org/record/1227121/files/TBUS_16k.zip + +wget https://zenodo.org/record/1227121/files/STRAFFIC_16k.zip + +wget https://zenodo.org/record/1227121/files/SPSQUARE_16k.zip + +unzip '*.zip' + +mkdir -p ./combined_demand_channels/ +for file in */*.wav; do +parentdir="$(dirname "$file")" +echo $parentdir +fname="$(basename "$file")" +cp $file ./combined_demand_channels/$parentdir+$fname +done diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/evaluation.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/evaluation.py new file mode 100644 index 0000000..38ba576 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/evaluation.py @@ -0,0 +1,349 @@ +""" +Evaluation script to compute the Raw Pitch Accuracy +Procedure: + - Look at all voiced frames in file + - Compute number of pitches in those frames that lie within a 50 cent threshold + RPA = (Total number of pitches within threshold summed across all files)/(Total number of voiced frames summed accross all files) +""" + +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "0" + +from prettytable import PrettyTable +import numpy as np +import glob +import random +import tqdm +import torch +import librosa +import json +from utils import stft, random_filter, feature_xform +import subprocess +import crepe + +from models import PitchDNN, PitchDNNIF, PitchDNNXcorr + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +def rca(reference,input,voicing,thresh = 25): + idx_voiced = np.where(voicing != 0)[0] + acc = np.where(np.abs(reference - input)[idx_voiced] < thresh)[0] + return acc.shape[0] + +def sweep_rca(reference,input,voicing,thresh = 25,ind_arr = np.arange(-10,10)): + l = [] + for i in ind_arr: + l.append(rca(reference,np.roll(input,i),voicing,thresh)) + l = np.array(l) + + return np.max(l) + +def rpa(model,device = 'cpu',data_format = 'if'): + list_files = glob.glob('/home/ubuntu/Code/Datasets/SPEECH DATA/combined_mic_16k_raw/*.raw') + dir_f0 = '/home/ubuntu/Code/Datasets/SPEECH DATA/combine_f0_ptdb/' + # random_shuffle = list(np.random.permutation(len(list_files))) + random.shuffle(list_files) + list_files = list_files[:1000] + + C_all = 0 + C_all_m = 0 + C_all_f = 0 + list_rca_model_all = [] + list_rca_male_all = [] + list_rca_female_all = [] + + thresh = 50 + N = 320 + H = 160 + freq_keep = 30 + + for idx in tqdm.trange(len(list_files)): + audio_file = list_files[idx] + file_name = os.path.basename(list_files[idx])[:-4] + + audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) + offset = 432 + audio = audio[offset:] + rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160)) + + spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T + phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) + phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) + idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) + feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T + feature_if = feature[:,idx_save] + + data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') + data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) + + subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32']) + feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) + ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) + feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) + # feature_xcorr = feature_xform(feature_xcorr) + + os.remove('./temp.raw') + os.remove('./temp_xcorr.f32') + + if data_format == 'if': + feature = feature_if + elif data_format == 'xcorr': + feature = feature_xcorr + else: + indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) + feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) + + + pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" + pitch = np.loadtxt(pitch_file_name)[:,0] + voicing = np.loadtxt(pitch_file_name)[:,1] + indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) + pitch = pitch[:indmin] + voicing = voicing[:indmin] + rmse = rmse[:indmin] + voicing = voicing*(rmse > 0.05*np.max(rmse)) + if "mic_F" in audio_file: + idx_correct = np.where(pitch < 125) + voicing[idx_correct] = 0 + + cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') + + + model_cents = model(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) + model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() + + num_frames = min(cent.shape[0],model_cents.shape[0]) + pitch = pitch[:num_frames] + cent = cent[:num_frames] + voicing = voicing[:num_frames] + model_cents = model_cents[:num_frames] + + voicing_all = np.copy(voicing) + # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model + force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) + voicing_all[force_out_of_pitch] = 0 + C_all = C_all + np.where(voicing_all != 0)[0].shape[0] + + list_rca_model_all.append(rca(cent,model_cents,voicing_all,thresh)) + + if "mic_M" in audio_file: + list_rca_male_all.append(rca(cent,model_cents,voicing_all,thresh)) + C_all_m = C_all_m + np.where(voicing_all != 0)[0].shape[0] + else: + list_rca_female_all.append(rca(cent,model_cents,voicing_all,thresh)) + C_all_f = C_all_f + np.where(voicing_all != 0)[0].shape[0] + + list_rca_model_all = np.array(list_rca_model_all) + list_rca_male_all = np.array(list_rca_male_all) + list_rca_female_all = np.array(list_rca_female_all) + + + x = PrettyTable() + + x.field_names = ["Experiment", "Mean RPA"] + x.add_row(["Both all pitches", np.sum(list_rca_model_all)/C_all]) + + x.add_row(["Male all pitches", np.sum(list_rca_male_all)/C_all_m]) + + x.add_row(["Female all pitches", np.sum(list_rca_female_all)/C_all_f]) + + print(x) + + return None + +def cycle_eval(checkpoint_list, noise_type = 'synthetic', noise_dataset = None, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = None,fraction = 0.1,thresh = 50): + """ + Cycle through SNR evaluation for list of checkpoints + """ + list_files = glob.glob(ptdb_dataset_path + 'combined_mic_16k/*.raw') + dir_f0 = ptdb_dataset_path + 'combined_reference_f0/' + random.shuffle(list_files) + list_files = list_files[:(int)(fraction*len(list_files))] + + dict_models = {} + list_snr.append(np.inf) + + for f in checkpoint_list: + if (f!='crepe') and (f!='lpcnet'): + + checkpoint = torch.load(f, map_location='cpu') + dict_params = checkpoint['config'] + if dict_params['data_format'] == 'if': + from models import large_if_ccode as model + pitch_nn = PitchDNNIF(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim']) + elif dict_params['data_format'] == 'xcorr': + from models import large_xcorr as model + pitch_nn = PitchDNNXcorr(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']) + else: + from models import large_joint as model + pitch_nn = PitchDNN(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']) + + pitch_nn.load_state_dict(checkpoint['state_dict']) + + N = dict_params['window_size'] + H = dict_params['hop_factor'] + freq_keep = dict_params['freq_keep'] + + list_mean = [] + list_std = [] + for snr_dB in list_snr: + C_all = 0 + C_correct = 0 + for idx in tqdm.trange(len(list_files)): + audio_file = list_files[idx] + file_name = os.path.basename(list_files[idx])[:-4] + + audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) + offset = 432 + audio = audio[offset:] + rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = N,hop_length = H)) + + if noise_type != 'synthetic': + list_noisefiles = noise_dataset + '*.wav' + noise_file = random.choice(glob.glob(list_noisefiles)) + n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) + rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing + n = n[rand_range:rand_range + audio.shape[0]] + else: + n = np.random.randn(audio.shape[0]) + n = random_filter(n) + + snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) + audio = audio + snr_multiplier*n + + spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T + phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) + phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) + idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) + feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T + feature_if = feature[:,idx_save] + + data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') + # data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) + data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16) + + subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32']) + feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) + ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) + feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) + + os.remove('./temp.raw') + os.remove('./temp_xcorr.f32') + + if dict_params['data_format'] == 'if': + feature = feature_if + elif dict_params['data_format'] == 'xcorr': + feature = feature_xcorr + else: + indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) + feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) + + pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" + pitch = np.loadtxt(pitch_file_name)[:,0] + voicing = np.loadtxt(pitch_file_name)[:,1] + indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) + pitch = pitch[:indmin] + voicing = voicing[:indmin] + rmse = rmse[:indmin] + voicing = voicing*(rmse > 0.05*np.max(rmse)) + if "mic_F" in audio_file: + idx_correct = np.where(pitch < 125) + voicing[idx_correct] = 0 + + cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') + + model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) + model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() + + num_frames = min(cent.shape[0],model_cents.shape[0]) + pitch = pitch[:num_frames] + cent = cent[:num_frames] + voicing = voicing[:num_frames] + model_cents = model_cents[:num_frames] + + voicing_all = np.copy(voicing) + # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model + force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) + voicing_all[force_out_of_pitch] = 0 + C_all = C_all + np.where(voicing_all != 0)[0].shape[0] + + C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh) + list_mean.append(C_correct/C_all) + else: + fname = f + list_mean = [] + list_std = [] + for snr_dB in list_snr: + C_all = 0 + C_correct = 0 + for idx in tqdm.trange(len(list_files)): + audio_file = list_files[idx] + file_name = os.path.basename(list_files[idx])[:-4] + + audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) + offset = 432 + audio = audio[offset:] + rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160)) + + if noise_type != 'synthetic': + list_noisefiles = noise_dataset + '*.wav' + noise_file = random.choice(glob.glob(list_noisefiles)) + n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) + rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing + n = n[rand_range:rand_range + audio.shape[0]] + else: + n = np.random.randn(audio.shape[0]) + n = random_filter(n) + + snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) + audio = audio + snr_multiplier*n + + if (f == 'crepe'): + _, model_frequency, _, _ = crepe.predict(np.concatenate([np.zeros(80),audio]), 16000, viterbi=True,center=True,verbose=0) + model_cents = 1200*np.log2(model_frequency/(16000/256) + 1.0e-8) + else: + data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') + # data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) + data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16) + + subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32', './temp_period.f32']) + feature_xcorr = np.fromfile('./temp_period.f32', dtype='float32') + model_cents = 1200*np.log2((256/feature_xcorr + 1.0e-8) + 1.0e-8) + + os.remove('./temp.raw') + os.remove('./temp_xcorr.f32') + os.remove('./temp_period.f32') + + + pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" + pitch = np.loadtxt(pitch_file_name)[:,0] + voicing = np.loadtxt(pitch_file_name)[:,1] + indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) + pitch = pitch[:indmin] + voicing = voicing[:indmin] + rmse = rmse[:indmin] + voicing = voicing*(rmse > 0.05*np.max(rmse)) + if "mic_F" in audio_file: + idx_correct = np.where(pitch < 125) + voicing[idx_correct] = 0 + + cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') + num_frames = min(cent.shape[0],model_cents.shape[0]) + pitch = pitch[:num_frames] + cent = cent[:num_frames] + voicing = voicing[:num_frames] + model_cents = model_cents[:num_frames] + + voicing_all = np.copy(voicing) + # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model + force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) + voicing_all[force_out_of_pitch] = 0 + C_all = C_all + np.where(voicing_all != 0)[0].shape[0] + + C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh) + list_mean.append(C_correct/C_all) + dict_models[fname] = {} + dict_models[fname]['list_SNR'] = list_mean[:-1] + dict_models[fname]['inf'] = list_mean[-1] + + return dict_models diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/experiments.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/experiments.py new file mode 100644 index 0000000..bc8ea7e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/experiments.py @@ -0,0 +1,38 @@ +""" +Running the experiments; + 1. RCA vs SNR for our models, CREPE, LPCNet +""" + +import argparse +parser = argparse.ArgumentParser() + +parser.add_argument('ptdb_root', type=str, help='Root Directory for PTDB generated by running ptdb_process.sh ') +parser.add_argument('output', type=str, help='Output dump file name') +parser.add_argument('method', type=str, help='Output Directory to save experiment dumps',choices=['model','lpcnet','crepe']) +parser.add_argument('--noise_dataset', type=str, help='Location of the Demand Datset',default = './',required=False) +parser.add_argument('--noise_type', type=str, help='Type of additive noise',default = 'synthetic',choices=['synthetic','demand'],required=False) +parser.add_argument('--pth_file', type=str, help='.pth file to analyze',default = './',required = False) +parser.add_argument('--fraction_files_analyze', type=float, help='Fraction of PTDB dataset to test on',default = 1,required = False) +parser.add_argument('--threshold_rca', type=float, help='Cent threshold when computing RCA',default = 50,required = False) +parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) + +args = parser.parse_args() + +import os +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) + +import json +from evaluation import cycle_eval + +if args.method == 'model': + dict_store = cycle_eval([args.pth_file], noise_type = args.noise_type, noise_dataset = args.noise_dataset, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = args.ptdb_root,fraction = args.fraction_files_analyze,thresh = args.threshold_rca) +else: + dict_store = cycle_eval([args.method], noise_type = args.noise_type, noise_dataset = args.noise_dataset, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = args.ptdb_root,fraction = args.fraction_files_analyze,thresh = args.threshold_rca) + +dict_store["method"] = args.method +if args.method == 'model': + dict_store['pth'] = args.pth_file + +with open(args.output, 'w') as fp: + json.dump(dict_store, fp) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py new file mode 100644 index 0000000..577ec88 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py @@ -0,0 +1,109 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) + + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint', type=str, help='model checkpoint') +parser.add_argument('output_dir', type=str, help='output folder') + +args = parser.parse_args() + +import torch +import numpy as np + +from models import PitchDNN +from wexchange.torch import dump_torch_weights +from wexchange.c_export import CWriter, print_vector + +def c_export(args, model): + + message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" + + writer = CWriter(os.path.join(args.output_dir, "pitchdnn_data"), message=message, model_struct_name='PitchDNN') + writer.header.write( +f""" +#include "opus_types.h" +""" + ) + + dense_layers = [ + ('if_upsample.0', "dense_if_upsampler_1"), + ('if_upsample.2', "dense_if_upsampler_2"), + ('downsample.0', "dense_downsampler"), + ("upsample.0", "dense_final_upsampler") + ] + + + for name, export_name in dense_layers: + layer = model.get_submodule(name) + dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=True, scale=None) + + conv_layers = [ + ('conv.1', "conv2d_1"), + ('conv.4', "conv2d_2") + ] + + + for name, export_name in conv_layers: + layer = model.get_submodule(name) + dump_torch_weights(writer, layer, name=export_name, verbose=True) + + + gru_layers = [ + ("GRU", "gru_1"), + ] + + max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) + for name, export_name in gru_layers]) + + writer.header.write( +f""" + +#define PITCH_DNN_MAX_RNN_UNITS {max_rnn_units} + +""" + ) + + writer.close() + + +if __name__ == "__main__": + + os.makedirs(args.output_dir, exist_ok=True) + model = PitchDNN() + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model.load_state_dict(checkpoint['state_dict']) + c_export(args, model) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/models.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/models.py new file mode 100644 index 0000000..ce4977f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/models.py @@ -0,0 +1,178 @@ +""" +Pitch Estimation Models and dataloaders + - Classification Based (Input features, output logits) +""" + +import torch +import numpy as np + +class PitchDNNIF(torch.nn.Module): + + def __init__(self, input_dim=88, gru_dim=64, output_dim=192): + super().__init__() + + self.activation = torch.nn.Tanh() + self.initial = torch.nn.Linear(input_dim, gru_dim) + self.hidden = torch.nn.Linear(gru_dim, gru_dim) + self.gru = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, batch_first=True) + self.upsample = torch.nn.Linear(gru_dim, output_dim) + + def forward(self, x): + + x = self.initial(x) + x = self.activation(x) + x = self.hidden(x) + x = self.activation(x) + x,_ = self.gru(x) + x = self.upsample(x) + x = self.activation(x) + x = x.permute(0,2,1) + + return x + +class PitchDNNXcorr(torch.nn.Module): + + def __init__(self, input_dim=90, gru_dim=64, output_dim=192): + super().__init__() + + self.activation = torch.nn.Tanh() + + self.conv = torch.nn.Sequential( + torch.nn.ZeroPad2d((2, 0, 1, 1)), + torch.nn.Conv2d(1, 8, 3, bias=True), + self.activation, + torch.nn.ZeroPad2d((2,0,1,1)), + torch.nn.Conv2d(8, 8, 3, bias=True), + self.activation, + torch.nn.ZeroPad2d((2,0,1,1)), + torch.nn.Conv2d(8, 1, 3, bias=True), + self.activation, + ) + + self.downsample = torch.nn.Sequential( + torch.nn.Linear(input_dim, gru_dim), + self.activation + ) + self.GRU = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, num_layers=1, batch_first=True) + self.upsample = torch.nn.Sequential( + torch.nn.Linear(gru_dim,output_dim), + self.activation + ) + + def forward(self, x): + x = self.conv(x.unsqueeze(-1).permute(0,3,2,1)).squeeze(1) + x,_ = self.GRU(self.downsample(x.permute(0,2,1))) + x = self.upsample(x).permute(0,2,1) + + return x + +class PitchDNN(torch.nn.Module): + """ + Joint IF-xcorr + 1D CNN on IF, merge with xcorr, 2D CNN on merged + GRU + """ + + def __init__(self,input_IF_dim=88, input_xcorr_dim=224, gru_dim=64, output_dim=192): + super().__init__() + + self.activation = torch.nn.Tanh() + + self.if_upsample = torch.nn.Sequential( + torch.nn.Linear(input_IF_dim,64), + self.activation, + torch.nn.Linear(64,64), + self.activation, + ) + + self.conv = torch.nn.Sequential( + torch.nn.ZeroPad2d((2,0,1,1)), + torch.nn.Conv2d(1, 4, 3, bias=True), + self.activation, + torch.nn.ZeroPad2d((2,0,1,1)), + torch.nn.Conv2d(4, 1, 3, bias=True), + self.activation, + ) + + self.downsample = torch.nn.Sequential( + torch.nn.Linear(64 + input_xcorr_dim, gru_dim), + self.activation + ) + self.GRU = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, num_layers=1, batch_first=True) + self.upsample = torch.nn.Sequential( + torch.nn.Linear(gru_dim, output_dim) + ) + + def forward(self, x): + xcorr_feat = x[:,:,:224] + if_feat = x[:,:,224:] + xcorr_feat = self.conv(xcorr_feat.unsqueeze(-1).permute(0,3,2,1)).squeeze(1).permute(0,2,1) + if_feat = self.if_upsample(if_feat) + x = torch.cat([xcorr_feat,if_feat],axis = - 1) + x,_ = self.GRU(self.downsample(x)) + x = self.upsample(x).permute(0,2,1) + + return x + + +# Dataloaders +class Loader(torch.utils.data.Dataset): + def __init__(self, features_if, file_pitch, confidence_threshold=0.4, dimension_if=30, context=100): + self.if_feat = np.memmap(features_if, dtype=np.float32).reshape(-1,3*dimension_if) + + # Resolution of 20 cents + self.cents = np.rint(np.load(file_pitch)[0,:]/20) + self.cents = np.clip(self.cents,0,179) + self.confidence = np.load(file_pitch)[1,:] + + # Filter confidence for CREPE + self.confidence[self.confidence < confidence_threshold] = 0 + self.context = context + # Clip both to same size + size_common = min(self.if_feat.shape[0], self.cents.shape[0]) + self.if_feat = self.if_feat[:size_common,:] + self.cents = self.cents[:size_common] + self.confidence = self.confidence[:size_common] + + frame_max = self.if_feat.shape[0]//context + self.if_feat = np.reshape(self.if_feat[:frame_max*context, :],(frame_max, context,3*dimension_if)) + self.cents = np.reshape(self.cents[:frame_max * context],(frame_max, context)) + self.confidence = np.reshape(self.confidence[:frame_max*context],(frame_max, context)) + + def __len__(self): + return self.if_feat.shape[0] + + def __getitem__(self, index): + return torch.from_numpy(self.if_feat[index,:,:]), torch.from_numpy(self.cents[index]), torch.from_numpy(self.confidence[index]) + +class PitchDNNDataloader(torch.utils.data.Dataset): + def __init__(self, features, file_pitch, confidence_threshold=0.4, context=100, choice_data='both'): + self.feat = np.memmap(features, mode='r', dtype=np.int8).reshape(-1,312) + self.xcorr = self.feat[:,:224] + self.if_feat = self.feat[:,224:] + ground_truth = np.memmap(file_pitch, mode='r', dtype=np.float32).reshape(-1,2) + self.cents = np.rint(60*np.log2(ground_truth[:,0]/62.5)) + mask = (self.cents>=0).astype('float32') * (self.cents<=180).astype('float32') + self.cents = np.clip(self.cents,0,179) + self.confidence = ground_truth[:,1] * mask + # Filter confidence for CREPE + self.confidence[self.confidence < confidence_threshold] = 0 + self.context = context + + self.choice_data = choice_data + + frame_max = self.if_feat.shape[0]//context + self.if_feat = np.reshape(self.if_feat[:frame_max*context,:], (frame_max, context, 88)) + self.cents = np.reshape(self.cents[:frame_max*context], (frame_max,context)) + self.xcorr = np.reshape(self.xcorr[:frame_max*context,:], (frame_max,context, 224)) + self.confidence = np.reshape(self.confidence[:frame_max*context], (frame_max, context)) + + def __len__(self): + return self.if_feat.shape[0] + + def __getitem__(self, index): + if self.choice_data == 'both': + return torch.cat([torch.from_numpy((1./127)*self.xcorr[index,:,:]), torch.from_numpy((1./127)*self.if_feat[index,:,:])], dim=-1), torch.from_numpy(self.cents[index]), torch.from_numpy(self.confidence[index]) + elif self.choice_data == 'if': + return torch.from_numpy((1./127)*self.if_feat[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index]) + else: + return torch.from_numpy((1./127)*self.xcorr[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index]) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/neural_pitch_update.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/neural_pitch_update.py new file mode 100644 index 0000000..aa2caf9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/neural_pitch_update.py @@ -0,0 +1,179 @@ +import argparse +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='Features generated from dump_data') +parser.add_argument('data', type=str, help='Data generated from dump_data (offset by 5ms)') +parser.add_argument('output', type=str, help='output .f32 feature file with replaced neural pitch') +parser.add_argument('checkpoint', type=str, help='model checkpoint file') +parser.add_argument('path_lpcnet_extractor', type=str, help='path to LPCNet extractor object file (generated on compilation)') +parser.add_argument('--device', type=str, help='compute device',default = None,required = False) +parser.add_argument('--replace_xcorr', type = bool, default = False, help='Replace LPCNet xcorr with updated one') + +args = parser.parse_args() + +import os + +from utils import stft, random_filter +import subprocess +import numpy as np +import json +import torch +import tqdm + +from models import PitchDNNIF, PitchDNNXcorr, PitchDNN + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +if device is not None: + device = torch.device(args.device) + +# Loading the appropriate model +checkpoint = torch.load(args.checkpoint, map_location='cpu') +dict_params = checkpoint['config'] + +if dict_params['data_format'] == 'if': + pitch_nn = PitchDNNIF(dict_params['freq_keep']*3, dict_params['gru_dim'], dict_params['output_dim']) +elif dict_params['data_format'] == 'xcorr': + pitch_nn = PitchDNNXcorr(dict_params['xcorr_dim'], dict_params['gru_dim'], dict_params['output_dim']) +else: + pitch_nn = PitchDNN(dict_params['freq_keep']*3, dict_params['xcorr_dim'], dict_params['gru_dim'], dict_params['output_dim']) + +pitch_nn.load_state_dict(checkpoint['state_dict']) +pitch_nn = pitch_nn.to(device) + +N = dict_params['window_size'] +H = dict_params['hop_factor'] +freq_keep = dict_params['freq_keep'] + +os.environ["OMP_NUM_THREADS"] = "16" + + +def run_lpc(signal, lpcs, frame_length=160): + num_frames, lpc_order = lpcs.shape + + prediction = np.concatenate( + [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] + ) + error = signal[lpc_order :] - prediction + + return prediction, error + + +if __name__ == "__main__": + args = parser.parse_args() + + features = np.memmap(args.features, dtype=np.float32,mode = 'r').reshape((-1, 36)) + data = np.memmap(args.data, dtype=np.int16,mode = 'r').reshape((-1, 2)) + + num_frames = features.shape[0] + feature_dim = features.shape[1] + + assert feature_dim == 36 + + output = np.memmap(args.output, dtype=np.float32, shape=(num_frames, feature_dim), mode='w+') + output[:, :36] = features + + # lpc coefficients and signal + lpcs = features[:, 20:36] + sig = data[:, 1] + + # parameters + + # constants + pitch_min = 32 + pitch_max = 256 + lpc_order = 16 + fs = 16000 + frame_length = 160 + overlap_frames = 100 + chunk_size = 10000 + history_length = frame_length * overlap_frames + history = np.zeros(history_length, dtype=np.int16) + pitch_position=18 + xcorr_position=19 + conf_position=36 + + num_frames = len(sig) // 160 - 1 + + frame_start = 0 + frame_stop = min(frame_start + chunk_size, num_frames) + signal_start = 0 + signal_stop = frame_stop * frame_length + + niters = (num_frames - 1)//chunk_size + for i in tqdm.trange(niters): + if (frame_start > num_frames - 1): + break + chunk = np.concatenate((history, sig[signal_start:signal_stop])) + chunk_la = np.concatenate((history, sig[signal_start:signal_stop + 80])) + + # Feature computation + spec = stft(x = np.concatenate([np.zeros(80),chunk_la/(2**15 - 1)]), w = 'boxcar', N = N, H = H).T + phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) + phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) + idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) + feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T + feature_if = feature[:,idx_save] + + data_temp = np.memmap('./temp_featcompute_' + dict_params['data_format'] + '_.raw', dtype=np.int16, shape=(chunk.shape[0]), mode='w+') + data_temp[:chunk.shape[0]] = chunk_la[80:].astype(np.int16) + + subprocess.run([args.path_lpcnet_extractor, './temp_featcompute_' + dict_params['data_format'] + '_.raw', './temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw']) + feature_xcorr = np.flip(np.fromfile('./temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) + ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) + feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) + + os.remove('./temp_featcompute_' + dict_params['data_format'] + '_.raw') + os.remove('./temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw') + + if dict_params['data_format'] == 'if': + feature = feature_if + elif dict_params['data_format'] == 'xcorr': + feature = feature_xcorr + else: + indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) + feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) + + # Compute pitch with my model + model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) + model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() + frequency = 62.5*2**(model_cents/1200) + + frequency = frequency[overlap_frames : overlap_frames + frame_stop - frame_start] + + # convert frequencies to periods + periods = np.round(fs / frequency) + + periods = np.clip(periods, pitch_min, pitch_max) + + output[frame_start:frame_stop, pitch_position] = (periods - 100) / 50 + + frame_offset = (pitch_max + frame_length - 1) // frame_length + offset = frame_offset * frame_length + padding = lpc_order + + + if frame_start < frame_offset: + lpc_coeffs = np.concatenate((np.zeros((frame_offset - frame_start, lpc_order), dtype=np.float32), lpcs[:frame_stop])) + else: + lpc_coeffs = lpcs[frame_start - frame_offset : frame_stop] + + pred, error = run_lpc(chunk[history_length - offset - padding :], lpc_coeffs, frame_length=frame_length) + + xcorr = np.zeros(frame_stop - frame_start) + for i, p in enumerate(periods.astype(np.int16)): + if p > 0: + f1 = error[offset + i * frame_length : offset + (i + 1) * frame_length] + f2 = error[offset + i * frame_length - p : offset + (i + 1) * frame_length - p] + xcorr[i] = np.dot(f1, f2) / np.sqrt(np.dot(f1, f1) * np.dot(f2, f2) + 1e-6) + + output[frame_start:frame_stop, xcorr_position] = xcorr - 0.5 + + # update buffers and indices + history = chunk[-history_length :] + + frame_start += chunk_size + frame_stop += chunk_size + frame_stop = min(frame_stop, num_frames) + + signal_start = frame_start * frame_length + signal_stop = frame_stop * frame_length diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/ptdb_process.sh b/managed_components/78__esp-opus/dnn/torch/neural-pitch/ptdb_process.sh new file mode 100644 index 0000000..f4df546 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/ptdb_process.sh @@ -0,0 +1,34 @@ +# Copy into PTDB root directory and run to combine all the male/female raw audio/references into below directories + +# Make folder for combined audio +mkdir -p './combined_mic_16k/' +# Make folder for combined pitch reference +mkdir -p './combined_reference_f0/' + +# Resample Male Audio +for i in ./MALE/MIC/**/*.wav; do +j="$(basename "$i" .wav)" +echo $j +sox -r 48000 -b 16 -e signed-integer "$i" -r 16000 -b 16 -e signed-integer ./combined_mic_16k/$j.raw +done + +# Resample Female Audio +for i in ./FEMALE/MIC/**/*.wav; do +j="$(basename "$i" .wav)" +echo $j +sox -r 48000 -b 16 -e signed-integer "$i" -r 16000 -b 16 -e signed-integer ./combined_mic_16k/$j.raw +done + +# Shift Male reference pitch files +for i in ./MALE/REF/**/*.f0; do +j="$(basename "$i" .wav)" +echo $j +cp "$i" ./combined_reference_f0/ +done + +# Shift Female reference pitch files +for i in ./FEMALE/REF/**/*.f0; do +j="$(basename "$i" .wav)" +echo $j +cp "$i" ./combined_reference_f0/ +done \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/run_crepe.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/run_crepe.py new file mode 100644 index 0000000..25d6524 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/run_crepe.py @@ -0,0 +1,72 @@ +""" +Perform Data Augmentation (Gain, Additive Noise, Random Filtering) on Input TTS Data +1. Read in chunks and compute clean pitch first +2. Then add in augmentation (Noise/Level/Response) + - Adds filtered noise from the "Demand" dataset, https://zenodo.org/record/1227121#.XRKKxYhKiUk + - When using the Demand Dataset, consider each channel as a possible noise input, and keep the first 4 minutes of noise for training +3. Use this "augmented" audio for feature computation, and compute pitch using CREPE on the clean input + +Notes: To ensure consistency with the discovered CREPE offset, we do the following +- We pad the input audio to the zero-centered CREPE estimator with 80 zeros +- We pad the input audio to our feature computation with 160 zeros to center them +""" + +import argparse +parser = argparse.ArgumentParser() + +parser.add_argument('data', type=str, help='input raw audio data') +parser.add_argument('output', type=str, help='output directory') +parser.add_argument('--gpu-index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) +parser.add_argument('--chunk-size-frames', type=int, help='Number of frames to process at a time',default = 100000,required = False) + +args = parser.parse_args() + +import os +os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) + +import numpy as np +import tqdm +import crepe + +data = np.memmap(args.data, dtype=np.int16,mode = 'r') + +# list_features = [] +list_cents = [] +list_confidences = [] + +min_period = 32 +max_period = 256 +f_ref = 16000/max_period +chunk_size_frames = args.chunk_size_frames +chunk_size = chunk_size_frames*160 + +nb_chunks = (data.shape[0]+79)//chunk_size+1 + +output_data = np.zeros((0,2),dtype='float32') + +for i in tqdm.trange(nb_chunks): + if i==0: + chunk = np.concatenate([np.zeros(80),data[:chunk_size-80]]) + elif i==nb_chunks-1: + chunk = data[i*chunk_size-80:] + else: + chunk = data[i*chunk_size-80:(i+1)*chunk_size-80] + chunk = chunk/np.array(32767.,dtype='float32') + + # Clean Pitch/Confidence Estimate + # Padding input to CREPE by 80 samples to ensure it aligns + _, pitch, confidence, _ = crepe.predict(chunk, 16000, center=True, viterbi=True,verbose=0) + pitch = pitch[:chunk_size_frames] + confidence = confidence[:chunk_size_frames] + + + # Filter out of range pitches/confidences + confidence[pitch < 16000/max_period] = 0 + confidence[pitch > 16000/min_period] = 0 + pitch = np.reshape(pitch, (-1, 1)) + confidence = np.reshape(confidence, (-1, 1)) + out = np.concatenate([pitch, confidence], axis=-1, dtype='float32') + output_data = np.concatenate([output_data, out], axis=0) + + +output_data.tofile(args.output) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/training.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/training.py new file mode 100644 index 0000000..62da135 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/training.py @@ -0,0 +1,162 @@ +""" +Training the neural pitch estimator + +""" + +import os +import argparse +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='.f32 IF Features for training (generated by augmentation script)') +parser.add_argument('features_pitch', type=str, help='.npy Pitch file for training (generated by augmentation script)') +parser.add_argument('output_folder', type=str, help='Output directory to store the model weights and config') +parser.add_argument('data_format', type=str, help='Choice of Input Data',choices=['if','xcorr','both']) +parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) +parser.add_argument('--confidence_threshold', type=float, help='Confidence value below which pitch will be neglected during training',default = 0.4,required = False) +parser.add_argument('--context', type=int, help='Sequence length during training',default = 100,required = False) +parser.add_argument('--N', type=int, help='STFT window size',default = 320,required = False) +parser.add_argument('--H', type=int, help='STFT Hop size',default = 160,required = False) +parser.add_argument('--xcorr_dimension', type=int, help='Dimension of Input cross-correlation',default = 257,required = False) +parser.add_argument('--freq_keep', type=int, help='Number of Frequencies to keep',default = 30,required = False) +parser.add_argument('--gru_dim', type=int, help='GRU Dimension',default = 64,required = False) +parser.add_argument('--output_dim', type=int, help='Output dimension',default = 192,required = False) +parser.add_argument('--learning_rate', type=float, help='Learning Rate',default = 1.0e-3,required = False) +parser.add_argument('--epochs', type=int, help='Number of training epochs',default = 50,required = False) +parser.add_argument('--choice_cel', type=str, help='Choice of Cross Entropy Loss (default or robust)',choices=['default','robust'],default = 'default',required = False) +parser.add_argument('--prefix', type=str, help="prefix for model export, default: model", default='model') +parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) + + +args = parser.parse_args() + +# import os +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) + +# Fixing the seeds for reproducability +import time +np_seed = int(time.time()) +torch_seed = int(time.time()) + +import torch +torch.manual_seed(torch_seed) +import numpy as np +np.random.seed(np_seed) +from utils import count_parameters +import tqdm +from models import PitchDNN, PitchDNNIF, PitchDNNXcorr, PitchDNNDataloader + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +if args.data_format == 'if': + pitch_nn = PitchDNNIF(3 * args.freq_keep - 2, args.gru_dim, args.output_dim) +elif args.data_format == 'xcorr': + pitch_nn = PitchDNNXcorr(args.xcorr_dimension, args.gru_dim, args.output_dim) +else: + pitch_nn = PitchDNN(3 * args.freq_keep - 2, 224, args.gru_dim, args.output_dim) + +if type(args.initial_checkpoint) != type(None): + checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') + pitch_nn.load_state_dict(checkpoint['state_dict'], strict=False) + + +dataset_training = PitchDNNDataloader(args.features,args.features_pitch,args.confidence_threshold,args.context,args.data_format) + +def loss_custom(logits,labels,confidence,choice = 'default',nmax = 192,q = 0.7): + logits_softmax = torch.nn.Softmax(dim = 1)(logits).permute(0,2,1) + labels_one_hot = torch.nn.functional.one_hot(labels.long(),nmax) + + if choice == 'default': + # Categorical Cross Entropy + CE = -torch.sum(torch.log(logits_softmax*labels_one_hot + 1.0e-6)*labels_one_hot,dim=-1) + CE = torch.mean(confidence*CE) + + else: + # Robust Cross Entropy + CE = (1.0/q)*(1 - torch.sum(torch.pow(logits_softmax*labels_one_hot + 1.0e-7,q),dim=-1) ) + CE = torch.sum(confidence*CE) + + return CE + +def accuracy(logits,labels,confidence,choice = 'default',nmax = 192,q = 0.7): + logits_softmax = torch.nn.Softmax(dim = 1)(logits).permute(0,2,1) + pred_pitch = torch.argmax(logits_softmax, 2) + accuracy = (pred_pitch != labels.long())*1. + return 1.-torch.mean(confidence*accuracy) + +train_dataset, test_dataset = torch.utils.data.random_split(dataset_training, [0.95,0.05], generator=torch.Generator().manual_seed(torch_seed)) + +batch_size = 256 +train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=False) +test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=False) + +pitch_nn = pitch_nn.to(device) +num_params = count_parameters(pitch_nn) +learning_rate = args.learning_rate +model_opt = torch.optim.Adam(pitch_nn.parameters(), lr = learning_rate) + +num_epochs = args.epochs + +for epoch in range(num_epochs): + losses = [] + accs = [] + pitch_nn.train() + with tqdm.tqdm(train_dataloader) as train_epoch: + for i, (xi, yi, ci) in enumerate(train_epoch): + yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True) + pi = pitch_nn(xi.float()) + loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) + acc = accuracy(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) + acc = acc.detach() + + model_opt.zero_grad() + loss.backward() + model_opt.step() + + losses.append(loss.item()) + accs.append(acc.item()) + avg_loss = np.mean(losses) + avg_acc = np.mean(accs) + train_epoch.set_postfix({"Train Epoch" : epoch, "Train Loss":avg_loss, "acc" : avg_acc.item()}) + + if epoch % 5 == 0: + pitch_nn.eval() + losses = [] + with tqdm.tqdm(test_dataloader) as test_epoch: + for i, (xi, yi, ci) in enumerate(test_epoch): + yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True) + pi = pitch_nn(xi.float()) + loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) + losses.append(loss.item()) + avg_loss = np.mean(losses) + test_epoch.set_postfix({"Epoch" : epoch, "Test Loss":avg_loss}) + +pitch_nn.eval() + +config = dict( + data_format=args.data_format, + epochs=num_epochs, + window_size= args.N, + hop_factor= args.H, + freq_keep=args.freq_keep, + batch_size=batch_size, + learning_rate=learning_rate, + confidence_threshold=args.confidence_threshold, + model_parameters=num_params, + np_seed=np_seed, + torch_seed=torch_seed, + xcorr_dim=args.xcorr_dimension, + dim_input=3*args.freq_keep - 2, + gru_dim=args.gru_dim, + output_dim=args.output_dim, + choice_cel=args.choice_cel, + context=args.context, +) + +model_save_path = os.path.join(args.output_folder, f"{args.prefix}_{args.data_format}.pth") +checkpoint = { + 'state_dict': pitch_nn.state_dict(), + 'config': config +} +torch.save(checkpoint, model_save_path) diff --git a/managed_components/78__esp-opus/dnn/torch/neural-pitch/utils.py b/managed_components/78__esp-opus/dnn/torch/neural-pitch/utils.py new file mode 100644 index 0000000..8930ad1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/neural-pitch/utils.py @@ -0,0 +1,59 @@ +""" +Utility functions that are commonly used +""" + +import numpy as np +from scipy.signal import windows, lfilter +from prettytable import PrettyTable + + +# Source: https://gist.github.com/thongonary/026210fc186eb5056f2b6f1ca362d912 +def count_parameters(model): + table = PrettyTable(["Modules", "Parameters"]) + total_params = 0 + for name, parameter in model.named_parameters(): + if not parameter.requires_grad: continue + param = parameter.numel() + table.add_row([name, param]) + total_params+=param + print(table) + print(f"Total Trainable Params: {total_params}") + return total_params + +def stft(x, w = 'boxcar', N = 320, H = 160): + x = np.concatenate([x,np.zeros(N)]) + # win_custom = np.concatenate([windows.hann(80)[:40],np.ones(240),windows.hann(80)[40:]]) + return np.stack([np.fft.rfft(x[i:i + N]*windows.get_window(w,N)) for i in np.arange(0,x.shape[0]-N,H)]) + +def random_filter(x): + # Randomly filter x with second order IIR filter with coefficients in between -3/8,3/8 + filter_coeff = np.random.uniform(low = -3.0/8, high = 3.0/8, size = 4) + b = [1,filter_coeff[0],filter_coeff[1]] + a = [1,filter_coeff[2],filter_coeff[3]] + return lfilter(b,a,x) + +def feature_xform(feature): + """ + Take as input the (N * 256) xcorr features output by LPCNet and perform the following + 1. Downsample and Upsample by 2 (followed by smoothing) + 2. Append positional embeddings (of dim k) coresponding to each xcorr lag + """ + + from scipy.signal import resample_poly, lfilter + + + feature_US = lfilter([0.25,0.5,0.25],[1],resample_poly(feature,2,1,axis = 1),axis = 1)[:,:feature.shape[1]] + feature_DS = lfilter([0.5,0.5],[1],resample_poly(feature,1,2,axis = 1),axis = 1) + Z_append = np.zeros((feature.shape[0],feature.shape[1] - feature_DS.shape[1])) + feature_DS = np.concatenate([feature_DS,Z_append],axis = -1) + + # pos_embedding = [] + # for i in range(k): + # pos_embedding.append(np.cos((2**i)*np.pi*((np.repeat(np.arange(feature.shape[1]).reshape(feature.shape[1],1),feature.shape[0],axis = 1)).T/(2*feature.shape[1])))) + + # pos_embedding = np.stack(pos_embedding,axis = -1) + + feature = np.stack((feature_DS,feature,feature_US),axis = -1) + # feature = np.concatenate((feature,pos_embedding),axis = -1) + + return feature diff --git a/managed_components/78__esp-opus/dnn/torch/osce/README.md b/managed_components/78__esp-opus/dnn/torch/osce/README.md new file mode 100644 index 0000000..696d7e1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/README.md @@ -0,0 +1,66 @@ +# Opus Speech Coding Enhancement + +This folder hosts models for enhancing Opus SILK. + +## Environment setup +The code is tested with python 3.11. Conda setup is done via + + +`conda create -n osce python=3.11` + +`conda activate osce` + +`python -m pip install -r requirements.txt` + + +## Generating training data +First step is to convert all training items to 16 kHz and 16 bit pcm and then concatenate them. A convenient way to do this is to create a file list and then run + +`python scripts/concatenator.py filelist 16000 dataset/clean.s16 --db_min -40 --db_max 0` + +which on top provides some random scaling. Data is taken from the datasets listed in dnn/datasets.txt and the exact list of items used for training and validation is +located in dnn/torch/osce/resources. + +Second step is to run a patched version of opus_demo in the dataset folder, which will produce the coded output and add feature files. To build the patched opus_demo binary, check out the exp-neural-silk-enhancement branch and build opus_demo the usual way. Then run + +`cd dataset && /opus_demo voip 16000 1 9000 -silk_random_switching 249 clean.s16 coded.s16 ` + +The argument to -silk_random_switching specifies the number of frames after which parameters are switched randomly. + +## Regression loss based training +Create a default setup for LACE or NoLACE via + +`python make_default_setup.py model.yml --model lace/nolace --path2dataset ` + +Then run + +`python train_model.py model.yml --no-redirect` + +for running the training script in foreground or + +`nohup python train_model.py model.yml &` + +to run it in background. In the latter case the output is written to `/out.txt`. + +## Adversarial training (NoLACE only) +Create a default setup for NoLACE via + +`python make_default_setup.py nolace_adv.yml --model nolace --adversarial --path2dataset ` + +Then run + +`python adv_train_model.py nolace_adv.yml --no-redirect` + +for running the training script in foreground or + +`nohup python adv_train_model.py nolace_adv.yml &` + +to run it in background. In the latter case the output is written to `/out.txt`. + +## Inference +Generating inference data is analogous to generating training data. Given an item 'item1.wav' run +`mkdir item1.se && sox item1.wav -r 16000 -e signed-integer -b 16 item1.raw && cd item1.se && /opus_demo voip 16000 1 ../item1.raw noisy.s16` + +The folder item1.se then serves as input for the test_model.py script or for the --testdata argument of train_model.py resp. adv_train_model.py + +autogen.sh downloads pre-trained model weights to the subfolder dnn/models of the main repo. \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/adv_train_model.py b/managed_components/78__esp-opus/dnn/torch/osce/adv_train_model.py new file mode 100644 index 0000000..7db859e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/adv_train_model.py @@ -0,0 +1,462 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys +import math as m +import random + +import yaml + +from tqdm import tqdm + +try: + import git + has_git = True +except: + has_git = False + +import torch +from torch.optim.lr_scheduler import LambdaLR +import torch.nn.functional as F + +from scipy.io import wavfile +import numpy as np +import pesq + +from data import SilkEnhancementSet +from models import model_dict + + +from utils.silk_features import load_inference_data +from utils.misc import count_parameters, retain_grads, get_grad_norm, create_weights + +from losses.stft_loss import MRSTFTLoss, MRLogMelLoss + + +parser = argparse.ArgumentParser() + +parser.add_argument('setup', type=str, help='setup yaml file') +parser.add_argument('output', type=str, help='output path') +parser.add_argument('--device', type=str, help='compute device', default=None) +parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) +parser.add_argument('--testdata', type=str, help='path to features and signal for testing', default=None) +parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') + +args = parser.parse_args() + + +torch.set_num_threads(4) + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +checkpoint_prefix = 'checkpoint' +output_prefix = 'output' +setup_name = 'setup.yml' +output_file='out.txt' + + +# check model +if not 'name' in setup['model']: + print(f'warning: did not find model entry in setup, using default PitchPostFilter') + model_name = 'pitchpostfilter' +else: + model_name = setup['model']['name'] + +# prepare output folder +if os.path.exists(args.output): + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit() +else: + os.makedirs(args.output, exist_ok=True) + +checkpoint_dir = os.path.join(args.output, 'checkpoints') +os.makedirs(checkpoint_dir, exist_ok=True) + +# add repo info to setup +if has_git: + working_dir = os.path.split(__file__)[0] + try: + repo = git.Repo(working_dir, search_parent_directories=True) + setup['repo'] = dict() + hash = repo.head.object.hexsha + urls = list(repo.remote().urls) + is_dirty = repo.is_dirty() + + if is_dirty: + print("warning: repo is dirty") + + setup['repo']['hash'] = hash + setup['repo']['urls'] = urls + setup['repo']['dirty'] = is_dirty + except: + has_git = False + +# dump setup +with open(os.path.join(args.output, setup_name), 'w') as f: + yaml.dump(setup, f) + + +ref = None +if args.testdata is not None: + + testsignal, features, periods, numbits = load_inference_data(args.testdata, **setup['data']) + + inference_test = True + inference_folder = os.path.join(args.output, 'inference_test') + os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True) + + try: + ref = np.fromfile(os.path.join(args.testdata, 'clean.s16'), dtype=np.int16) + except: + pass +else: + inference_test = False + +# training parameters +batch_size = setup['training']['batch_size'] +epochs = setup['training']['epochs'] +lr = setup['training']['lr'] +lr_decay_factor = setup['training']['lr_decay_factor'] +lr_gen = lr * setup['training']['gen_lr_reduction'] +lambda_feat = setup['training']['lambda_feat'] +lambda_reg = setup['training']['lambda_reg'] +adv_target = setup['training'].get('adv_target', 'target') + +# load training dataset +data_config = setup['data'] +data = SilkEnhancementSet(setup['dataset'], **data_config) + +# load validation dataset if given +if 'validation_dataset' in setup: + validation_data = SilkEnhancementSet(setup['validation_dataset'], **data_config) + + validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) + + run_validation = True +else: + run_validation = False + +# create model +model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) + +# create discriminator +disc_name = setup['discriminator']['name'] +disc = model_dict[disc_name]( + *setup['discriminator']['args'], **setup['discriminator']['kwargs'] +) + +# set compute device +if type(args.device) == type(None): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +else: + device = torch.device(args.device) + +# dataloader +dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) + +# optimizer is introduced to trainable parameters +parameters = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(parameters, lr=lr_gen) + +# disc optimizer +parameters = [p for p in disc.parameters() if p.requires_grad] +optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9]) + +# learning rate scheduler +scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +if args.initial_checkpoint is not None: + print(f"loading state dict from {args.initial_checkpoint}...") + chkpt = torch.load(args.initial_checkpoint, map_location=device) + model.load_state_dict(chkpt['state_dict']) + + if 'disc_state_dict' in chkpt: + print(f"loading discriminator state dict from {args.initial_checkpoint}...") + disc.load_state_dict(chkpt['disc_state_dict']) + + if 'optimizer_state_dict' in chkpt: + print(f"loading optimizer state dict from {args.initial_checkpoint}...") + optimizer.load_state_dict(chkpt['optimizer_state_dict']) + + if 'disc_optimizer_state_dict' in chkpt: + print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...") + optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict']) + + if 'scheduler_state_disc' in chkpt: + print(f"loading scheduler state dict from {args.initial_checkpoint}...") + scheduler.load_state_dict(chkpt['scheduler_state_dict']) + + # if 'torch_rng_state' in chkpt: + # print(f"setting torch RNG state from {args.initial_checkpoint}...") + # torch.set_rng_state(chkpt['torch_rng_state']) + + if 'numpy_rng_state' in chkpt: + print(f"setting numpy RNG state from {args.initial_checkpoint}...") + np.random.set_state(chkpt['numpy_rng_state']) + + if 'python_rng_state' in chkpt: + print(f"setting Python RNG state from {args.initial_checkpoint}...") + random.setstate(chkpt['python_rng_state']) + +# loss +w_l1 = setup['training']['loss']['w_l1'] +w_lm = setup['training']['loss']['w_lm'] +w_slm = setup['training']['loss']['w_slm'] +w_sc = setup['training']['loss']['w_sc'] +w_logmel = setup['training']['loss']['w_logmel'] +w_wsc = setup['training']['loss']['w_wsc'] +w_xcorr = setup['training']['loss']['w_xcorr'] +w_sxcorr = setup['training']['loss']['w_sxcorr'] +w_l2 = setup['training']['loss']['w_l2'] + +w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 + +stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) +logmelloss = MRLogMelLoss().to(device) + +def xcorr_loss(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) + + return torch.mean(loss) + +def td_l2_norm(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) + + return loss.mean() + +def td_l1(y_true, y_pred, pow=0): + dims = list(range(1, len(y_true.shape))) + tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) + + return torch.mean(tmp) + +def criterion(x, y): + + return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) + + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum + + +# model checkpoint +checkpoint = { + 'setup' : setup, + 'state_dict' : model.state_dict(), + 'loss' : -1 +} + + +if not args.no_redirect: + print(f"re-directing output to {os.path.join(args.output, output_file)}") + sys.stdout = open(os.path.join(args.output, output_file), "w") + + +print("summary:") + +print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters") +if hasattr(model, 'flop_count'): + print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS") +print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters") + +if ref is not None: + noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) + initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') + print(f"initial MOS (PESQ): {initial_mos}") + +best_loss = 1e9 +log_interval = 10 + + +m_r = 0 +m_f = 0 +s_r = 1 +s_f = 1 + +def optimizer_to(optim, device): + for param in optim.state.values(): + if isinstance(param, torch.Tensor): + param.data = param.data.to(device) + if param._grad is not None: + param._grad.data = param._grad.data.to(device) + elif isinstance(param, dict): + for subparam in param.values(): + if isinstance(subparam, torch.Tensor): + subparam.data = subparam.data.to(device) + if subparam._grad is not None: + subparam._grad.data = subparam._grad.data.to(device) + +optimizer_to(optimizer, device) +optimizer_to(optimizer_disc, device) + +retain_grads(model) +retain_grads(disc) + +for ep in range(1, epochs + 1): + print(f"training epoch {ep}...") + + model.to(device) + disc.to(device) + model.train() + disc.train() + + running_disc_loss = 0 + running_adv_loss = 0 + running_feature_loss = 0 + running_reg_loss = 0 + running_disc_grad_norm = 0 + running_model_grad_norm = 0 + + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + for i, batch in enumerate(tepoch): + + # set gradients to zero + optimizer.zero_grad() + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'].to(device) + disc_target = batch[adv_target].to(device) + + # calculate model output + output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) + + # discriminator update + scores_gen = disc(output.detach()) + scores_real = disc(disc_target.unsqueeze(1)) + + disc_loss = 0 + for score in scores_gen: + disc_loss += (((score[-1]) ** 2)).mean() + m_f = 0.9 * m_f + 0.1 * score[-1].detach().mean().cpu().item() + s_f = 0.9 * s_f + 0.1 * score[-1].detach().std().cpu().item() + + for score in scores_real: + disc_loss += (((1 - score[-1]) ** 2)).mean() + m_r = 0.9 * m_r + 0.1 * score[-1].detach().mean().cpu().item() + s_r = 0.9 * s_r + 0.1 * score[-1].detach().std().cpu().item() + + disc_loss = 0.5 * disc_loss / len(scores_gen) + winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) + + disc.zero_grad() + disc_loss.backward() + + running_disc_grad_norm += get_grad_norm(disc).detach().cpu().item() + + optimizer_disc.step() + + # generator update + scores_gen = disc(output) + + # calculate loss + loss_reg = criterion(output.squeeze(1), target) + + num_discs = len(scores_gen) + gen_loss = 0 + for score in scores_gen: + gen_loss += (((1 - score[-1]) ** 2)).mean() / num_discs + + loss_feat = 0 + for k in range(num_discs): + num_layers = len(scores_gen[k]) - 1 + f = 4 / num_discs / num_layers + for l in range(num_layers): + loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) + + model.zero_grad() + + (gen_loss + lambda_feat * loss_feat + lambda_reg * loss_reg).backward() + + optimizer.step() + + # sparsification + if hasattr(model, 'sparsifier'): + model.sparsifier() + + running_model_grad_norm += get_grad_norm(model).detach().cpu().item() + running_adv_loss += gen_loss.detach().cpu().item() + running_disc_loss += disc_loss.detach().cpu().item() + running_feature_loss += lambda_feat * loss_feat.detach().cpu().item() + running_reg_loss += lambda_reg * loss_reg.detach().cpu().item() + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}", + disc_loss=f"{running_disc_loss/(i + 1):8.7f}", + feat_loss=f"{running_feature_loss/(i + 1):8.7f}", + reg_loss=f"{running_reg_loss/(i + 1):8.7f}", + model_gradnorm=f"{running_model_grad_norm/(i+1):8.7f}", + disc_gradnorm=f"{running_disc_grad_norm/(i+1):8.7f}", + wc=f"{100*winning_chance:5.2f}%") + + + # save checkpoint + checkpoint['state_dict'] = model.state_dict() + checkpoint['disc_state_dict'] = disc.state_dict() + checkpoint['optimizer_state_dict'] = optimizer.state_dict() + checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict() + checkpoint['scheduler_state_dict'] = scheduler.state_dict() + checkpoint['torch_rng_state'] = torch.get_rng_state() + checkpoint['numpy_rng_state'] = np.random.get_state() + checkpoint['python_rng_state'] = random.getstate() + checkpoint['adv_loss'] = running_adv_loss/(i + 1) + checkpoint['disc_loss'] = running_disc_loss/(i + 1) + checkpoint['feature_loss'] = running_feature_loss/(i + 1) + checkpoint['reg_loss'] = running_reg_loss/(i + 1) + + + if inference_test: + print("running inference test...") + out = model.process(testsignal, features, periods, numbits).cpu().numpy() + wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) + if ref is not None: + mos = pesq.pesq(16000, ref, out, mode='wb') + print(f"MOS (PESQ): {mos}") + + + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) + + + print() + +print('Done') diff --git a/managed_components/78__esp-opus/dnn/torch/osce/adv_train_vocoder.py b/managed_components/78__esp-opus/dnn/torch/osce/adv_train_vocoder.py new file mode 100644 index 0000000..73e3c9b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/adv_train_vocoder.py @@ -0,0 +1,451 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys +import math as m +import random + +import yaml + +from tqdm import tqdm + +try: + import git + has_git = True +except: + has_git = False + +import torch +from torch.optim.lr_scheduler import LambdaLR +import torch.nn.functional as F + +from scipy.io import wavfile +import numpy as np +import pesq + +from data import LPCNetVocodingDataset +from models import model_dict + + +from utils.lpcnet_features import load_lpcnet_features +from utils.misc import count_parameters + +from losses.stft_loss import MRSTFTLoss, MRLogMelLoss + + +parser = argparse.ArgumentParser() + +parser.add_argument('setup', type=str, help='setup yaml file') +parser.add_argument('output', type=str, help='output path') +parser.add_argument('--device', type=str, help='compute device', default=None) +parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) +parser.add_argument('--test-features', type=str, help='path to features for testing', default=None) +parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') + +args = parser.parse_args() + + +torch.set_num_threads(4) + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +checkpoint_prefix = 'checkpoint' +output_prefix = 'output' +setup_name = 'setup.yml' +output_file='out.txt' + + +# check model +if not 'name' in setup['model']: + print(f'warning: did not find model entry in setup, using default PitchPostFilter') + model_name = 'pitchpostfilter' +else: + model_name = setup['model']['name'] + +# prepare output folder +if os.path.exists(args.output): + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit() +else: + os.makedirs(args.output, exist_ok=True) + +checkpoint_dir = os.path.join(args.output, 'checkpoints') +os.makedirs(checkpoint_dir, exist_ok=True) + +# add repo info to setup +if has_git: + working_dir = os.path.split(__file__)[0] + try: + repo = git.Repo(working_dir, search_parent_directories=True) + setup['repo'] = dict() + hash = repo.head.object.hexsha + urls = list(repo.remote().urls) + is_dirty = repo.is_dirty() + + if is_dirty: + print("warning: repo is dirty") + + setup['repo']['hash'] = hash + setup['repo']['urls'] = urls + setup['repo']['dirty'] = is_dirty + except: + has_git = False + +# dump setup +with open(os.path.join(args.output, setup_name), 'w') as f: + yaml.dump(setup, f) + + +ref = None +# prepare inference test if wanted +inference_test = False +if type(args.test_features) != type(None): + test_features = load_lpcnet_features(args.test_features) + features = test_features['features'] + periods = test_features['periods'] + inference_folder = os.path.join(args.output, 'inference_test') + os.makedirs(inference_folder, exist_ok=True) + inference_test = True + + +# training parameters +batch_size = setup['training']['batch_size'] +epochs = setup['training']['epochs'] +lr = setup['training']['lr'] +lr_decay_factor = setup['training']['lr_decay_factor'] +lr_gen = lr * setup['training']['gen_lr_reduction'] +lambda_feat = setup['training']['lambda_feat'] +lambda_reg = setup['training']['lambda_reg'] +adv_target = setup['training'].get('adv_target', 'target') + + +# load training dataset +data_config = setup['data'] +data = LPCNetVocodingDataset(setup['dataset'], **data_config) + +# load validation dataset if given +if 'validation_dataset' in setup: + validation_data = LPCNetVocodingDataset(setup['validation_dataset'], **data_config) + + validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) + + run_validation = True +else: + run_validation = False + +# create model +model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) + + +# create discriminator +disc_name = setup['discriminator']['name'] +disc = model_dict[disc_name]( + *setup['discriminator']['args'], **setup['discriminator']['kwargs'] +) + + + +# set compute device +if type(args.device) == type(None): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +else: + device = torch.device(args.device) + + + +# dataloader +dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) + +# optimizer is introduced to trainable parameters +parameters = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(parameters, lr=lr_gen) + +# disc optimizer +parameters = [p for p in disc.parameters() if p.requires_grad] +optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9]) + +# learning rate scheduler +scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +if args.initial_checkpoint is not None: + print(f"loading state dict from {args.initial_checkpoint}...") + chkpt = torch.load(args.initial_checkpoint, map_location=device) + model.load_state_dict(chkpt['state_dict']) + + if 'disc_state_dict' in chkpt: + print(f"loading discriminator state dict from {args.initial_checkpoint}...") + disc.load_state_dict(chkpt['disc_state_dict']) + + if 'optimizer_state_dict' in chkpt: + print(f"loading optimizer state dict from {args.initial_checkpoint}...") + optimizer.load_state_dict(chkpt['optimizer_state_dict']) + + if 'disc_optimizer_state_dict' in chkpt: + print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...") + optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict']) + + if 'scheduler_state_disc' in chkpt: + print(f"loading scheduler state dict from {args.initial_checkpoint}...") + scheduler.load_state_dict(chkpt['scheduler_state_dict']) + + # if 'torch_rng_state' in chkpt: + # print(f"setting torch RNG state from {args.initial_checkpoint}...") + # torch.set_rng_state(chkpt['torch_rng_state']) + + if 'numpy_rng_state' in chkpt: + print(f"setting numpy RNG state from {args.initial_checkpoint}...") + np.random.set_state(chkpt['numpy_rng_state']) + + if 'python_rng_state' in chkpt: + print(f"setting Python RNG state from {args.initial_checkpoint}...") + random.setstate(chkpt['python_rng_state']) + +# loss +w_l1 = setup['training']['loss']['w_l1'] +w_lm = setup['training']['loss']['w_lm'] +w_slm = setup['training']['loss']['w_slm'] +w_sc = setup['training']['loss']['w_sc'] +w_logmel = setup['training']['loss']['w_logmel'] +w_wsc = setup['training']['loss']['w_wsc'] +w_xcorr = setup['training']['loss']['w_xcorr'] +w_sxcorr = setup['training']['loss']['w_sxcorr'] +w_l2 = setup['training']['loss']['w_l2'] + +w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 + +stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) +logmelloss = MRLogMelLoss().to(device) + +def xcorr_loss(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) + + return torch.mean(loss) + +def td_l2_norm(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) + + return loss.mean() + +def td_l1(y_true, y_pred, pow=0): + dims = list(range(1, len(y_true.shape))) + tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) + + return torch.mean(tmp) + +def criterion(x, y): + + return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) + + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum + + +# model checkpoint +checkpoint = { + 'setup' : setup, + 'state_dict' : model.state_dict(), + 'loss' : -1 +} + + +if not args.no_redirect: + print(f"re-directing output to {os.path.join(args.output, output_file)}") + sys.stdout = open(os.path.join(args.output, output_file), "w") + + +print("summary:") + +print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters") +if hasattr(model, 'flop_count'): + print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS") +print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters") + +if ref is not None: + noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) + initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') + print(f"initial MOS (PESQ): {initial_mos}") + +best_loss = 1e9 +log_interval = 10 + + +m_r = 0 +m_f = 0 +s_r = 1 +s_f = 1 + +def optimizer_to(optim, device): + for param in optim.state.values(): + if isinstance(param, torch.Tensor): + param.data = param.data.to(device) + if param._grad is not None: + param._grad.data = param._grad.data.to(device) + elif isinstance(param, dict): + for subparam in param.values(): + if isinstance(subparam, torch.Tensor): + subparam.data = subparam.data.to(device) + if subparam._grad is not None: + subparam._grad.data = subparam._grad.data.to(device) + +optimizer_to(optimizer, device) +optimizer_to(optimizer_disc, device) + + +for ep in range(1, epochs + 1): + print(f"training epoch {ep}...") + + model.to(device) + disc.to(device) + model.train() + disc.train() + + running_disc_loss = 0 + running_adv_loss = 0 + running_feature_loss = 0 + running_reg_loss = 0 + + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + for i, batch in enumerate(tepoch): + + # set gradients to zero + optimizer.zero_grad() + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'].to(device) + disc_target = batch[adv_target].to(device) + + # calculate model output + output = model(batch['features'], batch['periods']) + + # discriminator update + scores_gen = disc(output.detach()) + scores_real = disc(disc_target.unsqueeze(1)) + + disc_loss = 0 + for scale in scores_gen: + disc_loss += ((scale[-1]) ** 2).mean() + m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item() + s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item() + + for scale in scores_real: + disc_loss += ((1 - scale[-1]) ** 2).mean() + m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item() + s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item() + + disc_loss = 0.5 * disc_loss / len(scores_gen) + winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) + + disc.zero_grad() + disc_loss.backward() + optimizer_disc.step() + + # generator update + scores_gen = disc(output) + + + # calculate loss + loss_reg = criterion(output.squeeze(1), target) + + num_discs = len(scores_gen) + loss_gen = 0 + for scale in scores_gen: + loss_gen += ((1 - scale[-1]) ** 2).mean() / num_discs + + loss_feat = 0 + for k in range(num_discs): + num_layers = len(scores_gen[k]) - 1 + f = 4 / num_discs / num_layers + for l in range(num_layers): + loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) + + model.zero_grad() + + (loss_gen + lambda_feat * loss_feat + lambda_reg * loss_reg).backward() + + optimizer.step() + + running_adv_loss += loss_gen.detach().cpu().item() + running_disc_loss += disc_loss.detach().cpu().item() + running_feature_loss += lambda_feat * loss_feat.detach().cpu().item() + running_reg_loss += lambda_reg * loss_reg.detach().cpu().item() + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}", + disc_loss=f"{running_disc_loss/(i + 1):8.7f}", + feat_loss=f"{running_feature_loss/(i + 1):8.7f}", + reg_loss=f"{running_reg_loss/(i + 1):8.7f}", + wc=f"{100*winning_chance:5.2f}%") + + + # save checkpoint + checkpoint['state_dict'] = model.state_dict() + checkpoint['disc_state_dict'] = disc.state_dict() + checkpoint['optimizer_state_dict'] = optimizer.state_dict() + checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict() + checkpoint['scheduler_state_dict'] = scheduler.state_dict() + checkpoint['torch_rng_state'] = torch.get_rng_state() + checkpoint['numpy_rng_state'] = np.random.get_state() + checkpoint['python_rng_state'] = random.getstate() + checkpoint['adv_loss'] = running_adv_loss/(i + 1) + checkpoint['disc_loss'] = running_disc_loss/(i + 1) + checkpoint['feature_loss'] = running_feature_loss/(i + 1) + checkpoint['reg_loss'] = running_reg_loss/(i + 1) + + + if inference_test: + print("running inference test...") + out = model.process(features, periods).cpu().numpy() + wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) + if ref is not None: + mos = pesq.pesq(16000, ref, out, mode='wb') + print(f"MOS (PESQ): {mos}") + + + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) + + + print() + +print('Done') diff --git a/managed_components/78__esp-opus/dnn/torch/osce/create_testvectors.py b/managed_components/78__esp-opus/dnn/torch/osce/create_testvectors.py new file mode 100644 index 0000000..a037d0d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/create_testvectors.py @@ -0,0 +1,165 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import torch +import numpy as np + +from models import model_dict +from utils import endoscopy + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint_path', type=str, help='path to folder containing checkpoints "lace_checkpoint.pth" and nolace_checkpoint.pth"') +parser.add_argument('output_folder', type=str, help='output folder for testvectors') +parser.add_argument('--debug', action='store_true', help='add debug output to output folder') + + +def create_adaconv_testvector(prefix, adaconv, num_frames, debug=False): + feature_dim = adaconv.feature_dim + in_channels = adaconv.in_channels + out_channels = adaconv.out_channels + frame_size = adaconv.frame_size + + features = torch.randn((1, num_frames, feature_dim)) + x_in = torch.randn((1, in_channels, num_frames * frame_size)) + + x_out = adaconv(x_in, features, debug=debug) + + features = features[0].detach().numpy() + x_in = x_in[0].reshape(in_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy() + x_out = x_out[0].reshape(out_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy() + + features.tofile(prefix + '_features.f32') + x_in.tofile(prefix + '_x_in.f32') + x_out.tofile(prefix + '_x_out.f32') + +def create_adacomb_testvector(prefix, adacomb, num_frames, debug=False): + feature_dim = adacomb.feature_dim + in_channels = 1 + frame_size = adacomb.frame_size + + features = torch.randn((1, num_frames, feature_dim)) + x_in = torch.randn((1, in_channels, num_frames * frame_size)) + p_in = torch.randint(adacomb.kernel_size, 250, (1, num_frames)) + + x_out = adacomb(x_in, features, p_in, debug=debug) + + features = features[0].detach().numpy() + x_in = x_in[0].permute(1, 0).detach().numpy() + p_in = p_in[0].detach().numpy().astype(np.int32) + x_out = x_out[0].permute(1, 0).detach().numpy() + + features.tofile(prefix + '_features.f32') + x_in.tofile(prefix + '_x_in.f32') + p_in.tofile(prefix + '_p_in.s32') + x_out.tofile(prefix + '_x_out.f32') + +def create_adashape_testvector(prefix, adashape, num_frames): + feature_dim = adashape.feature_dim + frame_size = adashape.frame_size + + features = torch.randn((1, num_frames, feature_dim)) + x_in = torch.randn((1, 1, num_frames * frame_size)) + + x_out = adashape(x_in, features) + + features = features[0].detach().numpy() + x_in = x_in.flatten().detach().numpy() + x_out = x_out.flatten().detach().numpy() + + features.tofile(prefix + '_features.f32') + x_in.tofile(prefix + '_x_in.f32') + x_out.tofile(prefix + '_x_out.f32') + +def create_feature_net_testvector(prefix, model, num_frames): + num_features = model.num_features + num_subframes = 4 * num_frames + + input_features = torch.randn((1, num_subframes, num_features)) + periods = torch.randint(32, 300, (1, num_subframes)) + numbits = model.numbits_range[0] + torch.rand((1, num_frames, 2)) * (model.numbits_range[1] - model.numbits_range[0]) + + + pembed = model.pitch_embedding(periods) + nembed = torch.repeat_interleave(model.numbits_embedding(numbits).flatten(2), 4, dim=1) + full_features = torch.cat((input_features, pembed, nembed), dim=-1) + + cf = model.feature_net(full_features) + + input_features.float().numpy().tofile(prefix + "_in_features.f32") + periods.numpy().astype(np.int32).tofile(prefix + "_periods.s32") + numbits.float().numpy().tofile(prefix + "_numbits.f32") + full_features.detach().numpy().tofile(prefix + "_full_features.f32") + cf.detach().numpy().tofile(prefix + "_out_features.f32") + + + +if __name__ == "__main__": + args = parser.parse_args() + + os.makedirs(args.output_folder, exist_ok=True) + + lace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "lace_checkpoint.pth"), map_location='cpu') + nolace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "nolace_checkpoint.pth"), map_location='cpu') + + lace = model_dict['lace'](**lace_checkpoint['setup']['model']['kwargs']) + nolace = model_dict['nolace'](**nolace_checkpoint['setup']['model']['kwargs']) + + lace.load_state_dict(lace_checkpoint['state_dict']) + nolace.load_state_dict(nolace_checkpoint['state_dict']) + + if args.debug: + endoscopy.init(args.output_folder) + + # lace af1, 1 input channel, 1 output channel + create_adaconv_testvector(os.path.join(args.output_folder, "lace_af1"), lace.af1, 5, debug=args.debug) + + # nolace af1, 1 input channel, 2 output channels + create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af1"), nolace.af1, 5, debug=args.debug) + + # nolace af4, 2 input channel, 1 output channels + create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af4"), nolace.af4, 5, debug=args.debug) + + # nolace af2, 2 input channel, 2 output channels + create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af2"), nolace.af2, 5, debug=args.debug) + + # lace cf1 + create_adacomb_testvector(os.path.join(args.output_folder, "lace_cf1"), lace.cf1, 5, debug=args.debug) + + # nolace tdshape1 + create_adashape_testvector(os.path.join(args.output_folder, "nolace_tdshape1"), nolace.tdshape1, 5) + + # lace feature net + create_feature_net_testvector(os.path.join(args.output_folder, 'lace'), lace, 5) + + if args.debug: + endoscopy.close() diff --git a/managed_components/78__esp-opus/dnn/torch/osce/data/__init__.py b/managed_components/78__esp-opus/dnn/torch/osce/data/__init__.py new file mode 100644 index 0000000..8df4d56 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/data/__init__.py @@ -0,0 +1,2 @@ +from .silk_enhancement_set import SilkEnhancementSet +from .lpcnet_vocoding_dataset import LPCNetVocodingDataset \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py b/managed_components/78__esp-opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py new file mode 100644 index 0000000..36c8c72 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py @@ -0,0 +1,225 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" Dataset for LPCNet training """ +import os + +import yaml +import torch +import numpy as np +from torch.utils.data import Dataset + + +scale = 255.0/32768.0 +scale_1 = 32768.0/255.0 +def ulaw2lin(u): + u = u - 128 + s = np.sign(u) + u = np.abs(u) + return s*scale_1*(np.exp(u/128.*np.log(256))-1) + + +def lin2ulaw(x): + s = np.sign(x) + x = np.abs(x) + u = (s*(128*np.log(1+scale*x)/np.log(256))) + u = np.clip(128 + np.round(u), 0, 255) + return u + + +def run_lpc(signal, lpcs, frame_length=160): + num_frames, lpc_order = lpcs.shape + + prediction = np.concatenate( + [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] + ) + error = signal[lpc_order :] - prediction + + return prediction, error + +class LPCNetVocodingDataset(Dataset): + def __init__(self, + path_to_dataset, + features=['cepstrum', 'periods', 'pitch_corr'], + target='signal', + frames_per_sample=100, + feature_history=0, + feature_lookahead=0, + lpc_gamma=1): + + super().__init__() + + # load dataset info + self.path_to_dataset = path_to_dataset + with open(os.path.join(path_to_dataset, 'info.yml'), 'r') as f: + dataset = yaml.load(f, yaml.FullLoader) + + # dataset version + self.version = dataset['version'] + if self.version == 1: + self.getitem = self.getitem_v1 + elif self.version == 2: + self.getitem = self.getitem_v2 + else: + raise ValueError(f"dataset version {self.version} unknown") + + # features + self.feature_history = feature_history + self.feature_lookahead = feature_lookahead + self.frame_offset = 2 + self.feature_history + self.frames_per_sample = frames_per_sample + self.input_features = features + self.feature_frame_layout = dataset['feature_frame_layout'] + self.lpc_gamma = lpc_gamma + + # load feature file + self.feature_file = os.path.join(path_to_dataset, dataset['feature_file']) + self.features = np.memmap(self.feature_file, dtype=dataset['feature_dtype']) + self.feature_frame_length = dataset['feature_frame_length'] + + assert len(self.features) % self.feature_frame_length == 0 + self.features = self.features.reshape((-1, self.feature_frame_length)) + + # derive number of samples is dataset + self.dataset_length = (len(self.features) - self.frame_offset - self.feature_lookahead - 1 - 2) // self.frames_per_sample + + # signals + self.frame_length = dataset['frame_length'] + self.signal_frame_layout = dataset['signal_frame_layout'] + self.target = target + + # load signals + self.signal_file = os.path.join(path_to_dataset, dataset['signal_file']) + self.signals = np.memmap(self.signal_file, dtype=dataset['signal_dtype']) + self.signal_frame_length = dataset['signal_frame_length'] + self.signals = self.signals.reshape((-1, self.signal_frame_length)) + assert len(self.signals) == len(self.features) * self.frame_length + + + def __getitem__(self, index): + return self.getitem(index) + + def getitem_v2(self, index): + sample = dict() + + # extract features + frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history + frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead + + for feature in self.input_features: + feature_start, feature_stop = self.feature_frame_layout[feature] + sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] + + # convert periods + if 'periods' in self.input_features: + sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') + + signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length + signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length + + # last_signal and signal are always expected to be there + sample['last_signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] + sample['signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['signal']] + + # calculate prediction and error if lpc coefficients present and prediction not given + if 'lpc' in self.feature_frame_layout and 'prediction' not in self.signal_frame_layout: + # lpc coefficients with one frame lookahead + # frame positions (start one frame early for past excitation) + frame_start = self.frame_offset + self.frames_per_sample * index - 1 + frame_stop = self.frame_offset + self.frames_per_sample * (index + 1) + + # feature positions + lpc_start, lpc_stop = self.feature_frame_layout['lpc'] + lpc_order = lpc_stop - lpc_start + lpcs = self.features[frame_start : frame_stop, lpc_start : lpc_stop] + + # LPC weighting + lpc_order = lpc_stop - lpc_start + weights = np.array([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]) + lpcs = lpcs * weights + + # signal position (lpc_order samples as history) + signal_start = frame_start * self.frame_length - lpc_order + 1 + signal_stop = frame_stop * self.frame_length + 1 + noisy_signal = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] + clean_signal = self.signals[signal_start - 1 : signal_stop - 1, self.signal_frame_layout['signal']] + + noisy_prediction, noisy_error = run_lpc(noisy_signal, lpcs, frame_length=self.frame_length) + + # extract signals + offset = self.frame_length + sample['prediction'] = noisy_prediction[offset : offset + self.frame_length * self.frames_per_sample] + sample['last_error'] = noisy_error[offset - 1 : offset - 1 + self.frame_length * self.frames_per_sample] + # calculate error between real signal and noisy prediction + + + sample['error'] = sample['signal'] - sample['prediction'] + + + # concatenate features + feature_keys = [key for key in self.input_features if not key.startswith("periods")] + features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) + target = torch.FloatTensor(sample[self.target]) / 2**15 + periods = torch.LongTensor(sample['periods']) + + return {'features' : features, 'periods' : periods, 'target' : target} + + def getitem_v1(self, index): + sample = dict() + + # extract features + frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history + frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead + + for feature in self.input_features: + feature_start, feature_stop = self.feature_frame_layout[feature] + sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] + + # convert periods + if 'periods' in self.input_features: + sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') + + signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length + signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length + + # last_signal and signal are always expected to be there + for signal_name, index in self.signal_frame_layout.items(): + sample[signal_name] = self.signals[signal_start : signal_stop, index] + + # concatenate features + feature_keys = [key for key in self.input_features if not key.startswith("periods")] + features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) + signals = torch.cat([torch.LongTensor(sample[key]).unsqueeze(-1) for key in self.input_signals], dim=-1) + target = torch.LongTensor(sample[self.target]) + periods = torch.LongTensor(sample['periods']) + + return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} + + def __len__(self): + return self.dataset_length diff --git a/managed_components/78__esp-opus/dnn/torch/osce/data/silk_conversion_set.py b/managed_components/78__esp-opus/dnn/torch/osce/data/silk_conversion_set.py new file mode 100644 index 0000000..8f64675 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/data/silk_conversion_set.py @@ -0,0 +1,132 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + +from torch.utils.data import Dataset +import numpy as np + +from utils.silk_features import silk_feature_factory +from utils.pitch import hangover, calculate_acorr_window + + +class SilkEnhancementSet(Dataset): + def __init__(self, + path, + frames_per_sample=100, + no_pitch_value=9, + acorr_radius=2, + pitch_hangover=8, + num_bands_clean_spec=64, + num_bands_noisy_spec=18, + noisy_spec_scale='opus', + noisy_apply_dct=True, + add_offset=False, + add_double_lag_acorr=False + ): + + assert frames_per_sample % 4 == 0 + + self.frame_size = 80 + self.frames_per_sample = frames_per_sample + self.no_pitch_value = no_pitch_value + self.acorr_radius = acorr_radius + self.pitch_hangover = pitch_hangover + self.num_bands_clean_spec = num_bands_clean_spec + self.num_bands_noisy_spec = num_bands_noisy_spec + self.noisy_spec_scale = noisy_spec_scale + self.add_double_lag_acorr = add_double_lag_acorr + + self.lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) + self.ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) + self.periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) + self.gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) + self.num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32) + self.num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32) + self.offsets = np.fromfile(os.path.join(path, 'features_offset.f32'), dtype=np.float32) + self.lpcnet_features = np.from_file(os.path.join(path, 'features_lpcnet.f32'), dtype=np.float32).reshape(-1, 36) + + self.coded_signal = np.fromfile(os.path.join(path, 'coded.s16'), dtype=np.int16) + + self.create_features = silk_feature_factory(no_pitch_value, + acorr_radius, + pitch_hangover, + num_bands_clean_spec, + num_bands_noisy_spec, + noisy_spec_scale, + noisy_apply_dct, + add_offset, + add_double_lag_acorr) + + self.history_len = 700 if add_double_lag_acorr else 350 + # discard some frames to have enough signal history + self.skip_frames = 4 * ((self.history_len + 319) // 320 + 2) + + num_frames = self.clean_signal.shape[0] // 80 - self.skip_frames + + self.len = num_frames // frames_per_sample + + def __len__(self): + return self.len + + def __getitem__(self, index): + + frame_start = self.frames_per_sample * index + self.skip_frames + frame_stop = frame_start + self.frames_per_sample + + signal_start = frame_start * self.frame_size - self.skip + signal_stop = frame_stop * self.frame_size - self.skip + + coded_signal = self.coded_signal[signal_start : signal_stop].astype(np.float32) / 2**15 + + coded_signal_history = self.coded_signal[signal_start - self.history_len : signal_start].astype(np.float32) / 2**15 + + features, periods = self.create_features( + coded_signal, + coded_signal_history, + self.lpcs[frame_start : frame_stop], + self.gains[frame_start : frame_stop], + self.ltps[frame_start : frame_stop], + self.periods[frame_start : frame_stop], + self.offsets[frame_start : frame_stop] + ) + + lpcnet_features = self.lpcnet_features[frame_start // 2 : frame_stop // 2, :20] + + num_bits = np.repeat(self.num_bits[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) + num_bits_smooth = np.repeat(self.num_bits_smooth[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) + + numbits = np.concatenate((num_bits, num_bits_smooth), axis=-1) + + return { + 'silk_features' : features, + 'periods' : periods.astype(np.int64), + 'numbits' : numbits.astype(np.float32), + 'lpcnet_features' : lpcnet_features + } diff --git a/managed_components/78__esp-opus/dnn/torch/osce/data/silk_enhancement_set.py b/managed_components/78__esp-opus/dnn/torch/osce/data/silk_enhancement_set.py new file mode 100644 index 0000000..fd18c4d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/data/silk_enhancement_set.py @@ -0,0 +1,140 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + +from torch.utils.data import Dataset +import numpy as np + +from utils.silk_features import silk_feature_factory +from utils.pitch import hangover, calculate_acorr_window + + +class SilkEnhancementSet(Dataset): + def __init__(self, + path, + frames_per_sample=100, + no_pitch_value=256, + preemph=0.85, + skip=91, + acorr_radius=2, + pitch_hangover=8, + num_bands_clean_spec=64, + num_bands_noisy_spec=18, + noisy_spec_scale='opus', + noisy_apply_dct=True, + add_double_lag_acorr=False, + ): + + assert frames_per_sample % 4 == 0 + + self.frame_size = 80 + self.frames_per_sample = frames_per_sample + self.no_pitch_value = no_pitch_value + self.preemph = preemph + self.skip = skip + self.acorr_radius = acorr_radius + self.pitch_hangover = pitch_hangover + self.num_bands_clean_spec = num_bands_clean_spec + self.num_bands_noisy_spec = num_bands_noisy_spec + self.noisy_spec_scale = noisy_spec_scale + self.add_double_lag_acorr = add_double_lag_acorr + + self.lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) + self.ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) + self.periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) + self.gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) + self.num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32) + self.num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32) + + self.clean_signal_hp = np.fromfile(os.path.join(path, 'clean_hp.s16'), dtype=np.int16) + self.clean_signal = np.fromfile(os.path.join(path, 'clean.s16'), dtype=np.int16) + self.coded_signal = np.fromfile(os.path.join(path, 'coded.s16'), dtype=np.int16) + + self.create_features = silk_feature_factory(no_pitch_value, + acorr_radius, + pitch_hangover, + num_bands_clean_spec, + num_bands_noisy_spec, + noisy_spec_scale, + noisy_apply_dct, + add_double_lag_acorr) + + self.history_len = 700 if add_double_lag_acorr else 350 + # discard some frames to have enough signal history + self.skip_frames = 4 * ((skip + self.history_len + 319) // 320 + 2) + + num_frames = self.clean_signal_hp.shape[0] // 80 - self.skip_frames + + self.len = num_frames // frames_per_sample + + def __len__(self): + return self.len + + def __getitem__(self, index): + + frame_start = self.frames_per_sample * index + self.skip_frames + frame_stop = frame_start + self.frames_per_sample + + signal_start = frame_start * self.frame_size - self.skip + signal_stop = frame_stop * self.frame_size - self.skip + + clean_signal_hp = self.clean_signal_hp[signal_start : signal_stop].astype(np.float32) / 2**15 + clean_signal = self.clean_signal[signal_start : signal_stop].astype(np.float32) / 2**15 + coded_signal = self.coded_signal[signal_start : signal_stop].astype(np.float32) / 2**15 + + coded_signal_history = self.coded_signal[signal_start - self.history_len : signal_start].astype(np.float32) / 2**15 + + features, periods = self.create_features( + coded_signal, + coded_signal_history, + self.lpcs[frame_start : frame_stop], + self.gains[frame_start : frame_stop], + self.ltps[frame_start : frame_stop], + self.periods[frame_start : frame_stop] + ) + + if self.preemph > 0: + clean_signal[1:] -= self.preemph * clean_signal[: -1] + clean_signal_hp[1:] -= self.preemph * clean_signal_hp[: -1] + coded_signal[1:] -= self.preemph * coded_signal[: -1] + + num_bits = np.repeat(self.num_bits[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) + num_bits_smooth = np.repeat(self.num_bits_smooth[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) + + numbits = np.concatenate((num_bits, num_bits_smooth), axis=-1) + + return { + 'features' : features, + 'periods' : periods.astype(np.int64), + 'target_orig' : clean_signal.astype(np.float32), + 'target' : clean_signal_hp.astype(np.float32), + 'signals' : coded_signal.reshape(-1, 1).astype(np.float32), + 'numbits' : numbits.astype(np.float32) + } diff --git a/managed_components/78__esp-opus/dnn/torch/osce/engine/engine.py b/managed_components/78__esp-opus/dnn/torch/osce/engine/engine.py new file mode 100644 index 0000000..0762c89 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/engine/engine.py @@ -0,0 +1,103 @@ +import torch +from tqdm import tqdm +import sys + +def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): + + model.to(device) + model.train() + + running_loss = 0 + previous_running_loss = 0 + + + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + # set gradients to zero + optimizer.zero_grad() + + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) + + # calculate loss + if isinstance(output, list): + loss = torch.zeros(1, device=device) + for y in output: + loss = loss + criterion(target, y.squeeze(1)) + loss = loss / len(output) + else: + loss = criterion(target, output.squeeze(1)) + + # calculate gradients + loss.backward() + + # update weights + optimizer.step() + + # update learning rate + scheduler.step() + + # sparsification + if hasattr(model, 'sparsifier'): + model.sparsifier() + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss + +def evaluate(model, criterion, dataloader, device, log_interval=10): + + model.to(device) + model.eval() + + running_loss = 0 + previous_running_loss = 0 + + + with torch.no_grad(): + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) + + # calculate loss + loss = criterion(target, output.squeeze(1)) + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/engine/vocoder_engine.py b/managed_components/78__esp-opus/dnn/torch/osce/engine/vocoder_engine.py new file mode 100644 index 0000000..9eee49e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/engine/vocoder_engine.py @@ -0,0 +1,101 @@ +import torch +from tqdm import tqdm +import sys + +def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): + + model.to(device) + model.train() + + running_loss = 0 + previous_running_loss = 0 + + + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + # set gradients to zero + optimizer.zero_grad() + + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['features'], batch['periods']) + + # calculate loss + if isinstance(output, list): + loss = torch.zeros(1, device=device) + for y in output: + loss = loss + criterion(target, y.squeeze(1)) + loss = loss / len(output) + else: + loss = criterion(target, output.squeeze(1)) + + # calculate gradients + loss.backward() + + # update weights + optimizer.step() + + # update learning rate + scheduler.step() + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss + +def evaluate(model, criterion, dataloader, device, log_interval=10): + + model.to(device) + model.eval() + + running_loss = 0 + previous_running_loss = 0 + + + with torch.no_grad(): + with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: + + for i, batch in enumerate(tepoch): + + + + # push batch to device + for key in batch: + batch[key] = batch[key].to(device) + + target = batch['target'] + + # calculate model output + output = model(batch['features'], batch['periods']) + + # calculate loss + loss = criterion(target, output.squeeze(1)) + + # update running loss + running_loss += float(loss.cpu()) + + # update status bar + if i % log_interval == 0: + tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") + previous_running_loss = running_loss + + + running_loss /= len(dataloader) + + return running_loss \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/export_model_weights.py b/managed_components/78__esp-opus/dnn/torch/osce/export_model_weights.py new file mode 100644 index 0000000..a504a61 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/export_model_weights.py @@ -0,0 +1,174 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +import hashlib + +sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) + +import torch +import wexchange.torch +from wexchange.torch import dump_torch_weights +from models import model_dict + +from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d +from utils.layers.td_shaper import TDShaper +from utils.misc import remove_all_weight_norm +from wexchange.torch import dump_torch_weights + + + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint', type=str, help='LACE or NoLACE model checkpoint') +parser.add_argument('output_dir', type=str, help='output folder') +parser.add_argument('--quantize', action="store_true", help='quantization according to schedule') + +sparse_default=False +schedules = { + 'nolace': [ + ('pitch_embedding', dict()), + ('feature_net.conv1', dict()), + ('feature_net.conv2', dict(quantize=True, scale=None, sparse=sparse_default)), + ('feature_net.tconv', dict(quantize=True, scale=None, sparse=sparse_default)), + ('feature_net.gru', dict(quantize=True, scale=None, recurrent_scale=None, input_sparse=sparse_default, recurrent_sparse=sparse_default)), + ('cf1', dict(quantize=True, scale=None)), + ('cf2', dict(quantize=True, scale=None)), + ('af1', dict(quantize=True, scale=None)), + ('tdshape1', dict(quantize=True, scale=None)), + ('tdshape2', dict(quantize=True, scale=None)), + ('tdshape3', dict(quantize=True, scale=None)), + ('af2', dict(quantize=True, scale=None)), + ('af3', dict(quantize=True, scale=None)), + ('af4', dict(quantize=True, scale=None)), + ('post_cf1', dict(quantize=True, scale=None, sparse=sparse_default)), + ('post_cf2', dict(quantize=True, scale=None, sparse=sparse_default)), + ('post_af1', dict(quantize=True, scale=None, sparse=sparse_default)), + ('post_af2', dict(quantize=True, scale=None, sparse=sparse_default)), + ('post_af3', dict(quantize=True, scale=None, sparse=sparse_default)) + ], + 'lace' : [ + ('pitch_embedding', dict()), + ('feature_net.conv1', dict()), + ('feature_net.conv2', dict(quantize=True, scale=None, sparse=sparse_default)), + ('feature_net.tconv', dict(quantize=True, scale=None, sparse=sparse_default)), + ('feature_net.gru', dict(quantize=True, scale=None, recurrent_scale=None, input_sparse=sparse_default, recurrent_sparse=sparse_default)), + ('cf1', dict(quantize=True, scale=None)), + ('cf2', dict(quantize=True, scale=None)), + ('af1', dict(quantize=True, scale=None)) + ] +} + + +# auxiliary functions +def sha1(filename): + BUF_SIZE = 65536 + sha1 = hashlib.sha1() + + with open(filename, 'rb') as f: + while True: + data = f.read(BUF_SIZE) + if not data: + break + sha1.update(data) + + return sha1.hexdigest() + +def osce_dump_generic(writer, name, module): + if isinstance(module, torch.nn.Linear) or isinstance(module, torch.nn.Conv1d) \ + or isinstance(module, torch.nn.ConvTranspose1d) or isinstance(module, torch.nn.Embedding) \ + or isinstance(module, LimitedAdaptiveConv1d) or isinstance(module, LimitedAdaptiveComb1d) \ + or isinstance(module, TDShaper) or isinstance(module, torch.nn.GRU): + dump_torch_weights(writer, module, name=name, verbose=True) + else: + for child_name, child in module.named_children(): + osce_dump_generic(writer, (name + "_" + child_name).replace("feature_net", "fnet"), child) + + +def export_name(name): + name = name.replace('.', '_') + name = name.replace('feature_net', 'fnet') + return name + +def osce_scheduled_dump(writer, prefix, model, schedule): + if not prefix.endswith('_'): + prefix += '_' + + for name, kwargs in schedule: + dump_torch_weights(writer, model.get_submodule(name), prefix + export_name(name), **kwargs, verbose=True) + +if __name__ == "__main__": + args = parser.parse_args() + + checkpoint_path = args.checkpoint + outdir = args.output_dir + os.makedirs(outdir, exist_ok=True) + + # dump message + message = f"Auto generated from checkpoint {os.path.basename(checkpoint_path)} (sha1: {sha1(checkpoint_path)})" + + # create model and load weights + checkpoint = torch.load(checkpoint_path, map_location='cpu') + model = model_dict[checkpoint['setup']['model']['name']](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) + model.load_state_dict(checkpoint['state_dict']) + remove_all_weight_norm(model, verbose=True) + + # CWriter + model_name = checkpoint['setup']['model']['name'] + cwriter = wexchange.c_export.CWriter(os.path.join(outdir, model_name + "_data"), message=message, model_struct_name=model_name.upper() + 'Layers', add_typedef=True) + + # Add custom includes and global parameters + cwriter.header.write(f''' +#define {model_name.upper()}_PREEMPH {model.preemph}f +#define {model_name.upper()}_FRAME_SIZE {model.FRAME_SIZE} +#define {model_name.upper()}_OVERLAP_SIZE 40 +#define {model_name.upper()}_NUM_FEATURES {model.num_features} +#define {model_name.upper()}_PITCH_MAX {model.pitch_max} +#define {model_name.upper()}_PITCH_EMBEDDING_DIM {model.pitch_embedding_dim} +#define {model_name.upper()}_NUMBITS_RANGE_LOW {model.numbits_range[0]} +#define {model_name.upper()}_NUMBITS_RANGE_HIGH {model.numbits_range[1]} +#define {model_name.upper()}_NUMBITS_EMBEDDING_DIM {model.numbits_embedding_dim} +#define {model_name.upper()}_COND_DIM {model.cond_dim} +#define {model_name.upper()}_HIDDEN_FEATURE_DIM {model.hidden_feature_dim} +''') + + for i, s in enumerate(model.numbits_embedding.scale_factors): + cwriter.header.write(f"#define {model_name.upper()}_NUMBITS_SCALE_{i} {float(s.detach().cpu())}f\n") + + # dump layers + if model_name in schedules and args.quantize: + osce_scheduled_dump(cwriter, model_name, model, schedules[model_name]) + else: + osce_dump_generic(cwriter, model_name, model) + + cwriter.close() diff --git a/managed_components/78__esp-opus/dnn/torch/osce/losses/stft_loss.py b/managed_components/78__esp-opus/dnn/torch/osce/losses/stft_loss.py new file mode 100644 index 0000000..4c164cb --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/losses/stft_loss.py @@ -0,0 +1,277 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +"""STFT-based Loss modules.""" + +import torch +import torch.nn.functional as F +from torch import nn +import numpy as np +import torchaudio + + +def get_window(win_name, win_length, *args, **kwargs): + window_dict = { + 'bartlett_window' : torch.bartlett_window, + 'blackman_window' : torch.blackman_window, + 'hamming_window' : torch.hamming_window, + 'hann_window' : torch.hann_window, + 'kaiser_window' : torch.kaiser_window + } + + if not win_name in window_dict: + raise ValueError() + + return window_dict[win_name](win_length, *args, **kwargs) + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + """ + + win = get_window(window, win_length).to(x.device) + x_stft = torch.stft(x, fft_size, hop_size, win_length, win, return_complex=True) + + + return torch.clamp(torch.abs(x_stft), min=1e-7) + +def spectral_convergence_loss(Y_true, Y_pred): + dims=list(range(1, len(Y_pred.shape))) + return torch.mean(torch.norm(torch.abs(Y_true) - torch.abs(Y_pred), p="fro", dim=dims) / (torch.norm(Y_pred, p="fro", dim=dims) + 1e-6)) + + +def log_magnitude_loss(Y_true, Y_pred): + Y_true_log_abs = torch.log(torch.abs(Y_true) + 1e-15) + Y_pred_log_abs = torch.log(torch.abs(Y_pred) + 1e-15) + + return torch.mean(torch.abs(Y_true_log_abs - Y_pred_log_abs)) + +def spectral_xcorr_loss(Y_true, Y_pred): + Y_true = Y_true.abs() + Y_pred = Y_pred.abs() + dims=list(range(1, len(Y_pred.shape))) + xcorr = torch.sum(Y_true * Y_pred, dim=dims) / torch.sqrt(torch.sum(Y_true ** 2, dim=dims) * torch.sum(Y_pred ** 2, dim=dims) + 1e-9) + + return 1 - xcorr.mean() + + + +class MRLogMelLoss(nn.Module): + def __init__(self, + fft_sizes=[512, 256, 128, 64], + overlap=0.5, + fs=16000, + n_mels=18 + ): + + self.fft_sizes = fft_sizes + self.overlap = overlap + self.fs = fs + self.n_mels = n_mels + + super().__init__() + + self.mel_specs = [] + for fft_size in fft_sizes: + hop_size = int(round(fft_size * (1 - self.overlap))) + + n_mels = self.n_mels + if fft_size < 128: + n_mels //= 2 + + self.mel_specs.append(torchaudio.transforms.MelSpectrogram(fs, fft_size, hop_length=hop_size, n_mels=n_mels)) + + for i, mel_spec in enumerate(self.mel_specs): + self.add_module(f'mel_spec_{i+1}', mel_spec) + + def forward(self, y_true, y_pred): + + loss = torch.zeros(1, device=y_true.device) + + for mel_spec in self.mel_specs: + Y_true = mel_spec(y_true) + Y_pred = mel_spec(y_pred) + loss = loss + log_magnitude_loss(Y_true, Y_pred) + + loss = loss / len(self.mel_specs) + + return loss + +def create_weight_matrix(num_bins, bins_per_band=10): + m = torch.zeros((num_bins, num_bins), dtype=torch.float32) + + r0 = bins_per_band // 2 + r1 = bins_per_band - r0 + + for i in range(num_bins): + i0 = max(i - r0, 0) + j0 = min(i + r1, num_bins) + + m[i, i0: j0] += 1 + + if i < r0: + m[i, :r0 - i] += 1 + + if i > num_bins - r1: + m[i, num_bins - r1 - i:] += 1 + + return m / bins_per_band + +def weighted_spectral_convergence(Y_true, Y_pred, w): + + # calculate sfm based weights + logY = torch.log(torch.abs(Y_true) + 1e-9) + Y = torch.abs(Y_true) + + avg_logY = torch.matmul(logY.transpose(1, 2), w) + avg_Y = torch.matmul(Y.transpose(1, 2), w) + + sfm = torch.exp(avg_logY) / (avg_Y + 1e-9) + + weight = (torch.relu(1 - sfm) ** .5).transpose(1, 2) + + loss = torch.mean( + torch.mean(weight * torch.abs(torch.abs(Y_true) - torch.abs(Y_pred)), dim=[1, 2]) + / (torch.mean( weight * torch.abs(Y_true), dim=[1, 2]) + 1e-9) + ) + + return loss + +def gen_filterbank(N, Fs=16000): + in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] + out_freq = (np.arange(N, dtype='float32')/N*Fs/2)[:,None] + #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. + ERB_N = 24.7 + .108*in_freq + delta = np.abs(in_freq-out_freq)/ERB_N + center = (delta<.5).astype('float32') + R = -12*center*delta**2 + (1-center)*(3-12*delta) + RE = 10.**(R/10.) + norm = np.sum(RE, axis=1) + RE = RE/norm[:, np.newaxis] + return torch.from_numpy(RE) + +def smooth_log_mag(Y_true, Y_pred, filterbank): + Y_true_smooth = torch.matmul(filterbank, torch.abs(Y_true)) + Y_pred_smooth = torch.matmul(filterbank, torch.abs(Y_pred)) + + loss = torch.abs( + torch.log(Y_true_smooth + 1e-9) - torch.log(Y_pred_smooth + 1e-9) + ) + + loss = loss.mean() + + return loss + +class MRSTFTLoss(nn.Module): + def __init__(self, + fft_sizes=[2048, 1024, 512, 256, 128, 64], + overlap=0.5, + window='hann_window', + fs=16000, + log_mag_weight=1, + sc_weight=0, + wsc_weight=0, + smooth_log_mag_weight=0, + sxcorr_weight=0): + super().__init__() + + self.fft_sizes = fft_sizes + self.overlap = overlap + self.window = window + self.log_mag_weight = log_mag_weight + self.sc_weight = sc_weight + self.wsc_weight = wsc_weight + self.smooth_log_mag_weight = smooth_log_mag_weight + self.sxcorr_weight = sxcorr_weight + self.fs = fs + + # weights for SFM weighted spectral convergence loss + self.wsc_weights = torch.nn.ParameterDict() + for fft_size in fft_sizes: + width = min(11, int(1000 * fft_size / self.fs + .5)) + width += width % 2 + self.wsc_weights[str(fft_size)] = torch.nn.Parameter( + create_weight_matrix(fft_size // 2 + 1, width), + requires_grad=False + ) + + # filterbanks for smooth log magnitude loss + self.filterbanks = torch.nn.ParameterDict() + for fft_size in fft_sizes: + self.filterbanks[str(fft_size)] = torch.nn.Parameter( + gen_filterbank(fft_size//2), + requires_grad=False + ) + + + def __call__(self, y_true, y_pred): + + + lm_loss = torch.zeros(1, device=y_true.device) + sc_loss = torch.zeros(1, device=y_true.device) + wsc_loss = torch.zeros(1, device=y_true.device) + slm_loss = torch.zeros(1, device=y_true.device) + sxcorr_loss = torch.zeros(1, device=y_true.device) + + for fft_size in self.fft_sizes: + hop_size = int(round(fft_size * (1 - self.overlap))) + win_size = fft_size + + Y_true = stft(y_true, fft_size, hop_size, win_size, self.window) + Y_pred = stft(y_pred, fft_size, hop_size, win_size, self.window) + + if self.log_mag_weight > 0: + lm_loss = lm_loss + log_magnitude_loss(Y_true, Y_pred) + + if self.sc_weight > 0: + sc_loss = sc_loss + spectral_convergence_loss(Y_true, Y_pred) + + if self.wsc_weight > 0: + wsc_loss = wsc_loss + weighted_spectral_convergence(Y_true, Y_pred, self.wsc_weights[str(fft_size)]) + + if self.smooth_log_mag_weight > 0: + slm_loss = slm_loss + smooth_log_mag(Y_true, Y_pred, self.filterbanks[str(fft_size)]) + + if self.sxcorr_weight > 0: + sxcorr_loss = sxcorr_loss + spectral_xcorr_loss(Y_true, Y_pred) + + + total_loss = (self.log_mag_weight * lm_loss + self.sc_weight * sc_loss + + self.wsc_weight * wsc_loss + self.smooth_log_mag_weight * slm_loss + + self.sxcorr_weight * sxcorr_loss) / len(self.fft_sizes) + + return total_loss \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/losses/td_lowpass.py b/managed_components/78__esp-opus/dnn/torch/osce/losses/td_lowpass.py new file mode 100644 index 0000000..af422fb --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/losses/td_lowpass.py @@ -0,0 +1,34 @@ +import torch +import scipy.signal + + +from utils.layers.fir import FIR + +class TDLowpass(torch.nn.Module): + def __init__(self, numtaps, cutoff, power=2): + super().__init__() + + self.b = scipy.signal.firwin(numtaps, cutoff) + self.weight = torch.from_numpy(self.b).float().view(1, 1, -1) + self.power = power + + def forward(self, y_true, y_pred): + + assert len(y_true.shape) == 3 and len(y_pred.shape) == 3 + + diff = y_true - y_pred + diff_lp = torch.nn.functional.conv1d(diff, self.weight) + + loss = torch.mean(torch.abs(diff_lp ** self.power)) + + return loss, diff_lp + + def get_freqz(self): + freq, response = scipy.signal.freqz(self.b) + + return freq, response + + + + + \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/make_default_setup.py b/managed_components/78__esp-opus/dnn/torch/osce/make_default_setup.py new file mode 100644 index 0000000..d7365ff --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/make_default_setup.py @@ -0,0 +1,93 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import sys +import argparse + +import yaml + +from utils.templates import setup_dict + +parser = argparse.ArgumentParser() + +parser.add_argument('name', type=str, help='name of default setup file') +parser.add_argument('--model', choices=['lace', 'nolace', 'lavoce'], help='model name', default='lace') +parser.add_argument('--adversarial', action='store_true', help='setup for adversarial training') +parser.add_argument('--path2dataset', type=str, help='dataset path', default=None) + +args = parser.parse_args() + +key = args.model + "_adv" if args.adversarial else args.model + +try: + setup = setup_dict[key] +except KeyError: + print("setup not found, adversarial training possibly not specified for model") + sys.exit(1) + +# update dataset if given +if type(args.path2dataset) != type(None): + setup['dataset'] = args.path2dataset + +name = args.name +if not name.endswith('.yml'): + name += '.yml' + +if __name__ == '__main__': + with open(name, 'w') as f: + f.write(yaml.dump(setup)) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/__init__.py b/managed_components/78__esp-opus/dnn/torch/osce/models/__init__.py new file mode 100644 index 0000000..859db03 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/__init__.py @@ -0,0 +1,42 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from .lace import LACE +from .no_lace import NoLACE +from .lavoce import LaVoce +from .lavoce_400 import LaVoce400 +from .fd_discriminator import TFDMultiResolutionDiscriminator as FDMResDisc + +model_dict = { + 'lace': LACE, + 'nolace': NoLACE, + 'lavoce': LaVoce, + 'lavoce400': LaVoce400, + 'fdmresdisc': FDMResDisc, +} diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/fd_discriminator.py b/managed_components/78__esp-opus/dnn/torch/osce/models/fd_discriminator.py new file mode 100644 index 0000000..2294862 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/fd_discriminator.py @@ -0,0 +1,974 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import math as m +import copy + +import torch +import torch.nn.functional as F +from torch import nn +from torch.nn.utils import weight_norm, spectral_norm +import torchaudio + +from utils.spec import gen_filterbank + +# auxiliary functions + +def remove_all_weight_norms(module): + for m in module.modules(): + if hasattr(m, 'weight_v'): + nn.utils.remove_weight_norm(m) + + +def create_smoothing_kernel(h, w, gamma=1.5): + + ch = h / 2 - 0.5 + cw = w / 2 - 0.5 + + sh = gamma * ch + sw = gamma * cw + + vx = ((torch.arange(h) - ch) / sh) ** 2 + vy = ((torch.arange(w) - cw) / sw) ** 2 + vals = vx.view(-1, 1) + vy.view(1, -1) + kernel = torch.exp(- vals) + kernel = kernel / kernel.sum() + + return kernel + + +def create_kernel(h, w, sh, sw): + # proto kernel gives disjoint partition of 1 + proto_kernel = torch.ones((sh, sw)) + + # create smoothing kernel eta + h_eta, w_eta = h - sh + 1, w - sw + 1 + assert h_eta > 0 and w_eta > 0 + eta = create_smoothing_kernel(h_eta, w_eta).view(1, 1, h_eta, w_eta) + + kernel0 = F.pad(proto_kernel, [w_eta - 1, w_eta - 1, h_eta - 1, h_eta - 1]).unsqueeze(0).unsqueeze(0) + kernel = F.conv2d(kernel0, eta) + + return kernel + +# positional embeddings +class FrequencyPositionalEmbedding(nn.Module): + def __init__(self): + + super().__init__() + + def forward(self, x): + + N = x.size(2) + args = torch.arange(0, N, dtype=x.dtype, device=x.device) * torch.pi * 2 / N + cos = torch.cos(args).reshape(1, 1, -1, 1) + sin = torch.sin(args).reshape(1, 1, -1, 1) + zeros = torch.zeros_like(x[:, 0:1, :, :]) + + y = torch.cat((x, zeros + sin, zeros + cos), dim=1) + + return y + + +class PositionalEmbedding2D(nn.Module): + def __init__(self, d=5): + + super().__init__() + + self.d = d + + def forward(self, x): + + N = x.size(2) + M = x.size(3) + + h_args = torch.arange(0, N, dtype=x.dtype, device=x.device).reshape(1, 1, -1, 1) + w_args = torch.arange(0, M, dtype=x.dtype, device=x.device).reshape(1, 1, 1, -1) + coeffs = (10000 ** (-2 * torch.arange(0, self.d, dtype=x.dtype, device=x.device) / self.d)).reshape(1, -1, 1, 1) + + h_sin = torch.sin(coeffs * h_args) + h_cos = torch.sin(coeffs * h_args) + w_sin = torch.sin(coeffs * w_args) + w_cos = torch.sin(coeffs * w_args) + + zeros = torch.zeros_like(x[:, 0:1, :, :]) + + y = torch.cat((x, zeros + h_sin, zeros + h_cos, zeros + w_sin, zeros + w_cos), dim=1) + + return y + + +# spectral discriminator base class +class SpecDiscriminatorBase(nn.Module): + RECEPTIVE_FIELD_MAX_WIDTH=10000 + def __init__(self, + layers, + resolution, + fs=16000, + freq_roi=[50, 7000], + noise_gain=1e-3, + fmap_start_index=0 + ): + super().__init__() + + + self.layers = nn.ModuleList(layers) + self.resolution = resolution + self.fs = fs + self.noise_gain = noise_gain + self.fmap_start_index = fmap_start_index + + if fmap_start_index >= len(layers): + raise ValueError(f'fmap_start_index is larger than number of layers') + + # filter bank for noise shaping + n_fft = resolution[0] + + self.filterbank = nn.Parameter( + gen_filterbank(n_fft // 2, fs, keep_size=True), + requires_grad=False + ) + + # roi bins + f_step = fs / n_fft + self.start_bin = int(m.ceil(freq_roi[0] / f_step - 0.01)) + self.stop_bin = min(int(m.floor(freq_roi[1] / f_step + 0.01)), n_fft//2 + 1) + + self.init_weights() + + # determine receptive field size, offsets and strides + + hw = 1000 + while True: + x = torch.zeros((1, hw, hw)) + with torch.no_grad(): + y = self.run_layer_stack(x)[-1] + + pos0 = [y.size(-2) // 2, y.size(-1) // 2] + pos1 = [t + 1 for t in pos0] + + hs0, ws0 = self._receptive_field((hw, hw), pos0) + hs1, ws1 = self._receptive_field((hw, hw), pos1) + + h0 = hs0[1] - hs0[0] + 1 + h1 = hs1[1] - hs1[0] + 1 + w0 = ws0[1] - ws0[0] + 1 + w1 = ws1[1] - ws1[0] + 1 + + if h0 != h1 or w0 != w1: + hw = 2 * hw + else: + + # strides + sh = hs1[0] - hs0[0] + sw = ws1[0] - ws0[0] + + if sh == 0 or sw == 0: continue + + # offsets + oh = hs0[0] - sh * pos0[0] + ow = ws0[0] - sw * pos0[1] + + # overlap factor + overlap = w0 / sw + h0 / sh + + #print(f"{w0=} {h0=} {sw=} {sh=} {overlap=}") + self.receptive_field_params = {'width': [sw, ow, w0], 'height': [sh, oh, h0], 'overlap': overlap} + + break + + if hw > self.RECEPTIVE_FIELD_MAX_WIDTH: + print("warning: exceeded max size while trying to determine receptive field") + + # create transposed convolutional kernel + #self.tconv_kernel = nn.Parameter(create_kernel(h0, w0, sw, sw), requires_grad=False) + + def run_layer_stack(self, spec): + + output = [] + + x = spec.unsqueeze(1) + + for layer in self.layers: + x = layer(x) + output.append(x) + + return output + + def forward(self, x): + """ returns array with feature maps and final score at index -1 """ + + output = [] + + x = self.spectrogram(x) + + output = self.run_layer_stack(x) + + return output[self.fmap_start_index:] + + def receptive_field(self, output_pos): + + if self.receptive_field_params is not None: + s, o, h = self.receptive_field_params['height'] + h_min = output_pos[0] * s + o + self.start_bin + h_max = h_min + h + h_min = max(h_min, self.start_bin) + h_max = min(h_max, self.stop_bin) + + s, o, w = self.receptive_field_params['width'] + w_min = output_pos[1] * s + o + w_max = w_min + w + + return (h_min, h_max), (w_min, w_max) + + else: + return None, None + + + def _receptive_field(self, input_dims, output_pos): + """ determines receptive field probabilistically via autograd (slow) """ + + x = torch.randn((1,) + input_dims, requires_grad=True) + + # run input through layers + y = self.run_layer_stack(x)[-1] + b, c, h, w = y.shape + + if output_pos[0] >= h or output_pos[1] >= w: + raise ValueError("position out of range") + + mask = torch.zeros((b, c, h, w)) + mask[0, 0, output_pos[0], output_pos[1]] = 1 + + (mask * y).sum().backward() + + hs, ws = torch.nonzero(x.grad[0], as_tuple=True) + + h_min, h_max = hs.min().item(), hs.max().item() + w_min, w_max = ws.min().item(), ws.max().item() + + return [h_min, h_max], [w_min, w_max] + + + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + + def spectrogram(self, x): + n_fft, hop_length, win_length = self.resolution + x = x.squeeze(1) + window = getattr(torch, 'hann_window')(win_length).to(x.device) + + x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length,\ + window=window, return_complex=True) #[B, F, T] + x = torch.abs(x) + + # noise floor following spectral envelope + smoothed_x = torch.matmul(self.filterbank, x) + noise = torch.randn_like(x) * smoothed_x * self.noise_gain + x = x + noise + + # frequency ROI + x = x[:, self.start_bin : self.stop_bin + 1, ...] + + return torchaudio.functional.amplitude_to_DB(x,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80)#torch.sqrt(x) + + def grad_map(self, x): + self.zero_grad() + + n_fft, hop_length, win_length = self.resolution + + window = getattr(torch, 'hann_window')(win_length).to(x.device) + + y = torch.stft(x.squeeze(1), n_fft=n_fft, hop_length=hop_length, win_length=win_length, + window=window, return_complex=True) #[B, F, T] + y = torch.abs(y) + + specgram = torchaudio.functional.amplitude_to_DB(y,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) + + specgram.requires_grad = True + specgram.retain_grad() + + if specgram.grad is not None: + specgram.grad.zero_() + + y = specgram[:, self.start_bin : self.stop_bin + 1, ...] + + scores = self.run_layer_stack(y)[-1] + + loss = torch.mean((1 - scores) ** 2) + loss.backward() + + return specgram.data[0], torch.abs(specgram.grad)[0] + + def relevance_map(self, x): + + n_fft, hop_length, win_length = self.resolution + y = x.view(-1) + window = getattr(torch, 'hann_window')(win_length).to(x.device) + + y = torch.stft(y, n_fft=n_fft, hop_length=hop_length, win_length=win_length,\ + window=window, return_complex=True) #[B, F, T] + y = torch.abs(y) + + specgram = torchaudio.functional.amplitude_to_DB(y,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) + + + scores = self.forward(x)[-1] + + sh, _, h = self.receptive_field_params['height'] + sw, _, w = self.receptive_field_params['width'] + kernel = create_kernel(h, w, sh, sw).float().to(scores.device) + with torch.no_grad(): + pad_w = (w + sw - 1) // sw + pad_h = (h + sh - 1) // sh + padded_scores = F.pad(scores, (pad_w, pad_w, pad_h, pad_h), mode='replicate') + # CAVE: padding should be derived from offsets + rv = F.conv_transpose2d(padded_scores, kernel, bias=None, stride=(sh, sw), padding=(h//2, w//2)) + rv = rv[..., pad_h * sh : - pad_h * sh, pad_w * sw : -pad_w * sw] + + relevance = torch.zeros_like(specgram) + relevance[..., self.start_bin : self.start_bin + rv.size(-2), : rv.size(-1)] = rv + + + return specgram, relevance + + + def lrp(self, x, eps=1e-9, label='both', threshold=0.5, low=None, high=None, verbose=False): + """ layer-wise relevance propagation (https://git.tu-berlin.de/gmontavon/lrp-tutorial) """ + + # ToDo: this code is highly unsafe as it assumes that layers are nn.Sequential with suitable activations + + def newconv2d(layer,g): + + new_layer = nn.Conv2d(layer.in_channels, + layer.out_channels, + layer.kernel_size, + stride=layer.stride, + padding=layer.padding, + dilation=layer.dilation, + groups=layer.groups) + + try: new_layer.weight = nn.Parameter(g(layer.weight.data.clone())) + except AttributeError: pass + + try: new_layer.bias = nn.Parameter(g(layer.bias.data.clone())) + except AttributeError: pass + + return new_layer + + bounds = { + 64: [-85.82449722290039, 2.1755014657974243], + 128: [-84.49211349487305, 3.5078893899917607], + 256: [-80.33127822875977, 7.6687201976776125], + 512: [-73.79328079223633, 14.20672025680542], + 1024: [-67.59239501953125, 20.40760498046875], + 2048: [-62.31902580261231, 25.680974197387698], + } + + nfft = self.resolution[0] + if low is None: low = bounds[nfft][0] + if high is None: high = bounds[nfft][1] + + remove_all_weight_norms(self) + + for p in self.parameters(): + if p.grad is not None: + p.grad.zero_() + + num_layers = len(self.layers) + X = self.spectrogram(x). detach() + + + # forward pass + A = [X.unsqueeze(1)] + [None] * len(self.layers) + + for i in range(num_layers - 1): + A[i + 1] = self.layers[i](A[i]) + + # initial relevance is last layer without activation + r = A[-2] + last_layer_rs = [r] + layer = self.layers[-1] + for sublayer in list(layer)[:-1]: + r = sublayer(r) + last_layer_rs.append(r) + + + mask = torch.zeros_like(r) + mask.requires_grad_(False) + if verbose: + print(r.min(), r.max()) + if label in {'both', 'fake'}: + mask[r < -threshold] = 1 + if label in {'both', 'real'}: + mask[r > threshold] = 1 + r = r * mask + + # backward pass + R = [None] * num_layers + [r] + + for l in range(1, num_layers)[::-1]: + A[l] = (A[l]).data.requires_grad_(True) + + layer = nn.Sequential(*(list(self.layers[l])[:-1])) + z = layer(A[l]) + eps + s = (R[l+1] / z).data + (z*s).sum().backward() + c = A[l].grad + R[l] = (A[l] * c).data + + # first layer + A[0] = (A[0].data).requires_grad_(True) + + Xl = (torch.zeros_like(A[0].data) + low).requires_grad_(True) + Xh = (torch.zeros_like(A[0].data) + high).requires_grad_(True) + + if len(list(self.layers)) > 2: + # unsafe way to check for embedding layer + embed = list(self.layers[0])[0] + conv = list(self.layers[0])[1] + + layer = nn.Sequential(embed, conv) + layerl = nn.Sequential(embed, newconv2d(conv, lambda p: p.clamp(min=0))) + layerh = nn.Sequential(embed, newconv2d(conv, lambda p: p.clamp(max=0))) + + else: + layer = list(self.layers[0])[0] + layerl = newconv2d(layer, lambda p: p.clamp(min=0)) + layerh = newconv2d(layer, lambda p: p.clamp(max=0)) + + + z = layer(A[0]) + z -= layerl(Xl) + layerh(Xh) + s = (R[1] / z).data + (z * s).sum().backward() + c, cp, cm = A[0].grad, Xl.grad, Xh.grad + + R[0] = (A[0] * c + Xl * cp + Xh * cm) + #R[0] = (A[0] * c).data + + return X, R[0].mean(dim=1) + + + + + + + + + + +def create_3x3_conv_plan(num_layers : int, + f_stretch : int, + f_down : int, + t_stretch : int, + t_down : int + ): + + + """ creates a stride, dilation, padding plan for a 2d conv network + + Args: + num_layers (int): number of layers + f_stretch (int): log_2 of stretching factor along frequency axis + f_down (int): log_2 of downsampling factor along frequency axis + t_stretch (int): log_2 of stretching factor along time axis + t_down (int): log_2 of downsampling factor along time axis + + Returns: + list(list(tuple)): list containing entries [(stride_t, stride_f), (dilation_t, dilation_f), (padding_t, padding_f)] + """ + + assert num_layers > 0 and t_stretch >= 0 and t_down >= 0 and f_stretch >= 0 and f_down >= 0 + assert f_stretch < num_layers and t_stretch < num_layers + + def process_dimension(n_layers, stretch, down): + + stack_layers = n_layers - 1 + + stride_layers = min(min(down, stretch) , stack_layers) + dilation_layers = max(min(stack_layers - stride_layers - 1, stretch - stride_layers), 0) + final_stride = 2 ** (max(down - stride_layers, 0)) + + final_dilation = 1 + if stride_layers < stack_layers and stretch - stride_layers - dilation_layers > 0: + final_dilation = 2 + + strides, dilations, paddings = [], [], [] + processed_layers = 0 + current_dilation = 1 + + for _ in range(stride_layers): + # increase receptive field and downsample via stride = 2 + strides.append(2) + dilations.append(1) + paddings.append(1) + processed_layers += 1 + + if processed_layers < stack_layers: + strides.append(1) + dilations.append(1) + paddings.append(1) + processed_layers += 1 + + for _ in range(dilation_layers): + # increase receptive field via dilation = 2 + strides.append(1) + current_dilation *= 2 + dilations.append(current_dilation) + paddings.append(current_dilation) + processed_layers += 1 + + while processed_layers < n_layers - 1: + # fill up with std layers + strides.append(1) + dilations.append(current_dilation) + paddings.append(current_dilation) + processed_layers += 1 + + # final layer + strides.append(final_stride) + current_dilation * final_dilation + dilations.append(current_dilation) + paddings.append(current_dilation) + processed_layers += 1 + + assert processed_layers == n_layers + + return strides, dilations, paddings + + t_strides, t_dilations, t_paddings = process_dimension(num_layers, t_stretch, t_down) + f_strides, f_dilations, f_paddings = process_dimension(num_layers, f_stretch, f_down) + + plan = [] + + for i in range(num_layers): + plan.append([ + (f_strides[i], t_strides[i]), + (f_dilations[i], t_dilations[i]), + (f_paddings[i], t_paddings[i]), + ]) + + return plan + + +class DiscriminatorExperimental(SpecDiscriminatorBase): + + def __init__(self, + resolution, + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + num_channels=16, + max_channels=512, + num_layers=5, + use_spectral_norm=False): + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + + self.num_channels = num_channels + self.num_channels_max = max_channels + self.num_layers = num_layers + + layers = [] + stride = (2, 1) + padding= (1, 1) + in_channels = 1 + 2 + out_channels = self.num_channels + for _ in range(self.num_layers): + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), + nn.ReLU(inplace=True) + ) + ) + in_channels = out_channels + 2 + out_channels = min(2 * out_channels, self.num_channels_max) + + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)), + nn.Sigmoid() + ) + ) + + super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) + + # bias biases + bias_val = 0.1 + with torch.no_grad(): + for name, weight in self.named_parameters(): + if 'bias' in name: + weight = weight + bias_val + + +configs = { + 'f_down': { + 'stretch' : { + 64 : (0, 0), + 128: (1, 0), + 256: (2, 0), + 512: (3, 0), + 1024: (4, 0), + 2048: (5, 0) + }, + 'down' : { + 64 : (0, 0), + 128: (1, 0), + 256: (2, 0), + 512: (3, 0), + 1024: (4, 0), + 2048: (5, 0) + } + }, + 'ft_down': { + 'stretch' : { + 64 : (0, 4), + 128: (1, 3), + 256: (2, 2), + 512: (3, 1), + 1024: (4, 0), + 2048: (5, 0) + }, + 'down' : { + 64 : (0, 4), + 128: (1, 3), + 256: (2, 2), + 512: (3, 1), + 1024: (4, 0), + 2048: (5, 0) + } + }, + 'dilated': { + 'stretch' : { + 64 : (0, 4), + 128: (1, 3), + 256: (2, 2), + 512: (3, 1), + 1024: (4, 0), + 2048: (5, 0) + }, + 'down' : { + 64 : (0, 0), + 128: (0, 0), + 256: (0, 0), + 512: (0, 0), + 1024: (0, 0), + 2048: (0, 0) + } + }, + 'mixed': { + 'stretch' : { + 64 : (0, 4), + 128: (1, 3), + 256: (2, 2), + 512: (3, 1), + 1024: (4, 0), + 2048: (5, 0) + }, + 'down' : { + 64 : (0, 0), + 128: (1, 0), + 256: (2, 0), + 512: (3, 0), + 1024: (4, 0), + 2048: (5, 0) + } + }, +} + + +class DiscriminatorMagFree(SpecDiscriminatorBase): + + def __init__(self, + resolution, + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + num_channels=16, + max_channels=256, + num_layers=5, + use_spectral_norm=False, + design=None): + + if design is None: + raise ValueError('error: arch required in DiscriminatorMagFree') + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + + stretch = configs[design]['stretch'][resolution[0]] + down = configs[design]['down'][resolution[0]] + + self.num_channels = num_channels + self.num_channels_max = max_channels + self.num_layers = num_layers + self.stretch = stretch + self.down = down + + layers = [] + plan = create_3x3_conv_plan(num_layers + 1, stretch[0], down[0], stretch[1], down[1]) + in_channels = 1 + 2 + out_channels = self.num_channels + for i in range(self.num_layers): + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=plan[i][0], dilation=plan[i][1], padding=plan[i][2])), + nn.ReLU(inplace=True) + ) + ) + in_channels = out_channels + 2 + # product over strides + channel_factor = plan[i][0][0] * plan[i][0][1] + out_channels = min(channel_factor * out_channels, self.num_channels_max) + + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, 1, (3, 3), stride=plan[-1][0], dilation=plan[-1][1], padding=plan[-1][2])), + nn.Sigmoid() + ) + ) + + + + # for layer in layers: + # print(layer) + + # print("end\n\n") + + super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) + + # bias biases + bias_val = 0.1 + with torch.no_grad(): + for name, weight in self.named_parameters(): + if 'bias' in name: + weight = weight + bias_val + +class DiscriminatorMagFreqPosition(SpecDiscriminatorBase): + + def __init__(self, + resolution, + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + num_channels=16, + max_channels=512, + num_layers=5, + use_spectral_norm=False): + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + + self.num_channels = num_channels + self.num_channels_max = max_channels + self.num_layers = num_layers + + layers = [] + stride = (2, 1) + padding= (1, 1) + in_channels = 1 + 2 + out_channels = self.num_channels + for _ in range(self.num_layers): + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), + nn.LeakyReLU(0.2, inplace=True) + ) + ) + in_channels = out_channels + 2 + out_channels = min(2 * out_channels, self.num_channels_max) + + layers.append( + nn.Sequential( + FrequencyPositionalEmbedding(), + norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)) + ) + ) + + super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) + + + +class DiscriminatorMag2dPositional(SpecDiscriminatorBase): + + def __init__(self, + resolution, + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + num_channels=16, + max_channels=512, + num_layers=5, + d=5, + use_spectral_norm=False): + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.resolution = resolution + self.num_channels = num_channels + self.num_channels_max = max_channels + self.num_layers = num_layers + self.d = d + embedding_dim = 4 * d + + + layers = [] + stride = (2, 2) + padding= (1, 1) + in_channels = 1 + embedding_dim + out_channels = self.num_channels + for _ in range(self.num_layers): + layers.append( + nn.Sequential( + PositionalEmbedding2D(d), + norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), + nn.LeakyReLU(0.2, inplace=True) + ) + ) + in_channels = out_channels + embedding_dim + out_channels = min(2 * out_channels, self.num_channels_max) + + + layers.append( + nn.Sequential( + PositionalEmbedding2D(), + norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)) + ) + ) + + super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) + + + +class DiscriminatorMag(SpecDiscriminatorBase): + def __init__(self, + resolution, + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + num_channels=32, + num_layers=5, + use_spectral_norm=False): + + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + + self.num_channels = num_channels + self.num_layers = num_layers + + layers = [] + stride = (1, 1) + padding= (1, 1) + in_channels = 1 + out_channels = self.num_channels + for _ in range(self.num_layers): + layers.append( + nn.Sequential( + norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), + nn.LeakyReLU(0.2, inplace=True) + ) + ) + in_channels = out_channels + + layers.append(norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding))) + + super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) + + +discriminators = { + 'mag': DiscriminatorMag, + 'freqpos': DiscriminatorMagFreqPosition, + '2dpos': DiscriminatorMag2dPositional, + 'experimental': DiscriminatorExperimental, + 'free': DiscriminatorMagFree +} + +class TFDMultiResolutionDiscriminator(torch.nn.Module): + def __init__(self, + fft_sizes_16k=[64, 128, 256, 512, 1024, 2048], + architecture='mag', + fs=16000, + freq_roi=[50, 7400], + noise_gain=0, + use_spectral_norm=False, + **kwargs): + + super().__init__() + + + fft_sizes = [int(round(fft_size_16k * fs / 16000)) for fft_size_16k in fft_sizes_16k] + + resolutions = [[n_fft, n_fft // 4, n_fft] for n_fft in fft_sizes] + + + Disc = discriminators[architecture] + + discs = [Disc(resolutions[i], fs=fs, freq_roi=freq_roi, noise_gain=noise_gain, use_spectral_norm=use_spectral_norm, **kwargs) for i in range(len(resolutions))] + + self.discriminators = nn.ModuleList(discs) + + def forward(self, y): + outputs = [] + + for disc in self.discriminators: + outputs.append(disc(y)) + + return outputs + + +class FWGAN_disc_wrapper(nn.Module): + def __init__(self, disc): + super().__init__() + + self.disc = disc + + def forward(self, y, y_hat): + + out_real = self.disc(y) + out_fake = self.disc(y_hat) + + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + + for y_real, y_fake in zip(out_real, out_fake): + y_d_rs.append(y_real[-1]) + y_d_gs.append(y_fake[-1]) + fmap_rs.append(y_real[:-1]) + fmap_gs.append(y_fake[:-1]) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/lace.py b/managed_components/78__esp-opus/dnn/torch/osce/models/lace.py new file mode 100644 index 0000000..51d65c3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/lace.py @@ -0,0 +1,190 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +import numpy as np + +from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d + +from models.nns_base import NNSBase +from models.silk_feature_net_pl import SilkFeatureNetPL +from models.silk_feature_net import SilkFeatureNet +from .scale_embedding import ScaleEmbedding + +import sys +sys.path.append('../dnntools') + +from dnntools.sparsification import create_sparsifier + + +class LACE(NNSBase): + """ Linear-Adaptive Coding Enhancer """ + FRAME_SIZE=80 + + def __init__(self, + num_features=47, + pitch_embedding_dim=64, + cond_dim=256, + pitch_max=257, + kernel_size=15, + preemph=0.85, + skip=91, + comb_gain_limit_db=-6, + global_gain_limits_db=[-6, 6], + conv_gain_limits_db=[-6, 6], + numbits_range=[50, 650], + numbits_embedding_dim=8, + hidden_feature_dim=64, + partial_lookahead=True, + norm_p=2, + softquant=False, + sparsify=False, + sparsification_schedule=[10000, 30000, 100], + sparsification_density=0.5, + apply_weight_norm=False): + + super().__init__(skip=skip, preemph=preemph) + + + self.num_features = num_features + self.cond_dim = cond_dim + self.pitch_max = pitch_max + self.pitch_embedding_dim = pitch_embedding_dim + self.kernel_size = kernel_size + self.preemph = preemph + self.skip = skip + self.numbits_range = numbits_range + self.numbits_embedding_dim = numbits_embedding_dim + self.hidden_feature_dim = hidden_feature_dim + self.partial_lookahead = partial_lookahead + + # pitch embedding + self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) + + # numbits embedding + self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) + + # feature net + if partial_lookahead: + self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm) + else: + self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) + + # comb filters + left_pad = self.kernel_size // 2 + right_pad = self.kernel_size - 1 - left_pad + self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + + # spectral shaping + self.af1 = LimitedAdaptiveConv1d(1, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + + if sparsify: + self.sparsifier = create_sparsifier(self, *sparsification_schedule) + + def flop_count(self, rate=16000, verbose=False): + + frame_rate = rate / self.FRAME_SIZE + + # feature net + feature_net_flops = self.feature_net.flop_count(frame_rate) + comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) + af_flops = self.af1.flop_count(rate) + + if verbose: + print(f"feature net: {feature_net_flops / 1e6} MFLOPS") + print(f"comb filters: {comb_flops / 1e6} MFLOPS") + print(f"adaptive conv: {af_flops / 1e6} MFLOPS") + + return feature_net_flops + comb_flops + af_flops + + def forward(self, x, features, periods, numbits, debug=False): + + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + numbits_embedding = self.numbits_embedding(numbits).flatten(2) + + full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) + cf = self.feature_net(full_features) + + y = self.cf1(x, cf, periods, debug=debug) + + y = self.cf2(y, cf, periods, debug=debug) + + y = self.af1(y, cf, debug=debug) + + return y + + def get_impulse_responses(self, features, periods, numbits): + """ generates impoulse responses on frame centers (input without batch dimension) """ + + num_frames = features.size(0) + batch_size = 32 + max_len = 2 * (self.pitch_max + self.kernel_size) + 10 + + # spread out some pulses + x = np.zeros((batch_size, 1, num_frames * self.FRAME_SIZE)) + for b in range(batch_size): + x[b, :, self.FRAME_SIZE // 2 + b * self.FRAME_SIZE :: batch_size * self.FRAME_SIZE] = 1 + + # prepare input + x = torch.from_numpy(x).float().to(features.device) + features = torch.repeat_interleave(features.unsqueeze(0), batch_size, 0) + periods = torch.repeat_interleave(periods.unsqueeze(0), batch_size, 0) + numbits = torch.repeat_interleave(numbits.unsqueeze(0), batch_size, 0) + + # run network + with torch.no_grad(): + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + numbits_embedding = self.numbits_embedding(numbits).flatten(2) + full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) + cf = self.feature_net(full_features) + y = self.cf1(x, cf, periods, debug=False) + y = self.cf2(y, cf, periods, debug=False) + y = self.af1(y, cf, debug=False) + + # collect responses + y = y.detach().squeeze().cpu().numpy() + cut_frames = (max_len + self.FRAME_SIZE - 1) // self.FRAME_SIZE + num_responses = num_frames - cut_frames + responses = np.zeros((num_responses, max_len)) + + for i in range(num_responses): + b = i % batch_size + start = self.FRAME_SIZE // 2 + i * self.FRAME_SIZE + stop = start + max_len + + responses[i, :] = y[b, start:stop] + + return responses diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce.py b/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce.py new file mode 100644 index 0000000..fcfdc8b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce.py @@ -0,0 +1,274 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import torch +from torch import nn +import torch.nn.functional as F + +import numpy as np + +from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d +from utils.layers.td_shaper import TDShaper +from utils.layers.noise_shaper import NoiseShaper +from utils.complexity import _conv1d_flop_count +from utils.endoscopy import write_data + +from models.nns_base import NNSBase +from models.lpcnet_feature_net import LPCNetFeatureNet +from .scale_embedding import ScaleEmbedding + +def print_channels(y, prefix="", name="", rate=16000): + num_channels = y.size(1) + for i in range(num_channels): + channel_name = f"{prefix}_c{i:02d}" + if len(name) > 0: channel_name += "_" + name + ch = y[0,i,:].detach().cpu().numpy() + ch = ((2**14) * ch / np.max(ch)).astype(np.int16) + write_data(channel_name, ch, rate) + + + +class LaVoce(nn.Module): + """ Linear-Adaptive VOCodEr """ + FEATURE_FRAME_SIZE=160 + FRAME_SIZE=80 + + def __init__(self, + num_features=20, + pitch_embedding_dim=64, + cond_dim=256, + pitch_max=300, + kernel_size=15, + preemph=0.85, + comb_gain_limit_db=-6, + global_gain_limits_db=[-6, 6], + conv_gain_limits_db=[-6, 6], + norm_p=2, + avg_pool_k=4, + pulses=False, + innovate1=True, + innovate2=False, + innovate3=False, + ftrans_k=2): + + super().__init__() + + + self.num_features = num_features + self.cond_dim = cond_dim + self.pitch_max = pitch_max + self.pitch_embedding_dim = pitch_embedding_dim + self.kernel_size = kernel_size + self.preemph = preemph + self.pulses = pulses + self.ftrans_k = ftrans_k + + assert self.FEATURE_FRAME_SIZE % self.FRAME_SIZE == 0 + self.upsamp_factor = self.FEATURE_FRAME_SIZE // self.FRAME_SIZE + + # pitch embedding + self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) + + # feature net + self.feature_net = LPCNetFeatureNet(num_features + pitch_embedding_dim, cond_dim, self.upsamp_factor) + + # noise shaper + self.noise_shaper = NoiseShaper(cond_dim, self.FRAME_SIZE) + + # comb filters + left_pad = self.kernel_size // 2 + right_pad = self.kernel_size - 1 - left_pad + self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) + self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) + + + self.af_prescale = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af_mix = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # spectral shaping + self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # non-linear transforms + self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate1) + self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate2) + self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate3) + + # combinators + self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # feature transforms + self.post_cf1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) + self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) + self.post_af1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) + self.post_af2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) + self.post_af3 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) + + + def create_phase_signals(self, periods): + + batch_size = periods.size(0) + progression = torch.arange(1, self.FRAME_SIZE + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) + progression = torch.repeat_interleave(progression, batch_size, 0) + + phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) + chunks = [] + for sframe in range(periods.size(1)): + f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) + + if self.pulses: + alpha = torch.cos(f).view(batch_size, 1, 1) + chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + pulse_a = torch.relu(chunk_sin - alpha) / (1 - alpha) + pulse_b = torch.relu(-chunk_sin - alpha) / (1 - alpha) + + chunk = torch.cat((pulse_a, pulse_b), dim = 1) + else: + chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + chunk_cos = torch.cos(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + + chunk = torch.cat((chunk_sin, chunk_cos), dim = 1) + + phase0 = phase0 + self.FRAME_SIZE * f + + chunks.append(chunk) + + phase_signals = torch.cat(chunks, dim=-1) + + return phase_signals + + def flop_count(self, rate=16000, verbose=False): + + frame_rate = rate / self.FRAME_SIZE + + # feature net + feature_net_flops = self.feature_net.flop_count(frame_rate) + comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) + af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) + self.af_prescale.flop_count(rate) + self.af_mix.flop_count(rate) + feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) + + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) + + if verbose: + print(f"feature net: {feature_net_flops / 1e6} MFLOPS") + print(f"comb filters: {comb_flops / 1e6} MFLOPS") + print(f"adaptive conv: {af_flops / 1e6} MFLOPS") + print(f"feature transforms: {feature_flops / 1e6} MFLOPS") + + return feature_net_flops + comb_flops + af_flops + feature_flops + + def feature_transform(self, f, layer): + f = f.permute(0, 2, 1) + f = F.pad(f, [self.ftrans_k - 1, 0]) + f = torch.tanh(layer(f)) + return f.permute(0, 2, 1) + + def forward(self, features, periods, debug=False): + + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + + full_features = torch.cat((features, pitch_embedding), dim=-1) + cf = self.feature_net(full_features) + + # upsample periods + periods = torch.repeat_interleave(periods, self.upsamp_factor, 1) + + # pre-net + ref_phase = torch.tanh(self.create_phase_signals(periods)) + if debug: print_channels(ref_phase, prefix="lavoce_01", name="pulse") + x = self.af_prescale(ref_phase, cf) + noise = self.noise_shaper(cf) + if debug: print_channels(torch.cat((x, noise), dim=1), prefix="lavoce_02", name="inputs") + y = self.af_mix(torch.cat((x, noise), dim=1), cf) + if debug: print_channels(y, prefix="lavoce_03", name="postselect1") + + # temporal shaping + innovating + y1 = y[:, 0:1, :] + y2 = self.tdshape1(y[:, 1:2, :], cf) + if debug: print_channels(y2, prefix="lavoce_04", name="postshape1") + y = torch.cat((y1, y2), dim=1) + y = self.af2(y, cf, debug=debug) + if debug: print_channels(y, prefix="lavoce_05", name="postselect2") + cf = self.feature_transform(cf, self.post_af2) + + y1 = y[:, 0:1, :] + y2 = self.tdshape2(y[:, 1:2, :], cf) + if debug: print_channels(y2, prefix="lavoce_06", name="postshape2") + y = torch.cat((y1, y2), dim=1) + y = self.af3(y, cf, debug=debug) + if debug: print_channels(y, prefix="lavoce_07", name="postmix1") + cf = self.feature_transform(cf, self.post_af3) + + # spectral shaping + y = self.cf1(y, cf, periods, debug=debug) + if debug: print_channels(y, prefix="lavoce_08", name="postcomb1") + cf = self.feature_transform(cf, self.post_cf1) + + y = self.cf2(y, cf, periods, debug=debug) + if debug: print_channels(y, prefix="lavoce_09", name="postcomb2") + cf = self.feature_transform(cf, self.post_cf2) + + y = self.af1(y, cf, debug=debug) + if debug: print_channels(y, prefix="lavoce_10", name="postselect3") + cf = self.feature_transform(cf, self.post_af1) + + # final temporal env adjustment + y1 = y[:, 0:1, :] + y2 = self.tdshape3(y[:, 1:2, :], cf) + if debug: print_channels(y2, prefix="lavoce_11", name="postshape3") + y = torch.cat((y1, y2), dim=1) + y = self.af4(y, cf, debug=debug) + if debug: print_channels(y, prefix="lavoce_12", name="postmix2") + + return y + + def process(self, features, periods, debug=False): + + self.eval() + device = next(iter(self.parameters())).device + with torch.no_grad(): + + # run model + f = features.unsqueeze(0).to(device) + p = periods.unsqueeze(0).to(device) + + y = self.forward(f, p, debug=debug).squeeze() + + # deemphasis + if self.preemph > 0: + for i in range(len(y) - 1): + y[i + 1] += self.preemph * y[i] + + # clip to valid range + out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() + + return out \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce_400.py b/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce_400.py new file mode 100644 index 0000000..fe8263b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/lavoce_400.py @@ -0,0 +1,254 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import torch +from torch import nn +import torch.nn.functional as F + +import numpy as np + +from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d +from utils.layers.td_shaper import TDShaper +from utils.layers.noise_shaper import NoiseShaper +from utils.complexity import _conv1d_flop_count +from utils.endoscopy import write_data + +from models.nns_base import NNSBase +from models.lpcnet_feature_net import LPCNetFeatureNet +from .scale_embedding import ScaleEmbedding + +class LaVoce400(nn.Module): + """ Linear-Adaptive VOCodEr """ + FEATURE_FRAME_SIZE=160 + FRAME_SIZE=40 + + def __init__(self, + num_features=20, + pitch_embedding_dim=64, + cond_dim=256, + pitch_max=300, + kernel_size=15, + preemph=0.85, + comb_gain_limit_db=-6, + global_gain_limits_db=[-6, 6], + conv_gain_limits_db=[-6, 6], + norm_p=2, + avg_pool_k=4, + pulses=False): + + super().__init__() + + + self.num_features = num_features + self.cond_dim = cond_dim + self.pitch_max = pitch_max + self.pitch_embedding_dim = pitch_embedding_dim + self.kernel_size = kernel_size + self.preemph = preemph + self.pulses = pulses + + assert self.FEATURE_FRAME_SIZE % self.FRAME_SIZE == 0 + self.upsamp_factor = self.FEATURE_FRAME_SIZE // self.FRAME_SIZE + + # pitch embedding + self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) + + # feature net + self.feature_net = LPCNetFeatureNet(num_features + pitch_embedding_dim, cond_dim, self.upsamp_factor) + + # noise shaper + self.noise_shaper = NoiseShaper(cond_dim, self.FRAME_SIZE) + + # comb filters + left_pad = self.kernel_size // 2 + right_pad = self.kernel_size - 1 - left_pad + self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=20, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) + self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=20, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) + + + self.af_prescale = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af_mix = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # spectral shaping + self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # non-linear transforms + self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=True) + self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k) + self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k) + + # combinators + self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + # feature transforms + self.post_cf1 = nn.Conv1d(cond_dim, cond_dim, 2) + self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, 2) + self.post_af1 = nn.Conv1d(cond_dim, cond_dim, 2) + self.post_af2 = nn.Conv1d(cond_dim, cond_dim, 2) + self.post_af3 = nn.Conv1d(cond_dim, cond_dim, 2) + + + def create_phase_signals(self, periods): + + batch_size = periods.size(0) + progression = torch.arange(1, self.FRAME_SIZE + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) + progression = torch.repeat_interleave(progression, batch_size, 0) + + phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) + chunks = [] + for sframe in range(periods.size(1)): + f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) + + if self.pulses: + alpha = torch.cos(f).view(batch_size, 1, 1) + chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + pulse_a = torch.relu(chunk_sin - alpha) / (1 - alpha) + pulse_b = torch.relu(-chunk_sin - alpha) / (1 - alpha) + + chunk = torch.cat((pulse_a, pulse_b), dim = 1) + else: + chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + chunk_cos = torch.cos(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) + + chunk = torch.cat((chunk_sin, chunk_cos), dim = 1) + + phase0 = phase0 + self.FRAME_SIZE * f + + chunks.append(chunk) + + phase_signals = torch.cat(chunks, dim=-1) + + return phase_signals + + def flop_count(self, rate=16000, verbose=False): + + frame_rate = rate / self.FRAME_SIZE + + # feature net + feature_net_flops = self.feature_net.flop_count(frame_rate) + comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) + af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) + self.af_prescale.flop_count(rate) + self.af_mix.flop_count(rate) + feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) + + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) + + if verbose: + print(f"feature net: {feature_net_flops / 1e6} MFLOPS") + print(f"comb filters: {comb_flops / 1e6} MFLOPS") + print(f"adaptive conv: {af_flops / 1e6} MFLOPS") + print(f"feature transforms: {feature_flops / 1e6} MFLOPS") + + return feature_net_flops + comb_flops + af_flops + feature_flops + + def feature_transform(self, f, layer): + f = f.permute(0, 2, 1) + f = F.pad(f, [1, 0]) + f = torch.tanh(layer(f)) + return f.permute(0, 2, 1) + + def forward(self, features, periods, debug=False): + + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + + full_features = torch.cat((features, pitch_embedding), dim=-1) + cf = self.feature_net(full_features) + + # upsample periods + periods = torch.repeat_interleave(periods, self.upsamp_factor, 1) + + # pre-net + ref_phase = torch.tanh(self.create_phase_signals(periods)) + x = self.af_prescale(ref_phase, cf) + noise = self.noise_shaper(cf) + y = self.af_mix(torch.cat((x, noise), dim=1), cf) + + if debug: + ch0 = y[0,0,:].detach().cpu().numpy() + ch1 = y[0,1,:].detach().cpu().numpy() + ch0 = (2**15 * ch0 / np.max(ch0)).astype(np.int16) + ch1 = (2**15 * ch1 / np.max(ch1)).astype(np.int16) + write_data('prior_channel0', ch0, 16000) + write_data('prior_channel1', ch1, 16000) + + # temporal shaping + innovating + y1 = y[:, 0:1, :] + y2 = self.tdshape1(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af2(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af2) + + y1 = y[:, 0:1, :] + y2 = self.tdshape2(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af3(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af3) + + # spectral shaping + y = self.cf1(y, cf, periods, debug=debug) + cf = self.feature_transform(cf, self.post_cf1) + + y = self.cf2(y, cf, periods, debug=debug) + cf = self.feature_transform(cf, self.post_cf2) + + y = self.af1(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af1) + + # final temporal env adjustment + y1 = y[:, 0:1, :] + y2 = self.tdshape3(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af4(y, cf, debug=debug) + + return y + + def process(self, features, periods, debug=False): + + self.eval() + device = next(iter(self.parameters())).device + with torch.no_grad(): + + # run model + f = features.unsqueeze(0).to(device) + p = periods.unsqueeze(0).to(device) + + y = self.forward(f, p, debug=debug).squeeze() + + # deemphasis + if self.preemph > 0: + for i in range(len(y) - 1): + y[i + 1] += self.preemph * y[i] + + # clip to valid range + out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() + + return out \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/lpcnet_feature_net.py b/managed_components/78__esp-opus/dnn/torch/osce/models/lpcnet_feature_net.py new file mode 100644 index 0000000..b637d74 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/lpcnet_feature_net.py @@ -0,0 +1,91 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +from utils.complexity import _conv1d_flop_count + +class LPCNetFeatureNet(nn.Module): + + def __init__(self, + feature_dim=84, + num_channels=256, + upsamp_factor=2, + lookahead=True): + + super().__init__() + + self.feature_dim = feature_dim + self.num_channels = num_channels + self.upsamp_factor = upsamp_factor + self.lookahead = lookahead + + self.conv1 = nn.Conv1d(feature_dim, num_channels, 3) + self.conv2 = nn.Conv1d(num_channels, num_channels, 3) + + self.gru = nn.GRU(num_channels, num_channels, batch_first=True) + + self.tconv = nn.ConvTranspose1d(num_channels, num_channels, upsamp_factor, upsamp_factor) + + def flop_count(self, rate=100): + count = 0 + for conv in self.conv1, self.conv2, self.tconv: + count += _conv1d_flop_count(conv, rate) + + count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate + + return count + + + def forward(self, features, state=None): + """ features shape: (batch_size, num_frames, feature_dim) """ + + batch_size = features.size(0) + + if state is None: + state = torch.zeros((1, batch_size, self.num_channels), device=features.device) + + + features = features.permute(0, 2, 1) + if self.lookahead: + c = torch.tanh(self.conv1(F.pad(features, [1, 1]))) + c = torch.tanh(self.conv2(F.pad(c, [2, 0]))) + else: + c = torch.tanh(self.conv1(F.pad(features, [2, 0]))) + c = torch.tanh(self.conv2(F.pad(c, [2, 0]))) + + c = torch.tanh(self.tconv(c)) + + c = c.permute(0, 2, 1) + + c, _ = self.gru(c, state) + + return c \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/nns_base.py b/managed_components/78__esp-opus/dnn/torch/osce/models/nns_base.py new file mode 100644 index 0000000..6e667b9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/nns_base.py @@ -0,0 +1,69 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +class NNSBase(nn.Module): + + def __init__(self, skip=91, preemph=0.85): + super().__init__() + + self.skip = skip + self.preemph = preemph + + def process(self, sig, features, periods, numbits, debug=False): + + self.eval() + has_numbits = 'numbits' in self.forward.__code__.co_varnames + device = next(iter(self.parameters())).device + with torch.no_grad(): + + # run model + x = sig.view(1, 1, -1).to(device) + f = features.unsqueeze(0).to(device) + p = periods.unsqueeze(0).to(device) + n = numbits.unsqueeze(0).to(device) + + if has_numbits: + y = self.forward(x, f, p, n, debug=debug).squeeze() + else: + y = self.forward(x, f, p, debug=debug).squeeze() + + # deemphasis + if self.preemph > 0: + for i in range(len(y) - 1): + y[i + 1] += self.preemph * y[i] + + # delay compensation + y = torch.cat((y[self.skip:], torch.zeros(self.skip, dtype=y.dtype, device=y.device))) + out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() + + return out \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/no_lace.py b/managed_components/78__esp-opus/dnn/torch/osce/models/no_lace.py new file mode 100644 index 0000000..78c3a30 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/no_lace.py @@ -0,0 +1,218 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numbers + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm + + +import numpy as np + +from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d +from utils.layers.td_shaper import TDShaper +from utils.complexity import _conv1d_flop_count + +from models.nns_base import NNSBase +from models.silk_feature_net_pl import SilkFeatureNetPL +from models.silk_feature_net import SilkFeatureNet +from .scale_embedding import ScaleEmbedding + +import sys +sys.path.append('../dnntools') +from dnntools.quantization import soft_quant +from dnntools.sparsification import create_sparsifier, mark_for_sparsification + +class NoLACE(NNSBase): + """ Non-Linear Adaptive Coding Enhancer """ + FRAME_SIZE=80 + + def __init__(self, + num_features=47, + pitch_embedding_dim=64, + cond_dim=256, + pitch_max=257, + kernel_size=15, + preemph=0.85, + skip=91, + comb_gain_limit_db=-6, + global_gain_limits_db=[-6, 6], + conv_gain_limits_db=[-6, 6], + numbits_range=[50, 650], + numbits_embedding_dim=8, + hidden_feature_dim=64, + partial_lookahead=True, + norm_p=2, + avg_pool_k=4, + pool_after=False, + softquant=False, + sparsify=False, + sparsification_schedule=[100, 1000, 100], + sparsification_density=0.5, + apply_weight_norm=False): + + super().__init__(skip=skip, preemph=preemph) + + self.num_features = num_features + self.cond_dim = cond_dim + self.pitch_max = pitch_max + self.pitch_embedding_dim = pitch_embedding_dim + self.kernel_size = kernel_size + self.preemph = preemph + self.skip = skip + self.numbits_range = numbits_range + self.numbits_embedding_dim = numbits_embedding_dim + self.hidden_feature_dim = hidden_feature_dim + self.partial_lookahead = partial_lookahead + + if isinstance(sparsification_density, numbers.Number): + sparsification_density = 10 * [sparsification_density] + + norm = weight_norm if apply_weight_norm else lambda x, name=None: x + + # pitch embedding + self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) + + # numbits embedding + self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) + + # feature net + if partial_lookahead: + self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm) + else: + self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) + + # comb filters + left_pad = self.kernel_size // 2 + right_pad = self.kernel_size - 1 - left_pad + self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + + # spectral shaping + self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + + # non-linear transforms + self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) + + # combinators + self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.af3 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) + + # feature transforms + self.post_cf1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) + self.post_cf2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) + self.post_af1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) + self.post_af2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) + self.post_af3 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) + + if softquant: + self.post_cf1 = soft_quant(self.post_cf1) + self.post_cf2 = soft_quant(self.post_cf2) + self.post_af1 = soft_quant(self.post_af1) + self.post_af2 = soft_quant(self.post_af2) + self.post_af3 = soft_quant(self.post_af3) + + + if sparsify: + mark_for_sparsification(self.post_cf1, (sparsification_density[4], [8, 4])) + mark_for_sparsification(self.post_cf2, (sparsification_density[5], [8, 4])) + mark_for_sparsification(self.post_af1, (sparsification_density[6], [8, 4])) + mark_for_sparsification(self.post_af2, (sparsification_density[7], [8, 4])) + mark_for_sparsification(self.post_af3, (sparsification_density[8], [8, 4])) + + self.sparsifier = create_sparsifier(self, *sparsification_schedule) + + def flop_count(self, rate=16000, verbose=False): + + frame_rate = rate / self.FRAME_SIZE + + # feature net + feature_net_flops = self.feature_net.flop_count(frame_rate) + comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) + af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) + shape_flops = self.tdshape1.flop_count(rate) + self.tdshape2.flop_count(rate) + self.tdshape3.flop_count(rate) + feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) + + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) + + if verbose: + print(f"feature net: {feature_net_flops / 1e6} MFLOPS") + print(f"comb filters: {comb_flops / 1e6} MFLOPS") + print(f"adaptive conv: {af_flops / 1e6} MFLOPS") + print(f"feature transforms: {feature_flops / 1e6} MFLOPS") + + return feature_net_flops + comb_flops + af_flops + feature_flops + shape_flops + + def feature_transform(self, f, layer): + f0 = f.permute(0, 2, 1) + f = F.pad(f0, [1, 0]) + f = torch.tanh(layer(f)) + return f.permute(0, 2, 1) + + def forward(self, x, features, periods, numbits, debug=False): + + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + numbits_embedding = self.numbits_embedding(numbits).flatten(2) + + full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) + cf = self.feature_net(full_features) + + y = self.cf1(x, cf, periods, debug=debug) + cf = self.feature_transform(cf, self.post_cf1) + + y = self.cf2(y, cf, periods, debug=debug) + cf = self.feature_transform(cf, self.post_cf2) + + y = self.af1(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af1) + + y1 = y[:, 0:1, :] + y2 = self.tdshape1(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af2(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af2) + + y1 = y[:, 0:1, :] + y2 = self.tdshape2(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af3(y, cf, debug=debug) + cf = self.feature_transform(cf, self.post_af3) + + y1 = y[:, 0:1, :] + y2 = self.tdshape3(y[:, 1:2, :], cf) + y = torch.cat((y1, y2), dim=1) + y = self.af4(y, cf, debug=debug) + + return y diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/scale_embedding.py b/managed_components/78__esp-opus/dnn/torch/osce/models/scale_embedding.py new file mode 100644 index 0000000..5869530 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/scale_embedding.py @@ -0,0 +1,68 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import math as m +import torch +from torch import nn + + +class ScaleEmbedding(nn.Module): + def __init__(self, + dim, + min_val, + max_val, + logscale=False): + + super().__init__() + + if min_val >= max_val: + raise ValueError('min_val must be smaller than max_val') + + if min_val <= 0 and logscale: + raise ValueError('min_val must be positive when logscale is true') + + self.dim = dim + self.logscale = logscale + self.min_val = min_val + self.max_val = max_val + + if logscale: + self.min_val = m.log(self.min_val) + self.max_val = m.log(self.max_val) + + + self.offset = (self.min_val + self.max_val) / 2 + self.scale_factors = nn.Parameter( + torch.arange(1, dim+1, dtype=torch.float32) * torch.pi / (self.max_val - self.min_val) + ) + + def forward(self, x): + if self.logscale: x = torch.log(x) + x = torch.clip(x, self.min_val, self.max_val) - self.offset + return torch.sin(x.unsqueeze(-1) * self.scale_factors - 0.5) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/shape_up_48.py b/managed_components/78__esp-opus/dnn/torch/osce/models/shape_up_48.py new file mode 100644 index 0000000..6e717f0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/shape_up_48.py @@ -0,0 +1,179 @@ + +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +import numpy as np + +from utils.layers.silk_upsampler import SilkUpsampler +from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d +from utils.layers.td_shaper import TDShaper +from utils.layers.deemph import Deemph +from utils.misc import freeze_model + +from models.nns_base import NNSBase +from models.silk_feature_net_pl import SilkFeatureNetPL +from models.silk_feature_net import SilkFeatureNet +from .scale_embedding import ScaleEmbedding + + + +class ShapeUp48(NNSBase): + FRAME_SIZE16k=80 + + def __init__(self, + num_features=47, + pitch_embedding_dim=64, + cond_dim=256, + pitch_max=257, + kernel_size=15, + preemph=0.85, + skip=288, + conv_gain_limits_db=[-6, 6], + numbits_range=[50, 650], + numbits_embedding_dim=8, + hidden_feature_dim=64, + partial_lookahead=True, + norm_p=2, + target_fs=48000, + noise_amplitude=0, + prenet=None, + avg_pool_k=4): + + super().__init__(skip=skip, preemph=preemph) + + + self.num_features = num_features + self.cond_dim = cond_dim + self.pitch_max = pitch_max + self.pitch_embedding_dim = pitch_embedding_dim + self.kernel_size = kernel_size + self.preemph = preemph + self.skip = skip + self.numbits_range = numbits_range + self.numbits_embedding_dim = numbits_embedding_dim + self.hidden_feature_dim = hidden_feature_dim + self.partial_lookahead = partial_lookahead + self.frame_size48 = int(self.FRAME_SIZE16k * target_fs / 16000 + .1) + self.frame_size32 = self.FRAME_SIZE16k * 2 + self.noise_amplitude = noise_amplitude + self.prenet = prenet + + # freeze prenet if given + if prenet is not None: + freeze_model(self.prenet) + try: + self.deemph = Deemph(prenet.preemph) + except: + print("[warning] prenet model is expected to have preemph attribute") + self.deemph = Deemph(0) + + + + # upsampler + self.upsampler = SilkUpsampler() + + # pitch embedding + self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) + + # numbits embedding + self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) + + # feature net + if partial_lookahead: + self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim) + else: + self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) + + # non-linear transforms + self.tdshape1 = TDShaper(cond_dim, frame_size=self.frame_size32, avg_pool_k=avg_pool_k) + self.tdshape2 = TDShaper(cond_dim, frame_size=self.frame_size48, avg_pool_k=avg_pool_k) + + # spectral shaping + self.af_noise = LimitedAdaptiveConv1d(1, 1, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=[-30, 0], norm_p=norm_p) + self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af2 = LimitedAdaptiveConv1d(3, 2, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.frame_size48, overlap_size=self.frame_size48//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) + + + def flop_count(self, rate=16000, verbose=False): + + frame_rate = rate / self.FRAME_SIZE16k + + # feature net + feature_net_flops = self.feature_net.flop_count(frame_rate) + af_flops = self.af1.flop_count(rate) + self.af2.flop_count(2 * rate) + self.af3.flop_count(3 * rate) + + if verbose: + print(f"feature net: {feature_net_flops / 1e6} MFLOPS") + print(f"adaptive conv: {af_flops / 1e6} MFLOPS") + + return feature_net_flops + af_flops + + def forward(self, x, features, periods, numbits, debug=False): + + if self.prenet is not None: + with torch.no_grad(): + x = self.prenet(x, features, periods, numbits) + x = self.deemph(x) + + + + periods = periods.squeeze(-1) + pitch_embedding = self.pitch_embedding(periods) + numbits_embedding = self.numbits_embedding(numbits).flatten(2) + + full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) + cf = self.feature_net(full_features) + + y32 = self.upsampler.hq_2x_up(x) + + noise = self.noise_amplitude * torch.randn_like(y32) + noise = self.af_noise(noise, cf) + + y32 = self.af1(y32, cf, debug=debug) + + y32_1 = y32[:, 0:1, :] + y32_2 = self.tdshape1(y32[:, 1:2, :], cf) + y32 = torch.cat((y32_1, y32_2, noise), dim=1) + + y32 = self.af2(y32, cf, debug=debug) + + y48 = self.upsampler.interpolate_3_2(y32) + + y48_1 = y48[:, 0:1, :] + y48_2 = self.tdshape2(y48[:, 1:2, :], cf) + y48 = torch.cat((y48_1, y48_2), dim=1) + + y48 = self.af3(y48, cf, debug=debug) + + return y48 diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net.py b/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net.py new file mode 100644 index 0000000..ed22f52 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net.py @@ -0,0 +1,86 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import torch +from torch import nn +import torch.nn.functional as F + +from utils.complexity import _conv1d_flop_count + +class SilkFeatureNet(nn.Module): + + def __init__(self, + feature_dim=47, + num_channels=256, + lookahead=False): + + super(SilkFeatureNet, self).__init__() + + self.feature_dim = feature_dim + self.num_channels = num_channels + self.lookahead = lookahead + + self.conv1 = nn.Conv1d(feature_dim, num_channels, 3) + self.conv2 = nn.Conv1d(num_channels, num_channels, 3, dilation=2) + + self.gru = nn.GRU(num_channels, num_channels, batch_first=True) + + def flop_count(self, rate=200): + count = 0 + for conv in self.conv1, self.conv2: + count += _conv1d_flop_count(conv, rate) + + count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate + + return count + + + def forward(self, features, state=None): + """ features shape: (batch_size, num_frames, feature_dim) """ + + batch_size = features.size(0) + + if state is None: + state = torch.zeros((1, batch_size, self.num_channels), device=features.device) + + + features = features.permute(0, 2, 1) + if self.lookahead: + c = torch.tanh(self.conv1(F.pad(features, [1, 1]))) + c = torch.tanh(self.conv2(F.pad(c, [2, 2]))) + else: + c = torch.tanh(self.conv1(F.pad(features, [2, 0]))) + c = torch.tanh(self.conv2(F.pad(c, [4, 0]))) + + c = c.permute(0, 2, 1) + + c, _ = self.gru(c, state) + + return c \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net_pl.py b/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net_pl.py new file mode 100644 index 0000000..e6a536f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/models/silk_feature_net_pl.py @@ -0,0 +1,127 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" +import sys +sys.path.append('../dnntools') +import numbers + + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm + +from utils.complexity import _conv1d_flop_count + +from dnntools.quantization.softquant import soft_quant +from dnntools.sparsification import mark_for_sparsification + +class SilkFeatureNetPL(nn.Module): + """ feature net with partial lookahead """ + def __init__(self, + feature_dim=47, + num_channels=256, + hidden_feature_dim=64, + softquant=False, + sparsify=True, + sparsification_density=0.5, + apply_weight_norm=False): + + super(SilkFeatureNetPL, self).__init__() + + if isinstance(sparsification_density, numbers.Number): + sparsification_density = 4 * [sparsification_density] + + self.feature_dim = feature_dim + self.num_channels = num_channels + self.hidden_feature_dim = hidden_feature_dim + + norm = weight_norm if apply_weight_norm else lambda x, name=None: x + + self.conv1 = norm(nn.Conv1d(feature_dim, self.hidden_feature_dim, 1)) + self.conv2 = norm(nn.Conv1d(4 * self.hidden_feature_dim, num_channels, 2)) + self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4)) + self.gru = norm(norm(nn.GRU(num_channels, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0') + + if softquant: + self.conv2 = soft_quant(self.conv2) + self.tconv = soft_quant(self.tconv) + self.gru = soft_quant(self.gru, names=['weight_hh_l0', 'weight_ih_l0']) + + + if sparsify: + mark_for_sparsification(self.conv2, (sparsification_density[0], [8, 4])) + mark_for_sparsification(self.tconv, (sparsification_density[1], [8, 4])) + mark_for_sparsification( + self.gru, + { + 'W_ir' : (sparsification_density[2], [8, 4], False), + 'W_iz' : (sparsification_density[2], [8, 4], False), + 'W_in' : (sparsification_density[2], [8, 4], False), + 'W_hr' : (sparsification_density[3], [8, 4], True), + 'W_hz' : (sparsification_density[3], [8, 4], True), + 'W_hn' : (sparsification_density[3], [8, 4], True), + } + ) + + + def flop_count(self, rate=200): + count = 0 + for conv in self.conv1, self.conv2, self.tconv: + count += _conv1d_flop_count(conv, rate) + + count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate + + return count + + + def forward(self, features, state=None): + """ features shape: (batch_size, num_frames, feature_dim) """ + + batch_size = features.size(0) + num_frames = features.size(1) + + if state is None: + state = torch.zeros((1, batch_size, self.num_channels), device=features.device) + + features = features.permute(0, 2, 1) + # dimensionality reduction + c = torch.tanh(self.conv1(features)) + + # frame accumulation + c = c.permute(0, 2, 1) + c = c.reshape(batch_size, num_frames // 4, -1).permute(0, 2, 1) + c = torch.tanh(self.conv2(F.pad(c, [1, 0]))) + + # upsampling + c = torch.tanh(self.tconv(c)) + c = c.permute(0, 2, 1) + + c, _ = self.gru(c, state) + + return c \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/requirements.txt b/managed_components/78__esp-opus/dnn/torch/osce/requirements.txt new file mode 100644 index 0000000..0a92e65 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/requirements.txt @@ -0,0 +1,9 @@ +pyyaml==6.0.1 +torch==2.0.1 +numpy==1.25.2 +scipy==1.11.2 +pesq==0.0.4 +gitpython==3.1.41 +matplotlib==3.7.3 +torchaudio==2.0.2 +tqdm==4.66.1 diff --git 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+audio/hi_fi_tts_v0/hi_fi_tts_v0/audio/9017_clean/13130/dartagnan01_38_dumas_0142.wav +audio/kn_in_female/knf_00254_00847846395.wav +audio/hi_fi_tts_v0/hi_fi_tts_v0/audio/92_clean/9783/auntcretesemancipation_07_hill_0199.wav +audio/xh_za/xh_za/za/xho/wavs/xho_4280_4424415518.wav diff --git a/managed_components/78__esp-opus/dnn/torch/osce/scripts/concatenator.py b/managed_components/78__esp-opus/dnn/torch/osce/scripts/concatenator.py new file mode 100644 index 0000000..699d436 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/scripts/concatenator.py @@ -0,0 +1,85 @@ +import os +import argparse + +import numpy as np +from scipy import signal +from scipy.io import wavfile +import resampy + + + + +parser = argparse.ArgumentParser() + +parser.add_argument("filelist", type=str, help="file with filenames for concatenation in WAVE format") +parser.add_argument("target_fs", type=int, help="target sampling rate of concatenated file") +parser.add_argument("output", type=str, help="binary output file (integer16)") +parser.add_argument("--basedir", type=str, help="basedir for filenames in filelist, defaults to ./", default="./") +parser.add_argument("--normalize", action="store_true", help="apply normalization") +parser.add_argument("--db_max", type=float, help="max DB for random normalization", default=0) +parser.add_argument("--db_min", type=float, help="min DB for random normalization", default=0) +parser.add_argument("--verbose", action="store_true") + +def read_filelist(basedir, filelist): + with open(filelist, "r") as f: + files = f.readlines() + + fullfiles = [os.path.join(basedir, f.rstrip('\n')) for f in files if len(f.rstrip('\n')) > 0] + + return fullfiles + +def read_wave(file, target_fs): + fs, x = wavfile.read(file) + + if fs < target_fs: + return None + print(f"[read_wave] warning: file {file} will be up-sampled from {fs} to {target_fs} Hz") + + if fs != target_fs: + x = resampy.resample(x, fs, target_fs) + + return x.astype(np.float32) + +def random_normalize(x, db_min, db_max, max_val=2**15 - 1): + db = np.random.uniform(db_min, db_max, 1) + m = np.abs(x).max() + c = 10**(db/20) * max_val / m + + return c * x + + +def concatenate(filelist : str, output : str, target_fs: int, normalize=True, db_min=0, db_max=0, verbose=False): + + overlap_size = int(40 * target_fs / 8000) + overlap_mem = np.zeros(overlap_size, dtype=np.float32) + overlap_win1 = (0.5 + 0.5 * np.cos(np.arange(0, overlap_size) * np.pi / overlap_size)).astype(np.float32) + overlap_win2 = np.flipud(overlap_win1) + + with open(output, 'wb') as f: + for file in filelist: + x = read_wave(file, target_fs) + if x is None: continue + + if len(x) < 10 * overlap_size: + if verbose: print(f"skipping {file}...") + continue + elif verbose: + print(f"processing {file}...") + + if normalize: + x = random_normalize(x, db_min, db_max) + + x1 = x[:-overlap_size] + x1[:overlap_size] = overlap_win1 * overlap_mem + overlap_win2 * x1[:overlap_size] + + f.write(x1.astype(np.int16).tobytes()) + + overlap_mem = x1[-overlap_size] + + +if __name__ == "__main__": + args = parser.parse_args() + + filelist = read_filelist(args.basedir, args.filelist) + + concatenate(filelist, args.output, args.target_fs, normalize=args.normalize, db_min=args.db_min, db_max=args.db_max, verbose=args.verbose) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/silk_16_to_48.py b/managed_components/78__esp-opus/dnn/torch/osce/silk_16_to_48.py new file mode 100644 index 0000000..e59b6cc --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/silk_16_to_48.py @@ -0,0 +1,28 @@ +import argparse + +from scipy.io import wavfile +import torch +import numpy as np + +from utils.layers.silk_upsampler import SilkUpsampler + +parser = argparse.ArgumentParser() +parser.add_argument("input", type=str, help="input wave file") +parser.add_argument("output", type=str, help="output wave file") + +if __name__ == "__main__": + args = parser.parse_args() + + fs, x = wavfile.read(args.input) + + # being lazy for now + assert fs == 16000 and x.dtype == np.int16 + + x = torch.from_numpy(x.astype(np.float32)).view(1, 1, -1) + + upsampler = SilkUpsampler() + y = upsampler(x) + + y = y.squeeze().numpy().astype(np.int16) + + wavfile.write(args.output, 48000, y[13:]) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/commonvoice_clip_selection.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/commonvoice_clip_selection.py new file mode 100644 index 0000000..ac1923a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/commonvoice_clip_selection.py @@ -0,0 +1,123 @@ +import argparse +import os +import yaml +import subprocess + +import numpy as np + + + +parser = argparse.ArgumentParser() +parser.add_argument('commonvoice_base_dir') +parser.add_argument('output_dir') +parser.add_argument('--clips-per-language', required=False, type=int, default=10) +parser.add_argument('--seed', required=False, type=int, default=2024) + + +def select_clips(dir, num_clips=10): + + if num_clips % 2: + print(f"warning: number of clips will be reduced to {num_clips - 1}") + female = dict() + male = dict() + + clips = np.genfromtxt(os.path.join(dir, 'validated.tsv'), delimiter='\t', dtype=str, invalid_raise=False) + clips_by_client = dict() + + if len(clips.shape) < 2 or len(clips) < num_clips: + # not enough data to proceed + return None + + for client in set(clips[1:,0]): + client_clips = clips[clips[:, 0] == client] + f, m = False, False + if 'female_feminine' in client_clips[:, 8]: + female[client] = client_clips[client_clips[:, 8] == 'female_feminine'] + f = True + if 'male_masculine' in client_clips[:, 8]: + male[client] = client_clips[client_clips[:, 8] == 'male_masculine'] + m = True + + if f and m: + print(f"both male and female clips under client {client}") + + + if min(len(female), len(male)) < num_clips // 2: + return None + + # select num_clips // 2 random female clients + female_client_selection = np.array(list(female.keys()), dtype=str)[np.random.choice(len(female), num_clips//2, replace=False)] + female_clip_selection = [] + for c in female_client_selection: + s_idx = np.random.randint(0, len(female[c])) + female_clip_selection.append(os.path.join(dir, 'clips', female[c][s_idx, 1].item())) + + # select num_clips // 2 random female clients + male_client_selection = np.array(list(male.keys()), dtype=str)[np.random.choice(len(male), num_clips//2, replace=False)] + male_clip_selection = [] + for c in male_client_selection: + s_idx = np.random.randint(0, len(male[c])) + male_clip_selection.append(os.path.join(dir, 'clips', male[c][s_idx, 1].item())) + + return female_clip_selection + male_clip_selection + +def ffmpeg_available(): + try: + x = subprocess.run(['ffmpeg', '-h'], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + return x.returncode == 0 + except: + return False + + +def convert_clips(selection, outdir): + if not ffmpeg_available(): + raise RuntimeError("ffmpeg not available") + + clipdir = os.path.join(outdir, 'clips') + os.makedirs(clipdir, exist_ok=True) + + clipdict = dict() + + for lang, clips in selection.items(): + clipdict[lang] = [] + for clip in clips: + clipname = os.path.splitext(os.path.split(clip)[-1])[0] + target_name = os.path.join('clips', clipname + '.wav') + call_args = ['ffmpeg', '-i', clip, '-ar', '16000', os.path.join(outdir, target_name)] + print(call_args) + r = subprocess.run(call_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + if r.returncode != 0: + raise RuntimeError(f'could not execute {call_args}') + clipdict[lang].append(target_name) + + return clipdict + + +if __name__ == "__main__": + if not ffmpeg_available(): + raise RuntimeError("ffmpeg not available") + + args = parser.parse_args() + + base_dir = args.commonvoice_base_dir + output_dir = args.output_dir + seed = args.seed + + np.random.seed(seed) + + langs = os.listdir(base_dir) + selection = dict() + + for lang in langs: + print(f"processing {lang}...") + clips = select_clips(os.path.join(base_dir, lang)) + if clips is not None: + selection[lang] = clips + + + os.makedirs(output_dir, exist_ok=True) + + clips = convert_clips(selection, output_dir) + + with open(os.path.join(output_dir, 'clips.yml'), 'w') as f: + yaml.dump(clips, f) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh new file mode 100644 index 0000000..54bacb8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh @@ -0,0 +1,25 @@ +#!/bin/bash + + +INPUT="dataset/LibriSpeech" +OUTPUT="testdata" +OPUSDEMO="/local/experiments/ietf_enhancement_studies/bin/opus_demo_patched" +BITRATES=( 6000 7500 ) # 9000 12000 15000 18000 24000 32000 ) + + +mkdir -p $OUTPUT + +for fn in $(find $INPUT -name "*.wav") +do + name=$(basename ${fn%*.wav}) + sox $fn -r 16000 -b 16 -e signed-integer ${OUTPUT}/tmp.raw + for br in ${BITRATES[@]} + do + folder=${OUTPUT}/"${name}_${br}.se" + echo "creating ${folder}..." + mkdir -p $folder + cp ${OUTPUT}/tmp.raw ${folder}/clean.s16 + (cd ${folder} && $OPUSDEMO voip 16000 1 $br clean.s16 noisy.s16) + done + rm -f ${OUTPUT}/tmp.raw +done diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/env.rc b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/env.rc new file mode 100644 index 0000000..f1266b6 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/env.rc @@ -0,0 +1,7 @@ +#!/bin/bash + +export PYTHON=/home/ubuntu/opt/miniconda3/envs/torch/bin/python +export LACE="/local/experiments/ietf_enhancement_studies/checkpoints/lace_checkpoint.pth" +export NOLACE="/local/experiments/ietf_enhancement_studies/checkpoints/nolace_checkpoint.pth" +export TESTMODEL="/local/experiments/ietf_enhancement_studies/opus/dnn/torch/osce/test_model.py" +export OPUSDEMO="/local/experiments/ietf_enhancement_studies/bin/opus_demo_patched" \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/evaluate.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/evaluate.py new file mode 100644 index 0000000..54700db --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/evaluate.py @@ -0,0 +1,113 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + + +from scipy.io import wavfile +from pesq import pesq +import numpy as np +from moc import compare +from moc2 import compare as compare2 +#from warpq import compute_WAPRQ as warpq +from lace_loss_metric import compare as laceloss_compare + + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='folder with processed items') +parser.add_argument('metric', type=str, choices=['pesq', 'moc', 'moc2', 'laceloss'], help='metric to be used for evaluation') + + +def get_bitrates(folder): + with open(os.path.join(folder, 'bitrates.txt')) as f: + x = f.read() + + bitrates = [int(y) for y in x.rstrip('\n').split()] + + return bitrates + +def get_itemlist(folder): + with open(os.path.join(folder, 'items.txt')) as f: + lines = f.readlines() + + items = [x.split()[0] for x in lines] + + return items + + +def process_item(folder, item, bitrate, metric): + fs, x_clean = wavfile.read(os.path.join(folder, 'clean', f"{item}_{bitrate}_clean.wav")) + fs, x_opus = wavfile.read(os.path.join(folder, 'opus', f"{item}_{bitrate}_opus.wav")) + fs, x_lace = wavfile.read(os.path.join(folder, 'lace', f"{item}_{bitrate}_lace.wav")) + fs, x_nolace = wavfile.read(os.path.join(folder, 'nolace', f"{item}_{bitrate}_nolace.wav")) + + x_clean = x_clean.astype(np.float32) / 2**15 + x_opus = x_opus.astype(np.float32) / 2**15 + x_lace = x_lace.astype(np.float32) / 2**15 + x_nolace = x_nolace.astype(np.float32) / 2**15 + + if metric == 'pesq': + result = [pesq(fs, x_clean, x_opus), pesq(fs, x_clean, x_lace), pesq(fs, x_clean, x_nolace)] + elif metric =='moc': + result = [compare(x_clean, x_opus), compare(x_clean, x_lace), compare(x_clean, x_nolace)] + elif metric =='moc2': + result = [compare2(x_clean, x_opus), compare2(x_clean, x_lace), compare2(x_clean, x_nolace)] + # elif metric == 'warpq': + # result = [warpq(x_clean, x_opus), warpq(x_clean, x_lace), warpq(x_clean, x_nolace)] + elif metric == 'laceloss': + result = [laceloss_compare(x_clean, x_opus), laceloss_compare(x_clean, x_lace), laceloss_compare(x_clean, x_nolace)] + else: + raise ValueError(f'unknown metric {metric}') + + return result + +def process_bitrate(folder, items, bitrate, metric): + results = np.zeros((len(items), 3)) + + for i, item in enumerate(items): + results[i, :] = np.array(process_item(folder, item, bitrate, metric)) + + return results + + +if __name__ == "__main__": + args = parser.parse_args() + + items = get_itemlist(args.folder) + bitrates = get_bitrates(args.folder) + + results = dict() + for br in bitrates: + print(f"processing bitrate {br}...") + results[br] = process_bitrate(args.folder, items, br, args.metric) + + np.save(os.path.join(args.folder, f'results_{args.metric}.npy'), results) + + print("Done.") diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py new file mode 100644 index 0000000..b079058 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py @@ -0,0 +1,330 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +"""STFT-based Loss modules.""" + +import torch +import torch.nn.functional as F +from torch import nn +import numpy as np +import torchaudio + + +def get_window(win_name, win_length, *args, **kwargs): + window_dict = { + 'bartlett_window' : torch.bartlett_window, + 'blackman_window' : torch.blackman_window, + 'hamming_window' : torch.hamming_window, + 'hann_window' : torch.hann_window, + 'kaiser_window' : torch.kaiser_window + } + + if not win_name in window_dict: + raise ValueError() + + return window_dict[win_name](win_length, *args, **kwargs) + + +def stft(x, fft_size, hop_size, win_length, window): + """Perform STFT and convert to magnitude spectrogram. + Args: + x (Tensor): Input signal tensor (B, T). + fft_size (int): FFT size. + hop_size (int): Hop size. + win_length (int): Window length. + window (str): Window function type. + Returns: + Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). + """ + + win = get_window(window, win_length).to(x.device) + x_stft = torch.stft(x, fft_size, hop_size, win_length, win, return_complex=True) + + + return torch.clamp(torch.abs(x_stft), min=1e-7) + +def spectral_convergence_loss(Y_true, Y_pred): + dims=list(range(1, len(Y_pred.shape))) + return torch.mean(torch.norm(torch.abs(Y_true) - torch.abs(Y_pred), p="fro", dim=dims) / (torch.norm(Y_pred, p="fro", dim=dims) + 1e-6)) + + +def log_magnitude_loss(Y_true, Y_pred): + Y_true_log_abs = torch.log(torch.abs(Y_true) + 1e-15) + Y_pred_log_abs = torch.log(torch.abs(Y_pred) + 1e-15) + + return torch.mean(torch.abs(Y_true_log_abs - Y_pred_log_abs)) + +def spectral_xcorr_loss(Y_true, Y_pred): + Y_true = Y_true.abs() + Y_pred = Y_pred.abs() + dims=list(range(1, len(Y_pred.shape))) + xcorr = torch.sum(Y_true * Y_pred, dim=dims) / torch.sqrt(torch.sum(Y_true ** 2, dim=dims) * torch.sum(Y_pred ** 2, dim=dims) + 1e-9) + + return 1 - xcorr.mean() + + + +class MRLogMelLoss(nn.Module): + def __init__(self, + fft_sizes=[512, 256, 128, 64], + overlap=0.5, + fs=16000, + n_mels=18 + ): + + self.fft_sizes = fft_sizes + self.overlap = overlap + self.fs = fs + self.n_mels = n_mels + + super().__init__() + + self.mel_specs = [] + for fft_size in fft_sizes: + hop_size = int(round(fft_size * (1 - self.overlap))) + + n_mels = self.n_mels + if fft_size < 128: + n_mels //= 2 + + self.mel_specs.append(torchaudio.transforms.MelSpectrogram(fs, fft_size, hop_length=hop_size, n_mels=n_mels)) + + for i, mel_spec in enumerate(self.mel_specs): + self.add_module(f'mel_spec_{i+1}', mel_spec) + + def forward(self, y_true, y_pred): + + loss = torch.zeros(1, device=y_true.device) + + for mel_spec in self.mel_specs: + Y_true = mel_spec(y_true) + Y_pred = mel_spec(y_pred) + loss = loss + log_magnitude_loss(Y_true, Y_pred) + + loss = loss / len(self.mel_specs) + + return loss + +def create_weight_matrix(num_bins, bins_per_band=10): + m = torch.zeros((num_bins, num_bins), dtype=torch.float32) + + r0 = bins_per_band // 2 + r1 = bins_per_band - r0 + + for i in range(num_bins): + i0 = max(i - r0, 0) + j0 = min(i + r1, num_bins) + + m[i, i0: j0] += 1 + + if i < r0: + m[i, :r0 - i] += 1 + + if i > num_bins - r1: + m[i, num_bins - r1 - i:] += 1 + + return m / bins_per_band + +def weighted_spectral_convergence(Y_true, Y_pred, w): + + # calculate sfm based weights + logY = torch.log(torch.abs(Y_true) + 1e-9) + Y = torch.abs(Y_true) + + avg_logY = torch.matmul(logY.transpose(1, 2), w) + avg_Y = torch.matmul(Y.transpose(1, 2), w) + + sfm = torch.exp(avg_logY) / (avg_Y + 1e-9) + + weight = (torch.relu(1 - sfm) ** .5).transpose(1, 2) + + loss = torch.mean( + torch.mean(weight * torch.abs(torch.abs(Y_true) - torch.abs(Y_pred)), dim=[1, 2]) + / (torch.mean( weight * torch.abs(Y_true), dim=[1, 2]) + 1e-9) + ) + + return loss + +def gen_filterbank(N, Fs=16000): + in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] + out_freq = (np.arange(N, dtype='float32')/N*Fs/2)[:,None] + #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. + ERB_N = 24.7 + .108*in_freq + delta = np.abs(in_freq-out_freq)/ERB_N + center = (delta<.5).astype('float32') + R = -12*center*delta**2 + (1-center)*(3-12*delta) + RE = 10.**(R/10.) + norm = np.sum(RE, axis=1) + RE = RE/norm[:, np.newaxis] + return torch.from_numpy(RE) + +def smooth_log_mag(Y_true, Y_pred, filterbank): + Y_true_smooth = torch.matmul(filterbank, torch.abs(Y_true)) + Y_pred_smooth = torch.matmul(filterbank, torch.abs(Y_pred)) + + loss = torch.abs( + torch.log(Y_true_smooth + 1e-9) - torch.log(Y_pred_smooth + 1e-9) + ) + + loss = loss.mean() + + return loss + +class MRSTFTLoss(nn.Module): + def __init__(self, + fft_sizes=[2048, 1024, 512, 256, 128, 64], + overlap=0.5, + window='hann_window', + fs=16000, + log_mag_weight=0, + sc_weight=0, + wsc_weight=0, + smooth_log_mag_weight=2, + sxcorr_weight=1): + super().__init__() + + self.fft_sizes = fft_sizes + self.overlap = overlap + self.window = window + self.log_mag_weight = log_mag_weight + self.sc_weight = sc_weight + self.wsc_weight = wsc_weight + self.smooth_log_mag_weight = smooth_log_mag_weight + self.sxcorr_weight = sxcorr_weight + self.fs = fs + + # weights for SFM weighted spectral convergence loss + self.wsc_weights = torch.nn.ParameterDict() + for fft_size in fft_sizes: + width = min(11, int(1000 * fft_size / self.fs + .5)) + width += width % 2 + self.wsc_weights[str(fft_size)] = torch.nn.Parameter( + create_weight_matrix(fft_size // 2 + 1, width), + requires_grad=False + ) + + # filterbanks for smooth log magnitude loss + self.filterbanks = torch.nn.ParameterDict() + for fft_size in fft_sizes: + self.filterbanks[str(fft_size)] = torch.nn.Parameter( + gen_filterbank(fft_size//2), + requires_grad=False + ) + + + def __call__(self, y_true, y_pred): + + + lm_loss = torch.zeros(1, device=y_true.device) + sc_loss = torch.zeros(1, device=y_true.device) + wsc_loss = torch.zeros(1, device=y_true.device) + slm_loss = torch.zeros(1, device=y_true.device) + sxcorr_loss = torch.zeros(1, device=y_true.device) + + for fft_size in self.fft_sizes: + hop_size = int(round(fft_size * (1 - self.overlap))) + win_size = fft_size + + Y_true = stft(y_true, fft_size, hop_size, win_size, self.window) + Y_pred = stft(y_pred, fft_size, hop_size, win_size, self.window) + + if self.log_mag_weight > 0: + lm_loss = lm_loss + log_magnitude_loss(Y_true, Y_pred) + + if self.sc_weight > 0: + sc_loss = sc_loss + spectral_convergence_loss(Y_true, Y_pred) + + if self.wsc_weight > 0: + wsc_loss = wsc_loss + weighted_spectral_convergence(Y_true, Y_pred, self.wsc_weights[str(fft_size)]) + + if self.smooth_log_mag_weight > 0: + slm_loss = slm_loss + smooth_log_mag(Y_true, Y_pred, self.filterbanks[str(fft_size)]) + + if self.sxcorr_weight > 0: + sxcorr_loss = sxcorr_loss + spectral_xcorr_loss(Y_true, Y_pred) + + + total_loss = (self.log_mag_weight * lm_loss + self.sc_weight * sc_loss + + self.wsc_weight * wsc_loss + self.smooth_log_mag_weight * slm_loss + + self.sxcorr_weight * sxcorr_loss) / len(self.fft_sizes) + + return total_loss + + +def td_l2_norm(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) + + return loss.mean() + + +class LaceLoss(nn.Module): + def __init__(self): + super().__init__() + + + self.stftloss = MRSTFTLoss(log_mag_weight=0, sc_weight=0, wsc_weight=0, smooth_log_mag_weight=2, sxcorr_weight=1) + + + def forward(self, x, y): + specloss = self.stftloss(x, y) + phaseloss = td_l2_norm(x, y) + total_loss = (specloss + 10 * phaseloss) / 13 + + return total_loss + + def compare(self, x_ref, x_deg): + # trim items to same size + n = min(len(x_ref), len(x_deg)) + x_ref = x_ref[:n].copy() + x_deg = x_deg[:n].copy() + + # pre-emphasis + x_ref[1:] -= 0.85 * x_ref[:-1] + x_deg[1:] -= 0.85 * x_deg[:-1] + + device = next(iter(self.parameters())).device + + x = torch.from_numpy(x_ref).to(device) + y = torch.from_numpy(x_deg).to(device) + + with torch.no_grad(): + dist = 10 * self.forward(x, y) + + return dist.cpu().numpy().item() + + +lace_loss = LaceLoss() +device = 'cuda' if torch.cuda.is_available() else 'cpu' +lace_loss.to(device) + +def compare(x, y): + + return lace_loss.compare(x, y) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py new file mode 100644 index 0000000..f7ea778 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py @@ -0,0 +1,116 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import numpy as np +import matplotlib.pyplot as plt +from prettytable import PrettyTable +from matplotlib.patches import Patch + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') +parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') +parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') + +def load_data(folder): + data = dict() + + if os.path.isfile(os.path.join(folder, 'results_moc.npy')): + data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_moc2.npy')): + data['moc2'] = np.load(os.path.join(folder, 'results_moc2.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): + data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): + data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): + data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): + data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() + + return data + +def plot_data(filename, data, title=None): + compare_dict = dict() + for br in data.keys(): + compare_dict[f'Opus {br/1000:.1f} kb/s'] = data[br][:, 0] + compare_dict[f'LACE {br/1000:.1f} kb/s'] = data[br][:, 1] + compare_dict[f'NoLACE {br/1000:.1f} kb/s'] = data[br][:, 2] + + plt.rcParams.update({ + "text.usetex": True, + "font.family": "Helvetica", + "font.size": 32 + }) + + black = '#000000' + red = '#ff5745' + blue = '#007dbc' + colors = [black, red, blue] + legend_elements = [Patch(facecolor=colors[0], label='Opus SILK'), + Patch(facecolor=colors[1], label='LACE'), + Patch(facecolor=colors[2], label='NoLACE')] + + fig, ax = plt.subplots() + fig.set_size_inches(40, 20) + bplot = ax.boxplot(compare_dict.values(), showfliers=False, notch=True, patch_artist=True) + + for i, patch in enumerate(bplot['boxes']): + patch.set_facecolor(colors[i%3]) + + ax.set_xticklabels(compare_dict.keys(), rotation=290) + + if title is not None: + ax.set_title(title) + + ax.legend(handles=legend_elements) + + fig.savefig(filename, bbox_inches='tight') + +if __name__ == "__main__": + args = parser.parse_args() + data = load_data(args.folder) + + + metrics = list(data.keys()) if args.metric == 'all' else [args.metric] + folder = args.folder if args.output is None else args.output + os.makedirs(folder, exist_ok=True) + + for metric in metrics: + print(f"Plotting data for {metric} metric...") + plot_data(os.path.join(folder, f"boxplot_{metric}.png"), data[metric], title=metric.upper()) + + print("Done.") \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py new file mode 100644 index 0000000..ca65aba --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py @@ -0,0 +1,109 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import numpy as np +import matplotlib.pyplot as plt +from prettytable import PrettyTable +from matplotlib.patches import Patch + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') +parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') +parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') + +def load_data(folder): + data = dict() + + if os.path.isfile(os.path.join(folder, 'results_moc.npy')): + data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): + data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): + data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): + data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): + data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() + + return data + +def plot_data(filename, data, title=None): + compare_dict = dict() + for br in data.keys(): + compare_dict[f'Opus {br/1000:.1f} kb/s'] = data[br][:, 0] + compare_dict[f'LACE (MOC only) {br/1000:.1f} kb/s'] = data[br][:, 1] + compare_dict[f'LACE (MOC + TD) {br/1000:.1f} kb/s'] = data[br][:, 2] + + plt.rcParams.update({ + "text.usetex": True, + "font.family": "Helvetica", + "font.size": 32 + }) + colors = ['pink', 'lightblue', 'lightgreen'] + legend_elements = [Patch(facecolor=colors[0], label='Opus SILK'), + Patch(facecolor=colors[1], label='MOC loss only'), + Patch(facecolor=colors[2], label='MOC + TD loss')] + + fig, ax = plt.subplots() + fig.set_size_inches(40, 20) + bplot = ax.boxplot(compare_dict.values(), showfliers=False, notch=True, patch_artist=True) + + for i, patch in enumerate(bplot['boxes']): + patch.set_facecolor(colors[i%3]) + + ax.set_xticklabels(compare_dict.keys(), rotation=290) + + if title is not None: + ax.set_title(title) + + ax.legend(handles=legend_elements) + + fig.savefig(filename, bbox_inches='tight') + +if __name__ == "__main__": + args = parser.parse_args() + data = load_data(args.folder) + + + metrics = list(data.keys()) if args.metric == 'all' else [args.metric] + folder = args.folder if args.output is None else args.output + os.makedirs(folder, exist_ok=True) + + for metric in metrics: + print(f"Plotting data for {metric} metric...") + plot_data(os.path.join(folder, f"boxplot_{metric}.png"), data[metric], title=metric.upper()) + + print("Done.") \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables.py new file mode 100644 index 0000000..5608012 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables.py @@ -0,0 +1,124 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import numpy as np +import matplotlib.pyplot as plt +from prettytable import PrettyTable +from matplotlib.patches import Patch + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') +parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') +parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') + +def load_data(folder): + data = dict() + + if os.path.isfile(os.path.join(folder, 'results_moc.npy')): + data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_moc2.npy')): + data['moc2'] = np.load(os.path.join(folder, 'results_moc2.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): + data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): + data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): + data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): + data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() + + return data + +def make_table(filename, data, title=None): + + # mean values + tbl = PrettyTable() + tbl.field_names = ['bitrate (bps)', 'Opus', 'LACE', 'NoLACE'] + for br in data.keys(): + opus = data[br][:, 0] + lace = data[br][:, 1] + nolace = data[br][:, 2] + tbl.add_row([br, f"{float(opus.mean()):.3f} ({float(opus.std()):.2f})", f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) + + with open(filename + ".txt", "w") as f: + f.write(str(tbl)) + + with open(filename + ".html", "w") as f: + f.write(tbl.get_html_string()) + + with open(filename + ".csv", "w") as f: + f.write(tbl.get_csv_string()) + + print(tbl) + + +def make_diff_table(filename, data, title=None): + + # mean values + tbl = PrettyTable() + tbl.field_names = ['bitrate (bps)', 'LACE - Opus', 'NoLACE - Opus'] + for br in data.keys(): + opus = data[br][:, 0] + lace = data[br][:, 1] - opus + nolace = data[br][:, 2] - opus + tbl.add_row([br, f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) + + with open(filename + ".txt", "w") as f: + f.write(str(tbl)) + + with open(filename + ".html", "w") as f: + f.write(tbl.get_html_string()) + + with open(filename + ".csv", "w") as f: + f.write(tbl.get_csv_string()) + + print(tbl) + +if __name__ == "__main__": + args = parser.parse_args() + data = load_data(args.folder) + + metrics = list(data.keys()) if args.metric == 'all' else [args.metric] + folder = args.folder if args.output is None else args.output + os.makedirs(folder, exist_ok=True) + + for metric in metrics: + print(f"Plotting data for {metric} metric...") + make_table(os.path.join(folder, f"table_{metric}"), data[metric]) + make_diff_table(os.path.join(folder, f"table_diff_{metric}"), data[metric]) + + print("Done.") \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py new file mode 100644 index 0000000..3771806 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py @@ -0,0 +1,121 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import numpy as np +import matplotlib.pyplot as plt +from prettytable import PrettyTable +from matplotlib.patches import Patch + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') +parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') +parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') + +def load_data(folder): + data = dict() + + if os.path.isfile(os.path.join(folder, 'results_moc.npy')): + data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): + data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): + data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): + data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() + + if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): + data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() + + return data + +def make_table(filename, data, title=None): + + # mean values + tbl = PrettyTable() + tbl.field_names = ['bitrate (bps)', 'Opus', 'LACE', 'NoLACE'] + for br in data.keys(): + opus = data[br][:, 0] + lace = data[br][:, 1] + nolace = data[br][:, 2] + tbl.add_row([br, f"{float(opus.mean()):.3f} ({float(opus.std()):.2f})", f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) + + with open(filename + ".txt", "w") as f: + f.write(str(tbl)) + + with open(filename + ".html", "w") as f: + f.write(tbl.get_html_string()) + + with open(filename + ".csv", "w") as f: + f.write(tbl.get_csv_string()) + + print(tbl) + + +def make_diff_table(filename, data, title=None): + + # mean values + tbl = PrettyTable() + tbl.field_names = ['bitrate (bps)', 'LACE - Opus', 'NoLACE - Opus'] + for br in data.keys(): + opus = data[br][:, 0] + lace = data[br][:, 1] - opus + nolace = data[br][:, 2] - opus + tbl.add_row([br, f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) + + with open(filename + ".txt", "w") as f: + f.write(str(tbl)) + + with open(filename + ".html", "w") as f: + f.write(tbl.get_html_string()) + + with open(filename + ".csv", "w") as f: + f.write(tbl.get_csv_string()) + + print(tbl) + +if __name__ == "__main__": + args = parser.parse_args() + data = load_data(args.folder) + + metrics = list(data.keys()) if args.metric == 'all' else [args.metric] + folder = args.folder if args.output is None else args.output + os.makedirs(folder, exist_ok=True) + + for metric in metrics: + print(f"Plotting data for {metric} metric...") + make_table(os.path.join(folder, f"table_{metric}"), data[metric]) + make_diff_table(os.path.join(folder, f"table_diff_{metric}"), data[metric]) + + print("Done.") \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc.py new file mode 100644 index 0000000..bf004de --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc.py @@ -0,0 +1,182 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np +import scipy.signal + +def compute_vad_mask(x, fs, stop_db=-70): + + frame_length = (fs + 49) // 50 + x = x[: frame_length * (len(x) // frame_length)] + + frames = x.reshape(-1, frame_length) + frame_energy = np.sum(frames ** 2, axis=1) + frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') + + max_threshold = frame_energy.max() * 10 ** (stop_db/20) + vactive = np.ones_like(frames) + vactive[frame_energy_smooth < max_threshold, :] = 0 + vactive = vactive.reshape(-1) + + filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) + filter = filter / filter.sum() + + mask = np.convolve(vactive, filter, mode='same') + + return x, mask + +def convert_mask(mask, num_frames, frame_size=160, hop_size=40): + num_samples = frame_size + (num_frames - 1) * hop_size + if len(mask) < num_samples: + mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) + else: + mask = mask[:num_samples] + + new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) + + return new_mask + +def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): + num_spectra = (len(x) - window_size - hop_size) // hop_size + window = scipy.signal.get_window(window, window_size) + N = window_size // 2 + + frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window + psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 + + return psd + + +def frequency_mask(num_bands, up_factor, down_factor): + + up_mask = np.zeros((num_bands, num_bands)) + down_mask = np.zeros((num_bands, num_bands)) + + for i in range(num_bands): + up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) + down_mask[i, i :] = down_factor ** np.arange(num_bands - i) + + return down_mask @ up_mask + + +def rect_fb(band_limits, num_bins=None): + num_bands = len(band_limits) - 1 + if num_bins is None: + num_bins = band_limits[-1] + + fb = np.zeros((num_bands, num_bins)) + for i in range(num_bands): + fb[i, band_limits[i]:band_limits[i+1]] = 1 + + return fb + + +def compare(x, y, apply_vad=False): + """ Modified version of opus_compare for 16 kHz mono signals + + Args: + x (np.ndarray): reference input signal scaled to [-1, 1] + y (np.ndarray): test signal scaled to [-1, 1] + + Returns: + float: perceptually weighted error + """ + # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz + band_limits = [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75] + num_bands = len(band_limits) - 1 + fb = rect_fb(band_limits, num_bins=81) + + # trim samples to same size + num_samples = min(len(x), len(y)) + x = x[:num_samples] * 2**15 + y = y[:num_samples] * 2**15 + + psd_x = power_spectrum(x) + 100000 + psd_y = power_spectrum(y) + 100000 + + num_frames = psd_x.shape[0] + + # average band energies + be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) + + # frequecy masking + f_mask = frequency_mask(num_bands, 0.1, 0.03) + mask_x = be_x @ f_mask.T + + # temporal masking + for i in range(1, num_frames): + mask_x[i, :] += 0.5 * mask_x[i-1, :] + + # apply mask + masked_psd_x = psd_x + 0.1 * (mask_x @ fb) + masked_psd_y = psd_y + 0.1 * (mask_x @ fb) + + # 2-frame average + masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] + masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] + + # distortion metric + re = masked_psd_y / masked_psd_x + im = np.log(re) ** 2 + Eb = ((im @ fb.T) / np.sum(fb, axis=1)) + Ef = np.mean(Eb , axis=1) + + if apply_vad: + _, mask = compute_vad_mask(x, 16000) + mask = convert_mask(mask, Ef.shape[0]) + else: + mask = np.ones_like(Ef) + + err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) + + return float(err) + +if __name__ == "__main__": + import argparse + from scipy.io import wavfile + + parser = argparse.ArgumentParser() + parser.add_argument('ref', type=str, help='reference wav file') + parser.add_argument('deg', type=str, help='degraded wav file') + parser.add_argument('--apply-vad', action='store_true') + args = parser.parse_args() + + + fs1, x = wavfile.read(args.ref) + fs2, y = wavfile.read(args.deg) + + if max(fs1, fs2) != 16000: + raise ValueError('error: encountered sampling frequency diffrent from 16kHz') + + x = x.astype(np.float32) / 2**15 + y = y.astype(np.float32) / 2**15 + + err = compare(x, y, apply_vad=args.apply_vad) + + print(f"MOC: {err}") diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc2.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc2.py new file mode 100644 index 0000000..7e155f0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/moc2.py @@ -0,0 +1,190 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np +import scipy.signal + +def compute_vad_mask(x, fs, stop_db=-70): + + frame_length = (fs + 49) // 50 + x = x[: frame_length * (len(x) // frame_length)] + + frames = x.reshape(-1, frame_length) + frame_energy = np.sum(frames ** 2, axis=1) + frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') + + max_threshold = frame_energy.max() * 10 ** (stop_db/20) + vactive = np.ones_like(frames) + vactive[frame_energy_smooth < max_threshold, :] = 0 + vactive = vactive.reshape(-1) + + filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) + filter = filter / filter.sum() + + mask = np.convolve(vactive, filter, mode='same') + + return x, mask + +def convert_mask(mask, num_frames, frame_size=160, hop_size=40): + num_samples = frame_size + (num_frames - 1) * hop_size + if len(mask) < num_samples: + mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) + else: + mask = mask[:num_samples] + + new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) + + return new_mask + +def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): + num_spectra = (len(x) - window_size - hop_size) // hop_size + window = scipy.signal.get_window(window, window_size) + N = window_size // 2 + + frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window + psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 + + return psd + + +def frequency_mask(num_bands, up_factor, down_factor): + + up_mask = np.zeros((num_bands, num_bands)) + down_mask = np.zeros((num_bands, num_bands)) + + for i in range(num_bands): + up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) + down_mask[i, i :] = down_factor ** np.arange(num_bands - i) + + return down_mask @ up_mask + + +def rect_fb(band_limits, num_bins=None): + num_bands = len(band_limits) - 1 + if num_bins is None: + num_bins = band_limits[-1] + + fb = np.zeros((num_bands, num_bins)) + for i in range(num_bands): + fb[i, band_limits[i]:band_limits[i+1]] = 1 + + return fb + + +def _compare(x, y, apply_vad=False, factor=1): + """ Modified version of opus_compare for 16 kHz mono signals + + Args: + x (np.ndarray): reference input signal scaled to [-1, 1] + y (np.ndarray): test signal scaled to [-1, 1] + + Returns: + float: perceptually weighted error + """ + # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz + band_limits = [factor * b for b in [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75]] + window_size = factor * 160 + hop_size = factor * 40 + num_bins = window_size // 2 + 1 + num_bands = len(band_limits) - 1 + fb = rect_fb(band_limits, num_bins=num_bins) + + # trim samples to same size + num_samples = min(len(x), len(y)) + x = x[:num_samples].copy() * 2**15 + y = y[:num_samples].copy() * 2**15 + + psd_x = power_spectrum(x, window_size=window_size, hop_size=hop_size) + 100000 + psd_y = power_spectrum(y, window_size=window_size, hop_size=hop_size) + 100000 + + num_frames = psd_x.shape[0] + + # average band energies + be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) + + # frequecy masking + f_mask = frequency_mask(num_bands, 0.1, 0.03) + mask_x = be_x @ f_mask.T + + # temporal masking + for i in range(1, num_frames): + mask_x[i, :] += (0.5 ** factor) * mask_x[i-1, :] + + # apply mask + masked_psd_x = psd_x + 0.1 * (mask_x @ fb) + masked_psd_y = psd_y + 0.1 * (mask_x @ fb) + + # 2-frame average + masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] + masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] + + # distortion metric + re = masked_psd_y / masked_psd_x + #im = re - np.log(re) - 1 + im = np.log(re) ** 2 + Eb = ((im @ fb.T) / np.sum(fb, axis=1)) + Ef = np.mean(Eb ** 1, axis=1) + + if apply_vad: + _, mask = compute_vad_mask(x, 16000) + mask = convert_mask(mask, Ef.shape[0]) + else: + mask = np.ones_like(Ef) + + err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) + + return float(err) + +def compare(x, y, apply_vad=False): + err = np.linalg.norm([_compare(x, y, apply_vad=apply_vad, factor=1)], ord=2) + return err + +if __name__ == "__main__": + import argparse + from scipy.io import wavfile + + parser = argparse.ArgumentParser() + parser.add_argument('ref', type=str, help='reference wav file') + parser.add_argument('deg', type=str, help='degraded wav file') + parser.add_argument('--apply-vad', action='store_true') + args = parser.parse_args() + + + fs1, x = wavfile.read(args.ref) + fs2, y = wavfile.read(args.deg) + + if max(fs1, fs2) != 16000: + raise ValueError('error: encountered sampling frequency diffrent from 16kHz') + + x = x.astype(np.float32) / 2**15 + y = y.astype(np.float32) / 2**15 + + err = compare(x, y, apply_vad=args.apply_vad) + + print(f"MOC: {err}") diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh new file mode 100644 index 0000000..a490da9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh @@ -0,0 +1,98 @@ +#!/bin/bash + +if [ ! -f "$PYTHON" ] +then + echo "PYTHON variable does not link to a file. Please point it to your python executable." + exit 1 +fi + +if [ ! -f "$TESTMODEL" ] +then + echo "TESTMODEL variable does not link to a file. Please point it to your copy of test_model.py" + exit 1 +fi + +if [ ! -f "$OPUSDEMO" ] +then + echo "OPUSDEMO variable does not link to a file. Please point it to your patched version of opus_demo." + exit 1 +fi + +if [ ! -f "$LACE" ] +then + echo "LACE variable does not link to a file. Please point it to your copy of the LACE checkpoint." + exit 1 +fi + +if [ ! -f "$NOLACE" ] +then + echo "LACE variable does not link to a file. Please point it to your copy of the NOLACE checkpoint." + exit 1 +fi + +case $# in + 2) INPUT=$1; OUTPUT=$2;; + *) echo "process_dataset.sh "; exit 1;; +esac + +if [ -d $OUTPUT ] +then + echo "output folder $OUTPUT exists, aborting..." + exit 1 +fi + +mkdir -p $OUTPUT + +if [ "$BITRATES" == "" ] +then + BITRATES=( 6000 7500 9000 12000 15000 18000 24000 32000 ) + echo "BITRATES variable not defined. Proceeding with default bitrates ${BITRATES[@]}." +fi + + +echo "LACE=${LACE}" > ${OUTPUT}/info.txt +echo "NOLACE=${NOLACE}" >> ${OUTPUT}/info.txt + +ITEMFILE=${OUTPUT}/items.txt +BITRATEFILE=${OUTPUT}/bitrates.txt + +FPROCESSING=${OUTPUT}/processing +FCLEAN=${OUTPUT}/clean +FOPUS=${OUTPUT}/opus +FLACE=${OUTPUT}/lace +FNOLACE=${OUTPUT}/nolace + +mkdir -p $FPROCESSING $FCLEAN $FOPUS $FLACE $FNOLACE + +echo "${BITRATES[@]}" > $BITRATEFILE + +for fn in $(find $INPUT -type f -name "*.wav") +do + UUID=$(uuid) + echo "$UUID $fn" >> $ITEMFILE + PIDS=( ) + for br in ${BITRATES[@]} + do + # run opus + pfolder=${FPROCESSING}/${UUID}_${br} + mkdir -p $pfolder + sox $fn -c 1 -r 16000 -b 16 -e signed-integer $pfolder/clean.s16 + (cd ${pfolder} && $OPUSDEMO voip 16000 1 $br clean.s16 noisy.s16) + + # copy clean and opus + sox -c 1 -r 16000 -b 16 -e signed-integer $pfolder/clean.s16 $FCLEAN/${UUID}_${br}_clean.wav + sox -c 1 -r 16000 -b 16 -e signed-integer $pfolder/noisy.s16 $FOPUS/${UUID}_${br}_opus.wav + + # run LACE + $PYTHON $TESTMODEL $pfolder $LACE $FLACE/${UUID}_${br}_lace.wav & + PIDS+=( "$!" ) + + # run NoLACE + $PYTHON $TESTMODEL $pfolder $NOLACE $FNOLACE/${UUID}_${br}_nolace.wav & + PIDS+=( "$!" ) + done + for pid in ${PIDS[@]} + do + wait $pid + done +done diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py new file mode 100644 index 0000000..0267bc9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py @@ -0,0 +1,138 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import tempfile +import shutil + +import pandas as pd +from scipy.spatial.distance import cdist +from scipy.io import wavfile +import numpy as np + +from nomad_audio.nomad import Nomad + + +parser = argparse.ArgumentParser() +parser.add_argument('folder', type=str, help='folder with processed items') +parser.add_argument('--full-reference', action='store_true', help='use NOMAD as full-reference metric') +parser.add_argument('--device', type=str, default=None, help='device for Nomad') + + +def get_bitrates(folder): + with open(os.path.join(folder, 'bitrates.txt')) as f: + x = f.read() + + bitrates = [int(y) for y in x.rstrip('\n').split()] + + return bitrates + +def get_itemlist(folder): + with open(os.path.join(folder, 'items.txt')) as f: + lines = f.readlines() + + items = [x.split()[0] for x in lines] + + return items + + +def nomad_wrapper(ref_folder, deg_folder, full_reference=False, ref_embeddings=None, device=None): + model = Nomad(device=device) + if not full_reference: + results = model.predict(nmr=ref_folder, deg=deg_folder)[0].to_dict()['NOMAD'] + return results, None + else: + if ref_embeddings is None: + print(f"Computing reference embeddings from {ref_folder}") + ref_data = pd.DataFrame(sorted(os.listdir(ref_folder))) + ref_data.columns = ['filename'] + ref_data['filename'] = [os.path.join(ref_folder, x) for x in ref_data['filename']] + ref_embeddings = model.get_embeddings_csv(model.model, ref_data).set_index('filename') + + print(f"Computing degraded embeddings from {deg_folder}") + deg_data = pd.DataFrame(sorted(os.listdir(deg_folder))) + deg_data.columns = ['filename'] + deg_data['filename'] = [os.path.join(deg_folder, x) for x in deg_data['filename']] + deg_embeddings = model.get_embeddings_csv(model.model, deg_data).set_index('filename') + + dist = np.diag(cdist(ref_embeddings, deg_embeddings)) # wasteful + test_files = [x.split('/')[-1].split('.')[0] for x in deg_embeddings.index] + + results = dict(zip(test_files, dist)) + + return results, ref_embeddings + + + + +def nomad_process_all(folder, full_reference=False, device=None): + bitrates = get_bitrates(folder) + items = get_itemlist(folder) + with tempfile.TemporaryDirectory() as dir: + cleandir = os.path.join(dir, 'clean') + opusdir = os.path.join(dir, 'opus') + lacedir = os.path.join(dir, 'lace') + nolacedir = os.path.join(dir, 'nolace') + + # prepare files + for d in [cleandir, opusdir, lacedir, nolacedir]: os.makedirs(d) + for br in bitrates: + for item in items: + for cond in ['clean', 'opus', 'lace', 'nolace']: + shutil.copyfile(os.path.join(folder, cond, f"{item}_{br}_{cond}.wav"), os.path.join(dir, cond, f"{item}_{br}.wav")) + + nomad_opus, ref_embeddings = nomad_wrapper(cleandir, opusdir, full_reference=full_reference, ref_embeddings=None) + nomad_lace, ref_embeddings = nomad_wrapper(cleandir, lacedir, full_reference=full_reference, ref_embeddings=ref_embeddings) + nomad_nolace, ref_embeddings = nomad_wrapper(cleandir, nolacedir, full_reference=full_reference, ref_embeddings=ref_embeddings) + + results = dict() + for br in bitrates: + results[br] = np.zeros((len(items), 3)) + for i, item in enumerate(items): + key = f"{item}_{br}" + results[br][i, 0] = nomad_opus[key] + results[br][i, 1] = nomad_lace[key] + results[br][i, 2] = nomad_nolace[key] + + return results + + + +if __name__ == "__main__": + args = parser.parse_args() + + items = get_itemlist(args.folder) + bitrates = get_bitrates(args.folder) + + results = nomad_process_all(args.folder, full_reference=args.full_reference, device=args.device) + + np.save(os.path.join(args.folder, f'results_nomad.npy'), results) + + print("Done.") diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_osce_test.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_osce_test.py new file mode 100644 index 0000000..59bff83 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/evaluation/run_osce_test.py @@ -0,0 +1,196 @@ +import os +import argparse +import yaml +import subprocess + +import numpy as np + +from moc2 import compare as moc + +DEBUG=False + +parser = argparse.ArgumentParser() + +parser.add_argument('inputdir', type=str, help='Input folder with test items') +parser.add_argument('outputdir', type=str, help='Output folder') +parser.add_argument('bitrate', type=int, help='bitrate to test') +parser.add_argument('--reference_opus_demo', type=str, default='./opus_demo', help='reference opus_demo binary for generating bitstreams and reference output') +parser.add_argument('--encoder_options', type=str, default="", help='encoder options (e.g. -complexity 5)') +parser.add_argument('--test_opus_demo', type=str, default='./opus_demo', help='opus_demo binary under test') +parser.add_argument('--test_opus_demo_options', type=str, default='-dec_complexity 7', help='options for test opus_demo (e.g. "-dec_complexity 7")') +parser.add_argument('--verbose', type=int, default=0, help='verbosity level: 0 for quiet (default), 1 for reporting individual test results, 2 for reporting per-item scores in failed tests') + +def run_opus_encoder(opus_demo_path, input_pcm_path, bitstream_path, application, fs, num_channels, bitrate, options=[], verbose=False): + + call_args = [ + opus_demo_path, + "-e", + application, + str(fs), + str(num_channels), + str(bitrate), + "-bandwidth", + "WB" + ] + + call_args += options + + call_args += [ + input_pcm_path, + bitstream_path + ] + + try: + if verbose: + print(f"running {call_args}...") + subprocess.run(call_args) + else: + subprocess.run(call_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + except: + return 1 + + return 0 + + +def run_opus_decoder(opus_demo_path, bitstream_path, output_pcm_path, fs, num_channels, options=[], verbose=False): + + call_args = [ + opus_demo_path, + "-d", + str(fs), + str(num_channels) + ] + + call_args += options + + call_args += [ + bitstream_path, + output_pcm_path + ] + + try: + if verbose: + print(f"running {call_args}...") + subprocess.run(call_args) + else: + subprocess.run(call_args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + except: + return 1 + + return 0 + +def compute_moc_score(reference_pcm, test_pcm, delay=91): + x_ref = np.fromfile(reference_pcm, dtype=np.int16).astype(np.float32) / (2 ** 15) + x_cut = np.fromfile(test_pcm, dtype=np.int16).astype(np.float32) / (2 ** 15) + + moc_score = moc(x_ref, x_cut[delay:]) + + return moc_score + +def sox(*call_args): + try: + call_args = ["sox"] + list(call_args) + subprocess.run(call_args) + return 0 + except: + return 1 + +def process_clip_factory(ref_opus_demo, test_opus_demo, enc_options, test_options): + def process_clip(clip_path, processdir, bitrate): + # derive paths + clipname = os.path.splitext(os.path.split(clip_path)[1])[0] + pcm_path = os.path.join(processdir, clipname + ".raw") + bitstream_path = os.path.join(processdir, clipname + ".bin") + ref_path = os.path.join(processdir, clipname + "_ref.raw") + test_path = os.path.join(processdir, clipname + "_test.raw") + + # run sox + sox(clip_path, pcm_path) + + # run encoder + run_opus_encoder(ref_opus_demo, pcm_path, bitstream_path, "voip", 16000, 1, bitrate, enc_options) + + # run decoder + run_opus_decoder(ref_opus_demo, bitstream_path, ref_path, 16000, 1) + run_opus_decoder(test_opus_demo, bitstream_path, test_path, 16000, 1, options=test_options) + + d_ref = compute_moc_score(pcm_path, ref_path) + d_test = compute_moc_score(pcm_path, test_path) + + return d_ref, d_test + + + return process_clip + +def main(inputdir, outputdir, bitrate, reference_opus_demo, test_opus_demo, enc_option_string, test_option_string, verbose): + + # load clips list + with open(os.path.join(inputdir, 'clips.yml'), "r") as f: + clips = yaml.safe_load(f) + + # parse test options + enc_options = enc_option_string.split() + test_options = test_option_string.split() + + process_clip = process_clip_factory(reference_opus_demo, test_opus_demo, enc_options, test_options) + + os.makedirs(outputdir, exist_ok=True) + processdir = os.path.join(outputdir, 'process') + os.makedirs(processdir, exist_ok=True) + + num_passed = 0 + results = dict() + min_rel_diff = 1000 + min_mean = 1000 + worst_clip = None + worst_lang = None + for lang, lang_clips in clips.items(): + if verbose > 0: print(f"processing language {lang}...") + results[lang] = np.zeros((len(lang_clips), 2)) + for i, clip in enumerate(lang_clips): + clip_path = os.path.join(inputdir, clip) + d_ref, d_test = process_clip(clip_path, processdir, bitrate) + results[lang][i, 0] = d_ref + results[lang][i, 1] = d_test + + alpha = 0.5 + rel_diff = ((results[lang][:, 0] ** alpha - results[lang][:, 1] ** alpha) /(results[lang][:, 0] ** alpha)) + + min_idx = np.argmin(rel_diff).item() + if rel_diff[min_idx] < min_rel_diff: + min_rel_diff = rel_diff[min_idx] + worst_clip = lang_clips[min_idx] + + if np.mean(rel_diff) < min_mean: + min_mean = np.mean(rel_diff).item() + worst_lang = lang + + if np.min(rel_diff) < -0.1 or np.mean(rel_diff) < -0.025: + if verbose > 0: print(f"FAIL ({np.mean(results[lang], axis=0)} {np.mean(rel_diff)} {np.min(rel_diff)})") + if verbose > 1: + for i, c in enumerate(lang_clips): + print(f" {c:50s} {results[lang][i]} {rel_diff[i]}") + else: + if verbose > 0: print(f"PASS ({np.mean(results[lang], axis=0)} {np.mean(rel_diff)} {np.min(rel_diff)})") + num_passed += 1 + + print(f"{num_passed}/{len(clips)} tests passed!") + + print(f"worst case occured at clip {worst_clip} with relative difference of {min_rel_diff}") + print(f"worst mean relative difference was {min_mean} for test {worst_lang}") + + np.save(os.path.join(outputdir, f'results_' + "_".join(test_options) + f"_{bitrate}.npy"), results, allow_pickle=True) + + + +if __name__ == "__main__": + args = parser.parse_args() + + main(args.inputdir, + args.outputdir, + args.bitrate, + args.reference_opus_demo, + args.test_opus_demo, + args.encoder_options, + args.test_opus_demo_options, + args.verbose) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/endoscopy.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/endoscopy.py new file mode 100644 index 0000000..05dd475 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/endoscopy.py @@ -0,0 +1,205 @@ +""" module for inspecting models during inference """ + +import os + +import yaml +import matplotlib.pyplot as plt +import matplotlib.animation as animation + +import torch +import numpy as np + +# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} +_state = dict() +_folder = 'endoscopy' + +def get_gru_gates(gru, input, state): + hidden_size = gru.hidden_size + + direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) + recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) + + # reset gate + start, stop = 0 * hidden_size, 1 * hidden_size + reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # update gate + start, stop = 1 * hidden_size, 2 * hidden_size + update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # new gate + start, stop = 2 * hidden_size, 3 * hidden_size + new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) + + return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} + + +def init(folder='endoscopy'): + """ sets up output folder for endoscopy data """ + + global _folder + _folder = folder + + if not os.path.exists(folder): + os.makedirs(folder) + else: + print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") + +def write_data(key, data, fs): + """ appends data to previous data written under key """ + + global _state + + # convert to numpy if torch.Tensor is given + if isinstance(data, torch.Tensor): + data = data.detach().numpy() + + if not key in _state: + _state[key] = { + 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), + 'fs' : fs, + 'dim' : tuple(data.shape), + 'dtype' : str(data.dtype) + } + + with open(os.path.join(_folder, key + '.yml'), 'w') as f: + f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) + else: + if _state[key]['fs'] != fs: + raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") + if _state[key]['dtype'] != str(data.dtype): + raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") + if _state[key]['dim'] != tuple(data.shape): + raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") + + _state[key]['fid'].write(data.tobytes()) + +def close(folder='endoscopy'): + """ clean up """ + for key in _state.keys(): + _state[key]['fid'].close() + + +def read_data(folder='endoscopy'): + """ retrieves written data as numpy arrays """ + + + keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] + + return_dict = dict() + + for key in keys: + with open(os.path.join(folder, key + '.yml'), 'r') as f: + value = yaml.load(f.read(), yaml.FullLoader) + + with open(os.path.join(folder, key + '.bin'), 'rb') as f: + data = np.frombuffer(f.read(), dtype=value['dtype']) + + value['data'] = data.reshape((-1,) + value['dim']) + + return_dict[key] = value + + return return_dict + +def get_best_reshape(shape, target_ratio=1): + """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" + + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return (1,) + + num_columns = int((pixel_count / target_ratio)**.5) + + while (pixel_count % num_columns): + num_columns -= 1 + + num_rows = pixel_count // num_columns + + return (num_rows, num_columns) + +def get_type_and_shape(shape): + + # can happen if data is one dimensional + if len(shape) == 0: + shape = (1,) + + # calculate pixel count + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return 'plot', (1, ) + + # stay with shape if already 2-dimensional + if len(shape) == 2: + if (shape[0] != pixel_count) or (shape[1] != pixel_count): + return 'image', shape + + return 'image', get_best_reshape(shape) + +def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): + + # determine plot setup + num_keys = len(data.keys()) + + num_rows = int((num_keys * 3/4) ** .5) + + num_cols = (num_keys + num_rows - 1) // num_rows + + fig, axs = plt.subplots(num_rows, num_cols) + fig.set_size_inches(num_cols * 5, num_rows * 5) + + display = dict() + + fs_max = max([val['fs'] for val in data.values()]) + + num_samples = max([val['data'].shape[0] for val in data.values()]) + + keys = sorted(data.keys()) + + # inspect data + for i, key in enumerate(keys): + axs[i // num_cols, i % num_cols].title.set_text(key) + + display[key] = dict() + + display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) + display[key]['down_factor'] = data[key]['fs'] / fs_max + + start_index = max(start_index, half_signal_window_length) + while stop_index < 0: + stop_index += num_samples + + stop_index = min(stop_index, num_samples - half_signal_window_length) + + # actual plotting + frames = [] + for index in range(start_index, stop_index): + ims = [] + for i, key in enumerate(keys): + feature_index = int(round(index * display[key]['down_factor'])) + + if display[key]['type'] == 'plot': + ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) + + elif display[key]['type'] == 'image': + ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) + + frames.append(ims) + + ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) + + if not filename.endswith('.mp4'): + filename += '.mp4' + + ani.save(filename) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb new file mode 100644 index 0000000..5e1820a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation\n", + "from scipy.io import wavfile\n", + "import scipy.signal\n", + "import torch\n", + "\n", + "from playback import make_playback_animation\n", + "from endoscopy import read_data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"Helvetica\",\n", + " \"font.size\": 24\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# comparison opus@6kb/s vs. LACE@6kb/s\n", + "for name in ['opus_6kbps', 'lace_6kbps']:\n", + " fs, y = wavfile.read(f'lace_demo/{name}.wav')\n", + " spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", + " spec = 10*np.log10(spec)\n", + " \n", + " make_playback_animation(f'lace_demo/{name}_spec.mp4', spec, len(y)/16)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = read_data('lace_demo/endoscopy/')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_adaconv_impz(data, prefix, frame_size=80, overlap_size=40):\n", + " win1 = .5 + .5 * np.cos((np.arange(overlap_size) + 0.5) * torch.pi / overlap_size)\n", + " win1 = win1[:, np.newaxis]\n", + " win2 = win1[::-1]\n", + " kernels = data[prefix + '_kernels']['data'][0] * data[prefix + '_gains']['data'][0, :, np.newaxis]\n", + " kernels = kernels[:, ::-1]\n", + " num_frames = len(kernels)\n", + " impz = np.repeat(kernels, frame_size, 0)\n", + " for i in range(num_frames - 2, 0, -1):\n", + " idx = i * frame_size\n", + " impz[idx : idx + overlap_size] = win2 * impz[idx : idx + overlap_size] + win1 * impz[idx - overlap_size : idx]\n", + " \n", + " return impz" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_adacomb_impz(data, prefix, frame_size=80, overlap_size=40):\n", + " win1 = .5 + .5 * np.cos((np.arange(overlap_size) + 0.5) * torch.pi / overlap_size)\n", + " win1 = win1[:, np.newaxis]\n", + " win2 = win1[::-1]\n", + " kernels = data[prefix + '_kernels']['data'][0]\n", + " gg = data[prefix + '_global_conv_gains']['data'][0]\n", + " g = data[prefix + '_gains']['data'][0]\n", + " lags = data[prefix + '_lags']['data'][0]\n", + " \n", + " num_frames = len(kernels)\n", + " max_lag = int(lags.max())\n", + " kernel_size = kernels.shape[1]\n", + " padding = kernel_size // 2\n", + " \n", + " impz = np.zeros((num_frames, max_lag + padding + 1))\n", + " for i in range(num_frames):\n", + " p = int(lags[i])\n", + " impz[i, 0] = gg[i]\n", + " impz[i, p - padding : p - padding + kernel_size] = gg[i] * g[i] * kernels[i, ::-1]\n", + " \n", + " impz = np.repeat(impz, frame_size, 0)\n", + " for i in range(num_frames - 2, 0, -1):\n", + " idx = i * frame_size\n", + " impz[idx : idx + overlap_size] = win2 * impz[idx : idx + overlap_size] + win1 * impz[idx - overlap_size : idx]\n", + " \n", + " return impz" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "impz_comb1 = get_adacomb_impz(data, 'limited_adaptive_comb1d_1')\n", + "impz_comb2 = get_adacomb_impz(data, 'limited_adaptive_comb1d_2')\n", + "impz_conv1 = get_adaconv_impz(data, 'limited_adaptive_conv1d_1')\n", + "\n", + "phi = data['hidden_features']['data'][0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# LACE animation\n", + "\n", + "fig, axs = plt.subplots(4, 2)\n", + "frame_duration=40\n", + "fig.set_size_inches(12, 18)\n", + "axs[0, 0].set_xlabel('Tap')\n", + "axs[0, 0].set_ylabel('Amplitude')\n", + "axs[0, 1].set_xlabel('Frequency (kHz)')\n", + "axs[0, 1].set_ylabel('Amplitude (dB)')\n", + "axs[1, 0].set_xlabel('Tap')\n", + "axs[1, 0].set_ylabel('Amplitude')\n", + "axs[1, 1].set_xlabel('Frequency (kHz)')\n", + "axs[1, 1].set_ylabel('Amplitude (dB)')\n", + "axs[2, 0].set_xlabel('Tap')\n", + "axs[2, 0].set_ylabel('Amplitude')\n", + "axs[2, 1].set_xlabel('Frequency (kHz)')\n", + "axs[2, 1].set_ylabel('Amplitude (dB)')\n", + "axs[3, 0].set_xlabel('Dim')\n", + "axs[3, 1].set_visible(False)\n", + "fig.tight_layout()\n", + "\n", + "frames = []\n", + "for i in range(12800, 14000, 4):\n", + " f = impz_comb1[i]\n", + " w, h = scipy.signal.freqz(f, fs=16000)\n", + " frame = axs[0, 0].plot(f, 'b') + axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + " \n", + " f = impz_comb2[i]\n", + " w, h = scipy.signal.freqz(f, fs=16000)\n", + " frame += axs[1, 0].plot(f, 'b') + axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + " \n", + " f = impz_conv1[i]\n", + " w, h = scipy.signal.freqz(f, fs=16000)\n", + " frame += axs[2, 0].plot(f, 'b') + axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + " \n", + " frame += axs[3, 0].plot(phi[i//80], 'b')\n", + " \n", + " frames.append(frame)\n", + " \n", + "ani = matplotlib.animation.ArtistAnimation(fig, frames, blit=True, interval=frame_duration)\n", + "ani.save('lace_demo/responses2.mp4', dpi=720)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# static plot for Jean-Marc's on-site presentation\n", + "\n", + "fig, axs = plt.subplots(4, 2)\n", + "frame_duration=40\n", + "fig.set_size_inches(12, 18)\n", + "axs[0, 0].set_xlabel('Tap')\n", + "axs[0, 0].set_ylabel('Amplitude')\n", + "axs[0, 1].set_xlabel('Frequency (kHz)')\n", + "axs[0, 1].set_ylabel('Amplitude (dB)')\n", + "axs[1, 0].set_xlabel('Tap')\n", + "axs[1, 0].set_ylabel('Amplitude')\n", + "axs[1, 1].set_xlabel('Frequency (kHz)')\n", + "axs[1, 1].set_ylabel('Amplitude (dB)')\n", + "axs[2, 0].set_xlabel('Tap')\n", + "axs[2, 0].set_ylabel('Amplitude')\n", + "axs[2, 1].set_xlabel('Frequency (kHz)')\n", + "axs[2, 1].set_ylabel('Amplitude (dB)')\n", + "axs[3, 0].set_xlabel('Tap')\n", + "axs[3, 0].set_ylabel('Amplitude')\n", + "axs[3, 1].set_xlabel('Frequency (kHz)')\n", + "axs[3, 1].set_ylabel('Amplitude (dB)')\n", + "fig.tight_layout()\n", + "i=10*80\n", + "\n", + "f = impz_comb1[i]\n", + "w, h = scipy.signal.freqz(f, fs=16000)\n", + "axs[0, 0].plot(f, 'b')\n", + "axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + "\n", + "f = impz_comb2[i]\n", + "w, h = scipy.signal.freqz(f, fs=16000)\n", + "axs[1, 0].plot(f, 'b')\n", + "axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + "\n", + "f = impz_conv1[i]\n", + "w, h = scipy.signal.freqz(f, fs=16000)\n", + "axs[2, 0].plot(f, 'b')\n", + "axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + "\n", + "f = np.convolve(np.convolve(impz_comb1[i], impz_comb2[i], mode='full'), impz_conv1[i])\n", + "w, h = scipy.signal.freqz(f, fs=16000)\n", + "axs[3, 0].plot(f, 'b')\n", + "axs[3, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", + "\n", + "fig.savefig('plots/lace_snapshot_unvoiced.png')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb new file mode 100644 index 0000000..a465fb9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation\n", + "from scipy.io import wavfile\n", + "import scipy.signal\n", + "import torch\n", + "\n", + "from playback import make_playback_animation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_lpcnet_features(feature_file, version=2):\n", + " if version == 2 or version == 3:\n", + " layout = {\n", + " 'cepstrum': [0,18],\n", + " 'periods': [18, 19],\n", + " 'pitch_corr': [19, 20],\n", + " 'lpc': [20, 36]\n", + " }\n", + " frame_length = 36\n", + "\n", + " elif version == 1:\n", + " layout = {\n", + " 'cepstrum': [0,18],\n", + " 'periods': [36, 37],\n", + " 'pitch_corr': [37, 38],\n", + " 'lpc': [39, 55],\n", + " }\n", + " frame_length = 55\n", + " else:\n", + " raise ValueError(f'unknown feature version: {version}')\n", + "\n", + "\n", + " raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32'))\n", + " raw_features = raw_features.reshape((-1, frame_length))\n", + "\n", + " features = torch.cat(\n", + " [\n", + " raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]],\n", + " raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]]\n", + " ],\n", + " dim=1\n", + " )\n", + "\n", + " lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]]\n", + " if version < 3:\n", + " periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long()\n", + " else:\n", + " periods = torch.round(torch.clip(256./2**(raw_features[:, layout['periods'][0] : layout['periods'][1]] + 1.5), 32, 256)).long()\n", + "\n", + " return {'features' : features, 'periods' : periods, 'lpcs' : lpcs}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def run_lpc(signal, lpcs, frame_length=160):\n", + " num_frames, lpc_order = lpcs.shape\n", + "\n", + " prediction = np.concatenate(\n", + " [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)]\n", + " )\n", + " error = signal[lpc_order :] - prediction\n", + "\n", + " return prediction, error" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "lpcnet_features = load_lpcnet_features('lp/features.f32')\n", + "\n", + "features = lpcnet_features['features'].numpy()\n", + "periods = lpcnet_features['periods'].squeeze(-1).numpy()\n", + "lpcs = lpcnet_features['lpcs'].numpy()\n", + "\n", + "x = np.fromfile('data/a3_short.pcm', dtype=np.int16).astype(np.float32) / 2**15\n", + "x = np.concatenate((np.zeros(80), x, np.zeros(320)))\n", + "x_preemph = x.copy()\n", + "x_preemph[1:] -= 0.85 * x_preemph[:-1]\n", + "\n", + "num_frames = features.shape[0]\n", + "x = x[:160 * num_frames]\n", + "x_preemph = x_preemph[:160 * num_frames]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# short-term prediction\n", + "pred, error = run_lpc(np.concatenate((np.zeros(16), x_preemph)), lpcs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# long-term prediction\n", + "offset = 256\n", + "padded_error = np.concatenate((np.zeros(offset), error))\n", + "ltp_error = padded_error.copy()\n", + "for i, p in enumerate(list(periods)):\n", + " t0 = i * 160 + offset\n", + " t1 = t0 + 160\n", + " \n", + " past = padded_error[t0 - p : t1 - p]\n", + " current = padded_error[t0 : t1]\n", + " \n", + " gain = np.dot(past, current) / (np.dot(past, past) + 1e-6)\n", + " ltp_error[t0 : t1] -= gain * past\n", + " \n", + " \n", + "ltp_error = ltp_error[offset:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "spec_ltp_error, freqs, t, im = plt.specgram(ltp_error, NFFT=512, cmap='inferno')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "x_in = (x * (2 ** 15)).astype(np.int16)\n", + "x_stp = (error * (2 ** 15)).astype(np.int16)\n", + "x_ltp = (ltp_error * (2 ** 15)).astype(np.int16)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "wavfile.write('lp/x_in.wav', 16000, x_in)\n", + "wavfile.write('lp/x_stp.wav', 16000, x_stp)\n", + "wavfile.write('lp/x_ltp.wav', 16000, x_ltp)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for y, name in [(x_in, 'x_in'), (x_stp, 'x_stp'), (x_ltp, 'x_ltp')]:\n", + " spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", + " spec = 10*np.log10(spec)\n", + " \n", + " make_playback_animation(f'lp/{name}_spec.mp4', spec, len(y)/16)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/playback.py b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/playback.py new file mode 100644 index 0000000..4d7e6c7 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/playback.py @@ -0,0 +1,25 @@ +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.animation + +def make_playback_animation(savepath, spec, duration_ms, vmin=20, vmax=90): + fig, axs = plt.subplots() + axs.set_axis_off() + fig.set_size_inches((duration_ms / 1000 * 5, 5)) + frames = [] + frame_duration=20 + num_frames = int(duration_ms / frame_duration + .99) + + spec_height, spec_width = spec.shape + for i in range(num_frames): + xpos = (i - 1) / (num_frames - 3) * (spec_width - 1) + new_frame = axs.imshow(spec, cmap='inferno', origin='lower', aspect='auto', vmin=vmin, vmax=vmax) + if i in {0, num_frames - 1}: + frames.append([new_frame]) + else: + line = axs.plot([xpos, xpos], [0, spec_height-1], color='white', alpha=0.8)[0] + frames.append([new_frame, line]) + + + ani = matplotlib.animation.ArtistAnimation(fig, frames, blit=True, interval=frame_duration) + ani.save(savepath, dpi=720) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb new file mode 100644 index 0000000..3a225ae --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation\n", + "from scipy.io import wavfile\n", + "import scipy.signal\n", + "import torch\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"Helvetica\",\n", + " \"font.size\": 20\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def load_lpcnet_features(feature_file, version=2):\n", + " if version == 2 or version == 3:\n", + " layout = {\n", + " 'cepstrum': [0,18],\n", + " 'periods': [18, 19],\n", + " 'pitch_corr': [19, 20],\n", + " 'lpc': [20, 36]\n", + " }\n", + " frame_length = 36\n", + "\n", + " elif version == 1:\n", + " layout = {\n", + " 'cepstrum': [0,18],\n", + " 'periods': [36, 37],\n", + " 'pitch_corr': [37, 38],\n", + " 'lpc': [39, 55],\n", + " }\n", + " frame_length = 55\n", + " else:\n", + " raise ValueError(f'unknown feature version: {version}')\n", + "\n", + "\n", + " raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32'))\n", + " raw_features = raw_features.reshape((-1, frame_length))\n", + "\n", + " features = torch.cat(\n", + " [\n", + " raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]],\n", + " raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]]\n", + " ],\n", + " dim=1\n", + " )\n", + "\n", + " lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]]\n", + " if version < 3:\n", + " periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long()\n", + " else:\n", + " periods = torch.round(torch.clip(256./2**(raw_features[:, layout['periods'][0] : layout['periods'][1]] + 1.5), 32, 256)).long()\n", + "\n", + " return {'features' : features, 'periods' : periods, 'lpcs' : lpcs}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "lpcnet_features = load_lpcnet_features('lp/features.f32')\n", + "\n", + "features = lpcnet_features['features'].numpy()\n", + "periods = lpcnet_features['periods'].squeeze(-1).numpy()\n", + "lpcs = lpcnet_features['lpcs'].numpy()\n", + "\n", + "x = np.fromfile('data/critical_3.pcm', dtype=np.int16).astype(np.float32) / 2**15\n", + "x = np.concatenate((np.zeros(80), x, np.zeros(320)))\n", + "x_preemph = x.copy()\n", + "x_preemph[1:] -= 0.85 * x_preemph[:-1]\n", + "\n", + "num_frames = features.shape[0]\n", + "x = x[:160 * num_frames]\n", + "x_preemph = x_preemph[:160 * num_frames]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def stp(A, alpha=0.8, beta=0.5):\n", + " A_num = A * (beta ** np.arange(len(A)))\n", + " A_den = A * (alpha ** np.arange(len(A)))\n", + " \n", + " return A_num, A_den" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "frame_idx = 31" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "A = np.concatenate((np.ones(1), lpcs[frame_idx]))\n", + "A_num, A_den = stp(A)\n", + "w, h = scipy.signal.freqz([1], A, fs=16000)\n", + "w, h_stp = scipy.signal.freqz(A_num, A_den, fs=16000)\n", + "plt.plot(w/1000, 10*np.log10(np.abs(h)))\n", + "plt.plot(w/1000, 10*np.log10(np.abs(h_stp)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "x_frame = x_preemph[frame_idx * 160 - 80: (frame_idx + 1) * 160 + 80]\n", + "\n", + "window = scipy.signal.get_window('hamming', 320)\n", + "spec = np.fft.fft(x_frame, n=1024)\n", + "\n", + "log_mag = 10*np.log10(np.abs(spec[:512]) + 1e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(9, 6))\n", + "\n", + "plt.plot(w/1000, log_mag - log_mag.mean(), alpha=0.7, label='spectrum')\n", + "plt.plot(w/1000, 10*np.log10(np.abs(h)), \"r--\", label='LPC spectrum', linewidth=3, alpha=0.9)\n", + "plt.plot(w/1000, 10*np.log10(np.abs(h_stp)), \"k--\", label='short-term postfilter', linewidth=3, alpha=0.9)\n", + "plt.legend()\n", + "plt.xlabel('kHz')\n", + "plt.ylabel('Amplitude (dB)')\n", + "plt.show()\n", + "fig.savefig('plots/short_term_postfilter.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "periods[frame_idx]\n", + "\n", + "p = int(periods[frame_idx])\n", + "ltp_num = np.zeros(p+1)\n", + "ltp_den = np.zeros(p+1)\n", + "\n", + "ltp_num[0] = ltp_den[0] = 1\n", + "ltp_num[p] = 0.25\n", + "ltp_den[p] = -0.25\n", + "\n", + "w, h_ltp = scipy.signal.freqz(ltp_num, ltp_den, fs=16000)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(9, 6))\n", + "plt.plot(w/1000, log_mag - log_mag.mean(), alpha=0.7, label='spectrum')\n", + "plt.plot(w/1000, 10*np.log10(np.abs(h_ltp)), \"k\", alpha=1, label='long-term postfilter', linewidth=1.5)\n", + "plt.legend()\n", + "plt.xlabel('kHz')\n", + "plt.ylabel('Amplitude (dB)')\n", + "plt.show()\n", + "fig.savefig('plots/long_term_postfilter.png')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "torch", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb new file mode 100644 index 0000000..9d3d96f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "04cba77c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_252797/4199785080.py:9: WavFileWarning: Chunk (non-data) not understood, skipping it.\n", + " fs, x = wavfile.read(f'data/a3_short_opus_{br}bps.wav')\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.io import wavfile\n", + "import scipy.signal\n", + "from playback import make_playback_animation\n", + "\n", + "# coding noise demonstration\n", + "for br in [\"inf\", 24000, 15000, 12000, 9000, 8000, 7000, 6000]:\n", + " fs, x = wavfile.read(f'data/a3_short_opus_{br}bps.wav')\n", + " spec, freqs, t, im = plt.specgram(x, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", + " spec = 10*np.log10(spec)\n", + " \n", + " make_playback_animation(f'animations/opus_{br}bps.mp4', spec, len(x)/16)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8408ca1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/managed_components/78__esp-opus/dnn/torch/osce/test_model.py b/managed_components/78__esp-opus/dnn/torch/osce/test_model.py new file mode 100644 index 0000000..616a0ec --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/test_model.py @@ -0,0 +1,96 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse + +import torch + +from scipy.io import wavfile + + +from models import model_dict +from utils.silk_features import load_inference_data +from utils import endoscopy + +debug = False +if debug: + args = type('dummy', (object,), + { + 'input' : 'testitems/all_0_orig.se', + 'checkpoint' : 'testout/checkpoints/checkpoint_epoch_5.pth', + 'output' : 'out.wav', + })() +else: + parser = argparse.ArgumentParser() + + parser.add_argument('input', type=str, help='path to folder with features and signals') + parser.add_argument('checkpoint', type=str, help='checkpoint file') + parser.add_argument('output', type=str, help='output file') + parser.add_argument('--debug', action='store_true', help='enables debug output') + + + args = parser.parse_args() + + +torch.set_num_threads(2) + +input_folder = args.input +checkpoint_file = args.checkpoint + + +output_file = args.output +if not output_file.endswith('.wav'): + output_file += '.wav' + +checkpoint = torch.load(checkpoint_file, map_location="cpu") + +# check model +if not 'name' in checkpoint['setup']['model']: + print(f'warning: did not find model name entry in setup, using pitchpostfilter per default') + model_name = 'pitchpostfilter' +else: + model_name = checkpoint['setup']['model']['name'] + +model = model_dict[model_name](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) + +model.load_state_dict(checkpoint['state_dict']) + +# generate model input +setup = checkpoint['setup'] +signal, features, periods, numbits = load_inference_data(input_folder, **setup['data']) + +if args.debug: + endoscopy.init() + +output = model.process(signal, features, periods, numbits, debug=args.debug) + +wavfile.write(output_file, 16000, output.cpu().numpy()) + +if args.debug: + endoscopy.close() diff --git a/managed_components/78__esp-opus/dnn/torch/osce/test_vocoder.py b/managed_components/78__esp-opus/dnn/torch/osce/test_vocoder.py new file mode 100644 index 0000000..e71a5c3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/test_vocoder.py @@ -0,0 +1,103 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import argparse + +import torch + +from scipy.io import wavfile + +from time import time + + +from models import model_dict +from utils.lpcnet_features import load_lpcnet_features +from utils import endoscopy + +debug = False +if debug: + args = type('dummy', (object,), + { + 'input' : 'testitems/all_0_orig.se', + 'checkpoint' : 'testout/checkpoints/checkpoint_epoch_5.pth', + 'output' : 'out.wav', + })() +else: + parser = argparse.ArgumentParser() + + parser.add_argument('input', type=str, help='path to input features') + parser.add_argument('checkpoint', type=str, help='checkpoint file') + parser.add_argument('output', type=str, help='output file') + parser.add_argument('--debug', action='store_true', help='enables debug output') + + + args = parser.parse_args() + + +torch.set_num_threads(2) + +input_folder = args.input +checkpoint_file = args.checkpoint + + +output_file = args.output +if not output_file.endswith('.wav'): + output_file += '.wav' + +checkpoint = torch.load(checkpoint_file, map_location="cpu") + +# check model +if not 'name' in checkpoint['setup']['model']: + print(f'warning: did not find model name entry in setup, using pitchpostfilter per default') + model_name = 'pitchpostfilter' +else: + model_name = checkpoint['setup']['model']['name'] + +model = model_dict[model_name](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) + +model.load_state_dict(checkpoint['state_dict']) + +# generate model input +setup = checkpoint['setup'] +testdata = load_lpcnet_features(input_folder) +features = testdata['features'] +periods = testdata['periods'] + +if args.debug: + endoscopy.init() + +start = time() +output = model.process(features, periods, debug=args.debug) +elapsed = time() - start +print(f"[timing] inference took {elapsed * 1000} ms") + +wavfile.write(output_file, 16000, output.cpu().numpy()) + +if args.debug: + endoscopy.close() diff --git a/managed_components/78__esp-opus/dnn/torch/osce/train_model.py b/managed_components/78__esp-opus/dnn/torch/osce/train_model.py new file mode 100644 index 0000000..e8f94dc --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/train_model.py @@ -0,0 +1,307 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +seed=1888 + +import os +import argparse +import sys +import random +random.seed(seed) + +import yaml + +try: + import git + has_git = True +except: + has_git = False + +import torch +torch.manual_seed(seed) +torch.backends.cudnn.benchmark = False +from torch.optim.lr_scheduler import LambdaLR + +import numpy as np +np.random.seed(seed) + +from scipy.io import wavfile + +import pesq + +from data import SilkEnhancementSet +from models import model_dict +from engine.engine import train_one_epoch, evaluate + + +from utils.silk_features import load_inference_data +from utils.misc import count_parameters, count_nonzero_parameters + +from losses.stft_loss import MRSTFTLoss, MRLogMelLoss + + +parser = argparse.ArgumentParser() + +parser.add_argument('setup', type=str, help='setup yaml file') +parser.add_argument('output', type=str, help='output path') +parser.add_argument('--device', type=str, help='compute device', default=None) +parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) +parser.add_argument('--testdata', type=str, help='path to features and signal for testing', default=None) +parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') + +args = parser.parse_args() + + + +torch.set_num_threads(4) + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +checkpoint_prefix = 'checkpoint' +output_prefix = 'output' +setup_name = 'setup.yml' +output_file='out.txt' + + +# check model +if not 'name' in setup['model']: + print(f'warning: did not find model entry in setup, using default PitchPostFilter') + model_name = 'pitchpostfilter' +else: + model_name = setup['model']['name'] + +# prepare output folder +if os.path.exists(args.output): + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit(0) +else: + os.makedirs(args.output, exist_ok=True) + +checkpoint_dir = os.path.join(args.output, 'checkpoints') +os.makedirs(checkpoint_dir, exist_ok=True) + +# add repo info to setup +if has_git: + working_dir = os.path.split(__file__)[0] + try: + repo = git.Repo(working_dir, search_parent_directories=True) + setup['repo'] = dict() + hash = repo.head.object.hexsha + urls = list(repo.remote().urls) + is_dirty = repo.is_dirty() + + if is_dirty: + print("warning: repo is dirty") + with open(os.path.join(args.output, 'repo.diff'), "w") as f: + f.write(repo.git.execute(["git", "diff"])) + + setup['repo']['hash'] = hash + setup['repo']['urls'] = urls + setup['repo']['dirty'] = is_dirty + except: + has_git = False + +# dump setup +with open(os.path.join(args.output, setup_name), 'w') as f: + yaml.dump(setup, f) + +ref = None +if args.testdata is not None: + + testsignal, features, periods, numbits = load_inference_data(args.testdata, **setup['data']) + + inference_test = True + inference_folder = os.path.join(args.output, 'inference_test') + os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True) + + try: + ref = np.fromfile(os.path.join(args.testdata, 'clean.s16'), dtype=np.int16) + except: + pass +else: + inference_test = False + +# training parameters +batch_size = setup['training']['batch_size'] +epochs = setup['training']['epochs'] +lr = setup['training']['lr'] +lr_decay_factor = setup['training']['lr_decay_factor'] + +# load training dataset +data_config = setup['data'] +data = SilkEnhancementSet(setup['dataset'], **data_config) + +# load validation dataset if given +if 'validation_dataset' in setup: + validation_data = SilkEnhancementSet(setup['validation_dataset'], **data_config) + + validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=8) + + run_validation = True +else: + run_validation = False + +# create model +model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) + +if args.initial_checkpoint is not None: + print(f"loading state dict from {args.initial_checkpoint}...") + chkpt = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(chkpt['state_dict']) + +# set compute device +if type(args.device) == type(None): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +else: + device = torch.device(args.device) + +# push model to device +model.to(device) + +# dataloader +dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8) + +# optimizer is introduced to trainable parameters +parameters = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(parameters, lr=lr) + +# learning rate scheduler +scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +# loss +w_l1 = setup['training']['loss']['w_l1'] +w_lm = setup['training']['loss']['w_lm'] +w_slm = setup['training']['loss']['w_slm'] +w_sc = setup['training']['loss']['w_sc'] +w_logmel = setup['training']['loss']['w_logmel'] +w_wsc = setup['training']['loss']['w_wsc'] +w_xcorr = setup['training']['loss']['w_xcorr'] +w_sxcorr = setup['training']['loss']['w_sxcorr'] +w_l2 = setup['training']['loss']['w_l2'] + +w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 + +stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) +logmelloss = MRLogMelLoss().to(device) + +def xcorr_loss(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) + + return torch.mean(loss) + +def td_l2_norm(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) + + return loss.mean() + +def td_l1(y_true, y_pred, pow=0): + dims = list(range(1, len(y_true.shape))) + tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) + + return torch.mean(tmp) + +def criterion(x, y): + + return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) + + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum + + + +# model checkpoint +checkpoint = { + 'setup' : setup, + 'state_dict' : model.state_dict(), + 'loss' : -1 +} + + + + +if not args.no_redirect: + print(f"re-directing output to {os.path.join(args.output, output_file)}") + sys.stdout = open(os.path.join(args.output, output_file), "w") + +print("summary:") + +print(f"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters") +if hasattr(model, 'flop_count'): + print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS") + +if ref is not None: + noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) + initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') + print(f"initial MOS (PESQ): {initial_mos}") + +best_loss = 1e9 + +for ep in range(1, epochs + 1): + print(f"training epoch {ep}...") + new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) + + + # save checkpoint + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = new_loss + + if run_validation: + print("running validation...") + validation_loss = evaluate(model, criterion, validation_dataloader, device) + checkpoint['validation_loss'] = validation_loss + + if validation_loss < best_loss: + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) + best_loss = validation_loss + + if inference_test: + print("running inference test...") + out = model.process(testsignal, features, periods, numbits).cpu().numpy() + wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) + if ref is not None: + mos = pesq.pesq(16000, ref, out, mode='wb') + print(f"MOS (PESQ): {mos}") + + + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) + + + print(f"non-zero parameters: {count_nonzero_parameters(model)}\n") + +print('Done') diff --git a/managed_components/78__esp-opus/dnn/torch/osce/train_vocoder.py b/managed_components/78__esp-opus/dnn/torch/osce/train_vocoder.py new file mode 100644 index 0000000..590e6d1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/train_vocoder.py @@ -0,0 +1,287 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +import yaml + +try: + import git + has_git = True +except: + has_git = False + +import torch +from torch.optim.lr_scheduler import LambdaLR + +from scipy.io import wavfile + +import pesq + +from data import LPCNetVocodingDataset +from models import model_dict +from engine.vocoder_engine import train_one_epoch, evaluate + + +from utils.lpcnet_features import load_lpcnet_features +from utils.misc import count_parameters + +from losses.stft_loss import MRSTFTLoss, MRLogMelLoss + + +parser = argparse.ArgumentParser() + +parser.add_argument('setup', type=str, help='setup yaml file') +parser.add_argument('output', type=str, help='output path') +parser.add_argument('--device', type=str, help='compute device', default=None) +parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) +parser.add_argument('--test-features', type=str, help='path to features for testing', default=None) +parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') + +args = parser.parse_args() + + +torch.set_num_threads(4) + +with open(args.setup, 'r') as f: + setup = yaml.load(f.read(), yaml.FullLoader) + +checkpoint_prefix = 'checkpoint' +output_prefix = 'output' +setup_name = 'setup.yml' +output_file='out.txt' + + +# check model +if not 'name' in setup['model']: + print(f'warning: did not find model entry in setup, using default PitchPostFilter') + model_name = 'pitchpostfilter' +else: + model_name = setup['model']['name'] + +# prepare output folder +if os.path.exists(args.output): + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit() +else: + os.makedirs(args.output, exist_ok=True) + +checkpoint_dir = os.path.join(args.output, 'checkpoints') +os.makedirs(checkpoint_dir, exist_ok=True) + +# add repo info to setup +if has_git: + working_dir = os.path.split(__file__)[0] + try: + repo = git.Repo(working_dir, search_parent_directories=True) + setup['repo'] = dict() + hash = repo.head.object.hexsha + urls = list(repo.remote().urls) + is_dirty = repo.is_dirty() + + if is_dirty: + print("warning: repo is dirty") + + setup['repo']['hash'] = hash + setup['repo']['urls'] = urls + setup['repo']['dirty'] = is_dirty + except: + has_git = False + +# dump setup +with open(os.path.join(args.output, setup_name), 'w') as f: + yaml.dump(setup, f) + +ref = None +# prepare inference test if wanted +inference_test = False +if type(args.test_features) != type(None): + test_features = load_lpcnet_features(args.test_features) + features = test_features['features'] + periods = test_features['periods'] + inference_folder = os.path.join(args.output, 'inference_test') + os.makedirs(inference_folder, exist_ok=True) + inference_test = True + + +# training parameters +batch_size = setup['training']['batch_size'] +epochs = setup['training']['epochs'] +lr = setup['training']['lr'] +lr_decay_factor = setup['training']['lr_decay_factor'] + +# load training dataset +data_config = setup['data'] +data = LPCNetVocodingDataset(setup['dataset'], **data_config) + +# load validation dataset if given +if 'validation_dataset' in setup: + validation_data = LPCNetVocodingDataset(setup['validation_dataset'], **data_config) + + validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=8) + + run_validation = True +else: + run_validation = False + +# create model +model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) + +if args.initial_checkpoint is not None: + print(f"loading state dict from {args.initial_checkpoint}...") + chkpt = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(chkpt['state_dict']) + +# set compute device +if type(args.device) == type(None): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +else: + device = torch.device(args.device) + +# push model to device +model.to(device) + +# dataloader +dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8) + +# optimizer is introduced to trainable parameters +parameters = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(parameters, lr=lr) + +# learning rate scheduler +scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +# loss +w_l1 = setup['training']['loss']['w_l1'] +w_lm = setup['training']['loss']['w_lm'] +w_slm = setup['training']['loss']['w_slm'] +w_sc = setup['training']['loss']['w_sc'] +w_logmel = setup['training']['loss']['w_logmel'] +w_wsc = setup['training']['loss']['w_wsc'] +w_xcorr = setup['training']['loss']['w_xcorr'] +w_sxcorr = setup['training']['loss']['w_sxcorr'] +w_l2 = setup['training']['loss']['w_l2'] + +w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 + +stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) +logmelloss = MRLogMelLoss().to(device) + +def xcorr_loss(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) + + return torch.mean(loss) + +def td_l2_norm(y_true, y_pred): + dims = list(range(1, len(y_true.shape))) + + loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) + + return loss.mean() + +def td_l1(y_true, y_pred, pow=0): + dims = list(range(1, len(y_true.shape))) + tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) + + return torch.mean(tmp) + +def criterion(x, y): + + return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) + + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum + + + +# model checkpoint +checkpoint = { + 'setup' : setup, + 'state_dict' : model.state_dict(), + 'loss' : -1 +} + + +if not args.no_redirect: + print(f"re-directing output to {os.path.join(args.output, output_file)}") + sys.stdout = open(os.path.join(args.output, output_file), "w") + +print("summary:") + +print(f"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters") +if hasattr(model, 'flop_count'): + print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS") + +if ref is not None: + pass + +best_loss = 1e9 + +for ep in range(1, epochs + 1): + print(f"training epoch {ep}...") + new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) + + + # save checkpoint + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = new_loss + + if run_validation: + print("running validation...") + validation_loss = evaluate(model, criterion, validation_dataloader, device) + checkpoint['validation_loss'] = validation_loss + + if validation_loss < best_loss: + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) + best_loss = validation_loss + + if inference_test: + print("running inference test...") + out = model.process(features, periods).cpu().numpy() + wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) + if ref is not None: + mos = pesq.pesq(16000, ref, out, mode='wb') + print(f"MOS (PESQ): {mos}") + + + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) + torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) + + + print() + +print('Done') diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/ada_conv.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/ada_conv.py new file mode 100644 index 0000000..b5b93f8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/ada_conv.py @@ -0,0 +1,71 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +# x is (batch, nb_in_channels, nb_frames*frame_size) +# kernels is (batch, nb_out_channels, nb_in_channels, nb_frames, coeffs) +def adaconv_kernel(x, kernels, half_window, fft_size=256): + device=x.device + overlap_size=half_window.size(-1) + nb_frames=kernels.size(3) + nb_batches=kernels.size(0) + nb_out_channels=kernels.size(1) + nb_in_channels=kernels.size(2) + kernel_size = kernels.size(-1) + x = x.reshape(nb_batches, 1, nb_in_channels, nb_frames, -1) + frame_size = x.size(-1) + # build window: [zeros, rising window, ones, falling window, zeros] + window = torch.cat( + [ + torch.zeros(frame_size, device=device), + half_window, + torch.ones(frame_size - overlap_size, device=device), + 1 - half_window, + torch.zeros(fft_size - 2 * frame_size - overlap_size,device=device) + ]) + x_prev = torch.cat([torch.zeros_like(x[:, :, :, :1, :]), x[:, :, :, :-1, :]], dim=-2) + x_next = torch.cat([x[:, :, :, 1:, :overlap_size], torch.zeros_like(x[:, :, :, -1:, :overlap_size])], dim=-2) + x_padded = torch.cat([x_prev, x, x_next, torch.zeros(nb_batches, 1, nb_in_channels, nb_frames, fft_size - 2 * frame_size - overlap_size, device=device)], -1) + k_padded = torch.cat([torch.flip(kernels, [-1]), torch.zeros(nb_batches, nb_out_channels, nb_in_channels, nb_frames, fft_size-kernel_size, device=device)], dim=-1) + + # compute convolution + X = torch.fft.rfft(x_padded, dim=-1) + K = torch.fft.rfft(k_padded, dim=-1) + + out = torch.fft.irfft(X * K, dim=-1) + # combine in channels + out = torch.sum(out, dim=2) + # apply the cross-fading + out = window.reshape(1, 1, 1, -1)*out + crossfaded = out[:,:,:,frame_size:2*frame_size] + torch.cat([torch.zeros(nb_batches, nb_out_channels, 1, frame_size, device=device), out[:, :, :-1, 2*frame_size:3*frame_size]], dim=-2) + + return crossfaded.reshape(nb_batches, nb_out_channels, -1) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/complexity.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/complexity.py new file mode 100644 index 0000000..4ee6e3f --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/complexity.py @@ -0,0 +1,8 @@ + + +def _conv1d_flop_count(layer, rate): + return 2 * ((layer.in_channels + 1) * layer.out_channels * rate / layer.stride[0] ) * layer.kernel_size[0] + + +def _dense_flop_count(layer, rate): + return 2 * ((layer.in_features + 1) * layer.out_features * rate ) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/endoscopy.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/endoscopy.py new file mode 100644 index 0000000..05dd475 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/endoscopy.py @@ -0,0 +1,205 @@ +""" module for inspecting models during inference """ + +import os + +import yaml +import matplotlib.pyplot as plt +import matplotlib.animation as animation + +import torch +import numpy as np + +# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} +_state = dict() +_folder = 'endoscopy' + +def get_gru_gates(gru, input, state): + hidden_size = gru.hidden_size + + direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) + recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) + + # reset gate + start, stop = 0 * hidden_size, 1 * hidden_size + reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # update gate + start, stop = 1 * hidden_size, 2 * hidden_size + update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) + + # new gate + start, stop = 2 * hidden_size, 3 * hidden_size + new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) + + return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} + + +def init(folder='endoscopy'): + """ sets up output folder for endoscopy data """ + + global _folder + _folder = folder + + if not os.path.exists(folder): + os.makedirs(folder) + else: + print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") + +def write_data(key, data, fs): + """ appends data to previous data written under key """ + + global _state + + # convert to numpy if torch.Tensor is given + if isinstance(data, torch.Tensor): + data = data.detach().numpy() + + if not key in _state: + _state[key] = { + 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), + 'fs' : fs, + 'dim' : tuple(data.shape), + 'dtype' : str(data.dtype) + } + + with open(os.path.join(_folder, key + '.yml'), 'w') as f: + f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) + else: + if _state[key]['fs'] != fs: + raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") + if _state[key]['dtype'] != str(data.dtype): + raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") + if _state[key]['dim'] != tuple(data.shape): + raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") + + _state[key]['fid'].write(data.tobytes()) + +def close(folder='endoscopy'): + """ clean up """ + for key in _state.keys(): + _state[key]['fid'].close() + + +def read_data(folder='endoscopy'): + """ retrieves written data as numpy arrays """ + + + keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] + + return_dict = dict() + + for key in keys: + with open(os.path.join(folder, key + '.yml'), 'r') as f: + value = yaml.load(f.read(), yaml.FullLoader) + + with open(os.path.join(folder, key + '.bin'), 'rb') as f: + data = np.frombuffer(f.read(), dtype=value['dtype']) + + value['data'] = data.reshape((-1,) + value['dim']) + + return_dict[key] = value + + return return_dict + +def get_best_reshape(shape, target_ratio=1): + """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" + + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return (1,) + + num_columns = int((pixel_count / target_ratio)**.5) + + while (pixel_count % num_columns): + num_columns -= 1 + + num_rows = pixel_count // num_columns + + return (num_rows, num_columns) + +def get_type_and_shape(shape): + + # can happen if data is one dimensional + if len(shape) == 0: + shape = (1,) + + # calculate pixel count + if len(shape) > 1: + pixel_count = 1 + for s in shape: + pixel_count *= s + else: + pixel_count = shape[0] + + if pixel_count == 1: + return 'plot', (1, ) + + # stay with shape if already 2-dimensional + if len(shape) == 2: + if (shape[0] != pixel_count) or (shape[1] != pixel_count): + return 'image', shape + + return 'image', get_best_reshape(shape) + +def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): + + # determine plot setup + num_keys = len(data.keys()) + + num_rows = int((num_keys * 3/4) ** .5) + + num_cols = (num_keys + num_rows - 1) // num_rows + + fig, axs = plt.subplots(num_rows, num_cols) + fig.set_size_inches(num_cols * 5, num_rows * 5) + + display = dict() + + fs_max = max([val['fs'] for val in data.values()]) + + num_samples = max([val['data'].shape[0] for val in data.values()]) + + keys = sorted(data.keys()) + + # inspect data + for i, key in enumerate(keys): + axs[i // num_cols, i % num_cols].title.set_text(key) + + display[key] = dict() + + display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) + display[key]['down_factor'] = data[key]['fs'] / fs_max + + start_index = max(start_index, half_signal_window_length) + while stop_index < 0: + stop_index += num_samples + + stop_index = min(stop_index, num_samples - half_signal_window_length) + + # actual plotting + frames = [] + for index in range(start_index, stop_index): + ims = [] + for i, key in enumerate(keys): + feature_index = int(round(index * display[key]['down_factor'])) + + if display[key]['type'] == 'plot': + ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) + + elif display[key]['type'] == 'image': + ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) + + frames.append(ims) + + ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) + + if not filename.endswith('.mp4'): + filename += '.mp4' + + ani.save(filename) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/fir.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/fir.py new file mode 100644 index 0000000..7eeb3e4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/fir.py @@ -0,0 +1,27 @@ +import numpy as np +import scipy.signal +import torch +from torch import nn +import torch.nn.functional as F + + +class FIR(nn.Module): + def __init__(self, numtaps, bands, desired, fs=2): + super().__init__() + + if numtaps % 2 == 0: + print(f"warning: numtaps must be odd, increasing numtaps to {numtaps + 1}") + numtaps += 1 + + a = scipy.signal.firls(numtaps, bands, desired, fs=fs) + + self.weight = torch.from_numpy(a.astype(np.float32)) + + def forward(self, x): + num_channels = x.size(1) + + weight = torch.repeat_interleave(self.weight.view(1, 1, -1), num_channels, 0) + + y = F.conv1d(x, weight, groups=num_channels) + + return y \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py new file mode 100644 index 0000000..0d87ca1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py @@ -0,0 +1,230 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +from utils.endoscopy import write_data +from utils.softquant import soft_quant + +class LimitedAdaptiveComb1d(nn.Module): + COUNTER = 1 + + def __init__(self, + kernel_size, + feature_dim, + frame_size=160, + overlap_size=40, + padding=None, + max_lag=256, + name=None, + gain_limit_db=10, + global_gain_limits_db=[-6, 6], + norm_p=2, + softquant=False, + apply_weight_norm=False, + **kwargs): + """ + + Parameters: + ----------- + + feature_dim : int + dimension of features from which kernels, biases and gains are computed + + frame_size : int, optional + frame size, defaults to 160 + + overlap_size : int, optional + overlap size for filter cross-fade. Cross-fade is done on the first overlap_size samples of every frame, defaults to 40 + + use_bias : bool, optional + if true, biases will be added to output channels. Defaults to True + + padding : List[int, int], optional + left and right padding. Defaults to [(kernel_size - 1) // 2, kernel_size - 1 - (kernel_size - 1) // 2] + + max_lag : int, optional + maximal pitch lag, defaults to 256 + + have_a0 : bool, optional + If true, the filter coefficient a0 will be learned as a positive gain (requires in_channels == out_channels). Otherwise, a0 is set to 0. Defaults to False + + name: str or None, optional + specifies a name attribute for the module. If None the name is auto generated as comb_1d_COUNT, where COUNT is an instance counter for LimitedAdaptiveComb1d + + """ + + super(LimitedAdaptiveComb1d, self).__init__() + + self.in_channels = 1 + self.out_channels = 1 + self.feature_dim = feature_dim + self.kernel_size = kernel_size + self.frame_size = frame_size + self.overlap_size = overlap_size + self.max_lag = max_lag + self.limit_db = gain_limit_db + self.norm_p = norm_p + + if name is None: + self.name = "limited_adaptive_comb1d_" + str(LimitedAdaptiveComb1d.COUNTER) + LimitedAdaptiveComb1d.COUNTER += 1 + else: + self.name = name + + norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x + + # network for generating convolution weights + self.conv_kernel = norm(nn.Linear(feature_dim, kernel_size)) + + if softquant: + self.conv_kernel = soft_quant(self.conv_kernel) + + + # comb filter gain + self.filter_gain = norm(nn.Linear(feature_dim, 1)) + self.log_gain_limit = gain_limit_db * 0.11512925464970229 + with torch.no_grad(): + self.filter_gain.bias[:] = max(0.1, 4 + self.log_gain_limit) + + self.global_filter_gain = norm(nn.Linear(feature_dim, 1)) + log_min, log_max = global_gain_limits_db[0] * 0.11512925464970229, global_gain_limits_db[1] * 0.11512925464970229 + self.filter_gain_a = (log_max - log_min) / 2 + self.filter_gain_b = (log_max + log_min) / 2 + + if type(padding) == type(None): + self.padding = [kernel_size // 2, kernel_size - 1 - kernel_size // 2] + else: + self.padding = padding + + self.overlap_win = nn.Parameter(.5 + .5 * torch.cos((torch.arange(self.overlap_size) + 0.5) * torch.pi / overlap_size), requires_grad=False) + + def forward(self, x, features, lags, debug=False): + """ adaptive 1d convolution + + + Parameters: + ----------- + x : torch.tensor + input signal of shape (batch_size, in_channels, num_samples) + + feathres : torch.tensor + frame-wise features of shape (batch_size, num_frames, feature_dim) + + lags: torch.LongTensor + frame-wise lags for comb-filtering + + """ + + batch_size = x.size(0) + num_frames = features.size(1) + num_samples = x.size(2) + frame_size = self.frame_size + overlap_size = self.overlap_size + kernel_size = self.kernel_size + win1 = torch.flip(self.overlap_win, [0]) + win2 = self.overlap_win + + if num_samples // self.frame_size != num_frames: + raise ValueError('non matching sizes in AdaptiveConv1d.forward') + + conv_kernels = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channels, self.kernel_size)) + conv_kernels = conv_kernels / (1e-6 + torch.norm(conv_kernels, p=self.norm_p, dim=-1, keepdim=True)) + + conv_gains = torch.exp(- torch.relu(self.filter_gain(features).permute(0, 2, 1)) + self.log_gain_limit) + # calculate gains + global_conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.global_filter_gain(features).permute(0, 2, 1)) + self.filter_gain_b) + + if debug and batch_size == 1: + key = self.name + "_gains" + write_data(key, conv_gains.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + key = self.name + "_kernels" + write_data(key, conv_kernels.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + key = self.name + "_lags" + write_data(key, lags.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + key = self.name + "_global_conv_gains" + write_data(key, global_conv_gains.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + + + # frame-wise convolution with overlap-add + output_frames = [] + overlap_mem = torch.zeros((batch_size, self.out_channels, self.overlap_size), device=x.device) + x = F.pad(x, self.padding) + x = F.pad(x, [self.max_lag, self.overlap_size]) + + idx = torch.arange(frame_size + kernel_size - 1 + overlap_size).to(x.device).view(1, 1, -1) + idx = torch.repeat_interleave(idx, batch_size, 0) + idx = torch.repeat_interleave(idx, self.in_channels, 1) + + + for i in range(num_frames): + + cidx = idx + i * frame_size + self.max_lag - lags[..., i].view(batch_size, 1, 1) + xx = torch.gather(x, -1, cidx).reshape((1, batch_size * self.in_channels, -1)) + + new_chunk = torch.conv1d(xx, conv_kernels[:, i, ...].reshape((batch_size * self.out_channels, self.in_channels, self.kernel_size)), groups=batch_size).reshape(batch_size, self.out_channels, -1) + + offset = self.max_lag + self.padding[0] + new_chunk = global_conv_gains[:, :, i : i + 1] * (new_chunk * conv_gains[:, :, i : i + 1] + x[..., offset + i * frame_size : offset + (i + 1) * frame_size + overlap_size]) + + # overlapping part + output_frames.append(new_chunk[:, :, : overlap_size] * win1 + overlap_mem * win2) + + # non-overlapping part + output_frames.append(new_chunk[:, :, overlap_size : frame_size]) + + # mem for next frame + overlap_mem = new_chunk[:, :, frame_size :] + + # concatenate chunks + output = torch.cat(output_frames, dim=-1) + + return output + + def flop_count(self, rate): + frame_rate = rate / self.frame_size + overlap = self.overlap_size + overhead = overlap / self.frame_size + + count = 0 + + # kernel computation and filtering + count += 2 * (frame_rate * self.feature_dim * self.kernel_size) + count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate) + count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels + + # a0 computation + count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels + + # windowing + count += overlap * frame_rate * 3 * self.out_channels + + return count diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py new file mode 100644 index 0000000..55df8c1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py @@ -0,0 +1,200 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +from utils.endoscopy import write_data + +from utils.ada_conv import adaconv_kernel +from utils.softquant import soft_quant + +class LimitedAdaptiveConv1d(nn.Module): + COUNTER = 1 + + def __init__(self, + in_channels, + out_channels, + kernel_size, + feature_dim, + frame_size=160, + overlap_size=40, + padding=None, + name=None, + gain_limits_db=[-6, 6], + shape_gain_db=0, + norm_p=2, + softquant=False, + apply_weight_norm=False, + **kwargs): + """ + + Parameters: + ----------- + + in_channels : int + number of input channels + + out_channels : int + number of output channels + + feature_dim : int + dimension of features from which kernels, biases and gains are computed + + frame_size : int + frame size + + overlap_size : int + overlap size for filter cross-fade. Cross-fade is done on the first overlap_size samples of every frame + + use_bias : bool + if true, biases will be added to output channels + + + padding : List[int, int] + + """ + + super(LimitedAdaptiveConv1d, self).__init__() + + + + self.in_channels = in_channels + self.out_channels = out_channels + self.feature_dim = feature_dim + self.kernel_size = kernel_size + self.frame_size = frame_size + self.overlap_size = overlap_size + self.gain_limits_db = gain_limits_db + self.shape_gain_db = shape_gain_db + self.norm_p = norm_p + + if name is None: + self.name = "limited_adaptive_conv1d_" + str(LimitedAdaptiveConv1d.COUNTER) + LimitedAdaptiveConv1d.COUNTER += 1 + else: + self.name = name + + norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x + + # network for generating convolution weights + self.conv_kernel = norm(nn.Linear(feature_dim, in_channels * out_channels * kernel_size)) + if softquant: + self.conv_kernel = soft_quant(self.conv_kernel) + + self.shape_gain = min(1, 10**(shape_gain_db / 20)) + + self.filter_gain = norm(nn.Linear(feature_dim, out_channels)) + log_min, log_max = gain_limits_db[0] * 0.11512925464970229, gain_limits_db[1] * 0.11512925464970229 + self.filter_gain_a = (log_max - log_min) / 2 + self.filter_gain_b = (log_max + log_min) / 2 + + if type(padding) == type(None): + self.padding = [kernel_size // 2, kernel_size - 1 - kernel_size // 2] + else: + self.padding = padding + + self.overlap_win = nn.Parameter(.5 + .5 * torch.cos((torch.arange(self.overlap_size) + 0.5) * torch.pi / overlap_size), requires_grad=False) + + + def flop_count(self, rate): + frame_rate = rate / self.frame_size + overlap = self.overlap_size + overhead = overlap / self.frame_size + + count = 0 + + # kernel computation and filtering + count += 2 * (frame_rate * self.feature_dim * self.kernel_size) + count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate) + + # gain computation + + count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels + + # windowing + count += 3 * overlap * frame_rate * self.out_channels + + return count + + def forward(self, x, features, debug=False): + """ adaptive 1d convolution + + + Parameters: + ----------- + x : torch.tensor + input signal of shape (batch_size, in_channels, num_samples) + + feathres : torch.tensor + frame-wise features of shape (batch_size, num_frames, feature_dim) + + """ + + batch_size = x.size(0) + num_frames = features.size(1) + num_samples = x.size(2) + frame_size = self.frame_size + overlap_size = self.overlap_size + kernel_size = self.kernel_size + win1 = torch.flip(self.overlap_win, [0]) + win2 = self.overlap_win + + if num_samples // self.frame_size != num_frames: + raise ValueError('non matching sizes in AdaptiveConv1d.forward') + + conv_kernels = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channels, self.kernel_size)) + + # normalize kernels (TODO: switch to L1 and normalize over kernel and input channel dimension) + conv_kernels = conv_kernels / (1e-6 + torch.norm(conv_kernels, p=self.norm_p, dim=[-2, -1], keepdim=True)) + + # limit shape + id_kernels = torch.zeros_like(conv_kernels) + id_kernels[..., self.padding[1]] = 1 + + conv_kernels = self.shape_gain * conv_kernels + (1 - self.shape_gain) * id_kernels + + # calculate gains + conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.filter_gain(features)) + self.filter_gain_b) + if debug and batch_size == 1: + key = self.name + "_gains" + write_data(key, conv_gains.permute(0, 2, 1).detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + key = self.name + "_kernels" + write_data(key, conv_kernels.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) + + + conv_kernels = conv_kernels * conv_gains.view(batch_size, num_frames, self.out_channels, 1, 1) + + conv_kernels = conv_kernels.permute(0, 2, 3, 1, 4) + + output = adaconv_kernel(x, conv_kernels, win1, fft_size=256) + + + return output \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/noise_shaper.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/noise_shaper.py new file mode 100644 index 0000000..ba8a3af --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/noise_shaper.py @@ -0,0 +1,100 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + +from utils.complexity import _conv1d_flop_count + +class NoiseShaper(nn.Module): + + def __init__(self, + feature_dim, + frame_size=160 + ): + """ + + Parameters: + ----------- + + feature_dim : int + dimension of input features + + frame_size : int + frame size + + """ + + super().__init__() + + self.feature_dim = feature_dim + self.frame_size = frame_size + + # feature transform + self.feature_alpha1 = nn.Conv1d(self.feature_dim, frame_size, 2) + self.feature_alpha2 = nn.Conv1d(frame_size, frame_size, 2) + + + def flop_count(self, rate): + + frame_rate = rate / self.frame_size + + shape_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1, self.feature_alpha2)]) + 11 * frame_rate * self.frame_size + + return shape_flops + + + def forward(self, features): + """ creates temporally shaped noise + + + Parameters: + ----------- + features : torch.tensor + frame-wise features of shape (batch_size, num_frames, feature_dim) + + """ + + batch_size = features.size(0) + num_frames = features.size(1) + frame_size = self.frame_size + num_samples = num_frames * frame_size + + # feature path + f = F.pad(features.permute(0, 2, 1), [1, 0]) + alpha = F.leaky_relu(self.feature_alpha1(f), 0.2) + alpha = torch.exp(self.feature_alpha2(F.pad(alpha, [1, 0]))) + alpha = alpha.permute(0, 2, 1) + + # signal generation + y = torch.randn((batch_size, num_frames, frame_size), dtype=features.dtype, device=features.device) + y = alpha * y + + return y.reshape(batch_size, 1, num_samples) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py new file mode 100644 index 0000000..ef58ae8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py @@ -0,0 +1,84 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch import nn +import torch.nn.functional as F + + +class PitchAutoCorrelator(nn.Module): + def __init__(self, + frame_size=80, + pitch_min=32, + pitch_max=300, + radius=2): + + super().__init__() + + self.frame_size = frame_size + self.pitch_min = pitch_min + self.pitch_max = pitch_max + self.radius = radius + + + def forward(self, x, periods): + # x of shape (batch_size, channels, num_samples) + # periods of shape (batch_size, num_frames) + + num_frames = periods.size(1) + batch_size = periods.size(0) + num_samples = self.frame_size * num_frames + channels = x.size(1) + + assert num_samples == x.size(-1) + + range = torch.arange(-self.radius, self.radius + 1, device=x.device) + idx = torch.arange(self.frame_size * num_frames, device=x.device) + p_up = torch.repeat_interleave(periods, self.frame_size, 1) + lookup = idx + self.pitch_max - p_up + lookup = lookup.unsqueeze(-1) + range + lookup = lookup.unsqueeze(1) + + # padding + x_pad = F.pad(x, [self.pitch_max, 0]) + x_ext = torch.repeat_interleave(x_pad.unsqueeze(-1), 2 * self.radius + 1, -1) + + # framing + x_select = torch.gather(x_ext, 2, lookup) + x_frames = x_pad[..., self.pitch_max : ].reshape(batch_size, channels, num_frames, self.frame_size, 1) + lag_frames = x_select.reshape(batch_size, 1, num_frames, self.frame_size, -1) + + # calculate auto-correlation + dotp = torch.sum(x_frames * lag_frames, dim=-2) + frame_nrg = torch.sum(x_frames * x_frames, dim=-2) + lag_frame_nrg = torch.sum(lag_frames * lag_frames, dim=-2) + + acorr = dotp / torch.sqrt(frame_nrg * lag_frame_nrg + 1e-9) + + return acorr diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/silk_upsampler.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/silk_upsampler.py new file mode 100644 index 0000000..0d20b8a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/silk_upsampler.py @@ -0,0 +1,167 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" This module implements the SILK upsampler from 16kHz to 24 or 48 kHz """ + +import torch +from torch import nn +import torch.nn.functional as F + +import numpy as np + +frac_fir = np.array( + [ + [189, -600, 617, 30567, 2996, -1375, 425, -46], + [117, -159, -1070, 29704, 5784, -2143, 611, -71], + [52, 221, -2392, 28276, 8798, -2865, 773, -91], + [-4, 529, -3350, 26341, 11950, -3487, 896, -103], + [-48, 758, -3956, 23973, 15143, -3957, 967, -107], + [-80, 905, -4235, 21254, 18278, -4222, 972, -99], + [-99, 972, -4222, 18278, 21254, -4235, 905, -80], + [-107, 967, -3957, 15143, 23973, -3956, 758, -48], + [-103, 896, -3487, 11950, 26341, -3350, 529, -4], + [-91, 773, -2865, 8798, 28276, -2392, 221, 52], + [-71, 611, -2143, 5784, 29704, -1070, -159, 117], + [-46, 425, -1375, 2996, 30567, 617, -600, 189] + ], + dtype=np.float32 +) / 2**15 + + +hq_2x_up_c_even = [x / 2**16 for x in [1746, 14986, 39083 - 65536]] +hq_2x_up_c_odd = [x / 2**16 for x in [6854, 25769, 55542 - 65536]] + + +def get_impz(coeffs, n): + s = 3*[0] + y = np.zeros(n) + x = 1 + + for i in range(n): + Y = x - s[0] + X = Y * coeffs[0] + tmp1 = s[0] + X + s[0] = x + X + + Y = tmp1 - s[1] + X = Y * coeffs[1] + tmp2 = s[1] + X + s[1] = tmp1 + X + + Y = tmp2 - s[2] + X = Y * (1 + coeffs[2]) + tmp3 = s[2] + X + s[2] = tmp2 + X + + y[i] = tmp3 + x = 0 + + return y + + + +class SilkUpsampler(nn.Module): + SUPPORTED_TARGET_RATES = {24000, 48000} + SUPPORTED_SOURCE_RATES = {16000} + def __init__(self, + fs_in=16000, + fs_out=48000): + + super().__init__() + self.fs_in = fs_in + self.fs_out = fs_out + + if fs_in not in self.SUPPORTED_SOURCE_RATES: + raise ValueError(f'SilkUpsampler currently only supports upsampling from {self.SUPPORTED_SOURCE_RATES} Hz') + + + if fs_out not in self.SUPPORTED_TARGET_RATES: + raise ValueError(f'SilkUpsampler currently only supports upsampling to {self.SUPPORTED_TARGET_RATES} Hz') + + + # hq 2x upsampler as FIR approximation + hq_2x_up_even = get_impz(hq_2x_up_c_even, 128)[::-1].copy() + hq_2x_up_odd = get_impz(hq_2x_up_c_odd , 128)[::-1].copy() + + self.hq_2x_up_even = nn.Parameter(torch.from_numpy(hq_2x_up_even).float().view(1, 1, -1), requires_grad=False) + self.hq_2x_up_odd = nn.Parameter(torch.from_numpy(hq_2x_up_odd ).float().view(1, 1, -1), requires_grad=False) + self.hq_2x_up_padding = [127, 0] + + # interpolation filters + frac_01_24 = frac_fir[0] + frac_17_24 = frac_fir[8] + frac_09_24 = frac_fir[4] + + self.frac_01_24 = nn.Parameter(torch.from_numpy(frac_01_24).view(1, 1, -1), requires_grad=False) + self.frac_17_24 = nn.Parameter(torch.from_numpy(frac_17_24).view(1, 1, -1), requires_grad=False) + self.frac_09_24 = nn.Parameter(torch.from_numpy(frac_09_24).view(1, 1, -1), requires_grad=False) + + self.stride = 1 if fs_out == 48000 else 2 + + def hq_2x_up(self, x): + + num_channels = x.size(1) + + weight_even = torch.repeat_interleave(self.hq_2x_up_even, num_channels, 0) + weight_odd = torch.repeat_interleave(self.hq_2x_up_odd , num_channels, 0) + + x_pad = F.pad(x, self.hq_2x_up_padding) + y_even = F.conv1d(x_pad, weight_even, groups=num_channels) + y_odd = F.conv1d(x_pad, weight_odd , groups=num_channels) + + y = torch.cat((y_even.unsqueeze(-1), y_odd.unsqueeze(-1)), dim=-1).flatten(2) + + return y + + def interpolate_3_2(self, x): + + num_channels = x.size(1) + + weight_01_24 = torch.repeat_interleave(self.frac_01_24, num_channels, 0) + weight_17_24 = torch.repeat_interleave(self.frac_17_24, num_channels, 0) + weight_09_24 = torch.repeat_interleave(self.frac_09_24, num_channels, 0) + + x_pad = F.pad(x, [8, 0]) + y_01_24 = F.conv1d(x_pad, weight_01_24, stride=2, groups=num_channels) + y_17_24 = F.conv1d(x_pad, weight_17_24, stride=2, groups=num_channels) + y_09_24_sh1 = F.conv1d(torch.roll(x_pad, -1, -1), weight_09_24, stride=2, groups=num_channels) + + + y = torch.cat( + (y_01_24.unsqueeze(-1), y_17_24.unsqueeze(-1), y_09_24_sh1.unsqueeze(-1)), + dim=-1).flatten(2) + + return y[..., :-3] + + def forward(self, x): + + y_2x = self.hq_2x_up(x) + y_3x = self.interpolate_3_2(y_2x) + + return y_3x[:, :, ::self.stride] diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/td_shaper.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/td_shaper.py new file mode 100644 index 0000000..fa7bf34 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/layers/td_shaper.py @@ -0,0 +1,145 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from utils.complexity import _conv1d_flop_count +from utils.softquant import soft_quant + +class TDShaper(nn.Module): + COUNTER = 1 + + def __init__(self, + feature_dim, + frame_size=160, + avg_pool_k=4, + innovate=False, + pool_after=False, + softquant=False, + apply_weight_norm=False + ): + """ + + Parameters: + ----------- + + + feature_dim : int + dimension of input features + + frame_size : int + frame size + + avg_pool_k : int, optional + kernel size and stride for avg pooling + + padding : List[int, int] + + """ + + super().__init__() + + + self.feature_dim = feature_dim + self.frame_size = frame_size + self.avg_pool_k = avg_pool_k + self.innovate = innovate + self.pool_after = pool_after + + assert frame_size % avg_pool_k == 0 + self.env_dim = frame_size // avg_pool_k + 1 + + norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x + + # feature transform + self.feature_alpha1_f = norm(nn.Conv1d(self.feature_dim, frame_size, 2)) + self.feature_alpha1_t = norm(nn.Conv1d(self.env_dim, frame_size, 2)) + self.feature_alpha2 = norm(nn.Conv1d(frame_size, frame_size, 2)) + + if softquant: + self.feature_alpha1_f = soft_quant(self.feature_alpha1_f) + + if self.innovate: + self.feature_alpha1b = norm(nn.Conv1d(self.feature_dim + self.env_dim, frame_size, 2)) + self.feature_alpha1c = norm(nn.Conv1d(self.feature_dim + self.env_dim, frame_size, 2)) + + self.feature_alpha2b = norm(nn.Conv1d(frame_size, frame_size, 2)) + self.feature_alpha2c = norm(nn.Conv1d(frame_size, frame_size, 2)) + + + def flop_count(self, rate): + + frame_rate = rate / self.frame_size + + shape_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1_f, self.feature_alpha1_t, self.feature_alpha2)]) + 11 * frame_rate * self.frame_size + + if self.innovate: + inno_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1b, self.feature_alpha2b, self.feature_alpha1c, self.feature_alpha2c)]) + 22 * frame_rate * self.frame_size + else: + inno_flops = 0 + + return shape_flops + inno_flops + + def envelope_transform(self, x): + + x = torch.abs(x) + if self.pool_after: + x = torch.log(x + .5**16) + x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k) + else: + x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k) + x = torch.log(x + .5**16) + + x = x.reshape(x.size(0), -1, self.env_dim - 1) + avg_x = torch.mean(x, -1, keepdim=True) + + x = torch.cat((x - avg_x, avg_x), dim=-1) + + return x + + def forward(self, x, features, debug=False): + """ innovate signal parts with temporal shaping + + + Parameters: + ----------- + x : torch.tensor + input signal of shape (batch_size, 1, num_samples) + + features : torch.tensor + frame-wise features of shape (batch_size, num_frames, feature_dim) + + """ + + batch_size = x.size(0) + num_frames = features.size(1) + num_samples = x.size(2) + frame_size = self.frame_size + + # generate temporal envelope + tenv = self.envelope_transform(x) + + # feature path + f = F.pad(features.permute(0, 2, 1), [1, 0]) + t = F.pad(tenv.permute(0, 2, 1), [1, 0]) + alpha = self.feature_alpha1_f(f) + self.feature_alpha1_t(t) + alpha = F.leaky_relu(alpha, 0.2) + alpha = torch.exp(self.feature_alpha2(F.pad(alpha, [1, 0]))) + alpha = alpha.permute(0, 2, 1) + + if self.innovate: + inno_alpha = F.leaky_relu(self.feature_alpha1b(f), 0.2) + inno_alpha = torch.exp(self.feature_alpha2b(F.pad(inno_alpha, [1, 0]))) + inno_alpha = inno_alpha.permute(0, 2, 1) + + inno_x = F.leaky_relu(self.feature_alpha1c(f), 0.2) + inno_x = torch.tanh(self.feature_alpha2c(F.pad(inno_x, [1, 0]))) + inno_x = inno_x.permute(0, 2, 1) + + # signal path + y = x.reshape(batch_size, num_frames, -1) + y = alpha * y + + if self.innovate: + y = y + inno_alpha * inno_x + + return y.reshape(batch_size, 1, num_samples) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/lpcnet_features.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/lpcnet_features.py new file mode 100644 index 0000000..3d109fd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/lpcnet_features.py @@ -0,0 +1,112 @@ +import os + +import torch +import numpy as np + +def load_lpcnet_features(feature_file, version=2): + if version == 2: + layout = { + 'cepstrum': [0,18], + 'periods': [18, 19], + 'pitch_corr': [19, 20], + 'lpc': [20, 36] + } + frame_length = 36 + + elif version == 1: + layout = { + 'cepstrum': [0,18], + 'periods': [36, 37], + 'pitch_corr': [37, 38], + 'lpc': [39, 55], + } + frame_length = 55 + else: + raise ValueError(f'unknown feature version: {version}') + + + raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32')) + raw_features = raw_features.reshape((-1, frame_length)) + + features = torch.cat( + [ + raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]], + raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]] + ], + dim=1 + ) + + lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]] + periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long() + + return {'features' : features, 'periods' : periods, 'lpcs' : lpcs} + + + +def create_new_data(signal_path, reference_data_path, new_data_path, offset=320, preemph_factor=0.85): + ref_data = np.memmap(reference_data_path, dtype=np.int16) + signal = np.memmap(signal_path, dtype=np.int16) + + signal_preemph_path = os.path.splitext(signal_path)[0] + '_preemph.raw' + signal_preemph = np.memmap(signal_preemph_path, dtype=np.int16, mode='write', shape=signal.shape) + + + assert len(signal) % 160 == 0 + num_frames = len(signal) // 160 + mem = np.zeros(1) + for fr in range(len(signal)//160): + signal_preemph[fr * 160 : (fr + 1) * 160] = np.convolve(np.concatenate((mem, signal[fr * 160 : (fr + 1) * 160])), [1, -preemph_factor], mode='valid') + mem = signal[(fr + 1) * 160 - 1 : (fr + 1) * 160] + + new_data = np.memmap(new_data_path, dtype=np.int16, mode='write', shape=ref_data.shape) + + new_data[:] = 0 + N = len(signal) - offset + new_data[1 : 2*N + 1: 2] = signal_preemph[offset:] + new_data[2 : 2*N + 2: 2] = signal_preemph[offset:] + + +def parse_warpq_scores(output_file): + """ extracts warpq scores from output file """ + + with open(output_file, "r") as f: + lines = f.readlines() + + scores = [float(line.split("WARP-Q score:")[-1]) for line in lines if line.startswith("WARP-Q score:")] + + return scores + + +def parse_stats_file(file): + + with open(file, "r") as f: + lines = f.readlines() + + mean = float(lines[0].split(":")[-1]) + bt_mean = float(lines[1].split(":")[-1]) + top_mean = float(lines[2].split(":")[-1]) + + return mean, bt_mean, top_mean + +def collect_test_stats(test_folder): + """ collects statistics for all discovered metrics from test folder """ + + metrics = {'pesq', 'warpq', 'pitch_error', 'voicing_error'} + + results = dict() + + content = os.listdir(test_folder) + + stats_files = [file for file in content if file.startswith('stats_')] + + for file in stats_files: + metric = file[len("stats_") : -len(".txt")] + + if metric not in metrics: + print(f"warning: unknown metric {metric}") + + mean, bt_mean, top_mean = parse_stats_file(os.path.join(test_folder, file)) + + results[metric] = [mean, bt_mean, top_mean] + + return results diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/misc.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/misc.py new file mode 100644 index 0000000..68ee4bf --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/misc.py @@ -0,0 +1,95 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +from torch.nn.utils import remove_weight_norm + +def count_parameters(model, verbose=False): + total = 0 + for name, p in model.named_parameters(): + count = torch.ones_like(p).sum().item() + + if verbose: + print(f"{name}: {count} parameters") + + total += count + + return total + +def count_nonzero_parameters(model, verbose=False): + total = 0 + for name, p in model.named_parameters(): + count = torch.count_nonzero(p).item() + + if verbose: + print(f"{name}: {count} non-zero parameters") + + total += count + + return total +def retain_grads(module): + for p in module.parameters(): + if p.requires_grad: + p.retain_grad() + +def get_grad_norm(module, p=2): + norm = 0 + for param in module.parameters(): + if param.requires_grad: + norm = norm + (torch.abs(param.grad) ** p).sum() + + return norm ** (1/p) + +def create_weights(s_real, s_gen, alpha): + weights = [] + with torch.no_grad(): + for sr, sg in zip(s_real, s_gen): + weight = torch.exp(alpha * (sr[-1] - sg[-1])) + weights.append(weight) + + return weights + + +def _get_candidates(module: torch.nn.Module): + candidates = [] + for key in module.__dict__.keys(): + if hasattr(module, key + '_v'): + candidates.append(key) + return candidates + +def remove_all_weight_norm(model : torch.nn.Module, verbose=False): + for name, m in model.named_modules(): + candidates = _get_candidates(m) + + for candidate in candidates: + try: + remove_weight_norm(m, name=candidate) + if verbose: print(f'removed weight norm on weight {name}.{candidate}') + except: + pass diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/moc.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/moc.py new file mode 100644 index 0000000..3dffcfd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/moc.py @@ -0,0 +1,153 @@ +import numpy as np +import scipy.signal + +def compute_vad_mask(x, fs, stop_db=-70): + + frame_length = (fs + 49) // 50 + x = x[: frame_length * (len(x) // frame_length)] + + frames = x.reshape(-1, frame_length) + frame_energy = np.sum(frames ** 2, axis=1) + frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') + + max_threshold = frame_energy.max() * 10 ** (stop_db/20) + vactive = np.ones_like(frames) + vactive[frame_energy_smooth < max_threshold, :] = 0 + vactive = vactive.reshape(-1) + + filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) + filter = filter / filter.sum() + + mask = np.convolve(vactive, filter, mode='same') + + return x, mask + +def convert_mask(mask, num_frames, frame_size=160, hop_size=40): + num_samples = frame_size + (num_frames - 1) * hop_size + if len(mask) < num_samples: + mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) + else: + mask = mask[:num_samples] + + new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) + + return new_mask + +def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): + num_spectra = (len(x) - window_size - hop_size) // hop_size + window = scipy.signal.get_window(window, window_size) + N = window_size // 2 + + frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window + psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 + + return psd + + +def frequency_mask(num_bands, up_factor, down_factor): + + up_mask = np.zeros((num_bands, num_bands)) + down_mask = np.zeros((num_bands, num_bands)) + + for i in range(num_bands): + up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) + down_mask[i, i :] = down_factor ** np.arange(num_bands - i) + + return down_mask @ up_mask + + +def rect_fb(band_limits, num_bins=None): + num_bands = len(band_limits) - 1 + if num_bins is None: + num_bins = band_limits[-1] + + fb = np.zeros((num_bands, num_bins)) + for i in range(num_bands): + fb[i, band_limits[i]:band_limits[i+1]] = 1 + + return fb + + +def compare(x, y, apply_vad=False): + """ Modified version of opus_compare for 16 kHz mono signals + + Args: + x (np.ndarray): reference input signal scaled to [-1, 1] + y (np.ndarray): test signal scaled to [-1, 1] + + Returns: + float: perceptually weighted error + """ + # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz + band_limits = [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75] + num_bands = len(band_limits) - 1 + fb = rect_fb(band_limits, num_bins=81) + + # trim samples to same size + num_samples = min(len(x), len(y)) + x = x[:num_samples] * 2**15 + y = y[:num_samples] * 2**15 + + psd_x = power_spectrum(x) + 100000 + psd_y = power_spectrum(y) + 100000 + + num_frames = psd_x.shape[0] + + # average band energies + be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) + + # frequecy masking + f_mask = frequency_mask(num_bands, 0.1, 0.03) + mask_x = be_x @ f_mask.T + + # temporal masking + for i in range(1, num_frames): + mask_x[i, :] += 0.5 * mask_x[i-1, :] + + # apply mask + masked_psd_x = psd_x + 0.1 * (mask_x @ fb) + masked_psd_y = psd_y + 0.1 * (mask_x @ fb) + + # 2-frame average + masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] + masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] + + # distortion metric + re = masked_psd_y / masked_psd_x + im = np.log(re) ** 2 + Eb = ((im @ fb.T) / np.sum(fb, axis=1)) + Ef = np.mean(Eb , axis=1) + + if apply_vad: + _, mask = compute_vad_mask(x, 16000) + mask = convert_mask(mask, Ef.shape[0]) + else: + mask = np.ones_like(Ef) + + err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) + + return float(err) + +if __name__ == "__main__": + import argparse + from scipy.io import wavfile + + parser = argparse.ArgumentParser() + parser.add_argument('ref', type=str, help='reference wav file') + parser.add_argument('deg', type=str, help='degraded wav file') + parser.add_argument('--apply-vad', action='store_true') + args = parser.parse_args() + + + fs1, x = wavfile.read(args.ref) + fs2, y = wavfile.read(args.deg) + + if max(fs1, fs2) != 16000: + raise ValueError('error: encountered sampling frequency diffrent from 16kHz') + + x = x.astype(np.float32) / 2**15 + y = y.astype(np.float32) / 2**15 + + err = compare(x, y, apply_vad=args.apply_vad) + + print(f"MOC: {err}") diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/pitch.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/pitch.py new file mode 100644 index 0000000..2a23381 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/pitch.py @@ -0,0 +1,122 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np + +def hangover(lags, num_frames=10): + lags = lags.copy() + count = 0 + last_lag = 0 + + for i in range(len(lags)): + lag = lags[i] + + if lag == 0: + if count < num_frames: + lags[i] = last_lag + count += 1 + else: + count = 0 + last_lag = lag + + return lags + + +def smooth_pitch_lags(lags, d=2): + + assert d < 4 + + num_silk_frames = len(lags) // 4 + + smoothed_lags = lags.copy() + + tmp = np.arange(1, d+1) + kernel = np.concatenate((tmp, [d+1], tmp[::-1]), dtype=np.float32) + kernel = kernel / np.sum(kernel) + + last = lags[0:d][::-1] + for i in range(num_silk_frames): + frame = lags[i * 4: (i+1) * 4] + + if np.max(np.abs(frame)) == 0: + last = frame[4-d:] + continue + + if i == num_silk_frames - 1: + next = frame[4-d:][::-1] + else: + next = lags[(i+1) * 4 : (i+1) * 4 + d] + + if np.max(np.abs(next)) == 0: + next = frame[4-d:][::-1] + + if np.max(np.abs(last)) == 0: + last = frame[0:d][::-1] + + smoothed_frame = np.convolve(np.concatenate((last, frame, next), dtype=np.float32), kernel, mode='valid') + + smoothed_lags[i * 4: (i+1) * 4] = np.round(smoothed_frame) + + last = frame[4-d:] + + return smoothed_lags + +def calculate_acorr_window(x, frame_size, lags, history=None, max_lag=300, radius=2, add_double_lag_acorr=False, no_pitch_threshold=32): + eps = 1e-9 + + lag_multiplier = 2 if add_double_lag_acorr else 1 + + if history is None: + history = np.zeros(lag_multiplier * max_lag + radius, dtype=x.dtype) + + offset = len(history) + + assert offset >= max_lag + radius + assert len(x) % frame_size == 0 + + num_frames = len(x) // frame_size + lags = lags.copy() + + x_ext = np.concatenate((history, x), dtype=x.dtype) + + d = radius + num_acorrs = 2 * d + 1 + acorrs = np.zeros((num_frames, lag_multiplier * num_acorrs), dtype=x.dtype) + + for idx in range(num_frames): + lag = lags[idx].item() + frame = x_ext[offset + idx * frame_size : offset + (idx + 1) * frame_size] + + for k in range(lag_multiplier): + lag1 = (k + 1) * lag if lag >= no_pitch_threshold else lag + for j in range(num_acorrs): + past = x_ext[offset + idx * frame_size - lag1 + j - d : offset + (idx + 1) * frame_size - lag1 + j - d] + acorrs[idx, j + k * num_acorrs] = np.dot(frame, past) / np.sqrt(np.dot(frame, frame) * np.dot(past, past) + eps) + + return acorrs, lags \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/silk_features.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/silk_features.py new file mode 100644 index 0000000..8c5dbf0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/silk_features.py @@ -0,0 +1,144 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + +import numpy as np +import torch + +import scipy +import scipy.signal + +from utils.pitch import hangover, calculate_acorr_window +from utils.spec import create_filter_bank, cepstrum, log_spectrum, log_spectrum_from_lpc + +def spec_from_lpc(a, n_fft=128, eps=1e-9): + order = a.shape[-1] + assert order + 1 < n_fft + + x = np.zeros((*a.shape[:-1], n_fft )) + x[..., 0] = 1 + x[..., 1:1 + order] = -a + + X = np.fft.fft(x, axis=-1) + X = np.abs(X[..., :n_fft//2 + 1]) ** 2 + + S = 1 / (X + eps) + + return S + +def silk_feature_factory(no_pitch_value=256, + acorr_radius=2, + pitch_hangover=8, + num_bands_clean_spec=64, + num_bands_noisy_spec=18, + noisy_spec_scale='opus', + noisy_apply_dct=True, + add_double_lag_acorr=False + ): + + w = scipy.signal.windows.cosine(320) + fb_clean_spec = create_filter_bank(num_bands_clean_spec, 320, scale='erb', round_center_bins=True, normalize=True) + fb_noisy_spec = create_filter_bank(num_bands_noisy_spec, 320, scale=noisy_spec_scale, round_center_bins=True, normalize=True) + + def create_features(noisy, noisy_history, lpcs, gains, ltps, periods): + + periods = periods.copy() + + if pitch_hangover > 0: + periods = hangover(periods, num_frames=pitch_hangover) + + periods[periods == 0] = no_pitch_value + + clean_spectrum = 0.3 * log_spectrum_from_lpc(lpcs, fb=fb_clean_spec, n_fft=320) + + if noisy_apply_dct: + noisy_cepstrum = np.repeat( + cepstrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0) + else: + noisy_cepstrum = np.repeat( + log_spectrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0) + + log_gains = np.log(gains + 1e-9).reshape(-1, 1) + + acorr, _ = calculate_acorr_window(noisy, 80, periods, noisy_history, radius=acorr_radius, add_double_lag_acorr=add_double_lag_acorr) + + features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains), axis=-1, dtype=np.float32) + + return features, periods.astype(np.int64) + + return create_features + + + +def load_inference_data(path, + no_pitch_value=256, + skip=92, + preemph=0.85, + acorr_radius=2, + pitch_hangover=8, + num_bands_clean_spec=64, + num_bands_noisy_spec=18, + noisy_spec_scale='opus', + noisy_apply_dct=True, + add_double_lag_acorr=False, + **kwargs): + + print(f"[load_inference_data]: ignoring keyword arguments {kwargs.keys()}...") + + lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) + ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) + gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) + periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) + num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32).astype(np.float32).reshape(-1, 1) + num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32).reshape(-1, 1) + + # load signal, add back delay and pre-emphasize + signal = np.fromfile(os.path.join(path, 'noisy.s16'), dtype=np.int16).astype(np.float32) / (2 ** 15) + signal = np.concatenate((np.zeros(skip, dtype=np.float32), signal), dtype=np.float32) + + create_features = silk_feature_factory(no_pitch_value, acorr_radius, pitch_hangover, num_bands_clean_spec, num_bands_noisy_spec, noisy_spec_scale, noisy_apply_dct, add_double_lag_acorr) + + num_frames = min((len(signal) // 320) * 4, len(lpcs)) + signal = signal[: num_frames * 80] + lpcs = lpcs[: num_frames] + ltps = ltps[: num_frames] + gains = gains[: num_frames] + periods = periods[: num_frames] + num_bits = num_bits[: num_frames // 4] + num_bits_smooth = num_bits[: num_frames // 4] + + numbits = np.repeat(np.concatenate((num_bits, num_bits_smooth), axis=-1, dtype=np.float32), 4, axis=0) + + features, periods = create_features(signal, np.zeros(350, dtype=signal.dtype), lpcs, gains, ltps, periods) + + if preemph > 0: + signal[1:] -= preemph * signal[:-1] + + return torch.from_numpy(signal), torch.from_numpy(features), torch.from_numpy(periods), torch.from_numpy(numbits) diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/softquant.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/softquant.py new file mode 100644 index 0000000..5fca5b2 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/softquant.py @@ -0,0 +1,110 @@ +import torch + +@torch.no_grad() +def compute_optimal_scale(weight): + with torch.no_grad(): + n_out, n_in = weight.shape + assert n_in % 4 == 0 + if n_out % 8: + # add padding + pad = n_out - n_out % 8 + weight = torch.cat((weight, torch.zeros((pad, n_in), dtype=weight.dtype, device=weight.device)), dim=0) + + weight_max_abs, _ = torch.max(torch.abs(weight), dim=1) + weight_max_sum, _ = torch.max(torch.abs(weight[:, : n_in : 2] + weight[:, 1 : n_in : 2]), dim=1) + scale_max = weight_max_abs / 127 + scale_sum = weight_max_sum / 129 + + scale = torch.maximum(scale_max, scale_sum) + + return scale[:n_out] + +@torch.no_grad() +def q_scaled_noise(module, weight): + if isinstance(module, torch.nn.Conv1d): + w = weight.permute(0, 2, 1).flatten(1) + noise = torch.rand_like(w) - 0.5 + scale = compute_optimal_scale(w) + noise = noise * scale.unsqueeze(-1) + noise = noise.reshape(weight.size(0), weight.size(2), weight.size(1)).permute(0, 2, 1) + elif isinstance(module, torch.nn.ConvTranspose1d): + i, o, k = weight.shape + w = weight.permute(2, 1, 0).reshape(k * o, i) + noise = torch.rand_like(w) - 0.5 + scale = compute_optimal_scale(w) + noise = noise * scale.unsqueeze(-1) + noise = noise.reshape(k, o, i).permute(2, 1, 0) + elif len(weight.shape) == 2: + noise = torch.rand_like(weight) - 0.5 + scale = compute_optimal_scale(weight) + noise = noise * scale.unsqueeze(-1) + else: + raise ValueError('unknown quantization setting') + + return noise + +class SoftQuant: + name: str + + def __init__(self, names: str, scale: float) -> None: + self.names = names + self.quantization_noise = None + self.scale = scale + + def __call__(self, module, inputs, *args, before=True): + if not module.training: return + + if before: + self.quantization_noise = dict() + for name in self.names: + weight = getattr(module, name) + if self.scale is None: + self.quantization_noise[name] = q_scaled_noise(module, weight) + else: + self.quantization_noise[name] = \ + self.scale * weight.abs().max() * (torch.rand_like(weight) - 0.5) + with torch.no_grad(): + weight.data[:] = weight + self.quantization_noise[name] + else: + for name in self.names: + weight = getattr(module, name) + with torch.no_grad(): + weight.data[:] = weight - self.quantization_noise[name] + self.quantization_noise = None + + def apply(module, names=['weight'], scale=None): + fn = SoftQuant(names, scale) + + for name in names: + if not hasattr(module, name): + raise ValueError("") + + fn_before = lambda *x : fn(*x, before=True) + fn_after = lambda *x : fn(*x, before=False) + setattr(fn_before, 'sqm', fn) + setattr(fn_after, 'sqm', fn) + + + module.register_forward_pre_hook(fn_before) + module.register_forward_hook(fn_after) + + module + + return fn + + +def soft_quant(module, names=['weight'], scale=None): + fn = SoftQuant.apply(module, names, scale) + return module + +def remove_soft_quant(module, names=['weight']): + for k, hook in module._forward_pre_hooks.items(): + if hasattr(hook, 'sqm'): + if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: + del module._forward_pre_hooks[k] + for k, hook in module._forward_hooks.items(): + if hasattr(hook, 'sqm'): + if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: + del module._forward_hooks[k] + + return module \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/spec.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/spec.py new file mode 100644 index 0000000..59f5353 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/spec.py @@ -0,0 +1,210 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import math as m +import numpy as np +import scipy +import scipy.fftpack +import torch + +def erb(f): + return 24.7 * (4.37 * f + 1) + +def inv_erb(e): + return (e / 24.7 - 1) / 4.37 + +def bark(f): + return 6 * m.asinh(f/600) + +def inv_bark(b): + return 600 * m.sinh(b / 6) + + +scale_dict = { + 'bark': [bark, inv_bark], + 'erb': [erb, inv_erb] +} + +def gen_filterbank(N, Fs=16000, keep_size=False): + in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] + M = N + 1 if keep_size else N + out_freq = (np.arange(M, dtype='float32')/N*Fs/2)[:,None] + #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. + ERB_N = 24.7 + .108*in_freq + delta = np.abs(in_freq-out_freq)/ERB_N + center = (delta<.5).astype('float32') + R = -12*center*delta**2 + (1-center)*(3-12*delta) + RE = 10.**(R/10.) + norm = np.sum(RE, axis=1) + RE = RE/norm[:, np.newaxis] + return torch.from_numpy(RE) + +def create_filter_bank(num_bands, n_fft=320, fs=16000, scale='bark', round_center_bins=False, return_upper=False, normalize=False): + + f0 = 0 + num_bins = n_fft // 2 + 1 + f1 = fs / n_fft * (num_bins - 1) + fstep = fs / n_fft + + if scale == 'opus': + bins_5ms = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40] + fac = 1000 * n_fft / fs / 5 + if num_bands != 18: + print("warning: requested Opus filter bank with num_bands != 18. Adjusting num_bands.") + num_bands = 18 + center_bins = np.array([fac * bin for bin in bins_5ms]) + else: + to_scale, from_scale = scale_dict[scale] + + s0 = to_scale(f0) + s1 = to_scale(f1) + + center_freqs = np.array([f0] + [from_scale(s0 + i * (s1 - s0) / (num_bands)) for i in range(1, num_bands - 1)] + [f1]) + center_bins = (center_freqs - f0) / fstep + + if round_center_bins: + center_bins = np.round(center_bins) + + filter_bank = np.zeros((num_bands, num_bins)) + + band = 0 + for bin in range(num_bins): + # update band index + if bin > center_bins[band + 1]: + band += 1 + + # calculate filter coefficients + frac = (center_bins[band + 1] - bin) / (center_bins[band + 1] - center_bins[band]) + filter_bank[band][bin] = frac + filter_bank[band + 1][bin] = 1 - frac + + if return_upper: + extend = n_fft - num_bins + filter_bank = np.concatenate((filter_bank, np.fliplr(filter_bank[:, 1:extend+1])), axis=1) + + if normalize: + filter_bank = filter_bank / np.sum(filter_bank, axis=1).reshape(-1, 1) + + return filter_bank + + +def compressed_log_spec(pspec): + + lpspec = np.zeros_like(pspec) + num_bands = pspec.shape[-1] + + log_max = -2 + follow = -2 + + for i in range(num_bands): + tmp = np.log10(pspec[i] + 1e-9) + tmp = max(log_max, max(follow - 2.5, tmp)) + lpspec[i] = tmp + log_max = max(log_max, tmp) + follow = max(follow - 2.5, tmp) + + return lpspec + +def log_spectrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False, power=1): + """ calculates cepstrum from SILK lpcs """ + order = a.shape[-1] + assert order + 1 < n_fft + + a = a * (gamma ** (1 + np.arange(order))).astype(np.float32) + + x = np.zeros((*a.shape[:-1], n_fft )) + x[..., 0] = 1 + x[..., 1:1 + order] = -a + + X = np.fft.fft(x, axis=-1) + X = np.abs(X[..., :n_fft//2 + 1]) ** power + + S = 1 / (X + eps) + + if fb is None: + Sf = S + else: + Sf = np.matmul(S, fb.T) + + if compress: + Sf = np.apply_along_axis(compressed_log_spec, -1, Sf) + else: + Sf = np.log(Sf + eps) + + return Sf + +def cepstrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False): + """ calculates cepstrum from SILK lpcs """ + + Sf = log_spectrum_from_lpc(a, fb, n_fft, eps, gamma, compress) + + cepstrum = scipy.fftpack.dct(Sf, 2, norm='ortho') + + return cepstrum + + + +def log_spectrum(x, frame_size, fb=None, window=None, power=1): + """ calculate cepstrum on 50% overlapping frames """ + + assert(2*len(x)) % frame_size == 0 + assert frame_size % 2 == 0 + + n = len(x) + num_even = n // frame_size + num_odd = (n - frame_size // 2) // frame_size + num_bins = frame_size // 2 + 1 + + x_even = x[:num_even * frame_size].reshape(-1, frame_size) + x_odd = x[frame_size//2 : frame_size//2 + frame_size * num_odd].reshape(-1, frame_size) + + x_unfold = np.empty((x_even.size + x_odd.size), dtype=x.dtype).reshape((-1, frame_size)) + x_unfold[::2, :] = x_even + x_unfold[1::2, :] = x_odd + + if window is not None: + x_unfold *= window.reshape(1, -1) + + X = np.abs(np.fft.fft(x_unfold, n=frame_size, axis=-1))[:, :num_bins] ** power + + if fb is not None: + X = np.matmul(X, fb.T) + + + return np.log(X + 1e-9) + + +def cepstrum(x, frame_size, fb=None, window=None): + """ calculate cepstrum on 50% overlapping frames """ + + X = log_spectrum(x, frame_size, fb, window) + + cepstrum = scipy.fftpack.dct(X, 2, norm='ortho') + + return cepstrum \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/osce/utils/templates.py b/managed_components/78__esp-opus/dnn/torch/osce/utils/templates.py new file mode 100644 index 0000000..5fc84ef --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/osce/utils/templates.py @@ -0,0 +1,347 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +setup_dict = dict() + +lace_setup = { + 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', + 'validation_dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/validation', + 'model': { + 'name': 'lace', + 'args': [], + 'kwargs': { + 'comb_gain_limit_db': 10, + 'cond_dim': 128, + 'conv_gain_limits_db': [-12, 12], + 'global_gain_limits_db': [-6, 6], + 'hidden_feature_dim': 96, + 'kernel_size': 15, + 'num_features': 93, + 'numbits_embedding_dim': 8, + 'numbits_range': [50, 650], + 'partial_lookahead': True, + 'pitch_embedding_dim': 64, + 'pitch_max': 300, + 'preemph': 0.85, + 'skip': 91, + 'softquant': True, + 'sparsify': False, + 'sparsification_density': 0.4, + 'sparsification_schedule': [10000, 40000, 200] + } + }, + 'data': { + 'frames_per_sample': 100, + 'no_pitch_value': 7, + 'preemph': 0.85, + 'skip': 91, + 'pitch_hangover': 8, + 'acorr_radius': 2, + 'num_bands_clean_spec': 64, + 'num_bands_noisy_spec': 18, + 'noisy_spec_scale': 'opus', + 'pitch_hangover': 0, + }, + 'training': { + 'batch_size': 256, + 'lr': 5.e-4, + 'lr_decay_factor': 2.5e-5, + 'epochs': 50, + 'loss': { + 'w_l1': 0, + 'w_lm': 0, + 'w_logmel': 0, + 'w_sc': 0, + 'w_wsc': 0, + 'w_xcorr': 0, + 'w_sxcorr': 1, + 'w_l2': 10, + 'w_slm': 2 + } + } +} + + +nolace_setup = { + 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', + 'validation_dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/validation', + 'model': { + 'name': 'nolace', + 'args': [], + 'kwargs': { + 'avg_pool_k': 4, + 'comb_gain_limit_db': 10, + 'cond_dim': 256, + 'conv_gain_limits_db': [-12, 12], + 'global_gain_limits_db': [-6, 6], + 'hidden_feature_dim': 96, + 'kernel_size': 15, + 'num_features': 93, + 'numbits_embedding_dim': 8, + 'numbits_range': [50, 650], + 'partial_lookahead': True, + 'pitch_embedding_dim': 64, + 'pitch_max': 300, + 'preemph': 0.85, + 'skip': 91, + 'softquant': True, + 'sparsify': False, + 'sparsification_density': 0.4, + 'sparsification_schedule': [10000, 40000, 200] + } + }, + 'data': { + 'frames_per_sample': 100, + 'no_pitch_value': 7, + 'preemph': 0.85, + 'skip': 91, + 'pitch_hangover': 8, + 'acorr_radius': 2, + 'num_bands_clean_spec': 64, + 'num_bands_noisy_spec': 18, + 'noisy_spec_scale': 'opus', + 'pitch_hangover': 0, + }, + 'training': { + 'batch_size': 256, + 'lr': 5.e-4, + 'lr_decay_factor': 2.5e-5, + 'epochs': 50, + 'loss': { + 'w_l1': 0, + 'w_lm': 0, + 'w_logmel': 0, + 'w_sc': 0, + 'w_wsc': 0, + 'w_xcorr': 0, + 'w_sxcorr': 1, + 'w_l2': 10, + 'w_slm': 2 + } + } +} + +nolace_setup_adv = { + 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', + 'model': { + 'name': 'nolace', + 'args': [], + 'kwargs': { + 'avg_pool_k': 4, + 'comb_gain_limit_db': 10, + 'cond_dim': 256, + 'conv_gain_limits_db': [-12, 12], + 'global_gain_limits_db': [-6, 6], + 'hidden_feature_dim': 96, + 'kernel_size': 15, + 'num_features': 93, + 'numbits_embedding_dim': 8, + 'numbits_range': [50, 650], + 'partial_lookahead': True, + 'pitch_embedding_dim': 64, + 'pitch_max': 300, + 'preemph': 0.85, + 'skip': 91, + 'softquant': True, + 'sparsify': False, + 'sparsification_density': 0.4, + 'sparsification_schedule': [0, 0, 200] + } + }, + 'data': { + 'frames_per_sample': 100, + 'no_pitch_value': 7, + 'preemph': 0.85, + 'skip': 91, + 'pitch_hangover': 8, + 'acorr_radius': 2, + 'num_bands_clean_spec': 64, + 'num_bands_noisy_spec': 18, + 'noisy_spec_scale': 'opus', + 'pitch_hangover': 0, + }, + 'discriminator': { + 'args': [], + 'kwargs': { + 'architecture': 'free', + 'design': 'f_down', + 'fft_sizes_16k': [ + 64, + 128, + 256, + 512, + 1024, + 2048, + ], + 'freq_roi': [0, 7400], + 'fs': 16000, + 'max_channels': 256, + 'noise_gain': 0.0, + }, + 'name': 'fdmresdisc', + }, + 'training': { + 'adv_target': 'target_orig', + 'batch_size': 64, + 'epochs': 50, + 'gen_lr_reduction': 1, + 'lambda_feat': 1.0, + 'lambda_reg': 0.6, + 'loss': { + 'w_l1': 0, + 'w_l2': 10, + 'w_lm': 0, + 'w_logmel': 0, + 'w_sc': 0, + 'w_slm': 20, + 'w_sxcorr': 1, + 'w_wsc': 0, + 'w_xcorr': 0, + }, + 'lr': 0.0001, + 'lr_decay_factor': 2.5e-09, + } +} + + +lavoce_setup = { + 'data': { + 'frames_per_sample': 100, + 'target': 'signal' + }, + 'dataset': '/local/datasets/lpcnet_large/training', + 'model': { + 'args': [], + 'kwargs': { + 'comb_gain_limit_db': 10, + 'cond_dim': 256, + 'conv_gain_limits_db': [-12, 12], + 'global_gain_limits_db': [-6, 6], + 'kernel_size': 15, + 'num_features': 19, + 'pitch_embedding_dim': 64, + 'pitch_max': 300, + 'preemph': 0.85, + 'pulses': True + }, + 'name': 'lavoce' + }, + 'training': { + 'batch_size': 256, + 'epochs': 50, + 'loss': { + 'w_l1': 0, + 'w_l2': 0, + 'w_lm': 0, + 'w_logmel': 0, + 'w_sc': 0, + 'w_slm': 2, + 'w_sxcorr': 1, + 'w_wsc': 0, + 'w_xcorr': 0 + }, + 'lr': 0.0005, + 'lr_decay_factor': 2.5e-05 + }, + 'validation_dataset': '/local/datasets/lpcnet_large/validation' +} + +lavoce_setup_adv = { + 'data': { + 'frames_per_sample': 100, + 'target': 'signal' + }, + 'dataset': '/local/datasets/lpcnet_large/training', + 'discriminator': { + 'args': [], + 'kwargs': { + 'architecture': 'free', + 'design': 'f_down', + 'fft_sizes_16k': [ + 64, + 128, + 256, + 512, + 1024, + 2048, + ], + 'freq_roi': [0, 7400], + 'fs': 16000, + 'max_channels': 256, + 'noise_gain': 0.0, + }, + 'name': 'fdmresdisc', + }, + 'model': { + 'args': [], + 'kwargs': { + 'comb_gain_limit_db': 10, + 'cond_dim': 256, + 'conv_gain_limits_db': [-12, 12], + 'global_gain_limits_db': [-6, 6], + 'kernel_size': 15, + 'num_features': 19, + 'pitch_embedding_dim': 64, + 'pitch_max': 300, + 'preemph': 0.85, + 'pulses': True + }, + 'name': 'lavoce' + }, + 'training': { + 'batch_size': 64, + 'epochs': 50, + 'gen_lr_reduction': 1, + 'lambda_feat': 1.0, + 'lambda_reg': 0.6, + 'loss': { + 'w_l1': 0, + 'w_l2': 0, + 'w_lm': 0, + 'w_logmel': 0, + 'w_sc': 0, + 'w_slm': 2, + 'w_sxcorr': 1, + 'w_wsc': 0, + 'w_xcorr': 0 + }, + 'lr': 0.0001, + 'lr_decay_factor': 2.5e-09 + }, +} + + +setup_dict = { + 'lace': lace_setup, + 'nolace': nolace_setup, + 'nolace_adv': nolace_setup_adv, + 'lavoce': lavoce_setup, + 'lavoce_adv': lavoce_setup_adv +} diff --git a/managed_components/78__esp-opus/dnn/torch/plc/export_plc.py b/managed_components/78__esp-opus/dnn/torch/plc/export_plc.py new file mode 100644 index 0000000..7f153c4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/plc/export_plc.py @@ -0,0 +1,100 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) + + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint', type=str, help='model checkpoint') +parser.add_argument('output_dir', type=str, help='output folder') + +args = parser.parse_args() + +import torch +import numpy as np + +import plc +from wexchange.torch import dump_torch_weights +from wexchange.c_export import CWriter, print_vector + +def c_export(args, model): + + message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" + + writer = CWriter(os.path.join(args.output_dir, "plc_data"), message=message, model_struct_name='PLCModel') + writer.header.write( +f""" +#include "opus_types.h" +""" + ) + + dense_layers = [ + ('dense_in', "plc_dense_in"), + ('dense_out', "plc_dense_out") + ] + + + for name, export_name in dense_layers: + layer = model.get_submodule(name) + dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None) + + + gru_layers = [ + ("gru1", "plc_gru1"), + ("gru2", "plc_gru2"), + ] + + max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) + for name, export_name in gru_layers]) + + writer.header.write( +f""" + +#define PLC_MAX_RNN_UNITS {max_rnn_units} + +""" + ) + + writer.close() + + +if __name__ == "__main__": + + os.makedirs(args.output_dir, exist_ok=True) + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs']) + model.load_state_dict(checkpoint['state_dict'], strict=False) + #checkpoint = torch.load(args.checkpoint, map_location='cpu') + #model.load_state_dict(checkpoint['state_dict']) + c_export(args, model) diff --git a/managed_components/78__esp-opus/dnn/torch/plc/plc.py b/managed_components/78__esp-opus/dnn/torch/plc/plc.py new file mode 100644 index 0000000..f08e564 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/plc/plc.py @@ -0,0 +1,144 @@ +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm +import math + +fid_dict = {} +def dump_signal(x, filename): + return + if filename in fid_dict: + fid = fid_dict[filename] + else: + fid = open(filename, "w") + fid_dict[filename] = fid + x = x.detach().numpy().astype('float32') + x.tofile(fid) + + +class IDCT(nn.Module): + def __init__(self, N, device=None): + super(IDCT, self).__init__() + + self.N = N + n = torch.arange(N, device=device) + k = torch.arange(N, device=device) + self.table = torch.cos(torch.pi/N * (n[:,None]+.5) * k[None,:]) + self.table[:,0] = self.table[:,0] * math.sqrt(.5) + self.table = self.table / math.sqrt(N/2) + + def forward(self, x): + return F.linear(x, self.table, None) + +def plc_loss(N, device=None, alpha=1.0, bias=1.): + idct = IDCT(18, device=device) + def loss(y_true,y_pred): + mask = y_true[:,:,-1:] + y_true = y_true[:,:,:-1] + e = (y_pred - y_true)*mask + e_bands = idct(e[:,:,:-2]) + bias_mask = torch.clamp(4*y_true[:,:,-1:], min=0., max=1.) + l1_loss = torch.mean(torch.abs(e)) + ceps_loss = torch.mean(torch.abs(e[:,:,:-2])) + band_loss = torch.mean(torch.abs(e_bands)) + biased_loss = torch.mean(bias_mask*torch.clamp(e_bands, min=0.)) + pitch_loss1 = torch.mean(torch.clamp(torch.abs(e[:,:,18:19]),max=1.)) + pitch_loss = torch.mean(torch.clamp(torch.abs(e[:,:,18:19]),max=.4)) + voice_bias = torch.mean(torch.clamp(-e[:,:,-1:], min=0.)) + tot = l1_loss + 0.1*voice_bias + alpha*(band_loss + bias*biased_loss) + pitch_loss1 + 8*pitch_loss + return tot, l1_loss, ceps_loss, band_loss, pitch_loss + return loss + + +# weight initialization and clipping +def init_weights(module): + if isinstance(module, nn.GRU): + for p in module.named_parameters(): + if p[0].startswith('weight_hh_'): + nn.init.orthogonal_(p[1]) + + +class GLU(nn.Module): + def __init__(self, feat_size): + super(GLU, self).__init__() + + torch.manual_seed(5) + + self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + out = x * torch.sigmoid(self.gate(x)) + + return out + +class FWConv(nn.Module): + def __init__(self, in_size, out_size, kernel_size=2): + super(FWConv, self).__init__() + + torch.manual_seed(5) + + self.in_size = in_size + self.kernel_size = kernel_size + self.conv = weight_norm(nn.Linear(in_size*self.kernel_size, out_size, bias=False)) + self.glu = GLU(out_size) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x, state): + xcat = torch.cat((state, x), -1) + out = self.glu(torch.tanh(self.conv(xcat))) + return out, xcat[:,self.in_size:] + +def n(x): + return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) + +class PLC(nn.Module): + def __init__(self, features_in=57, features_out=20, cond_size=128, gru_size=128): + super(PLC, self).__init__() + + self.features_in = features_in + self.features_out = features_out + self.cond_size = cond_size + self.gru_size = gru_size + + self.dense_in = nn.Linear(self.features_in, self.cond_size) + self.gru1 = nn.GRU(self.cond_size, self.gru_size, batch_first=True) + self.gru2 = nn.GRU(self.gru_size, self.gru_size, batch_first=True) + self.dense_out = nn.Linear(self.gru_size, features_out) + + self.apply(init_weights) + nb_params = sum(p.numel() for p in self.parameters()) + print(f"plc model: {nb_params} weights") + + def forward(self, features, lost, states=None): + device = features.device + batch_size = features.size(0) + if states is None: + gru1_state = torch.zeros((1, batch_size, self.gru_size), device=device) + gru2_state = torch.zeros((1, batch_size, self.gru_size), device=device) + else: + gru1_state = states[0] + gru2_state = states[1] + x = torch.cat([features, lost], dim=-1) + x = torch.tanh(self.dense_in(x)) + gru1_out, gru1_state = self.gru1(x, gru1_state) + gru2_out, gru2_state = self.gru2(gru1_out, gru2_state) + return self.dense_out(gru2_out), [gru1_state, gru2_state] diff --git a/managed_components/78__esp-opus/dnn/torch/plc/plc_dataset.py b/managed_components/78__esp-opus/dnn/torch/plc/plc_dataset.py new file mode 100644 index 0000000..2dfaaaf --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/plc/plc_dataset.py @@ -0,0 +1,60 @@ +import torch +import numpy as np + +class PLCDataset(torch.utils.data.Dataset): + def __init__(self, + feature_file, + loss_file, + sequence_length=1000, + nb_features=20, + nb_burg_features=36, + lpc_order=16): + + self.features_in = nb_features + nb_burg_features + self.nb_burg_features = nb_burg_features + total_features = self.features_in + lpc_order + self.sequence_length = sequence_length + self.nb_features = nb_features + + self.features = np.memmap(feature_file, dtype='float32', mode='r') + self.lost = np.memmap(loss_file, dtype='int8', mode='r') + self.lost = self.lost.astype('float32') + + self.nb_sequences = self.features.shape[0]//self.sequence_length//total_features + + self.features = self.features[:self.nb_sequences*self.sequence_length*total_features] + self.features = self.features.reshape((self.nb_sequences, self.sequence_length, total_features)) + self.features = self.features[:,:,:self.features_in] + + #self.lost = self.lost[:(len(self.lost)//features.shape[1]-1)*features.shape[1]] + #self.lost = self.lost.reshape((-1, self.sequence_length)) + + def __len__(self): + return self.nb_sequences + + def __getitem__(self, index): + features = self.features[index, :, :] + burg_lost = (np.random.rand(features.shape[0]) > .1).astype('float32') + burg_lost = np.reshape(burg_lost, (features.shape[0], 1)) + burg_mask = np.tile(burg_lost, (1,self.nb_burg_features)) + + lost_offset = np.random.randint(0, high=self.lost.shape[0]-self.sequence_length) + lost = self.lost[lost_offset:lost_offset+self.sequence_length] + #randomly add a few 10-ms losses so that the model learns to handle them + lost = lost * (np.random.rand(lost.shape[-1]) > .02).astype('float32') + #randomly break long consecutive losses so we don't try too hard to predict them + lost = 1 - ((1-lost) * (np.random.rand(lost.shape[-1]) > .1).astype('float32')) + lost = np.reshape(lost, (features.shape[0], 1)) + lost_mask = np.tile(lost, (1,features.shape[-1])) + in_features = features*lost_mask + in_features[:,:self.nb_burg_features] = in_features[:,:self.nb_burg_features]*burg_mask + + #For the first frame after a loss, we don't have valid features, but the Burg estimate is valid. + #in_features[:,1:,self.nb_burg_features:] = in_features[:,1:,self.nb_burg_features:]*lost_mask[:,:-1,self.nb_burg_features:] + out_lost = np.copy(lost) + #out_lost[:,1:,:] = out_lost[:,1:,:]*out_lost[:,:-1,:] + + out_features = np.concatenate([features[:,self.nb_burg_features:], 1.-out_lost], axis=-1) + burg_sign = 2*burg_lost - 1 + # last dim is 1 for received packet, 0 for lost packet, and -1 when just the Burg info is missing + return in_features*lost_mask, lost*burg_sign, out_features diff --git a/managed_components/78__esp-opus/dnn/torch/plc/train_plc.py b/managed_components/78__esp-opus/dnn/torch/plc/train_plc.py new file mode 100644 index 0000000..12b31c4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/plc/train_plc.py @@ -0,0 +1,145 @@ +import os +import argparse +import random +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F +import tqdm + +import plc +from plc_dataset import PLCDataset + +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='path to feature file in .f32 format') +parser.add_argument('loss', type=str, help='path to signal file in .s8 format') +parser.add_argument('output', type=str, help='path to output folder') + +parser.add_argument('--suffix', type=str, help="model name suffix", default="") +parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) + + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 128", default=128) +model_group.add_argument('--gru-size', type=int, help="GRU size, default: 128", default=128) + +training_group = parser.add_argument_group(title="training parameters") +training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512) +training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3) +training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20) +training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15) +training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4) +training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) + +args = parser.parse_args() + +if args.cuda_visible_devices != None: + os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices + +# checkpoints +checkpoint_dir = os.path.join(args.output, 'checkpoints') +checkpoint = dict() +os.makedirs(checkpoint_dir, exist_ok=True) + + +# training parameters +batch_size = args.batch_size +lr = args.lr +epochs = args.epochs +sequence_length = args.sequence_length +lr_decay = args.lr_decay + +adam_betas = [0.8, 0.95] +adam_eps = 1e-8 +features_file = args.features +loss_file = args.loss + +# model parameters +cond_size = args.cond_size + + +checkpoint['batch_size'] = batch_size +checkpoint['lr'] = lr +checkpoint['lr_decay'] = lr_decay +checkpoint['epochs'] = epochs +checkpoint['sequence_length'] = sequence_length +checkpoint['adam_betas'] = adam_betas + + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +checkpoint['model_args'] = () +checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gru_size': args.gru_size} +print(checkpoint['model_kwargs']) +model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs']) + +if type(args.initial_checkpoint) != type(None): + checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(checkpoint['state_dict'], strict=False) + +checkpoint['state_dict'] = model.state_dict() + + +dataset = PLCDataset(features_file, loss_file, sequence_length=sequence_length) +dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) + + +optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) + + +# learning rate scheduler +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) + +states = None + +plc_loss = plc.plc_loss(18, device=device) +if __name__ == '__main__': + model.to(device) + + for epoch in range(1, epochs + 1): + + running_loss = 0 + running_l1_loss = 0 + running_ceps_loss = 0 + running_band_loss = 0 + running_pitch_loss = 0 + + print(f"training epoch {epoch}...") + with tqdm.tqdm(dataloader, unit='batch') as tepoch: + for i, (features, lost, target) in enumerate(tepoch): + optimizer.zero_grad() + features = features.to(device) + lost = lost.to(device) + target = target.to(device) + + out, states = model(features, lost) + + loss, l1_loss, ceps_loss, band_loss, pitch_loss = plc_loss(target, out) + + loss.backward() + optimizer.step() + + #model.clip_weights() + + scheduler.step() + + running_loss += loss.detach().cpu().item() + running_l1_loss += l1_loss.detach().cpu().item() + running_ceps_loss += ceps_loss.detach().cpu().item() + running_band_loss += band_loss.detach().cpu().item() + running_pitch_loss += pitch_loss.detach().cpu().item() + tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", + l1_loss=f"{running_l1_loss/(i+1):8.5f}", + ceps_loss=f"{running_ceps_loss/(i+1):8.5f}", + band_loss=f"{running_band_loss/(i+1):8.5f}", + pitch_loss=f"{running_pitch_loss/(i+1):8.5f}", + ) + + # save checkpoint + checkpoint_path = os.path.join(checkpoint_dir, f'plc{args.suffix}_{epoch}.pth') + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = running_loss / len(dataloader) + checkpoint['epoch'] = epoch + torch.save(checkpoint, checkpoint_path) diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/README.md b/managed_components/78__esp-opus/dnn/torch/rdovae/README.md new file mode 100644 index 0000000..30eb24a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/README.md @@ -0,0 +1,55 @@ +# Deep REDundancy (DRED) with RDO-VAE + +This is a rate-distortion-optimized variational autoencoder (RDO-VAE) designed +to coding redundancy information. Pre-trained models are provided as C code +in the dnn/ directory with the corresponding model in dnn/models/ directory +(name starts with rdovae_). If you don't want to train a new DRED model, you can +skip straight to the Inference section. + +## Data preparation + +First, fetch all the data from the datasets.txt file using: +``` +./download_datasets.sh +``` + +Then concatenate and resample the data into a single 16-kHz file: +``` +./process_speech.sh +``` +The script will produce an all_speech.pcm speech file in raw 16-bit PCM format. + + +For data preparation you need to build Opus as detailed in the top-level README. +You will need to use the --enable-dred configure option. +The build will produce an executable named "dump_data". +To prepare the training data, run: +``` +./dump_data -train all_speech.pcm all_features.f32 /dev/null +``` + +## Training + +To perform training, run the following command: +``` +python ./train_rdovae.py --sequence-length 400 --split-mode random_split --state-dim 80 --batch-size 512 --epochs 400 --lambda-max 0.04 --lr 0.003 --lr-decay-factor 0.0001 all_features.f32 output_dir +``` +The final model will be in output_dir/checkpoints/chechpoint_400.pth. + +The model can be converted to C using: +``` +python export_rdovae_weights.py output_dir/checkpoints/chechpoint_400.pth dred_c_dir +``` +which will create a number of C source and header files in the fargan_c_dir directory. +Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus. + +## Inference + +DRED is integrated within the Opus codec and can be evaluated using the opus_demo +executable. For example: +``` +./opus_demo voip 16000 1 64000 -loss 50 -dred 100 -sim_loss 50 input.pcm output.pcm +``` +Will tell the encoder to encode a 16 kHz raw audio file at 64 kb/s using up to 1 second +of redundancy (units are based on 10-ms) and then simulate 50% loss. Refer to `opus_demo --help` +for more details. diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/download_datasets.sh b/managed_components/78__esp-opus/dnn/torch/rdovae/download_datasets.sh new file mode 100644 index 0000000..c1060df --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/download_datasets.sh @@ -0,0 +1,6 @@ +mkdir datasets +cd datasets +for i in `grep https ../../../datasets.txt` +do + wget $i +done diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/export_rdovae_weights.py b/managed_components/78__esp-opus/dnn/torch/rdovae/export_rdovae_weights.py new file mode 100644 index 0000000..3ef9fab --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/export_rdovae_weights.py @@ -0,0 +1,365 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) + +parser = argparse.ArgumentParser() + +parser.add_argument('checkpoint', type=str, help='rdovae model checkpoint') +parser.add_argument('output_dir', type=str, help='output folder') +parser.add_argument('--format', choices=['C', 'numpy'], help='output format, default: C', default='C') + +args = parser.parse_args() + +import torch +import numpy as np + +from rdovae import RDOVAE +from wexchange.torch import dump_torch_weights +from wexchange.c_export import CWriter, print_vector + +def print_xml(xmlout, val, param, anchor, name): + xmlout.write( +f""" + + {param} values for {name} + + + + +""") + for k in range(val.shape[1]): + xmlout.write(f" ") + for j in range(val.shape[0]): + xmlout.write(f"") + xmlout.write("\n") + xmlout.write( +f""" + +
kQ0Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11Q12Q13Q14Q15
{k}{val[j][k]}
+""") +def dump_statistical_model(writer, w, name, xmlout): + levels = w.shape[0] + + print("printing statistical model") + quant_scales = torch.nn.functional.softplus(w[:, 0, :]).numpy() + dead_zone = 0.05 * torch.nn.functional.softplus(w[:, 1, :]).numpy() + r = torch.sigmoid(w[:, 5 , :]).numpy() + p0 = torch.sigmoid(w[:, 4 , :]).numpy() + p0 = 1 - r ** (0.5 + 0.5 * p0) + + scales_norm = 255./256./(1e-15+np.max(quant_scales,axis=0)) + quant_scales = quant_scales*scales_norm + quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16) + dead_zone_q8 = np.clip(np.round(dead_zone * 2**8), 0, 255).astype(np.uint16) + r_q8 = np.clip(np.round(r * 2**8), 0, 255).astype(np.uint8) + p0_q8 = np.clip(np.round(p0 * 2**8), 0, 255).astype(np.uint16) + + mask = (np.max(r_q8,axis=0) > 0) * (np.min(p0_q8,axis=0) < 255) + quant_scales_q8 = quant_scales_q8[:, mask] + dead_zone_q8 = dead_zone_q8[:, mask] + r_q8 = r_q8[:, mask] + p0_q8 = p0_q8[:, mask] + N = r_q8.shape[-1] + + print_vector(writer.source, quant_scales_q8, f'dred_{name}_quant_scales_q8', dtype='opus_uint8', static=False) + print_vector(writer.source, dead_zone_q8, f'dred_{name}_dead_zone_q8', dtype='opus_uint8', static=False) + print_vector(writer.source, r_q8, f'dred_{name}_r_q8', dtype='opus_uint8', static=False) + print_vector(writer.source, p0_q8, f'dred_{name}_p0_q8', dtype='opus_uint8', static=False) + + print_xml(xmlout, quant_scales_q8, "Scale", "scale", name) + print_xml(xmlout, dead_zone_q8, "Dead zone", "deadzone", name) + print_xml(xmlout, r_q8, "Decay (r)", "decay", name) + print_xml(xmlout, p0_q8, "P(0)", "p0", name) + + writer.header.write( +f""" +extern const opus_uint8 dred_{name}_quant_scales_q8[{levels * N}]; +extern const opus_uint8 dred_{name}_dead_zone_q8[{levels * N}]; +extern const opus_uint8 dred_{name}_r_q8[{levels * N}]; +extern const opus_uint8 dred_{name}_p0_q8[{levels * N}]; + +""" + ) + return N, mask, torch.tensor(scales_norm[mask]) + + +def c_export(args, model): + + message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" + + enc_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_enc_data"), message=message, model_struct_name='RDOVAEEnc') + dec_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_dec_data"), message=message, model_struct_name='RDOVAEDec') + stats_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_stats_data"), message=message, enable_binary_blob=False) + constants_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_constants"), message=message, header_only=True, enable_binary_blob=False) + xmlout = open("stats.xml", "w") + + # some custom includes + for writer in [enc_writer, dec_writer]: + writer.header.write( +f""" +#include "opus_types.h" + +#include "dred_rdovae.h" + +#include "dred_rdovae_constants.h" + +""" + ) + + stats_writer.header.write( +f""" +#include "opus_types.h" + +#include "dred_rdovae_constants.h" + +""" + ) + + latent_out = model.get_submodule('core_encoder.module.z_dense') + state_out = model.get_submodule('core_encoder.module.state_dense_2') + orig_latent_dim = latent_out.weight.shape[0] + orig_state_dim = state_out.weight.shape[0] + # statistical model + qembedding = model.statistical_model.quant_embedding.weight.detach() + levels = qembedding.shape[0] + qembedding = torch.reshape(qembedding, (levels, 6, -1)) + + latent_dim, latent_mask, latent_scale = dump_statistical_model(stats_writer, qembedding[:, :, :orig_latent_dim], 'latent', xmlout) + state_dim, state_mask, state_scale = dump_statistical_model(stats_writer, qembedding[:, :, orig_latent_dim:], 'state', xmlout) + + padded_latent_dim = (latent_dim+7)//8*8 + latent_pad = padded_latent_dim - latent_dim; + w = latent_out.weight[latent_mask,:] + w = w/latent_scale[:, None] + w = torch.cat([w, torch.zeros(latent_pad, w.shape[1])], dim=0) + b = latent_out.bias[latent_mask] + b = b/latent_scale + b = torch.cat([b, torch.zeros(latent_pad)], dim=0) + latent_out.weight = torch.nn.Parameter(w) + latent_out.bias = torch.nn.Parameter(b) + + padded_state_dim = (state_dim+7)//8*8 + state_pad = padded_state_dim - state_dim; + w = state_out.weight[state_mask,:] + w = w/state_scale[:, None] + w = torch.cat([w, torch.zeros(state_pad, w.shape[1])], dim=0) + b = state_out.bias[state_mask] + b = b/state_scale + b = torch.cat([b, torch.zeros(state_pad)], dim=0) + state_out.weight = torch.nn.Parameter(w) + state_out.bias = torch.nn.Parameter(b) + + latent_in = model.get_submodule('core_decoder.module.dense_1') + state_in = model.get_submodule('core_decoder.module.hidden_init') + latent_in.weight = torch.nn.Parameter(latent_in.weight[:,latent_mask]*latent_scale) + state_in.weight = torch.nn.Parameter(state_in.weight[:,state_mask]*state_scale) + + # encoder + encoder_dense_layers = [ + ('core_encoder.module.dense_1' , 'enc_dense1', 'TANH', False,), + ('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR', True,), + ('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH', True,), + ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True) + ] + + for name, export_name, _, quantize in encoder_dense_layers: + layer = model.get_submodule(name) + dump_torch_weights(enc_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) + + + encoder_gru_layers = [ + ('core_encoder.module.gru1' , 'enc_gru1', 'TANH', True), + ('core_encoder.module.gru2' , 'enc_gru2', 'TANH', True), + ('core_encoder.module.gru3' , 'enc_gru3', 'TANH', True), + ('core_encoder.module.gru4' , 'enc_gru4', 'TANH', True), + ('core_encoder.module.gru5' , 'enc_gru5', 'TANH', True), + ] + + enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) + for name, export_name, _, quantize in encoder_gru_layers]) + + + encoder_conv_layers = [ + ('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH', True), + ('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH', True), + ('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH', True), + ('core_encoder.module.conv4.conv' , 'enc_conv4', 'TANH', True), + ('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', True), + ] + + enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in encoder_conv_layers]) + + + del enc_writer + + # decoder + decoder_dense_layers = [ + ('core_decoder.module.dense_1' , 'dec_dense1', 'TANH', False), + ('core_decoder.module.glu1.gate' , 'dec_glu1', 'TANH', True), + ('core_decoder.module.glu2.gate' , 'dec_glu2', 'TANH', True), + ('core_decoder.module.glu3.gate' , 'dec_glu3', 'TANH', True), + ('core_decoder.module.glu4.gate' , 'dec_glu4', 'TANH', True), + ('core_decoder.module.glu5.gate' , 'dec_glu5', 'TANH', True), + ('core_decoder.module.output' , 'dec_output', 'LINEAR', True), + ('core_decoder.module.hidden_init' , 'dec_hidden_init', 'TANH', False), + ('core_decoder.module.gru_init' , 'dec_gru_init','TANH', True), + ] + + for name, export_name, _, quantize in decoder_dense_layers: + layer = model.get_submodule(name) + dump_torch_weights(dec_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) + + + decoder_gru_layers = [ + ('core_decoder.module.gru1' , 'dec_gru1', 'TANH', True), + ('core_decoder.module.gru2' , 'dec_gru2', 'TANH', True), + ('core_decoder.module.gru3' , 'dec_gru3', 'TANH', True), + ('core_decoder.module.gru4' , 'dec_gru4', 'TANH', True), + ('core_decoder.module.gru5' , 'dec_gru5', 'TANH', True), + ] + + dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) + for name, export_name, _, quantize in decoder_gru_layers]) + + decoder_conv_layers = [ + ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True), + ('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH', True), + ('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH', True), + ('core_decoder.module.conv4.conv' , 'dec_conv4', 'TANH', True), + ('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', True), + ] + + dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in decoder_conv_layers]) + + del dec_writer + + del stats_writer + + # constants + constants_writer.header.write( +f""" +#define DRED_NUM_FEATURES {model.feature_dim} + +#define DRED_LATENT_DIM {latent_dim} + +#define DRED_STATE_DIM {state_dim} + +#define DRED_PADDED_LATENT_DIM {padded_latent_dim} + +#define DRED_PADDED_STATE_DIM {padded_state_dim} + +#define DRED_NUM_QUANTIZATION_LEVELS {model.quant_levels} + +#define DRED_MAX_RNN_NEURONS {max(enc_max_rnn_units, dec_max_rnn_units)} + +#define DRED_MAX_CONV_INPUTS {max(enc_max_conv_inputs, dec_max_conv_inputs)} + +#define DRED_ENC_MAX_RNN_NEURONS {enc_max_conv_inputs} + +#define DRED_ENC_MAX_CONV_INPUTS {enc_max_conv_inputs} + +#define DRED_DEC_MAX_RNN_NEURONS {dec_max_rnn_units} + +""" + ) + + del constants_writer + + +def numpy_export(args, model): + + exchange_name_to_name = { + 'encoder_stack_layer1_dense' : 'core_encoder.module.dense_1', + 'encoder_stack_layer3_dense' : 'core_encoder.module.dense_2', + 'encoder_stack_layer5_dense' : 'core_encoder.module.dense_3', + 'encoder_stack_layer7_dense' : 'core_encoder.module.dense_4', + 'encoder_stack_layer8_dense' : 'core_encoder.module.dense_5', + 'encoder_state_layer1_dense' : 'core_encoder.module.state_dense_1', + 'encoder_state_layer2_dense' : 'core_encoder.module.state_dense_2', + 'encoder_stack_layer2_gru' : 'core_encoder.module.gru_1', + 'encoder_stack_layer4_gru' : 'core_encoder.module.gru_2', + 'encoder_stack_layer6_gru' : 'core_encoder.module.gru_3', + 'encoder_stack_layer9_conv' : 'core_encoder.module.conv1', + 'statistical_model_embedding' : 'statistical_model.quant_embedding', + 'decoder_state1_dense' : 'core_decoder.module.gru_1_init', + 'decoder_state2_dense' : 'core_decoder.module.gru_2_init', + 'decoder_state3_dense' : 'core_decoder.module.gru_3_init', + 'decoder_stack_layer1_dense' : 'core_decoder.module.dense_1', + 'decoder_stack_layer3_dense' : 'core_decoder.module.dense_2', + 'decoder_stack_layer5_dense' : 'core_decoder.module.dense_3', + 'decoder_stack_layer7_dense' : 'core_decoder.module.dense_4', + 'decoder_stack_layer8_dense' : 'core_decoder.module.dense_5', + 'decoder_stack_layer9_dense' : 'core_decoder.module.output', + 'decoder_stack_layer2_gru' : 'core_decoder.module.gru_1', + 'decoder_stack_layer4_gru' : 'core_decoder.module.gru_2', + 'decoder_stack_layer6_gru' : 'core_decoder.module.gru_3' + } + + name_to_exchange_name = {value : key for key, value in exchange_name_to_name.items()} + + for name, exchange_name in name_to_exchange_name.items(): + print(f"printing layer {name}...") + dump_torch_weights(os.path.join(args.output_dir, exchange_name), model.get_submodule(name)) + + +if __name__ == "__main__": + + + os.makedirs(args.output_dir, exist_ok=True) + + + # load model from checkpoint + checkpoint = torch.load(args.checkpoint, map_location='cpu') + model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) + missing_keys, unmatched_keys = model.load_state_dict(checkpoint['state_dict'], strict=False) + def _remove_weight_norm(m): + try: + torch.nn.utils.remove_weight_norm(m) + except ValueError: # this module didn't have weight norm + return + model.apply(_remove_weight_norm) + + + if len(missing_keys) > 0: + raise ValueError(f"error: missing keys in state dict") + + if len(unmatched_keys) > 0: + print(f"warning: the following keys were unmatched {unmatched_keys}") + + if args.format == 'C': + c_export(args, model) + elif args.format == 'numpy': + numpy_export(args, model) + else: + raise ValueError(f'error: unknown export format {args.format}') diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/fec_encoder.py b/managed_components/78__esp-opus/dnn/torch/rdovae/fec_encoder.py new file mode 100644 index 0000000..20ab4ac --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/fec_encoder.py @@ -0,0 +1,212 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe and Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import subprocess +import argparse + +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +parser = argparse.ArgumentParser(description='Encode redundancy for Opus neural FEC. Designed for use with voip application and 20ms frames') + +parser.add_argument('input', metavar='', help='audio input (.wav or .raw or .pcm as int16)') +parser.add_argument('checkpoint', metavar='', help='model checkpoint') +parser.add_argument('q0', metavar='', type=int, help='quantization level for most recent frame') +parser.add_argument('q1', metavar='', type=int, help='quantization level for oldest frame') +parser.add_argument('output', type=str, help='output file (will be extended with .fec)') + +parser.add_argument('--dump-data', type=str, default='./dump_data', help='path to dump data executable (default ./dump_data)') +parser.add_argument('--num-redundancy-frames', default=52, type=int, help='number of redundancy frames per packet (default 52)') +parser.add_argument('--extra-delay', default=0, type=int, help="last features in packet are calculated with the decoder aligned samples, use this option to add extra delay (in samples at 16kHz)") +parser.add_argument('--lossfile', type=str, help='file containing loss trace (0 for frame received, 1 for lost)') +parser.add_argument('--debug-output', action='store_true', help='if set, differently assembled features are written to disk') + +args = parser.parse_args() + +import numpy as np +from scipy.io import wavfile +import torch + +from rdovae import RDOVAE +from packets import write_fec_packets + +torch.set_num_threads(4) + +checkpoint = torch.load(args.checkpoint, map_location="cpu") +model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) +model.load_state_dict(checkpoint['state_dict'], strict=False) +model.to("cpu") + +lpc_order = 16 + +## prepare input signal +# SILK frame size is 20ms and LPCNet subframes are 10ms +subframe_size = 160 +frame_size = 2 * subframe_size + +# 91 samples delay to align with SILK decoded frames +silk_delay = 91 + +# prepend zeros to have enough history to produce the first package +zero_history = (args.num_redundancy_frames - 1) * frame_size + +# dump data has a (feature) delay of 10ms +dump_data_delay = 160 + +total_delay = silk_delay + zero_history + args.extra_delay - dump_data_delay + +# load signal +if args.input.endswith('.raw') or args.input.endswith('.pcm'): + signal = np.fromfile(args.input, dtype='int16') + +elif args.input.endswith('.wav'): + fs, signal = wavfile.read(args.input) +else: + raise ValueError(f'unknown input signal format: {args.input}') + +# fill up last frame with zeros +padded_signal_length = len(signal) + total_delay +tail = padded_signal_length % frame_size +right_padding = (frame_size - tail) % frame_size + +signal = np.concatenate((np.zeros(total_delay, dtype=np.int16), signal, np.zeros(right_padding, dtype=np.int16))) + +padded_signal_file = os.path.splitext(args.input)[0] + '_padded.raw' +signal.tofile(padded_signal_file) + +# write signal and call dump_data to create features + +feature_file = os.path.splitext(args.input)[0] + '_features.f32' +command = f"{args.dump_data} -test {padded_signal_file} {feature_file}" +r = subprocess.run(command, shell=True) +if r.returncode != 0: + raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}") + +# load features +nb_features = model.feature_dim + lpc_order +nb_used_features = model.feature_dim + +# load features +features = np.fromfile(feature_file, dtype='float32') +num_subframes = len(features) // nb_features +num_subframes = 2 * (num_subframes // 2) +num_frames = num_subframes // 2 + +features = np.reshape(features, (1, -1, nb_features)) +features = features[:, :, :nb_used_features] +features = features[:, :num_subframes, :] + +# quant_ids in reverse decoding order +quant_ids = torch.round((args.q1 + (args.q0 - args.q1) * torch.arange(args.num_redundancy_frames // 2) / (args.num_redundancy_frames // 2 - 1))).long() + +print(f"using quantization levels {quant_ids}...") + +# convert input to torch tensors +features = torch.from_numpy(features) + + +# run encoder +print("running fec encoder...") +with torch.no_grad(): + + # encoding + z, states, state_size = model.encode(features) + + + # decoder on packet chunks + input_length = args.num_redundancy_frames // 2 + offset = args.num_redundancy_frames - 1 + + packets = [] + packet_sizes = [] + + for i in range(offset, num_frames): + print(f"processing frame {i - offset}...") + # quantize / unquantize latent vectors + zi = torch.clone(z[:, i - 2 * input_length + 2: i + 1 : 2, :]) + zi, rates = model.quantize(zi, quant_ids) + zi = model.unquantize(zi, quant_ids) + + features = model.decode(zi, states[:, i : i + 1, :]) + packets.append(features.squeeze(0).numpy()) + packet_size = 8 * int((torch.sum(rates) + 7 + state_size) / 8) + packet_sizes.append(packet_size) + + +# write packets +packet_file = args.output + '.fec' if not args.output.endswith('.fec') else args.output +write_fec_packets(packet_file, packets, packet_sizes) + + +print(f"average redundancy rate: {int(round(sum(packet_sizes) / len(packet_sizes) * 50 / 1000))} kbps") + +# assemble features according to loss file +if args.lossfile != None: + num_packets = len(packets) + loss = np.loadtxt(args.lossfile, dtype='int16') + fec_out = np.zeros((num_packets * 2, packets[0].shape[-1]), dtype='float32') + foffset = -2 + ptr = 0 + count = 2 + for i in range(num_packets): + if (loss[i] == 0) or (i == num_packets - 1): + + fec_out[ptr:ptr+count,:] = packets[i][foffset:, :] + + ptr += count + foffset = -2 + count = 2 + else: + count += 2 + foffset -= 2 + + fec_out_full = np.zeros((fec_out.shape[0], 36), dtype=np.float32) + fec_out_full[:, : fec_out.shape[-1]] = fec_out + + fec_out_full.tofile(packet_file[:-4] + f'_fec.f32') + + +if args.debug_output: + import itertools + + batches = [4] + offsets = [0, 2 * args.num_redundancy_frames - 4] + + # sanity checks + # 1. concatenate features at offset 0 + for batch, offset in itertools.product(batches, offsets): + + stop = packets[0].shape[1] - offset + test_features = np.concatenate([packet[stop - batch: stop, :] for packet in packets[::batch//2]], axis=0) + + test_features_full = np.zeros((test_features.shape[0], nb_features), dtype=np.float32) + test_features_full[:, :nb_used_features] = test_features[:, :] + + print(f"writing debug output {packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32'}") + test_features_full.tofile(packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32') diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/import_rdovae_weights.py b/managed_components/78__esp-opus/dnn/torch/rdovae/import_rdovae_weights.py new file mode 100644 index 0000000..c824986 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/import_rdovae_weights.py @@ -0,0 +1,143 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +import argparse + + + +parser = argparse.ArgumentParser() + +parser.add_argument('exchange_folder', type=str, help='exchange folder path') +parser.add_argument('output', type=str, help='path to output model checkpoint') + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--num-features', type=int, help="number of features, default: 20", default=20) +model_group.add_argument('--latent-dim', type=int, help="number of symbols produces by encoder, default: 80", default=80) +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) +model_group.add_argument('--cond-size2', type=int, help="second conditioning size, default: 256", default=256) +model_group.add_argument('--state-dim', type=int, help="dimensionality of transfered state, default: 24", default=24) +model_group.add_argument('--quant-levels', type=int, help="number of quantization levels, default: 40", default=40) + +args = parser.parse_args() + +import torch +from rdovae import RDOVAE +from wexchange.torch import load_torch_weights + +exchange_name_to_name = { + 'encoder_stack_layer1_dense' : 'core_encoder.module.dense_1', + 'encoder_stack_layer3_dense' : 'core_encoder.module.dense_2', + 'encoder_stack_layer5_dense' : 'core_encoder.module.dense_3', + 'encoder_stack_layer7_dense' : 'core_encoder.module.dense_4', + 'encoder_stack_layer8_dense' : 'core_encoder.module.dense_5', + 'encoder_state_layer1_dense' : 'core_encoder.module.state_dense_1', + 'encoder_state_layer2_dense' : 'core_encoder.module.state_dense_2', + 'encoder_stack_layer2_gru' : 'core_encoder.module.gru_1', + 'encoder_stack_layer4_gru' : 'core_encoder.module.gru_2', + 'encoder_stack_layer6_gru' : 'core_encoder.module.gru_3', + 'encoder_stack_layer9_conv' : 'core_encoder.module.conv1', + 'statistical_model_embedding' : 'statistical_model.quant_embedding', + 'decoder_state1_dense' : 'core_decoder.module.gru_1_init', + 'decoder_state2_dense' : 'core_decoder.module.gru_2_init', + 'decoder_state3_dense' : 'core_decoder.module.gru_3_init', + 'decoder_stack_layer1_dense' : 'core_decoder.module.dense_1', + 'decoder_stack_layer3_dense' : 'core_decoder.module.dense_2', + 'decoder_stack_layer5_dense' : 'core_decoder.module.dense_3', + 'decoder_stack_layer7_dense' : 'core_decoder.module.dense_4', + 'decoder_stack_layer8_dense' : 'core_decoder.module.dense_5', + 'decoder_stack_layer9_dense' : 'core_decoder.module.output', + 'decoder_stack_layer2_gru' : 'core_decoder.module.gru_1', + 'decoder_stack_layer4_gru' : 'core_decoder.module.gru_2', + 'decoder_stack_layer6_gru' : 'core_decoder.module.gru_3' +} + +if __name__ == "__main__": + checkpoint = dict() + + # parameters + num_features = args.num_features + latent_dim = args.latent_dim + quant_levels = args.quant_levels + cond_size = args.cond_size + cond_size2 = args.cond_size2 + state_dim = args.state_dim + + + # model + checkpoint['model_args'] = (num_features, latent_dim, quant_levels, cond_size, cond_size2) + checkpoint['model_kwargs'] = {'state_dim': state_dim} + model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) + + dense_layer_names = [ + 'encoder_stack_layer1_dense', + 'encoder_stack_layer3_dense', + 'encoder_stack_layer5_dense', + 'encoder_stack_layer7_dense', + 'encoder_stack_layer8_dense', + 'encoder_state_layer1_dense', + 'encoder_state_layer2_dense', + 'decoder_state1_dense', + 'decoder_state2_dense', + 'decoder_state3_dense', + 'decoder_stack_layer1_dense', + 'decoder_stack_layer3_dense', + 'decoder_stack_layer5_dense', + 'decoder_stack_layer7_dense', + 'decoder_stack_layer8_dense', + 'decoder_stack_layer9_dense' + ] + + gru_layer_names = [ + 'encoder_stack_layer2_gru', + 'encoder_stack_layer4_gru', + 'encoder_stack_layer6_gru', + 'decoder_stack_layer2_gru', + 'decoder_stack_layer4_gru', + 'decoder_stack_layer6_gru' + ] + + conv1d_layer_names = [ + 'encoder_stack_layer9_conv' + ] + + embedding_layer_names = [ + 'statistical_model_embedding' + ] + + for name in dense_layer_names + gru_layer_names + conv1d_layer_names + embedding_layer_names: + print(f"loading weights for layer {exchange_name_to_name[name]}") + layer = model.get_submodule(exchange_name_to_name[name]) + load_torch_weights(os.path.join(args.exchange_folder, name), layer) + + checkpoint['state_dict'] = model.state_dict() + + torch.save(checkpoint, args.output) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/packets/__init__.py b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/__init__.py new file mode 100644 index 0000000..fb71ab3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/__init__.py @@ -0,0 +1 @@ +from .fec_packets import write_fec_packets, read_fec_packets \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.c b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.c new file mode 100644 index 0000000..ee08ba9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.c @@ -0,0 +1,142 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include +#include + +#include "fec_packets.h" + +int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index) +{ + + int16_t version; + int16_t header_size; + int16_t num_packets; + int16_t packet_size; + int16_t subframe_size; + int16_t subframes_per_packet; + int16_t num_features; + long offset; + + FILE *fid = fopen(filename, "rb"); + + /* read header */ + if (fread(&version, sizeof(version), 1, fid) != 1) goto error; + if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; + if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; + if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; + if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; + if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; + if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; + + /* check if indices are valid */ + if (packet_index >= num_packets || subframe_index >= subframes_per_packet) + { + fprintf(stderr, "get_fec_frame: index out of bounds\n"); + goto error; + } + + /* calculate offset in file (+ 2 is for rate) */ + offset = header_size + packet_index * packet_size + 2 + subframe_index * subframe_size; + fseek(fid, offset, SEEK_SET); + + /* read features */ + if (fread(features, sizeof(*features), num_features, fid) != num_features) goto error; + + fclose(fid); + return 0; + +error: + fclose(fid); + return 1; +} + +int get_fec_rate(const char * const filename, int packet_index) +{ + int16_t version; + int16_t header_size; + int16_t num_packets; + int16_t packet_size; + int16_t subframe_size; + int16_t subframes_per_packet; + int16_t num_features; + long offset; + int16_t rate; + + FILE *fid = fopen(filename, "rb"); + + /* read header */ + if (fread(&version, sizeof(version), 1, fid) != 1) goto error; + if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; + if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; + if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; + if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; + if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; + if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; + + /* check if indices are valid */ + if (packet_index >= num_packets) + { + fprintf(stderr, "get_fec_rate: index out of bounds\n"); + goto error; + } + + /* calculate offset in file (+ 2 is for rate) */ + offset = header_size + packet_index * packet_size; + fseek(fid, offset, SEEK_SET); + + /* read rate */ + if (fread(&rate, sizeof(rate), 1, fid) != 1) goto error; + + fclose(fid); + return (int) rate; + +error: + fclose(fid); + return -1; +} + +#if 0 +int main() +{ + float features[20]; + int i; + + if (get_fec_frame("../test.fec", &features[0], 0, 127)) + { + return 1; + } + + for (i = 0; i < 20; i ++) + { + printf("%d %f\n", i, features[i]); + } + + printf("rate: %d\n", get_fec_rate("../test.fec", 0)); + +} +#endif \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.h b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.h new file mode 100644 index 0000000..35d3554 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.h @@ -0,0 +1,34 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef _FEC_PACKETS_H +#define _FEC_PACKETS_H + +int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index); +int get_fec_rate(const char * const filename, int packet_index); + +#endif \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.py b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.py new file mode 100644 index 0000000..f44c1a9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/packets/fec_packets.py @@ -0,0 +1,108 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np + + + +def write_fec_packets(filename, packets, rates=None): + """ writes packets in binary format """ + + assert np.dtype(np.float32).itemsize == 4 + assert np.dtype(np.int16).itemsize == 2 + + # derive some sizes + num_packets = len(packets) + subframes_per_packet = packets[0].shape[-2] + num_features = packets[0].shape[-1] + + # size of float is 4 + subframe_size = num_features * 4 + packet_size = subframe_size * subframes_per_packet + 2 # two bytes for rate + + version = 1 + # header size (version, header_size, num_packets, packet_size, subframe_size, subrames_per_packet, num_features) + header_size = 14 + + with open(filename, 'wb') as f: + + # header + f.write(np.int16(version).tobytes()) + f.write(np.int16(header_size).tobytes()) + f.write(np.int16(num_packets).tobytes()) + f.write(np.int16(packet_size).tobytes()) + f.write(np.int16(subframe_size).tobytes()) + f.write(np.int16(subframes_per_packet).tobytes()) + f.write(np.int16(num_features).tobytes()) + + # packets + for i, packet in enumerate(packets): + if type(rates) == type(None): + rate = 0 + else: + rate = rates[i] + + f.write(np.int16(rate).tobytes()) + + features = np.flip(packet, axis=-2) + f.write(features.astype(np.float32).tobytes()) + + +def read_fec_packets(filename): + """ reads packets from binary format """ + + assert np.dtype(np.float32).itemsize == 4 + assert np.dtype(np.int16).itemsize == 2 + + with open(filename, 'rb') as f: + + # header + version = np.frombuffer(f.read(2), dtype=np.int16).item() + header_size = np.frombuffer(f.read(2), dtype=np.int16).item() + num_packets = np.frombuffer(f.read(2), dtype=np.int16).item() + packet_size = np.frombuffer(f.read(2), dtype=np.int16).item() + subframe_size = np.frombuffer(f.read(2), dtype=np.int16).item() + subframes_per_packet = np.frombuffer(f.read(2), dtype=np.int16).item() + num_features = np.frombuffer(f.read(2), dtype=np.int16).item() + + dummy_features = np.zeros((subframes_per_packet, num_features), dtype=np.float32) + + # packets + rates = [] + packets = [] + for i in range(num_packets): + + rate = np.frombuffer(f.read(2), dtype=np.int16).item + rates.append(rate) + + features = np.reshape(np.frombuffer(f.read(subframe_size * subframes_per_packet), dtype=np.float32), dummy_features.shape) + packet = np.flip(features, axis=-2) + packets.append(packet) + + return packets \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/process_speech.sh b/managed_components/78__esp-opus/dnn/torch/rdovae/process_speech.sh new file mode 100644 index 0000000..f492dc3 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/process_speech.sh @@ -0,0 +1,7 @@ +#!/bin/sh + +cd datasets + +#parallel -j +2 'unzip -n {}' ::: *.zip + +find . -name "*.wav" | parallel -k -j 20 'sox --no-dither {} -t sw -r 16000 -c 1 -' > ../all_speech.sw diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/__init__.py b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/__init__.py new file mode 100644 index 0000000..b945add --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/__init__.py @@ -0,0 +1,2 @@ +from .rdovae import RDOVAE, distortion_loss, hard_rate_estimate, soft_rate_estimate +from .dataset import RDOVAEDataset diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/dataset.py b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/dataset.py new file mode 100644 index 0000000..cfb32b0 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/dataset.py @@ -0,0 +1,67 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import torch +import numpy as np + +class RDOVAEDataset(torch.utils.data.Dataset): + def __init__(self, + feature_file, + sequence_length, + num_used_features=20, + num_features=36, + lambda_min=0.0002, + lambda_max=0.0135, + quant_levels=16, + enc_stride=2): + + self.sequence_length = sequence_length + self.lambda_min = lambda_min + self.lambda_max = lambda_max + self.enc_stride = enc_stride + self.quant_levels = quant_levels + self.denominator = (quant_levels - 1) / np.log(lambda_max / lambda_min) + + if sequence_length % enc_stride: + raise ValueError(f"RDOVAEDataset.__init__: enc_stride {enc_stride} does not divide sequence length {sequence_length}") + + self.features = np.reshape(np.fromfile(feature_file, dtype=np.float32), (-1, num_features)) + self.features = self.features[:, :num_used_features] + self.num_sequences = self.features.shape[0] // sequence_length + + def __len__(self): + return self.num_sequences + + def __getitem__(self, index): + features = self.features[index * self.sequence_length: (index + 1) * self.sequence_length, :] + q_ids = np.random.randint(0, self.quant_levels, (1)).astype(np.int64) + q_ids = np.repeat(q_ids, self.sequence_length // self.enc_stride, axis=0) + rate_lambda = self.lambda_min * np.exp(q_ids.astype(np.float32) / self.denominator).astype(np.float32) + + return features, rate_lambda, q_ids diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/rdovae.py b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/rdovae.py new file mode 100644 index 0000000..a3f3e99 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/rdovae/rdovae.py @@ -0,0 +1,752 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +""" Pytorch implementations of rate distortion optimized variational autoencoder """ + +import math as m + +import torch +from torch import nn +import torch.nn.functional as F +import sys +import os +source_dir = os.path.split(os.path.abspath(__file__))[0] +sys.path.append(os.path.join(source_dir, "../../lpcnet/")) +from utils.sparsification import GRUSparsifier +from torch.nn.utils import weight_norm +sys.path.append(os.path.join(source_dir, "../../dnntools")) +from dnntools.quantization import soft_quant + +# Quantization and rate related utily functions + +def soft_pvq(x, k): + """ soft pyramid vector quantizer """ + + # L2 normalization + x_norm2 = x / (1e-15 + torch.norm(x, dim=-1, keepdim=True)) + + + with torch.no_grad(): + # quantization loop, no need to track gradients here + x_norm1 = x / torch.sum(torch.abs(x), dim=-1, keepdim=True) + + # set initial scaling factor to k + scale_factor = k + x_scaled = scale_factor * x_norm1 + x_quant = torch.round(x_scaled) + + # we aim for ||x_quant||_L1 = k + for _ in range(10): + # remove signs and calculate L1 norm + abs_x_quant = torch.abs(x_quant) + abs_x_scaled = torch.abs(x_scaled) + l1_x_quant = torch.sum(abs_x_quant, axis=-1) + + # increase, where target is too small and decrease, where target is too large + plus = 1.0001 * torch.min((abs_x_quant + 0.5) / (abs_x_scaled + 1e-15), dim=-1).values + minus = 0.9999 * torch.max((abs_x_quant - 0.5) / (abs_x_scaled + 1e-15), dim=-1).values + factor = torch.where(l1_x_quant > k, minus, plus) + factor = torch.where(l1_x_quant == k, torch.ones_like(factor), factor) + scale_factor = scale_factor * factor.unsqueeze(-1) + + # update x + x_scaled = scale_factor * x_norm1 + x_quant = torch.round(x_quant) + + # L2 normalization of quantized x + x_quant_norm2 = x_quant / (1e-15 + torch.norm(x_quant, dim=-1, keepdim=True)) + quantization_error = x_quant_norm2 - x_norm2 + + return x_norm2 + quantization_error.detach() + +def cache_parameters(func): + cache = dict() + def cached_func(*args): + if args in cache: + return cache[args] + else: + cache[args] = func(*args) + + return cache[args] + return cached_func + +@cache_parameters +def pvq_codebook_size(n, k): + + if k == 0: + return 1 + + if n == 0: + return 0 + + return pvq_codebook_size(n - 1, k) + pvq_codebook_size(n, k - 1) + pvq_codebook_size(n - 1, k - 1) + + +def soft_rate_estimate(z, r, reduce=True): + """ rate approximation with dependent theta Eq. (7)""" + + rate = torch.sum( + - torch.log2((1 - r)/(1 + r) * r ** torch.abs(z) + 1e-6), + dim=-1 + ) + + if reduce: + rate = torch.mean(rate) + + return rate + + +def hard_rate_estimate(z, r, theta, reduce=True): + """ hard rate approximation """ + + z_q = torch.round(z) + p0 = 1 - r ** (0.5 + 0.5 * theta) + alpha = torch.relu(1 - torch.abs(z_q)) ** 2 + rate = - torch.sum( + (alpha * torch.log2(p0 * r ** torch.abs(z_q) + 1e-6) + + (1 - alpha) * torch.log2(0.5 * (1 - p0) * (1 - r) * r ** (torch.abs(z_q) - 1) + 1e-6)), + dim=-1 + ) + + if reduce: + rate = torch.mean(rate) + + return rate + + + +def soft_dead_zone(x, dead_zone): + """ approximates application of a dead zone to x """ + d = dead_zone * 0.05 + return x - d * torch.tanh(x / (0.1 + d)) + + +def hard_quantize(x): + """ round with copy gradient trick """ + return x + (torch.round(x) - x).detach() + + +def noise_quantize(x): + """ simulates quantization with addition of random uniform noise """ + return x + (torch.rand_like(x) - 0.5) + + +# loss functions + + +def distortion_loss(y_true, y_pred, rate_lambda=None): + """ custom distortion loss for LPCNet features """ + + if y_true.size(-1) != 20: + raise ValueError('distortion loss is designed to work with 20 features') + + ceps_error = y_pred[..., :18] - y_true[..., :18] + pitch_error = 2*(y_pred[..., 18:19] - y_true[..., 18:19]) + corr_error = y_pred[..., 19:] - y_true[..., 19:] + pitch_weight = torch.relu(y_true[..., 19:] + 0.5) ** 2 + + loss = torch.mean(ceps_error ** 2 + (10/18) * torch.abs(pitch_error) * pitch_weight + (1/18) * corr_error ** 2, dim=-1) + + if type(rate_lambda) != type(None): + loss = loss / torch.sqrt(rate_lambda) + + loss = torch.mean(loss) + + return loss + + +# sampling functions + +import random + + +def random_split(start, stop, num_splits=3, min_len=3): + get_min_len = lambda x : min([x[i+1] - x[i] for i in range(len(x) - 1)]) + candidate = [start] + sorted([random.randint(start, stop-1) for i in range(num_splits)]) + [stop] + + while get_min_len(candidate) < min_len: + candidate = [start] + sorted([random.randint(start, stop-1) for i in range(num_splits)]) + [stop] + + return candidate + + + +# weight initialization and clipping +def init_weights(module): + + if isinstance(module, nn.GRU): + for p in module.named_parameters(): + if p[0].startswith('weight_hh_'): + nn.init.orthogonal_(p[1]) + + +def weight_clip_factory(max_value): + """ weight clipping function concerning sum of abs values of adjecent weights """ + def clip_weight_(w): + stop = w.size(1) + # omit last column if stop is odd + if stop % 2: + stop -= 1 + max_values = max_value * torch.ones_like(w[:, :stop]) + factor = max_value / torch.maximum(max_values, + torch.repeat_interleave( + torch.abs(w[:, :stop:2]) + torch.abs(w[:, 1:stop:2]), + 2, + 1)) + with torch.no_grad(): + w[:, :stop] *= factor + + def clip_weights(module): + if isinstance(module, nn.GRU) or isinstance(module, nn.Linear): + for name, w in module.named_parameters(): + if name.startswith('weight'): + clip_weight_(w) + + return clip_weights + +def n(x): + return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) + +# RDOVAE module and submodules + +sparsify_start = 12000 +sparsify_stop = 24000 +sparsify_interval = 100 +sparsify_exponent = 3 +#sparsify_start = 0 +#sparsify_stop = 0 + +sparse_params1 = { +# 'W_hr' : (1.0, [8, 4], True), +# 'W_hz' : (1.0, [8, 4], True), +# 'W_hn' : (1.0, [8, 4], True), + 'W_ir' : (0.6, [8, 4], False), + 'W_iz' : (0.4, [8, 4], False), + 'W_in' : (0.8, [8, 4], False) + } + +sparse_params2 = { +# 'W_hr' : (1.0, [8, 4], True), +# 'W_hz' : (1.0, [8, 4], True), +# 'W_hn' : (1.0, [8, 4], True), + 'W_ir' : (0.3, [8, 4], False), + 'W_iz' : (0.2, [8, 4], False), + 'W_in' : (0.4, [8, 4], False) + } + + +class MyConv(nn.Module): + def __init__(self, input_dim, output_dim, dilation=1, softquant=False): + super(MyConv, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.dilation=dilation + self.conv = nn.Conv1d(input_dim, output_dim, kernel_size=2, padding='valid', dilation=dilation) + + if softquant: + self.conv = soft_quant(self.conv) + + def forward(self, x, state=None): + device = x.device + conv_in = torch.cat([torch.zeros_like(x[:,0:self.dilation,:], device=device), x], -2).permute(0, 2, 1) + return torch.tanh(self.conv(conv_in)).permute(0, 2, 1) + +class GLU(nn.Module): + def __init__(self, feat_size, softquant=False): + super(GLU, self).__init__() + + torch.manual_seed(5) + + self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) + + if softquant: + self.gate = soft_quant(self.gate) + + self.init_weights() + + def init_weights(self): + + for m in self.modules(): + if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ + or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): + nn.init.orthogonal_(m.weight.data) + + def forward(self, x): + + out = x * torch.sigmoid(self.gate(x)) + + return out + +class CoreEncoder(nn.Module): + STATE_HIDDEN = 128 + FRAMES_PER_STEP = 2 + CONV_KERNEL_SIZE = 4 + + def __init__(self, feature_dim, output_dim, cond_size, cond_size2, state_size=24, softquant=False): + """ core encoder for RDOVAE + + Computes latents, initial states, and rate estimates from features and lambda parameter + + """ + + super(CoreEncoder, self).__init__() + + # hyper parameters + self.feature_dim = feature_dim + self.output_dim = output_dim + self.cond_size = cond_size + self.cond_size2 = cond_size2 + self.state_size = state_size + + # derived parameters + self.input_dim = self.FRAMES_PER_STEP * self.feature_dim + + # layers + self.dense_1 = nn.Linear(self.input_dim, 64) + self.gru1 = nn.GRU(64, 64, batch_first=True) + self.conv1 = MyConv(128, 96, softquant=True) + self.gru2 = nn.GRU(224, 64, batch_first=True) + self.conv2 = MyConv(288, 96, dilation=2, softquant=True) + self.gru3 = nn.GRU(384, 64, batch_first=True) + self.conv3 = MyConv(448, 96, dilation=2, softquant=True) + self.gru4 = nn.GRU(544, 64, batch_first=True) + self.conv4 = MyConv(608, 96, dilation=2, softquant=True) + self.gru5 = nn.GRU(704, 64, batch_first=True) + self.conv5 = MyConv(768, 96, dilation=2, softquant=True) + + self.z_dense = nn.Linear(864, self.output_dim) + + + self.state_dense_1 = nn.Linear(864, self.STATE_HIDDEN) + + self.state_dense_2 = nn.Linear(self.STATE_HIDDEN, self.state_size) + nb_params = sum(p.numel() for p in self.parameters()) + print(f"encoder: {nb_params} weights") + + # initialize weights + self.apply(init_weights) + + if softquant: + self.gru1 = soft_quant(self.gru1, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru2 = soft_quant(self.gru2, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru3 = soft_quant(self.gru3, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru4 = soft_quant(self.gru4, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru5 = soft_quant(self.gru5, names=['weight_hh_l0', 'weight_ih_l0']) + self.z_dense = soft_quant(self.z_dense) + self.state_dense_1 = soft_quant(self.state_dense_1) + self.state_dense_2 = soft_quant(self.state_dense_2) + + + def forward(self, features): + + # reshape features + x = torch.reshape(features, (features.size(0), features.size(1) // self.FRAMES_PER_STEP, self.FRAMES_PER_STEP * features.size(2))) + + batch = x.size(0) + device = x.device + + # run encoding layer stack + x = n(torch.tanh(self.dense_1(x))) + x = torch.cat([x, n(self.gru1(x)[0])], -1) + x = torch.cat([x, n(self.conv1(x))], -1) + x = torch.cat([x, n(self.gru2(x)[0])], -1) + x = torch.cat([x, n(self.conv2(x))], -1) + x = torch.cat([x, n(self.gru3(x)[0])], -1) + x = torch.cat([x, n(self.conv3(x))], -1) + x = torch.cat([x, n(self.gru4(x)[0])], -1) + x = torch.cat([x, n(self.conv4(x))], -1) + x = torch.cat([x, n(self.gru5(x)[0])], -1) + x = torch.cat([x, n(self.conv5(x))], -1) + z = self.z_dense(x) + + # init state for decoder + states = torch.tanh(self.state_dense_1(x)) + states = self.state_dense_2(states) + + return z, states + + + + +class CoreDecoder(nn.Module): + + FRAMES_PER_STEP = 4 + + def __init__(self, input_dim, output_dim, cond_size, cond_size2, state_size=24, softquant=False): + """ core decoder for RDOVAE + + Computes features from latents, initial state, and quantization index + + """ + + super(CoreDecoder, self).__init__() + + # hyper parameters + self.input_dim = input_dim + self.output_dim = output_dim + self.cond_size = cond_size + self.cond_size2 = cond_size2 + self.state_size = state_size + + self.input_size = self.input_dim + + # layers + self.dense_1 = nn.Linear(self.input_size, 96) + self.gru1 = nn.GRU(96, 96, batch_first=True) + self.conv1 = MyConv(192, 32, softquant=softquant) + self.gru2 = nn.GRU(224, 96, batch_first=True) + self.conv2 = MyConv(320, 32, softquant=softquant) + self.gru3 = nn.GRU(352, 96, batch_first=True) + self.conv3 = MyConv(448, 32, softquant=softquant) + self.gru4 = nn.GRU(480, 96, batch_first=True) + self.conv4 = MyConv(576, 32, softquant=softquant) + self.gru5 = nn.GRU(608, 96, batch_first=True) + self.conv5 = MyConv(704, 32, softquant=softquant) + self.output = nn.Linear(736, self.FRAMES_PER_STEP * self.output_dim) + self.glu1 = GLU(96, softquant=softquant) + self.glu2 = GLU(96, softquant=softquant) + self.glu3 = GLU(96, softquant=softquant) + self.glu4 = GLU(96, softquant=softquant) + self.glu5 = GLU(96, softquant=softquant) + self.hidden_init = nn.Linear(self.state_size, 128) + self.gru_init = nn.Linear(128, 480) + + nb_params = sum(p.numel() for p in self.parameters()) + print(f"decoder: {nb_params} weights") + # initialize weights + self.apply(init_weights) + self.sparsifier = [] + self.sparsifier.append(GRUSparsifier([(self.gru1, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) + self.sparsifier.append(GRUSparsifier([(self.gru2, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) + self.sparsifier.append(GRUSparsifier([(self.gru3, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) + self.sparsifier.append(GRUSparsifier([(self.gru4, sparse_params2)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) + self.sparsifier.append(GRUSparsifier([(self.gru5, sparse_params2)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) + + if softquant: + self.gru1 = soft_quant(self.gru1, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru2 = soft_quant(self.gru2, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru3 = soft_quant(self.gru3, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru4 = soft_quant(self.gru4, names=['weight_hh_l0', 'weight_ih_l0']) + self.gru5 = soft_quant(self.gru5, names=['weight_hh_l0', 'weight_ih_l0']) + self.output = soft_quant(self.output) + self.gru_init = soft_quant(self.gru_init) + + def sparsify(self): + for sparsifier in self.sparsifier: + sparsifier.step() + + def forward(self, z, initial_state): + + hidden = torch.tanh(self.hidden_init(initial_state)) + gru_state = torch.tanh(self.gru_init(hidden).permute(1, 0, 2)) + h1_state = gru_state[:,:,:96].contiguous() + h2_state = gru_state[:,:,96:192].contiguous() + h3_state = gru_state[:,:,192:288].contiguous() + h4_state = gru_state[:,:,288:384].contiguous() + h5_state = gru_state[:,:,384:].contiguous() + + # run decoding layer stack + x = n(torch.tanh(self.dense_1(z))) + + x = torch.cat([x, n(self.glu1(n(self.gru1(x, h1_state)[0])))], -1) + x = torch.cat([x, n(self.conv1(x))], -1) + x = torch.cat([x, n(self.glu2(n(self.gru2(x, h2_state)[0])))], -1) + x = torch.cat([x, n(self.conv2(x))], -1) + x = torch.cat([x, n(self.glu3(n(self.gru3(x, h3_state)[0])))], -1) + x = torch.cat([x, n(self.conv3(x))], -1) + x = torch.cat([x, n(self.glu4(n(self.gru4(x, h4_state)[0])))], -1) + x = torch.cat([x, n(self.conv4(x))], -1) + x = torch.cat([x, n(self.glu5(n(self.gru5(x, h5_state)[0])))], -1) + x = torch.cat([x, n(self.conv5(x))], -1) + + # output layer and reshaping + x10 = self.output(x) + features = torch.reshape(x10, (x10.size(0), x10.size(1) * self.FRAMES_PER_STEP, x10.size(2) // self.FRAMES_PER_STEP)) + + return features + + +class StatisticalModel(nn.Module): + def __init__(self, quant_levels, latent_dim, state_dim): + """ Statistical model for latent space + + Computes scaling, deadzone, r, and theta + + """ + + super(StatisticalModel, self).__init__() + + # copy parameters + self.latent_dim = latent_dim + self.state_dim = state_dim + self.total_dim = latent_dim + state_dim + self.quant_levels = quant_levels + self.embedding_dim = 6 * self.total_dim + + # quantization embedding + self.quant_embedding = nn.Embedding(quant_levels, self.embedding_dim) + + # initialize embedding to 0 + with torch.no_grad(): + self.quant_embedding.weight[:] = 0 + + + def forward(self, quant_ids): + """ takes quant_ids and returns statistical model parameters""" + + x = self.quant_embedding(quant_ids) + + # CAVE: theta_soft is not used anymore. Kick it out? + quant_scale = F.softplus(x[..., 0 * self.total_dim : 1 * self.total_dim]) + dead_zone = F.softplus(x[..., 1 * self.total_dim : 2 * self.total_dim]) + theta_soft = torch.sigmoid(x[..., 2 * self.total_dim : 3 * self.total_dim]) + r_soft = torch.sigmoid(x[..., 3 * self.total_dim : 4 * self.total_dim]) + theta_hard = torch.sigmoid(x[..., 4 * self.total_dim : 5 * self.total_dim]) + r_hard = torch.sigmoid(x[..., 5 * self.total_dim : 6 * self.total_dim]) + + + return { + 'quant_embedding' : x, + 'quant_scale' : quant_scale, + 'dead_zone' : dead_zone, + 'r_hard' : r_hard, + 'theta_hard' : theta_hard, + 'r_soft' : r_soft, + 'theta_soft' : theta_soft + } + + +class RDOVAE(nn.Module): + def __init__(self, + feature_dim, + latent_dim, + quant_levels, + cond_size, + cond_size2, + state_dim=24, + split_mode='split', + chunks_per_offset=4, + clip_weights=False, + pvq_num_pulses=82, + state_dropout_rate=0, + softquant=False): + + super(RDOVAE, self).__init__() + + self.feature_dim = feature_dim + self.latent_dim = latent_dim + self.quant_levels = quant_levels + self.cond_size = cond_size + self.cond_size2 = cond_size2 + self.split_mode = split_mode + self.chunks_per_offset = chunks_per_offset + self.state_dim = state_dim + self.pvq_num_pulses = pvq_num_pulses + self.state_dropout_rate = state_dropout_rate + + # submodules encoder and decoder share the statistical model + self.statistical_model = StatisticalModel(quant_levels, latent_dim, state_dim) + self.core_encoder = nn.DataParallel(CoreEncoder(feature_dim, latent_dim, cond_size, cond_size2, state_size=state_dim, softquant=softquant)) + self.core_decoder = nn.DataParallel(CoreDecoder(latent_dim, feature_dim, cond_size, cond_size2, state_size=state_dim, softquant=softquant)) + + self.enc_stride = CoreEncoder.FRAMES_PER_STEP + self.dec_stride = CoreDecoder.FRAMES_PER_STEP + + if clip_weights: + self.weight_clip_fn = weight_clip_factory(0.496) + else: + self.weight_clip_fn = None + + if self.dec_stride % self.enc_stride != 0: + raise ValueError(f"get_decoder_chunks_generic: encoder stride does not divide decoder stride") + + def clip_weights(self): + if not type(self.weight_clip_fn) == type(None): + self.apply(self.weight_clip_fn) + + def sparsify(self): + #self.core_encoder.module.sparsify() + self.core_decoder.module.sparsify() + + def get_decoder_chunks(self, z_frames, mode='split', chunks_per_offset = 4): + + enc_stride = self.enc_stride + dec_stride = self.dec_stride + + stride = dec_stride // enc_stride + + chunks = [] + + for offset in range(stride): + # start is the smalles number = offset mod stride that decodes to a valid range + start = offset + while enc_stride * (start + 1) - dec_stride < 0: + start += stride + + # check if start is a valid index + if start >= z_frames: + raise ValueError("get_decoder_chunks_generic: range too small") + + # stop is the smallest number outside [0, num_enc_frames] that's congruent to offset mod stride + stop = z_frames - (z_frames % stride) + offset + while stop < z_frames: + stop += stride + + # calculate split points + length = (stop - start) + if mode == 'split': + split_points = [start + stride * int(i * length / chunks_per_offset / stride) for i in range(chunks_per_offset)] + [stop] + elif mode == 'random_split': + split_points = [stride * x + start for x in random_split(0, (stop - start)//stride - 1, chunks_per_offset - 1, 1)] + elif mode == 'skewed_split': + split_points = [start + stride * int(i * length / 4 / chunks_per_offset / stride) for i in range(chunks_per_offset)] + [stop] + else: + raise ValueError(f"get_decoder_chunks_generic: unknown mode {mode}") + + + for i in range(chunks_per_offset): + # (enc_frame_start, enc_frame_stop, enc_frame_stride, stride, feature_frame_start, feature_frame_stop) + # encoder range(i, j, stride) maps to feature range(enc_stride * (i + 1) - dec_stride, enc_stride * j) + # provided that i - j = 1 mod stride + chunks.append({ + 'z_start' : split_points[i], + 'z_stop' : split_points[i + 1] - stride + 1, + 'z_stride' : stride, + 'features_start' : enc_stride * (split_points[i] + 1) - dec_stride, + 'features_stop' : enc_stride * (split_points[i + 1] - stride + 1) + }) + + return chunks + + + def forward(self, features, q_id): + + # calculate statistical model from quantization ID + statistical_model = self.statistical_model(q_id) + + # run encoder + z, states = self.core_encoder(features) + + # scaling, dead-zone and quantization + z = z * statistical_model['quant_scale'][:,:,:self.latent_dim] + z = soft_dead_zone(z, statistical_model['dead_zone'][:,:,:self.latent_dim]) + + # quantization + z_q = hard_quantize(z) / statistical_model['quant_scale'][:,:,:self.latent_dim] + z_n = noise_quantize(z) / statistical_model['quant_scale'][:,:,:self.latent_dim] + #states_q = soft_pvq(states, self.pvq_num_pulses) + states = states * statistical_model['quant_scale'][:,:,self.latent_dim:] + states = soft_dead_zone(states, statistical_model['dead_zone'][:,:,self.latent_dim:]) + + states_q = hard_quantize(states) / statistical_model['quant_scale'][:,:,self.latent_dim:] + states_n = noise_quantize(states) / statistical_model['quant_scale'][:,:,self.latent_dim:] + + if self.state_dropout_rate > 0: + drop = torch.rand(states_q.size(0)) < self.state_dropout_rate + mask = torch.ones_like(states_q) + mask[drop] = 0 + states_q = states_q * mask + + # decoder + chunks = self.get_decoder_chunks(z.size(1), mode=self.split_mode, chunks_per_offset=self.chunks_per_offset) + + outputs_hq = [] + outputs_sq = [] + for chunk in chunks: + # decoder with hard quantized input + z_dec_reverse = torch.flip(z_q[..., chunk['z_start'] : chunk['z_stop'] : chunk['z_stride'], :], [1]) + dec_initial_state = states_q[..., chunk['z_stop'] - 1 : chunk['z_stop'], :] + features_reverse = self.core_decoder(z_dec_reverse, dec_initial_state) + outputs_hq.append((torch.flip(features_reverse, [1]), chunk['features_start'], chunk['features_stop'])) + + + # decoder with soft quantized input + z_dec_reverse = torch.flip(z_n[..., chunk['z_start'] : chunk['z_stop'] : chunk['z_stride'], :], [1]) + dec_initial_state = states_n[..., chunk['z_stop'] - 1 : chunk['z_stop'], :] + features_reverse = self.core_decoder(z_dec_reverse, dec_initial_state) + outputs_sq.append((torch.flip(features_reverse, [1]), chunk['features_start'], chunk['features_stop'])) + + return { + 'outputs_hard_quant' : outputs_hq, + 'outputs_soft_quant' : outputs_sq, + 'z' : z, + 'states' : states, + 'statistical_model' : statistical_model + } + + def encode(self, features): + """ encoder with quantization and rate estimation """ + + z, states = self.core_encoder(features) + + # quantization of initial states + states = soft_pvq(states, self.pvq_num_pulses) + state_size = m.log2(pvq_codebook_size(self.state_dim, self.pvq_num_pulses)) + + return z, states, state_size + + def decode(self, z, initial_state): + """ decoder (flips sequences by itself) """ + + z_reverse = torch.flip(z, [1]) + features_reverse = self.core_decoder(z_reverse, initial_state) + features = torch.flip(features_reverse, [1]) + + return features + + def quantize(self, z, q_ids): + """ quantization of latent vectors """ + + stats = self.statistical_model(q_ids) + + zq = z * stats['quant_scale'][:self.latent_dim] + zq = soft_dead_zone(zq, stats['dead_zone'][:self.latent_dim]) + zq = torch.round(zq) + + sizes = hard_rate_estimate(zq, stats['r_hard'][:,:,:self.latent_dim], stats['theta_hard'][:,:,:self.latent_dim], reduce=False) + + return zq, sizes + + def unquantize(self, zq, q_ids): + """ re-scaling of latent vector """ + + stats = self.statistical_model(q_ids) + + z = zq / stats['quant_scale'][:,:,:self.latent_dim] + + return z + + def freeze_model(self): + + # freeze all parameters + for p in self.parameters(): + p.requires_grad = False + + for p in self.statistical_model.parameters(): + p.requires_grad = True diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/requirements.txt b/managed_components/78__esp-opus/dnn/torch/rdovae/requirements.txt new file mode 100644 index 0000000..9225ea8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/requirements.txt @@ -0,0 +1,4 @@ +numpy +scipy +torch +tqdm \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/rdovae/train_rdovae.py b/managed_components/78__esp-opus/dnn/torch/rdovae/train_rdovae.py new file mode 100644 index 0000000..444c573 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/rdovae/train_rdovae.py @@ -0,0 +1,290 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import argparse + +import torch +import tqdm + +from rdovae import RDOVAE, RDOVAEDataset, distortion_loss, hard_rate_estimate, soft_rate_estimate + + +parser = argparse.ArgumentParser() + +parser.add_argument('features', type=str, help='path to feature file in .f32 format') +parser.add_argument('output', type=str, help='path to output folder') + +parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: ''", default="") + + +model_group = parser.add_argument_group(title="model parameters") +model_group.add_argument('--latent-dim', type=int, help="number of symbols produces by encoder, default: 80", default=80) +model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) +model_group.add_argument('--cond-size2', type=int, help="second conditioning size, default: 256", default=256) +model_group.add_argument('--state-dim', type=int, help="dimensionality of transfered state, default: 24", default=24) +model_group.add_argument('--quant-levels', type=int, help="number of quantization levels, default: 16", default=16) +model_group.add_argument('--lambda-min', type=float, help="minimal value for rate lambda, default: 0.0002", default=2e-4) +model_group.add_argument('--lambda-max', type=float, help="maximal value for rate lambda, default: 0.0104", default=0.0104) +model_group.add_argument('--pvq-num-pulses', type=int, help="number of pulses for PVQ, default: 82", default=82) +model_group.add_argument('--state-dropout-rate', type=float, help="state dropout rate, default: 0", default=0.0) +model_group.add_argument('--softquant', action="store_true", help="enables soft quantization during training") + +training_group = parser.add_argument_group(title="training parameters") +training_group.add_argument('--batch-size', type=int, help="batch size, default: 32", default=32) +training_group.add_argument('--lr', type=float, help='learning rate, default: 3e-4', default=3e-4) +training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 100', default=100) +training_group.add_argument('--sequence-length', type=int, help='sequence length, needs to be divisible by chunks_per_offset, default: 400', default=400) +training_group.add_argument('--chunks-per-offset', type=int, help='chunks per offset', default=4) +training_group.add_argument('--lr-decay-factor', type=float, help='learning rate decay factor, default: 2.5e-5', default=2.5e-5) +training_group.add_argument('--split-mode', type=str, choices=['split', 'random_split', 'skewed_split'], help='splitting mode for decoder input, default: split', default='split') +training_group.add_argument('--enable-first-frame-loss', action='store_true', default=False, help='enables dedicated distortion loss on first 4 decoder frames') +training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) +training_group.add_argument('--train-decoder-only', action='store_true', help='freeze encoder and statistical model and train decoder only') + +args = parser.parse_args() + +# set visible devices +os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices + +# checkpoints +checkpoint_dir = os.path.join(args.output, 'checkpoints') +checkpoint = dict() +os.makedirs(checkpoint_dir, exist_ok=True) + +# training parameters +batch_size = args.batch_size +lr = args.lr +epochs = args.epochs +sequence_length = args.sequence_length +lr_decay_factor = args.lr_decay_factor +split_mode = args.split_mode +# not exposed +adam_betas = [0.8, 0.95] +adam_eps = 1e-8 + +checkpoint['batch_size'] = batch_size +checkpoint['lr'] = lr +checkpoint['lr_decay_factor'] = lr_decay_factor +checkpoint['split_mode'] = split_mode +checkpoint['epochs'] = epochs +checkpoint['sequence_length'] = sequence_length +checkpoint['adam_betas'] = adam_betas + +# logging +log_interval = 10 + +# device +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + +# model parameters +cond_size = args.cond_size +cond_size2 = args.cond_size2 +latent_dim = args.latent_dim +quant_levels = args.quant_levels +lambda_min = args.lambda_min +lambda_max = args.lambda_max +state_dim = args.state_dim +softquant = args.softquant +# not expsed +num_features = 20 + + +# training data +feature_file = args.features + +# model +checkpoint['model_args'] = (num_features, latent_dim, quant_levels, cond_size, cond_size2) +checkpoint['model_kwargs'] = {'state_dim': state_dim, 'split_mode' : split_mode, 'pvq_num_pulses': args.pvq_num_pulses, 'state_dropout_rate': args.state_dropout_rate, 'softquant': softquant, 'chunks_per_offset': args.chunks_per_offset} +model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) + +if type(args.initial_checkpoint) != type(None): + checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') + model.load_state_dict(checkpoint['state_dict'], strict=False) + +checkpoint['state_dict'] = model.state_dict() + +if args.train_decoder_only: + if args.initial_checkpoint is None: + print("warning: training decoder only without providing initial checkpoint") + + for p in model.core_encoder.module.parameters(): + p.requires_grad = False + + for p in model.statistical_model.parameters(): + p.requires_grad = False + +# dataloader +checkpoint['dataset_args'] = (feature_file, sequence_length, num_features, 36) +checkpoint['dataset_kwargs'] = {'lambda_min': lambda_min, 'lambda_max': lambda_max, 'enc_stride': model.enc_stride, 'quant_levels': quant_levels} +dataset = RDOVAEDataset(*checkpoint['dataset_args'], **checkpoint['dataset_kwargs']) +dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) + + + +# optimizer +params = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.Adam(params, lr=lr, betas=adam_betas, eps=adam_eps) + + +# learning rate scheduler +scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) + +if __name__ == '__main__': + + # push model to device + model.to(device) + + # training loop + + batch = 1 + for epoch in range(1, epochs + 1): + + print(f"training epoch {epoch}...") + + # running stats + running_rate_loss = 0 + running_soft_dist_loss = 0 + running_hard_dist_loss = 0 + running_hard_rate_loss = 0 + running_soft_rate_loss = 0 + running_total_loss = 0 + running_rate_metric = 0 + running_states_rate_metric = 0 + previous_total_loss = 0 + running_first_frame_loss = 0 + + with tqdm.tqdm(dataloader, unit='batch') as tepoch: + for i, (features, rate_lambda, q_ids) in enumerate(tepoch): + + # zero out gradients + optimizer.zero_grad() + + # push inputs to device + features = features.to(device) + q_ids = q_ids.to(device) + rate_lambda = rate_lambda.to(device) + + + rate_lambda_upsamp = torch.repeat_interleave(rate_lambda, 2, 1) + + # run model + model_output = model(features, q_ids) + + # collect outputs + z = model_output['z'] + states = model_output['states'] + outputs_hard_quant = model_output['outputs_hard_quant'] + outputs_soft_quant = model_output['outputs_soft_quant'] + statistical_model = model_output['statistical_model'] + + if type(args.initial_checkpoint) == type(None): + latent_lambda = (1. - .5/(1.+batch/1000)) + state_lambda = (1. - .9/(1.+batch/6000)) + else: + latent_lambda = 1. + state_lambda = 1. + + # rate loss + hard_rate = hard_rate_estimate(z, statistical_model['r_hard'][:,:,:latent_dim], statistical_model['theta_hard'][:,:,:latent_dim], reduce=False) + soft_rate = soft_rate_estimate(z, statistical_model['r_soft'][:,:,:latent_dim], reduce=False) + states_hard_rate = hard_rate_estimate(states, statistical_model['r_hard'][:,:,latent_dim:], statistical_model['theta_hard'][:,:,latent_dim:], reduce=False) + states_soft_rate = soft_rate_estimate(states, statistical_model['r_soft'][:,:,latent_dim:], reduce=False) + soft_rate_loss = torch.mean(torch.sqrt(rate_lambda) * (latent_lambda*soft_rate + .04*state_lambda*states_soft_rate)) + hard_rate_loss = torch.mean(torch.sqrt(rate_lambda) * (latent_lambda*hard_rate + .04*state_lambda*states_hard_rate)) + rate_loss = (soft_rate_loss + 0.1 * hard_rate_loss) + hard_rate_metric = torch.mean(hard_rate) + states_rate_metric = torch.mean(states_hard_rate) + + ## distortion losses + + # hard quantized decoder input + distortion_loss_hard_quant = torch.zeros_like(rate_loss) + for dec_features, start, stop in outputs_hard_quant: + distortion_loss_hard_quant += distortion_loss(features[..., start : stop, :], dec_features, rate_lambda_upsamp[..., start : stop]) / len(outputs_hard_quant) + + first_frame_loss = torch.zeros_like(rate_loss) + for dec_features, start, stop in outputs_hard_quant: + first_frame_loss += distortion_loss(features[..., stop-4 : stop, :], dec_features[..., -4:, :], rate_lambda_upsamp[..., stop - 4 : stop]) / len(outputs_hard_quant) + + # soft quantized decoder input + distortion_loss_soft_quant = torch.zeros_like(rate_loss) + for dec_features, start, stop in outputs_soft_quant: + distortion_loss_soft_quant += distortion_loss(features[..., start : stop, :], dec_features, rate_lambda_upsamp[..., start : stop]) / len(outputs_soft_quant) + + # total loss + total_loss = rate_loss + (distortion_loss_hard_quant + distortion_loss_soft_quant) / 2 + + if args.enable_first_frame_loss: + total_loss = .97*total_loss + 0.03 * first_frame_loss + + + total_loss.backward() + + optimizer.step() + + model.clip_weights() + model.sparsify() + + scheduler.step() + + # collect running stats + running_hard_dist_loss += float(distortion_loss_hard_quant.detach().cpu()) + running_soft_dist_loss += float(distortion_loss_soft_quant.detach().cpu()) + running_rate_loss += float(rate_loss.detach().cpu()) + running_rate_metric += float(hard_rate_metric.detach().cpu()) + running_states_rate_metric += float(states_rate_metric.detach().cpu()) + running_total_loss += float(total_loss.detach().cpu()) + running_first_frame_loss += float(first_frame_loss.detach().cpu()) + running_soft_rate_loss += float(soft_rate_loss.detach().cpu()) + running_hard_rate_loss += float(hard_rate_loss.detach().cpu()) + + if (i + 1) % log_interval == 0: + current_loss = (running_total_loss - previous_total_loss) / log_interval + tepoch.set_postfix( + current_loss=current_loss, + total_loss=running_total_loss / (i + 1), + dist_hq=running_hard_dist_loss / (i + 1), + dist_sq=running_soft_dist_loss / (i + 1), + rate_loss=running_rate_loss / (i + 1), + rate=running_rate_metric / (i + 1), + states_rate=running_states_rate_metric / (i + 1), + ffloss=running_first_frame_loss / (i + 1), + rateloss_hard=running_hard_rate_loss / (i + 1), + rateloss_soft=running_soft_rate_loss / (i + 1) + ) + previous_total_loss = running_total_loss + batch = batch+1 + + # save checkpoint + checkpoint_path = os.path.join(checkpoint_dir, f'checkpoint_epoch_{epoch}.pth') + checkpoint['state_dict'] = model.state_dict() + checkpoint['loss'] = running_total_loss / len(dataloader) + checkpoint['epoch'] = epoch + torch.save(checkpoint, checkpoint_path) diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/README.md b/managed_components/78__esp-opus/dnn/torch/testsuite/README.md new file mode 100644 index 0000000..cc76965 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/README.md @@ -0,0 +1,46 @@ +# lpcnet-testsuite + +## setup +The test script is written for Linux only. It requires sox to be installed and available. + +Setup is done as usual via + +``` +pip install -r requirements.txt +``` + +The test scrip run_warpq_test.py requires a setup file in yaml format, which specifies how +to generate a wave file OUTPUT from a wave file INPUT sampled resampled to the specified +sampling rate as a list of shell commands. This makes it easy to test other neural vocoders +with it as well. Two examples are given in examples. INPUT and OUTPUT will be replaced by using +the string.format(INPUT=input,OUTPUT=output) method. + +Here is one example: + +``` +test: "LPCNet reference test" +processing: + - "sox {INPUT} {INPUT}.raw" + - "/local/code/LPCNet/lpcnet_demo -features {INPUT}.raw {INPUT}.features.f32" + - "/local/code/LPCNet/lpcnet_demo -synthesis {INPUT}.features.f32 {INPUT}.decoded.raw" + - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT}" +``` + +The structure of the output folder is as follows: + +``` +output_folder ++-- html + +-- index.html + +-- items ++-- processing ++-- setup.yml ++-- stats.txt ++-- scores.txt +``` + +scores.txt contains the WARP-Q scores in descending order (best to worse) +stats.txt contains mean values over all, the 10 best and the 10 worst items +setup.yml contains all information to repeat the run +htms contains a self-contained website displaying the 10 best and 10 worst items +processing contains processing output \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml new file mode 100644 index 0000000..2858309 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml @@ -0,0 +1,6 @@ +test: "LPCNet reference test" +processing: + - "sox {INPUT} {INPUT}.raw" + - "/local/code/LPCNet/lpcnet_demo -features {INPUT}.raw {INPUT}.features.f32" + - "/local/code/LPCNet/lpcnet_demo -synthesis {INPUT}.features.f32 {INPUT}.decoded.raw" + - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT} trim 0.015" \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml new file mode 100644 index 0000000..b97b26d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml @@ -0,0 +1,5 @@ +test: "LPCNet reference test" +processing: + - "sox {INPUT} {INPUT}.raw" + - "/local/code/LPCNet/lpcnet_demo -plc_file causal {PLCFILE} {INPUT}.raw {INPUT}.decoded.raw" + - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT}" \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml new file mode 100644 index 0000000..631cbfa --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml @@ -0,0 +1,5 @@ +test: "no noise test" +processing: + - "sox {INPUT} {INPUT}.raw" + - "/home/ubuntu/bin/lpcnet_dump_data_v2 -test {INPUT}.raw {INPUT}.features.f32" + - "/home/ubuntu/opt/miniconda3/envs/torch/bin/python /local/code/lpcnext/test_lpcnet.py {INPUT}.features.f32 /local/experiments/noise_augmentation/output/lpcnet_384_2/checkpoints/checkpoint_epoch_20.pth {OUTPUT}" \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/requirements.txt b/managed_components/78__esp-opus/dnn/torch/testsuite/requirements.txt new file mode 100644 index 0000000..af2d991 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/requirements.txt @@ -0,0 +1,12 @@ +scipy +librosa +numpy +scikit-image +pyvad +speechpy +soundfile +pyyaml +pesq +AMFM_decompy +matplotlib +multiprocess \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/run_test.py b/managed_components/78__esp-opus/dnn/torch/testsuite/run_test.py new file mode 100644 index 0000000..69463dd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/run_test.py @@ -0,0 +1,375 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import multiprocess as multiprocessing +import random +import subprocess +import argparse +import shutil + +import yaml + +from utils.files import get_wave_file_list +from utils.pesq import compute_PESQ +from utils.pitch import compute_pitch_error + + +parser = argparse.ArgumentParser() +parser.add_argument('setup', type=str, help='setup yaml specifying end to end processing with model under test') +parser.add_argument('input_folder', type=str, help='input folder path') +parser.add_argument('output_folder', type=str, help='output folder path') +parser.add_argument('--num-testitems', type=int, help="number of testitems to be processed (default 100)", default=100) +parser.add_argument('--seed', type=int, help='seed for random item selection', default=None) +parser.add_argument('--fs', type=int, help="sampling rate at which input is presented as wave file (defaults to 16000)", default=16000) +parser.add_argument('--num-workers', type=int, help="number of subprocesses to be used (default=4)", default=4) +parser.add_argument('--plc-suffix', type=str, default="_is_lost.txt", help="suffix of plc error pattern file: only relevant if command chain uses PLCFILE (default=_is_lost.txt)") +parser.add_argument('--metrics', type=str, default='pesq', help='comma separated string of metrics, supported: {{"pesq", "pitch_error", "voicing_error"}}, default="pesq"') +parser.add_argument('--verbose', action='store_true', help='enables printouts of all commands run in the pipeline') + +def check_for_sox_in_path(): + r = subprocess.run("sox -h", shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + return r.returncode == 0 + + +def run_save_sh(command, verbose=False): + + if verbose: + print(f"[run_save_sh] running command {command}...") + + r = subprocess.run(command, shell=True) + if r.returncode != 0: + raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}") + + +def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'pesq'}, plc_suffix="_is_lost.txt", verbose=False): + + # prepare model input + model_input = output_path + ".resamp.wav" + run_save_sh(f"sox {input_path} -r {fs} {model_input}", verbose=verbose) + + plcfile = os.path.splitext(input_path)[0] + plc_suffix + if os.path.isfile(plcfile): + run_save_sh(f"cp {plcfile} {os.path.dirname(output_path)}") + + # generate model output + for command in model_commands: + run_save_sh(command.format(INPUT=model_input, OUTPUT=output_path, PLCFILE=plcfile), verbose=verbose) + + scores = dict() + cache = dict() + for metric in metrics: + if metric == 'pesq': + # run pesq + score = compute_PESQ(input_path, output_path, fs=fs) + elif metric == 'pitch_error': + if metric in cache: + score = cache[metric] + else: + rval = compute_pitch_error(input_path, output_path, fs=fs) + score = rval[metric] + cache['voicing_error'] = rval['voicing_error'] + elif metric == 'voicing_error': + if metric in cache: + score = cache[metric] + else: + rval = compute_pitch_error(input_path, output_path, fs=fs) + score = rval[metric] + cache['pitch_error'] = rval['pitch_error'] + else: + ValueError(f'error: unknown metric {metric}') + + scores[metric] = score + + return (output_path, scores) + + +def get_output_path(root_folder, input, output_folder): + + input_relpath = os.path.relpath(input, root_folder) + + os.makedirs(os.path.join(output_folder, 'processing', os.path.dirname(input_relpath)), exist_ok=True) + + output_path = os.path.join(output_folder, 'processing', input_relpath + '.output.wav') + + return output_path + + +def add_audio_table(f, html_folder, results, title, metric): + + item_folder = os.path.join(html_folder, 'items') + os.makedirs(item_folder, exist_ok=True) + + # table with results + f.write(f""" +
+

{title}

+ + + + + + + + + """) + + for i, r in enumerate(results): + item, score = r + item_name = os.path.basename(item) + new_item_path = os.path.join(item_folder, item_name) + shutil.copyfile(item, new_item_path) + shutil.copyfile(item + '.resamp.wav', os.path.join(item_folder, item_name + '.orig.wav')) + + f.write(f""" + + + + + + + + """) + + # footer + f.write(""" +
Rank Name {metric.upper()} Audio (out) Audio (orig)
{i + 1} {item_name.split('.')[0]} {score:.3f} + + + +
+
+ """) + + +def create_html(output_folder, results, title, metric): + + html_folder = output_folder + items_folder = os.path.join(html_folder, 'items') + os.makedirs(html_folder, exist_ok=True) + os.makedirs(items_folder, exist_ok=True) + + with open(os.path.join(html_folder, 'index.html'), 'w') as f: + # header and title + f.write(f""" + + + + + {title} + + + +

{title}

+
+ """) + + # top 20 + add_audio_table(f, html_folder, results[:-21: -1], "Top 20", metric) + + # 20 around median + N = len(results) // 2 + add_audio_table(f, html_folder, results[N + 10 : N - 10: -1], "Median 20", metric) + + # flop 20 + add_audio_table(f, html_folder, results[:20], "Flop 20", metric) + + # footer + f.write(""" +
+ + + """) + +metric_sorting_signs = { + 'pesq' : 1, + 'pitch_error' : -1, + 'voicing_error' : -1 +} + +def is_valid_result(data, metrics): + if not isinstance(data, dict): + return False + + for metric in metrics: + if not metric in data: + return False + + return True + + +def evaluate_results(output_folder, results, metric): + + results = sorted(results, key=lambda x : metric_sorting_signs[metric] * x[1]) + with open(os.path.join(args.output_folder, f'scores_{metric}.txt'), 'w') as f: + for result in results: + f.write(f"{os.path.relpath(result[0], args.output_folder)} {result[1]}\n") + + + # some statistics + mean = sum([r[1] for r in results]) / len(results) + top_mean = sum([r[1] for r in results[-20:]]) / 20 + bottom_mean = sum([r[1] for r in results[:20]]) / 20 + + with open(os.path.join(args.output_folder, f'stats_{metric}.txt'), 'w') as f: + f.write(f"mean score: {mean}\n") + f.write(f"bottom mean score: {bottom_mean}\n") + f.write(f"top mean score: {top_mean}\n") + + print(f"\nmean score: {mean}") + print(f"bottom mean score: {bottom_mean}") + print(f"top mean score: {top_mean}\n") + + # create output html + create_html(os.path.join(output_folder, 'html', metric), results, setup['test'], metric) + +if __name__ == "__main__": + args = parser.parse_args() + + # check for sox + if not check_for_sox_in_path(): + raise RuntimeError("script requires sox") + + + # prepare output folder + if os.path.exists(args.output_folder): + print("warning: output folder exists") + + reply = input('continue? (y/n): ') + while reply not in {'y', 'n'}: + reply = input('continue? (y/n): ') + + if reply == 'n': + os._exit() + else: + # start with a clean sleight + shutil.rmtree(args.output_folder) + + os.makedirs(args.output_folder, exist_ok=True) + + # extract metrics + metrics = args.metrics.split(",") + for metric in metrics: + if not metric in metric_sorting_signs: + print(f"unknown metric {metric}") + args.usage() + + # read setup + print(f"loading {args.setup}...") + with open(args.setup, "r") as f: + setup = yaml.load(f.read(), yaml.FullLoader) + + model_commands = setup['processing'] + + print("\nfound the following model commands:") + for command in model_commands: + print(command.format(INPUT='input.wav', OUTPUT='output.wav', PLCFILE='input_is_lost.txt')) + + # store setup to output folder + setup['input'] = os.path.abspath(args.input_folder) + setup['output'] = os.path.abspath(args.output_folder) + setup['seed'] = args.seed + with open(os.path.join(args.output_folder, 'setup.yml'), 'w') as f: + yaml.dump(setup, f) + + # get input + print(f"\nCollecting audio files from {args.input_folder}...") + file_list = get_wave_file_list(args.input_folder, check_for_features=False) + print(f"...{len(file_list)} files found\n") + + # sample from file list + file_list = sorted(file_list) + random.seed(args.seed) + random.shuffle(file_list) + num_testitems = min(args.num_testitems, len(file_list)) + file_list = file_list[:num_testitems] + + + print(f"\nlaunching test on {num_testitems} items...") + # helper function for parallel processing + def func(input_path): + output_path = get_output_path(args.input_folder, input_path, args.output_folder) + + try: + rval = run_processing_chain(input_path, output_path, model_commands, args.fs, metrics=metrics, plc_suffix=args.plc_suffix, verbose=args.verbose) + except: + rval = (input_path, -1) + + return rval + + with multiprocessing.Pool(args.num_workers) as p: + results = p.map(func, file_list) + + results_dict = dict() + for name, values in results: + if is_valid_result(values, metrics): + results_dict[name] = values + + print(results_dict) + + # evaluating results + num_failures = num_testitems - len(results_dict) + print(f"\nprocessing of {num_failures} items failed\n") + + for metric in metrics: + print(metric) + evaluate_results( + args.output_folder, + [(name, value[metric]) for name, value in results_dict.items()], + metric + ) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/utils/__init__.py b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/utils/files.py b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/files.py new file mode 100644 index 0000000..c3a1553 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/files.py @@ -0,0 +1,54 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + + +def get_wave_file_list(parent_folder, extensions=[".wav", ".flac"], check_for_features=False): + """ traverses subfolders of parent_folder in search for files that match the given extension """ + + file_list = [] + + for root, dirs, files in os.walk(parent_folder, topdown=True): + + for file in files: + + stem, ext = os.path.splitext(file) + + #check for extension + if not ext in extensions: + continue + + # check if feature file exists + if check_for_features and not os.path.isfile(os.path.join(root, stem + "_features.f32")): + continue + + file_list.append(os.path.join(root, file)) + + return file_list \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pesq.py b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pesq.py new file mode 100644 index 0000000..5a27f51 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pesq.py @@ -0,0 +1,43 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import pesq +import librosa + +def compute_PESQ(ref, test, fs=16000): + + if not ref.endswith('.wav') or not test.endswith('.wav'): + raise ValueError('error: expecting .wav as file extension') + + ref_item, _ = librosa.load(ref, sr=fs) + test_item, _ = librosa.load(test, sr=fs) + + score = pesq.pesq(fs, ref_item, test_item) + + return score \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pitch.py b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pitch.py new file mode 100644 index 0000000..f942965 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/testsuite/utils/pitch.py @@ -0,0 +1,61 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np +from scipy.io import wavfile +import amfm_decompy.pYAAPT as pYAAPT +import amfm_decompy.basic_tools as basic + +def get_voicing_info(x, sr=16000): + + signal = basic.SignalObj(x, sr) + pitch = pYAAPT.yaapt(signal, **{'frame_length' : 20.0, 'tda_frame_length' : 20.0}) + + pitch_values = pitch.samp_values + voiced_flags = pitch.vuv.astype('float') + + return pitch_values, voiced_flags + +def compute_pitch_error(ref_path, test_path, fs=16000): + fs_orig, x_orig = wavfile.read(ref_path) + fs_test, x_test = wavfile.read(test_path) + + min_length = min(len(x_orig), len(x_test)) + x_orig = x_orig[:min_length] + x_test = x_test[:min_length] + + assert fs_orig == fs_test == fs + + pitch_contour_orig, voicing_orig = get_voicing_info(x_orig.astype(np.float32)) + pitch_contour_test, voicing_test = get_voicing_info(x_test.astype(np.float32)) + + return { + 'pitch_error' : np.mean(np.abs(pitch_contour_orig - pitch_contour_test)).item(), + 'voicing_error' : np.sum(np.abs(voicing_orig - voicing_test)).item() / len(voicing_orig) + } \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/README.md b/managed_components/78__esp-opus/dnn/torch/weight-exchange/README.md new file mode 100644 index 0000000..f4818b5 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/README.md @@ -0,0 +1,21 @@ +# weight-exchange + + + +## Weight Exchange +Repo wor exchanging weights betweeen torch an tensorflow.keras modules, using an intermediate numpy format. + +Routines for loading/dumping torch weights are located in exchange/torch and can be loaded with +``` +import exchange.torch +``` +and routines for loading/dumping tensorflow weights are located in exchange/tf and can be loaded with +``` +import exchange.tf +``` + +Note that `exchange.torch` requires torch to be installed and `exchange.tf` requires tensorflow. To avoid the necessity of installing both torch and tensorflow in the working environment, none of these submodules is imported when calling `import exchange`. Similarly, the requirements listed in `requirements.txt` do include neither Tensorflow or Pytorch. + + +## C export +The module `exchange.c_export` contains routines to export weights to C files. On the long run it will be possible to call all `dump_...` functions with either a path string or a `CWriter` instance based on which the export format is chosen. This is currently only implemented for `torch.nn.GRU`, `torch.nn.Linear` and `torch.nn.Conv1d`. \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/requirements.txt b/managed_components/78__esp-opus/dnn/torch/weight-exchange/requirements.txt new file mode 100644 index 0000000..296d654 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/requirements.txt @@ -0,0 +1 @@ +numpy \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/setup.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/setup.py new file mode 100644 index 0000000..2b20440 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/setup.py @@ -0,0 +1,48 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +#!/usr/bin/env/python +import os +from setuptools import setup + +lib_folder = os.path.dirname(os.path.realpath(__file__)) + +with open(os.path.join(lib_folder, 'requirements.txt'), 'r') as f: + install_requires = list(f.read().splitlines()) + +print(install_requires) + +setup(name='wexchange', + version='1.6', + author='Jan Buethe', + author_email='jbuethe@amazon.de', + description='Weight-exchange library between Pytorch and Tensorflow', + packages=['wexchange', 'wexchange.tf', 'wexchange.torch', 'wexchange.c_export'], + install_requires=install_requires + ) diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/__init__.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/__init__.py new file mode 100644 index 0000000..a028614 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/__init__.py @@ -0,0 +1,30 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from . import c_export \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py new file mode 100644 index 0000000..2a580c8 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py @@ -0,0 +1,31 @@ +from .c_writer import CWriter +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from .common import print_gru_layer, print_dense_layer, print_conv1d_layer, print_tconv1d_layer, print_conv2d_layer, print_vector \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py new file mode 100644 index 0000000..74c8b55 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py @@ -0,0 +1,181 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +from collections import OrderedDict + +class CWriter: + def __init__(self, + filename_without_extension, + message=None, + header_only=False, + create_state_struct=False, + enable_binary_blob=True, + model_struct_name="Model", + nnet_header="nnet.h", + add_typedef=False): + """ + Writer class for creating souce and header files for weight exports to C + + Parameters: + ----------- + + filename_without_extension: str + filename from which .c and .h files are created + + message: str, optional + if given and not None, this message will be printed as comment in the header file + + header_only: bool, optional + if True, only a header file is created; defaults to False + + enable_binary_blob: bool, optional + if True, export is done in binary blob format and a model type is created; defaults to False + + create_state_struct: bool, optional + if True, a state struct type is created in the header file; if False, state sizes are defined as macros; defaults to False + + model_struct_name: str, optional + name used for the model struct type; only relevant when enable_binary_blob is True; defaults to "Model" + + nnet_header: str, optional + name of header nnet header file; defaults to nnet.h + + """ + + + self.header_only = header_only + self.enable_binary_blob = enable_binary_blob + self.create_state_struct = create_state_struct + self.model_struct_name = model_struct_name + self.add_typedef = add_typedef + + # for binary blob format, format is key=, value=(, ) + self.layer_dict = OrderedDict() + + # for binary blob format, format is key=, value= + self.weight_arrays = [] + + # form model struct, format is key=, value= + self.state_dict = OrderedDict() + + self.header = open(filename_without_extension + ".h", "w") + header_name = os.path.basename(filename_without_extension) + '.h' + + if message is not None: + self.header.write(f"/* {message} */\n\n") + + self.header_guard = os.path.basename(filename_without_extension).upper() + "_H" + self.header.write( +f''' +#ifndef {self.header_guard} +#define {self.header_guard} + +#include "{nnet_header}" + +''' + ) + + if not self.header_only: + self.source = open(filename_without_extension + ".c", "w") + if message is not None: + self.source.write(f"/* {message} */\n\n") + + self.source.write( +f""" +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +""") + self.source.write(f'#include "{header_name}"\n\n') + + + def _finalize_header(self): + + # create model type + if self.add_typedef: + self.header.write(f"\ntypedef struct {{") + else: + self.header.write(f"\nstruct {self.model_struct_name} {{") + for name, data in self.layer_dict.items(): + layer_type = data[0] + self.header.write(f"\n {layer_type} {name};") + if self.add_typedef: + self.header.write(f"\n}} {self.model_struct_name};\n") + else: + self.header.write(f"\n}};\n") + + init_prototype = f"int init_{self.model_struct_name.lower()}({self.model_struct_name} *model, const WeightArray *arrays)" + self.header.write(f"\n{init_prototype};\n") + + self.header.write(f"\n#endif /* {self.header_guard} */\n") + + def _finalize_source(self): + + + # create weight array + if len(set(self.weight_arrays)) != len(self.weight_arrays): + raise ValueError("error: detected duplicates in weight arrays") + if self.enable_binary_blob: self.source.write("\n#ifndef USE_WEIGHTS_FILE\n") + self.source.write(f"const WeightArray {self.model_struct_name.lower()}_arrays[] = {{\n") + for name in self.weight_arrays: + self.source.write(f"#ifdef WEIGHTS_{name}_DEFINED\n") + self.source.write(f' {{"{name}", WEIGHTS_{name}_TYPE, sizeof({name}), {name}}},\n') + self.source.write(f"#endif\n") + self.source.write(" {NULL, 0, 0, NULL}\n") + self.source.write("};\n") + + if self.enable_binary_blob: self.source.write("#endif /* USE_WEIGHTS_FILE */\n") + + # create init function definition + init_prototype = f"int init_{self.model_struct_name.lower()}({self.model_struct_name} *model, const WeightArray *arrays)" + if self.enable_binary_blob: self.source.write("\n#ifndef DUMP_BINARY_WEIGHTS\n") + self.source.write(f"{init_prototype} {{\n") + for name, data in self.layer_dict.items(): + self.source.write(f" if ({data[1]}) return 1;\n") + self.source.write(" return 0;\n") + self.source.write("}\n") + if self.enable_binary_blob:self.source.write("#endif /* DUMP_BINARY_WEIGHTS */\n") + + + def close(self): + + if not self.header_only: + self._finalize_source() + self.source.close() + + self._finalize_header() + self.header.close() + + def __del__(self): + try: + self.close() + except: + pass \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/common.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/common.py new file mode 100644 index 0000000..5801c7e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/c_export/common.py @@ -0,0 +1,386 @@ +'''Copyright (c) 2017-2018 Mozilla + Copyright (c) 2022 Amazon + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import numpy as np + +from .c_writer import CWriter + +def print_vector(writer, vector, name, dtype='float', reshape_8x4=False, static=True, debug_float=False): + + if isinstance(writer, CWriter): + f = writer.source + binary_blob = writer.enable_binary_blob + else: + f = writer + binary_blob = False + + dtype_suffix = { + 'float' : 'float', + 'opus_uint8' : 'uint8', + 'opus_int8' : 'int8', + 'opus_uint16' : 'uint16', + 'opus_int16' : 'int16', + 'int' : 'int', + 'qweight': 'qweight' + } + + + if binary_blob: + f.write( +f''' +#ifndef USE_WEIGHTS_FILE +''' + ) + writer.weight_arrays.append(name) + + if reshape_8x4: + vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8)) + vector = vector.transpose((2, 0, 3, 1)) + + v = np.reshape(vector, (-1)) + + if debug_float: + f.write('#ifndef DISABLE_DEBUG_FLOAT\n') + f.write( +f''' +#define WEIGHTS_{name}_DEFINED +#define WEIGHTS_{name}_TYPE WEIGHT_TYPE_{dtype_suffix[dtype]} +''' + ) + + if static: + f.write('static ') + + f.write(f'const {dtype} {name}[{len(v)}] = {{\n ') + + for i in range(0, len(v)): + + f.write(f'{v[i]}') + + if (i!=len(v)-1): + f.write(',') + else: + break + + if (i%8==7): + f.write("\n ") + else: + f.write(" ") + + f.write('\n};\n\n') + if debug_float: f.write('#endif /*DISABLE_DEBUG_FLOAT*/\n') + + if binary_blob: + f.write( +f''' +#endif /* USE_WEIGHTS_FILE */ +''' + ) + + return vector + + + +def extract_diagonal(A): + """ input shape is (N, k*N) """ + + N, M = A.shape + B = A.copy() + assert M % N == 0 + k = M // N + + diags = [] + for l in range(k): + diag = np.diag(B[:, l * N : (l+1) * N]).copy() + B[:, l * N : (l+1) * N] -= np.diag(diag) + diags.append(diag) + + diag = np.concatenate(diags) + + return diag, B + +def quantize_weight(weight, scale): + scale = scale + 1e-30 + Aq = np.round(weight / scale).astype('int') + if Aq.max() > 127 or Aq.min() <= -128: + raise ValueError("value out of bounds in quantize_weight") + Aq = np.clip(np.round(weight / scale).astype('int'), -128, 127) + return Aq + + +def print_sparse_weight(writer, A, name, scale=1/128, have_diag=True, quantize=False): + N = A.shape[0] + M = A.shape[1] + W = np.zeros((0,), dtype='int') + W0 = np.zeros((0,)) + + if have_diag: + diag, A = extract_diagonal(A) + print_vector(writer, diag, name + '_diag') + + if quantize: + Aq = quantize_weight(A, scale) + else: + Aq = A + + # extract blocks + idx = np.zeros((0,), dtype='int') + for i in range(M//8): + pos = idx.shape[0] + idx = np.append(idx, -1) + nb_nonzero = 0 + for j in range(N//4): + block = A[j*4:(j+1)*4, i*8:(i+1)*8] + qblock = Aq[j*4:(j+1)*4, i*8:(i+1)*8] + if np.sum(np.abs(block)) > 1e-10: + nb_nonzero = nb_nonzero + 1 + idx = np.append(idx, j*4) + vblock = qblock.transpose((1,0)).reshape((-1,)) + W0 = np.concatenate([W0, block.reshape((-1,))]) + W = np.concatenate([W, vblock]) + idx[pos] = nb_nonzero + + if quantize: print_vector(writer, W, name + '_int8', reshape_8x4=False, dtype='opus_int8') + print_vector(writer, W0, name + '_float', reshape_8x4=False, dtype='float', debug_float=quantize) + print_vector(writer, idx, name + '_idx', reshape_8x4=False, dtype='int') + + return Aq + + + +def compute_scaling(weight): + """ computes optimal scaling vector for weight of shape (features_in, features_out) """ + + n_in, n_out = weight.shape + assert n_in % 4 == 0 and n_out % 8 == 0 + + weight_max_abs = np.max(np.abs(weight), axis=0) + weight_max_sum = np.max(np.abs(weight[: n_in : 2] + weight[1 : n_in : 2]), axis=0) + scale_max = weight_max_abs / 127 + scale_sum = weight_max_sum / 129 + + scale = np.maximum(scale_max, scale_sum) + + return scale + +def qn(string): + if string == "NULL": return string + else: return '"' + string + '"' + +def print_linear_layer(writer : CWriter, + name : str, + weight : np.ndarray, + bias : np.ndarray, + scale : np.ndarray = None, + sparse : bool = False, + diagonal : bool = False, + quantize : bool = True): + + """ prints linear layer + + Parameters: + ----------- + name : str + layer name + weight: np.ndarray + ... + scale: np.ndarray or None + If None auto scaling will be applied. Otherwise, output channels will be multiplied by scale (the usual broadcasting rules apply). + + + """ + + if len(weight.shape) != 2: + raise ValueError('expecting 2-dim weight array in print_linear_layer') + + + bias_name = "NULL" if bias is None else name + "_bias" + subias_name = name + "_subias" if quantize else "NULL" + scale_name = name + "_scale" if quantize else "NULL" + idx_name = name + "_weights_idx" if sparse else "NULL" + float_weight_name = name + "_weights_float" + int_weight_name = name + "_weights_int8" if quantize else "NULL" + diag_name = name + "_weights_diag" if sparse and diagonal else "NULL" + + nb_inputs, nb_outputs = weight.shape + + if scale is None and quantize: + scale = compute_scaling(weight) + + + if sparse: + weight_q = print_sparse_weight(writer, weight, name + "_weights", scale=scale, have_diag=diagonal, quantize=quantize) + else: + if quantize: + weight_q = quantize_weight(weight, scale) + print_vector(writer, weight_q, name + "_weights_int8", dtype='opus_int8', reshape_8x4=True) + + print_vector(writer, weight, name + "_weights_float", dtype='float', reshape_8x4=False, debug_float=quantize) + + if quantize: + subias = (np.zeros(nb_outputs) if bias is None else bias) - np.sum(weight_q * scale, axis=0) + print_vector(writer, subias, name + "_subias") + + final_scale = scale / 127 * np.ones(nb_outputs) + print_vector(writer, final_scale, name + "_scale") + + if bias is not None: + print_vector(writer, bias, name + "_bias") + + + init_call = f'linear_init(&model->{name}, arrays, {qn(bias_name)}, {qn(subias_name)}, {qn(int_weight_name)},' \ + + f'{qn(float_weight_name)}, {qn(idx_name)}, {qn(diag_name)}, {qn(scale_name)}, {nb_inputs}, {nb_outputs})' + + writer.layer_dict[name] = ('LinearLayer', init_call) + + +def print_dense_layer(writer : CWriter, + name : str, + weight : np.ndarray, + bias : np.ndarray, + scale=1/128, + format : str = 'torch', + sparse=False, + diagonal=False, + quantize=False): + + if format == 'torch': + weight = weight.transpose() + + print_linear_layer(writer, name, weight, bias, scale=scale, sparse=sparse, diagonal=diagonal, quantize=quantize) + + writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[1]}\n") + + +def print_conv1d_layer(writer : CWriter, + name : str, + weight : np.ndarray, + bias : np.ndarray, + scale=1/128, + format : str = 'torch', + quantize=False, + sparse=False): + + + if format == "torch": + # convert to channels last + weight = np.transpose(weight, (2, 1, 0)) + + lin_weight = np.reshape(weight, (-1, weight.shape[-1])) + print_linear_layer(writer, name, lin_weight, bias, scale=scale, sparse=sparse, diagonal=False, quantize=quantize) + + + writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[2]}\n") + writer.header.write(f"\n#define {name.upper()}_IN_SIZE {weight.shape[1]}\n") + writer.header.write(f"\n#define {name.upper()}_STATE_SIZE ({weight.shape[1]} * ({weight.shape[0] - 1}))\n") + + return weight.shape[0] * weight.shape[1] + +def print_conv2d_layer(writer : CWriter, + name : str, + weight : np.ndarray, + bias : np.ndarray, + scale : float=1/128, + quantize : bool=False): + + if quantize: + print("[print_conv2d_layer] warning: quantize argument ignored") + + bias_name = name + "_bias" + float_weight_name = name + "_weight_float" + + print_vector(writer, weight, float_weight_name) + print_vector(writer, bias, bias_name) + + # init function + out_channels, in_channels, ksize1, ksize2 = weight.shape + init_call = f'conv2d_init(&model->{name}, arrays, "{bias_name}", "{float_weight_name}", {in_channels}, {out_channels}, {ksize1}, {ksize2})' + + writer.layer_dict[name] = ('Conv2dLayer', init_call) + + + +def print_gru_layer(writer : CWriter, + name : str, + weight : np.ndarray, + recurrent_weight : np.ndarray, + bias : np.ndarray, + recurrent_bias : np.ndarray, + format : str = 'torch', + quantize : bool = False, + input_sparse : bool = False, + recurrent_sparse : bool = False, + scale=1/128, + recurrent_scale=1/128 + ): + + if format == "torch": + # change gate ordering from rzn to zrn + + N = weight.shape[0] // 3 + for x in [weight, recurrent_weight, bias, recurrent_bias]: + if x is None: continue + tmp = x[0:N].copy() + x[0:N] = x[N:2*N] + x[N:2*N] = tmp + + weight = weight.transpose() + recurrent_weight = recurrent_weight.transpose() + else: + N = weight.shape[1] // 3 + + print_linear_layer(writer, name + "_input", weight, bias, scale=scale, sparse=input_sparse, quantize=quantize) + print_linear_layer(writer, name + "_recurrent", recurrent_weight, recurrent_bias, scale=recurrent_scale, sparse=recurrent_sparse, diagonal=recurrent_sparse, quantize=quantize) + + # wrapping it up + writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {N}\n") + writer.header.write(f"\n#define {name.upper()}_STATE_SIZE {N}\n") + + return N + + +def print_tconv1d_layer(writer : CWriter, + name : str, + weight : np.ndarray, + bias : np.ndarray, + stride: int, + scale=1/128, + quantize=False, + sparse=False): + + in_channels, out_channels, kernel_size = weight.shape + + + linear_weight = weight.transpose(2, 1, 0).reshape(kernel_size * out_channels, in_channels).transpose(1, 0) + linear_bias = np.repeat(bias[np.newaxis, :], kernel_size, 0).flatten() + + print_linear_layer(writer, name, linear_weight, linear_bias, scale=scale, quantize=quantize, sparse=sparse) + + writer.header.write(f"\n#define {name.upper()}_KERNEL_SIZE {kernel_size}\n") + writer.header.write(f"\n#define {name.upper()}_STRIDE {stride}\n") + writer.header.write(f"\n#define {name.upper()}_IN_CHANNELS {in_channels}\n") + writer.header.write(f"\n#define {name.upper()}_OUT_CHANNELS {out_channels}\n") diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py new file mode 100644 index 0000000..0267804 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py @@ -0,0 +1,5 @@ +from .tf import dump_tf_conv1d_weights, load_tf_conv1d_weights +from .tf import dump_tf_dense_weights, load_tf_dense_weights +from .tf import dump_tf_embedding_weights, load_tf_embedding_weights +from .tf import dump_tf_gru_weights, load_tf_gru_weights +from .tf import dump_tf_weights, load_tf_weights \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/tf.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/tf.py new file mode 100644 index 0000000..bebbb55 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/tf/tf.py @@ -0,0 +1,188 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os + +import tensorflow as tf +import numpy as np + +from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer + +def dump_tf_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): + + + assert gru.activation == tf.keras.activations.tanh + assert gru.recurrent_activation == tf.keras.activations.sigmoid + assert gru.reset_after == True + + w_ih = gru.weights[0].numpy().transpose().copy() + w_hh = gru.weights[1].numpy().transpose().copy() + b_ih = gru.weights[2].numpy()[0].copy() + b_hh = gru.weights[2].numpy()[1].copy() + + if isinstance(where, CWriter): + return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='tf', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) + else: + os.makedirs(where, exist_ok=True) + + # zrn => rzn + N = w_ih.shape[0] // 3 + for x in [w_ih, w_hh, b_ih, b_hh]: + tmp = x[0:N].copy() + x[0:N] = x[N:2*N] + x[N:2*N] = tmp + + np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) + np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) + np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) + np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) + + +def load_tf_gru_weights(path, gru): + + assert gru.activation == tf.keras.activations.tanh + assert gru.recurrent_activation == tf.keras.activations.sigmoid + assert gru.reset_after == True + + w_ih = np.load(os.path.join(path, 'weight_ih_rzn.npy')) + w_hh = np.load(os.path.join(path, 'weight_hh_rzn.npy')) + b_ih = np.load(os.path.join(path, 'bias_ih_rzn.npy')) + b_hh = np.load(os.path.join(path, 'bias_hh_rzn.npy')) + + # rzn => zrn + N = w_ih.shape[0] // 3 + for x in [w_ih, w_hh, b_ih, b_hh]: + tmp = x[0:N].copy() + x[0:N] = x[N:2*N] + x[N:2*N] = tmp + + gru.weights[0].assign(tf.convert_to_tensor(w_ih.transpose())) + gru.weights[1].assign(tf.convert_to_tensor(w_hh.transpose())) + gru.weights[2].assign(tf.convert_to_tensor(np.vstack((b_ih, b_hh)))) + + +def dump_tf_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False): + + w = dense.weights[0].numpy() + if dense.bias is None: + b = np.zeros(dense.units, dtype=w.dtype) + else: + b = dense.bias.numpy() + + + + if isinstance(where, CWriter): + return print_dense_layer(where, name, w, b, scale=scale, format='tf', sparse=sparse, diagonal=diagonal, quantize=quantize) + + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight.npy'), w.transpose()) + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_tf_dense_weights(path, dense): + + w = np.load(os.path.join(path, 'weight.npy')).transpose() + b = np.load(os.path.join(path, 'bias.npy')) + + dense.weights[0].assign(tf.convert_to_tensor(w)) + if dense.bias is not None: + dense.weights[1].assign(tf.convert_to_tensor(b)) + + +def dump_tf_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False): + + assert conv.data_format == 'channels_last' + + w = conv.weights[0].numpy().copy() + if conv.bias is None: + b = np.zeros(conv.filters, dtype=w.dtype) + else: + b = conv.bias.numpy() + + if isinstance(where, CWriter): + return print_conv1d_layer(where, name, w, b, scale=scale, format='tf', quantize=quantize) + else: + os.makedirs(where, exist_ok=True) + + w = np.transpose(w, (2, 1, 0)) + np.save(os.path.join(where, 'weight_oik.npy'), w) + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_tf_conv1d_weights(path, conv): + + w = np.load(os.path.join(path, 'weight_oik.npy')) + b = np.load(os.path.join(path, 'bias.npy')) + + w = np.transpose(w, (2, 1, 0)) + + conv.weights[0].assign(tf.convert_to_tensor(w)) + if conv.bias is not None: + conv.weights[1].assign(tf.convert_to_tensor(b)) + + +def dump_tf_embedding_weights(path, emb): + os.makedirs(path, exist_ok=True) + + w = emb.weights[0].numpy() + np.save(os.path.join(path, 'weight.npy'), w) + + + +def load_tf_embedding_weights(path, emb): + + w = np.load(os.path.join(path, 'weight.npy')) + emb.weights[0].assign(tf.convert_to_tensor(w)) + + +def dump_tf_weights(path, module): + if isinstance(module, tf.keras.layers.Dense): + dump_tf_dense_weights(path, module) + elif isinstance(module, tf.keras.layers.GRU): + dump_tf_gru_weights(path, module) + elif isinstance(module, tf.keras.layers.Conv1D): + dump_tf_conv1d_weights(path, module) + elif isinstance(module, tf.keras.layers.Embedding): + dump_tf_embedding_weights(path, module) + else: + raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported') + +def load_tf_weights(path, module): + if isinstance(module, tf.keras.layers.Dense): + load_tf_dense_weights(path, module) + elif isinstance(module, tf.keras.layers.GRU): + load_tf_gru_weights(path, module) + elif isinstance(module, tf.keras.layers.Conv1D): + load_tf_conv1d_weights(path, module) + elif isinstance(module, tf.keras.layers.Embedding): + load_tf_embedding_weights(path, module) + else: + raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported') \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py new file mode 100644 index 0000000..8245566 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py @@ -0,0 +1,37 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +from .torch import dump_torch_conv1d_weights, load_torch_conv1d_weights +from .torch import dump_torch_conv2d_weights, load_torch_conv2d_weights +from .torch import dump_torch_dense_weights, load_torch_dense_weights +from .torch import dump_torch_gru_weights, load_torch_gru_weights +from .torch import dump_torch_grucell_weights +from .torch import dump_torch_embedding_weights, load_torch_embedding_weights +from .torch import dump_torch_weights, load_torch_weights +from .torch import dump_torch_adaptive_conv1d_weights \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/torch.py b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/torch.py new file mode 100644 index 0000000..af5d3e5 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/torch/weight-exchange/wexchange/torch/torch.py @@ -0,0 +1,433 @@ +""" +/* Copyright (c) 2023 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import os +import sys + +import torch +import numpy as np + +sys.path.append(sys.path.append(os.path.join(os.path.dirname(__file__), '../osce'))) +try: + import utils.layers as osce_layers + from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d + from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d + from utils.layers.td_shaper import TDShaper + has_osce=True +except: + has_osce=False + +from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer, print_tconv1d_layer, print_conv2d_layer + +def dump_torch_adaptive_conv1d_weights(where, adaconv, name='adaconv', scale=1/128, quantize=False): + + + w_kernel = adaconv.conv_kernel.weight.detach().cpu().numpy().copy() + b_kernel = adaconv.conv_kernel.bias.detach().cpu().numpy().copy() + w_gain = adaconv.filter_gain.weight.detach().cpu().numpy().copy() + b_gain = adaconv.filter_gain.bias.detach().cpu().numpy().copy() + + if isinstance(where, CWriter): + # pad kernel for quantization + left_padding = adaconv.padding[0] + kernel_size = adaconv.kernel_size + in_channels = adaconv.in_channels + out_channels = adaconv.out_channels + feature_dim = adaconv.feature_dim + + if quantize and kernel_size % 8: + kernel_padding = 8 - (kernel_size % 8) + w_kernel = np.concatenate( + (np.zeros((out_channels, in_channels, kernel_padding, feature_dim)), w_kernel.reshape(out_channels, in_channels, kernel_size, feature_dim)), + dtype=w_kernel.dtype, + axis=2).reshape(-1, feature_dim) + b_kernel = np.concatenate( + (np.zeros((out_channels, in_channels, kernel_padding)), b_kernel.reshape(out_channels, in_channels, kernel_size)), + dtype=b_kernel.dtype, + axis=2).reshape(-1) + left_padding += kernel_padding + kernel_size += kernel_padding + + # write relevant scalar parameters to header file + where.header.write(f""" +#define {name.upper()}_FILTER_GAIN_A {adaconv.filter_gain_a:f}f +#define {name.upper()}_FILTER_GAIN_B {adaconv.filter_gain_b:f}f +#define {name.upper()}_SHAPE_GAIN {adaconv.shape_gain:f}f +#define {name.upper()}_KERNEL_SIZE {kernel_size} +#define {name.upper()}_FRAME_SIZE {adaconv.frame_size} +#define {name.upper()}_LEFT_PADDING {left_padding} +#define {name.upper()}_OVERLAP_SIZE {adaconv.overlap_size} +#define {name.upper()}_IN_CHANNELS {adaconv.in_channels} +#define {name.upper()}_OUT_CHANNELS {adaconv.out_channels} +#define {name.upper()}_NORM_P {adaconv.norm_p} +#define {name.upper()}_FEATURE_DIM {adaconv.feature_dim} +""" + ) + + print_dense_layer(where, name + "_kernel", w_kernel, b_kernel, scale=scale, format='torch', sparse=False, diagonal=False, quantize=quantize) + print_dense_layer(where, name + "_gain", w_gain, b_gain, format='torch', sparse=False, diagonal=False, quantize=False) + + + else: + np.save(where, 'weight_kernel.npy', w_kernel) + np.save(where, 'bias_kernel.npy', b_kernel) + np.save(where, 'weight_gain.npy', w_gain) + np.save(where, 'bias_gain.npy', b_gain) + + +def dump_torch_adaptive_comb1d_weights(where, adaconv, name='adaconv', scale=1/128, quantize=False): + + + w_kernel = adaconv.conv_kernel.weight.detach().cpu().numpy().copy() + b_kernel = adaconv.conv_kernel.bias.detach().cpu().numpy().copy() + w_gain = adaconv.filter_gain.weight.detach().cpu().numpy().copy() + b_gain = adaconv.filter_gain.bias.detach().cpu().numpy().copy() + w_global_gain = adaconv.global_filter_gain.weight.detach().cpu().numpy().copy() + b_global_gain = adaconv.global_filter_gain.bias.detach().cpu().numpy().copy() + + + if isinstance(where, CWriter): + # pad kernel for quantization + left_padding = adaconv.padding[0] + kernel_size = adaconv.kernel_size + + if quantize and w_kernel.shape[0] % 8: + kernel_padding = 8 - (w_kernel.shape[0] % 8) + w_kernel = np.concatenate((np.zeros((kernel_padding, w_kernel.shape[1])), w_kernel), dtype=w_kernel.dtype) + b_kernel = np.concatenate((np.zeros((kernel_padding)), b_kernel), dtype=b_kernel.dtype) + left_padding += kernel_padding + kernel_size += kernel_padding + # write relevant scalar parameters to header file + where.header.write(f""" +#define {name.upper()}_FILTER_GAIN_A {adaconv.filter_gain_a:f}f +#define {name.upper()}_FILTER_GAIN_B {adaconv.filter_gain_b:f}f +#define {name.upper()}_LOG_GAIN_LIMIT {adaconv.log_gain_limit:f}f +#define {name.upper()}_KERNEL_SIZE {kernel_size} +#define {name.upper()}_LEFT_PADDING {left_padding} +#define {name.upper()}_FRAME_SIZE {adaconv.frame_size} +#define {name.upper()}_OVERLAP_SIZE {adaconv.overlap_size} +#define {name.upper()}_IN_CHANNELS {adaconv.in_channels} +#define {name.upper()}_OUT_CHANNELS {adaconv.out_channels} +#define {name.upper()}_NORM_P {adaconv.norm_p} +#define {name.upper()}_FEATURE_DIM {adaconv.feature_dim} +#define {name.upper()}_MAX_LAG {adaconv.max_lag} +""" + ) + + print_dense_layer(where, name + "_kernel", w_kernel, b_kernel, scale=scale, format='torch', sparse=False, diagonal=False, quantize=quantize) + print_dense_layer(where, name + "_gain", w_gain, b_gain, format='torch', sparse=False, diagonal=False, quantize=False) + print_dense_layer(where, name + "_global_gain", w_global_gain, b_global_gain, format='torch', sparse=False, diagonal=False, quantize=False) + + + else: + np.save(where, 'weight_kernel.npy', w_kernel) + np.save(where, 'bias_kernel.npy', b_kernel) + np.save(where, 'weight_gain.npy', w_gain) + np.save(where, 'bias_gain.npy', b_gain) + np.save(where, 'weight_global_gain.npy', w_global_gain) + np.save(where, 'bias_global_gain.npy', b_global_gain) + +def dump_torch_tdshaper(where, shaper, name='tdshaper', quantize=False, scale=1/128): + + if isinstance(where, CWriter): + where.header.write(f""" +#define {name.upper()}_FEATURE_DIM {shaper.feature_dim} +#define {name.upper()}_FRAME_SIZE {shaper.frame_size} +#define {name.upper()}_AVG_POOL_K {shaper.avg_pool_k} +#define {name.upper()}_INNOVATE {1 if shaper.innovate else 0} +#define {name.upper()}_POOL_AFTER {1 if shaper.pool_after else 0} +""" + ) + + dump_torch_conv1d_weights(where, shaper.feature_alpha1_f, name + "_alpha1_f", quantize=quantize, scale=scale) + dump_torch_conv1d_weights(where, shaper.feature_alpha1_t, name + "_alpha1_t") + dump_torch_conv1d_weights(where, shaper.feature_alpha2, name + "_alpha2") + + if shaper.innovate: + dump_torch_conv1d_weights(where, shaper.feature_alpha1b, name + "_alpha1b") + dump_torch_conv1d_weights(where, shaper.feature_alpha1c, name + "_alpha1c") + dump_torch_conv1d_weights(where, shaper.feature_alpha2b, name + "_alpha2b") + dump_torch_conv1d_weights(where, shaper.feature_alpha2c, name + "_alpha2c") + + + +def dump_torch_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): + + assert gru.num_layers == 1 + assert gru.bidirectional == False + + w_ih = gru.weight_ih_l0.detach().cpu().numpy().copy() + w_hh = gru.weight_hh_l0.detach().cpu().numpy().copy() + if hasattr(gru, 'bias_ih_l0'): + b_ih = gru.bias_ih_l0.detach().cpu().numpy().copy() + else: + b_ih = None + if hasattr(gru, 'bias_hh_l0'): + b_hh = gru.bias_hh_l0.detach().cpu().numpy().copy() + else: + b_hh = None + + if isinstance(where, CWriter): + return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='torch', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) + np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) + np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) + np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) + + +def dump_torch_grucell_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): + + w_ih = gru.weight_ih.detach().cpu().numpy().copy() + w_hh = gru.weight_hh.detach().cpu().numpy().copy() + if hasattr(gru, 'bias_ih') and gru.bias_ih is not None: + b_ih = gru.bias_ih.detach().cpu().numpy().copy() + else: + b_ih = None + if hasattr(gru, 'bias_hh') and gru.bias_hh is not None: + b_hh = gru.bias_hh.detach().cpu().numpy().copy() + else: + b_hh = None + + if isinstance(where, CWriter): + return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='torch', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) + np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) + np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) + np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) + + + +def load_torch_gru_weights(where, gru): + + assert gru.num_layers == 1 + assert gru.bidirectional == False + + w_ih = np.load(os.path.join(where, 'weight_ih_rzn.npy')) + w_hh = np.load(os.path.join(where, 'weight_hh_rzn.npy')) + b_ih = np.load(os.path.join(where, 'bias_ih_rzn.npy')) + b_hh = np.load(os.path.join(where, 'bias_hh_rzn.npy')) + + with torch.no_grad(): + gru.weight_ih_l0.set_(torch.from_numpy(w_ih)) + gru.weight_hh_l0.set_(torch.from_numpy(w_hh)) + gru.bias_ih_l0.set_(torch.from_numpy(b_ih)) + gru.bias_hh_l0.set_(torch.from_numpy(b_hh)) + + +def dump_torch_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False): + + w = dense.weight.detach().cpu().numpy().copy() + if dense.bias is None: + b = np.zeros(dense.out_features, dtype=w.dtype) + else: + b = dense.bias.detach().cpu().numpy().copy() + + if isinstance(where, CWriter): + return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize) + + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight.npy'), w) + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_torch_dense_weights(where, dense): + + w = np.load(os.path.join(where, 'weight.npy')) + b = np.load(os.path.join(where, 'bias.npy')) + + with torch.no_grad(): + dense.weight.set_(torch.from_numpy(w)) + if dense.bias is not None: + dense.bias.set_(torch.from_numpy(b)) + + +def dump_torch_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False, sparse=False): + + w = conv.weight.detach().cpu().numpy().copy() + if conv.bias is None: + b = np.zeros(conv.out_channels, dtype=w.dtype) + else: + b = conv.bias.detach().cpu().numpy().copy() + + if isinstance(where, CWriter): + + return print_conv1d_layer(where, name, w, b, scale=scale, format='torch', quantize=quantize, sparse=sparse) + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight_oik.npy'), w) + + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_torch_conv1d_weights(where, conv): + + with torch.no_grad(): + w = np.load(os.path.join(where, 'weight_oik.npy')) + conv.weight.set_(torch.from_numpy(w)) + if type(conv.bias) != type(None): + b = np.load(os.path.join(where, 'bias.npy')) + if conv.bias is not None: + conv.bias.set_(torch.from_numpy(b)) + + +def dump_torch_tconv1d_weights(where, conv, name='conv', scale=1/128, quantize=False, sparse=False): + + w = conv.weight.detach().cpu().numpy().copy() + if conv.bias is None: + b = np.zeros(conv.out_channels, dtype=w.dtype) + else: + b = conv.bias.detach().cpu().numpy().copy() + + if isinstance(where, CWriter): + + return print_tconv1d_layer(where, name, w, b, conv.stride[0], scale=scale, quantize=quantize, sparse=sparse) + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight_oik.npy'), w) + + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_torch_tconv1d_weights(where, conv): + + with torch.no_grad(): + w = np.load(os.path.join(where, 'weight_oik.npy')) + conv.weight.set_(torch.from_numpy(w)) + if type(conv.bias) != type(None): + b = np.load(os.path.join(where, 'bias.npy')) + if conv.bias is not None: + conv.bias.set_(torch.from_numpy(b)) + + +def dump_torch_conv2d_weights(where, conv, name='conv', scale=1/128, quantize=False): + w = conv.weight.detach().cpu().permute(0, 1, 3, 2).numpy().copy() + if conv.bias is None: + b = np.zeros(conv.out_channels, dtype=w.dtype) + else: + b = conv.bias.detach().cpu().numpy().copy() + + if isinstance(where, CWriter): + return print_conv2d_layer(where, name, w, b, scale=scale, quantize=quantize) + + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight_oiwh.npy'), w) + + np.save(os.path.join(where, 'bias.npy'), b) + +def load_torch_conv2d_weights(where, conv): + with torch.no_grad(): + w = np.load(os.path.join(where, 'weight_oiwh.npy')) + conv.weight.set_(torch.from_numpy(w).permute(0, 1, 3, 2)) + if type(conv.bias) != type(None): + b = np.load(os.path.join(where, 'bias.npy')) + if conv.bias is not None: + conv.bias.set_(torch.from_numpy(b)) + + +def dump_torch_embedding_weights(where, embed, name='embed', scale=1/128, sparse=False, diagonal=False, quantize=False): + + w = embed.weight.detach().cpu().numpy().copy().transpose() + b = np.zeros(w.shape[0], dtype=w.dtype) + + if isinstance(where, CWriter): + return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize) + + else: + os.makedirs(where, exist_ok=True) + + np.save(os.path.join(where, 'weight.npy'), w) + np.save(os.path.join(where, 'bias.npy'), b) + + +def load_torch_embedding_weights(where, emb): + + w = np.load(os.path.join(where, 'weight.npy')) + + with torch.no_grad(): + emb.weight.set_(torch.from_numpy(w)) + +def dump_torch_weights(where, module, name=None, verbose=False, **kwargs): + """ generic function for dumping weights of some torch.nn.Module """ + if verbose and name is not None: + print(f"printing layer {name} of type {type(module)}...") + if isinstance(module, torch.nn.Linear): + return dump_torch_dense_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.GRU): + return dump_torch_gru_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.GRUCell): + return dump_torch_grucell_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.Conv1d): + return dump_torch_conv1d_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.Conv2d): + return dump_torch_conv2d_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.Embedding): + return dump_torch_embedding_weights(where, module, name, **kwargs) + elif isinstance(module, torch.nn.ConvTranspose1d): + return dump_torch_tconv1d_weights(where, module, name, **kwargs) + else: + if has_osce: + if isinstance(module, LimitedAdaptiveConv1d): + dump_torch_adaptive_conv1d_weights(where, module, name, **kwargs) + elif isinstance(module, LimitedAdaptiveComb1d): + dump_torch_adaptive_comb1d_weights(where, module, name, **kwargs) + elif isinstance(module, TDShaper): + dump_torch_tdshaper(where, module, name, **kwargs) + else: + raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported') + else: + raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported') + +def load_torch_weights(where, module): + """ generic function for loading weights of some torch.nn.Module """ + if isinstance(module, torch.nn.Linear): + load_torch_dense_weights(where, module) + elif isinstance(module, torch.nn.GRU): + load_torch_gru_weights(where, module) + elif isinstance(module, torch.nn.Conv1d): + load_torch_conv1d_weights(where, module) + elif isinstance(module, torch.nn.Conv2d): + load_torch_conv2d_weights(where, module) + elif isinstance(module, torch.nn.Embedding): + load_torch_embedding_weights(where, module) + elif isinstance(module, torch.nn.ConvTranspose1d): + return load_torch_tconv1d_weights(where, module) + else: + raise ValueError(f'load_torch_weights: layer of type {type(module)} not supported') diff --git a/managed_components/78__esp-opus/dnn/training_tf2/dataloader.py b/managed_components/78__esp-opus/dnn/training_tf2/dataloader.py new file mode 100644 index 0000000..ed441c1 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/dataloader.py @@ -0,0 +1,49 @@ +import numpy as np +from tensorflow.keras.utils import Sequence +from ulaw import lin2ulaw + +def lpc2rc(lpc): + #print("shape is = ", lpc.shape) + order = lpc.shape[-1] + rc = 0*lpc + for i in range(order, 0, -1): + rc[:,:,i-1] = lpc[:,:,-1] + ki = rc[:,:,i-1:i].repeat(i-1, axis=2) + lpc = (lpc[:,:,:-1] - ki*lpc[:,:,-2::-1])/(1-ki*ki) + return rc + +class LPCNetLoader(Sequence): + def __init__(self, data, features, periods, batch_size, e2e=False, lookahead=2): + self.batch_size = batch_size + self.nb_batches = np.minimum(np.minimum(data.shape[0], features.shape[0]), periods.shape[0])//self.batch_size + self.data = data[:self.nb_batches*self.batch_size, :] + self.features = features[:self.nb_batches*self.batch_size, :] + self.periods = periods[:self.nb_batches*self.batch_size, :] + self.e2e = e2e + self.lookahead = lookahead + self.on_epoch_end() + + def on_epoch_end(self): + self.indices = np.arange(self.nb_batches*self.batch_size) + np.random.shuffle(self.indices) + + def __getitem__(self, index): + data = self.data[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :] + in_data = data[: , :, :1] + out_data = data[: , :, 1:] + features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :-16] + periods = self.periods[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :] + outputs = [out_data] + inputs = [in_data, features, periods] + if self.lookahead > 0: + lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 4-self.lookahead:-self.lookahead, -16:] + else: + lpc = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], 4:, -16:] + if self.e2e: + outputs.append(lpc2rc(lpc)) + else: + inputs.append(lpc) + return (inputs, outputs) + + def __len__(self): + return self.nb_batches diff --git a/managed_components/78__esp-opus/dnn/training_tf2/decode_rdovae.py b/managed_components/78__esp-opus/dnn/training_tf2/decode_rdovae.py new file mode 100644 index 0000000..f9bf9bf --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/decode_rdovae.py @@ -0,0 +1,111 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + Copyright (c) 2018-2019 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +# Train an LPCNet model + +import argparse +#from plc_loader import PLCLoader + +parser = argparse.ArgumentParser(description='Train a PLC model') + +parser.add_argument('bits', metavar='', help='binary features file (int16)') +parser.add_argument('output', metavar='', help='output features') +parser.add_argument('--model', metavar='', default='rdovae', help='PLC model python definition (without .py)') +group1 = parser.add_mutually_exclusive_group() +group1.add_argument('--weights', metavar='', help='model weights') +parser.add_argument('--cond-size', metavar='', default=1024, type=int, help='number of units in conditioning network (default 1024)') +parser.add_argument('--batch-size', metavar='', default=1, type=int, help='batch size to use (default 128)') +parser.add_argument('--seq-length', metavar='', default=1000, type=int, help='sequence length to use (default 1000)') + + +args = parser.parse_args() + +import importlib +rdovae = importlib.import_module(args.model) + +import sys +import numpy as np +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger +import tensorflow.keras.backend as K +import h5py + +import tensorflow as tf +from rdovae import pvq_quantize +from rdovae import apply_dead_zone + +# Try reducing batch_size if you run out of memory on your GPU +batch_size = args.batch_size + +model, encoder, decoder, qembedding = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size) +model.load_weights(args.weights) + +lpc_order = 16 +nbits=80 + + +bits_file = args.bits +sequence_size = args.seq_length + +# u for unquantised, load 16 bit PCM samples and convert to mu-law + + +bits = np.memmap(bits_file + "-syms.f32", dtype='float32', mode='r') +nb_sequences = len(bits)//(40*sequence_size)//batch_size*batch_size +bits = bits[:nb_sequences*sequence_size*40] + +bits = np.reshape(bits, (nb_sequences, sequence_size//2, 20*4)) +print(bits.shape) + +lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1)) +quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16') +quant_id = quant_id[:,:,0] +quant_embed = qembedding(quant_id) +quant_scale = tf.math.softplus(quant_embed[:,:,:nbits]) +dead_zone = tf.math.softplus(quant_embed[:, :, nbits : 2 * nbits]) + +bits = bits*quant_scale +bits = np.round(apply_dead_zone([bits, dead_zone]).numpy()) +bits = bits/quant_scale + + +state = np.memmap(bits_file + "-state.f32", dtype='float32', mode='r') + +state = np.reshape(state, (nb_sequences, sequence_size//2, 24)) +state = state[:,-1,:] +state = pvq_quantize(state, 82) +#state = state/(1e-15+tf.norm(state, axis=-1,keepdims=True)) + +print("shapes are:") +print(bits.shape) +print(state.shape) + +bits = bits[:,1::2,:] +features = decoder.predict([bits, state], batch_size=batch_size) + +features.astype('float32').tofile(args.output) diff --git a/managed_components/78__esp-opus/dnn/training_tf2/diffembed.py b/managed_components/78__esp-opus/dnn/training_tf2/diffembed.py new file mode 100644 index 0000000..e04ae15 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/diffembed.py @@ -0,0 +1,49 @@ +""" +Modification of Tensorflow's Embedding Layer: + 1. Not restricted to be the first layer of a model + 2. Differentiable (allows non-integer lookups) + - For non integer lookup, this layer linearly interpolates between the adjacent embeddings in the following way to preserver gradient flow + - E = (1 - frac(x))*embed(floor(x)) + frac(x)*embed(ceil(x)) +""" + +import tensorflow as tf +from tensorflow.keras.layers import Layer + +class diff_Embed(Layer): + """ + Parameters: + - units: int + Dimension of the Embedding + - dict_size: int + Number of Embeddings to lookup + - pcm_init: boolean + Initialized for the embedding matrix + """ + def __init__(self, units=128, dict_size = 256, pcm_init = True, initializer = None, **kwargs): + super(diff_Embed, self).__init__(**kwargs) + self.units = units + self.dict_size = dict_size + self.pcm_init = pcm_init + self.initializer = initializer + + def build(self, input_shape): + w_init = tf.random_normal_initializer() + if self.pcm_init: + w_init = self.initializer + self.w = tf.Variable(initial_value=w_init(shape=(self.dict_size, self.units),dtype='float32'),trainable=True) + + def call(self, inputs): + alpha = inputs - tf.math.floor(inputs) + alpha = tf.expand_dims(alpha,axis = -1) + alpha = tf.tile(alpha,[1,1,1,self.units]) + inputs = tf.cast(inputs,'int32') + M = (1 - alpha)*tf.gather(self.w,inputs) + alpha*tf.gather(self.w,tf.clip_by_value(inputs + 1, 0, 255)) + return M + + def get_config(self): + config = super(diff_Embed, self).get_config() + config.update({"units": self.units}) + config.update({"dict_size" : self.dict_size}) + config.update({"pcm_init" : self.pcm_init}) + config.update({"initializer" : self.initializer}) + return config \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/training_tf2/dump_lpcnet.py b/managed_components/78__esp-opus/dnn/training_tf2/dump_lpcnet.py new file mode 100644 index 0000000..97ce0ce --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/dump_lpcnet.py @@ -0,0 +1,388 @@ +#!/usr/bin/python3 +'''Copyright (c) 2017-2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import os +import io +import lpcnet +import sys +import numpy as np +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding +from ulaw import ulaw2lin, lin2ulaw +from mdense import MDense +from diffembed import diff_Embed +from parameters import get_parameter +import h5py +import re +import argparse + + +# no cuda devices needed +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +# Flag for dumping e2e (differentiable lpc) network weights +flag_e2e = False + + +max_rnn_neurons = 1 +max_conv_inputs = 1 +max_mdense_tmp = 1 + +def printVector(f, vector, name, dtype='float', dotp=False): + global array_list + if dotp: + vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8)) + vector = vector.transpose((2, 0, 3, 1)) + v = np.reshape(vector, (-1)); + #print('static const float ', name, '[', len(v), '] = \n', file=f) + if name not in array_list: + array_list.append(name) + f.write('#ifndef USE_WEIGHTS_FILE\n') + f.write('#define WEIGHTS_{}_DEFINED\n'.format(name)) + f.write('#define WEIGHTS_{}_TYPE WEIGHT_TYPE_{}\n'.format(name, dtype)) + f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v))) + for i in range(0, len(v)): + f.write('{}'.format(v[i])) + if (i!=len(v)-1): + f.write(',') + else: + break; + if (i%8==7): + f.write("\n ") + else: + f.write(" ") + #print(v, file=f) + f.write('\n};\n') + f.write('#endif\n\n') + return; + +def printSparseVector(f, A, name, have_diag=True): + N = A.shape[0] + M = A.shape[1] + W = np.zeros((0,), dtype='int') + W0 = np.zeros((0,)) + if have_diag: + diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])]) + A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N])) + A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N])) + A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:])) + printVector(f, diag, name + '_diag') + AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int') + idx = np.zeros((0,), dtype='int') + for i in range(M//8): + pos = idx.shape[0] + idx = np.append(idx, -1) + nb_nonzero = 0 + for j in range(N//4): + block = A[j*4:(j+1)*4, i*8:(i+1)*8] + qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8] + if np.sum(np.abs(block)) > 1e-10: + nb_nonzero = nb_nonzero + 1 + idx = np.append(idx, j*4) + vblock = qblock.transpose((1,0)).reshape((-1,)) + W0 = np.concatenate([W0, block.reshape((-1,))]) + W = np.concatenate([W, vblock]) + idx[pos] = nb_nonzero + f.write('#ifdef DOT_PROD\n') + printVector(f, W, name, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + printVector(f, W0, name, dtype='qweight') + f.write('#endif /*DOT_PROD*/\n') + #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16) + printVector(f, idx, name + '_idx', dtype='int') + return AQ + +def dump_layer_ignore(self, f, hf): + print("ignoring layer " + self.name + " of type " + self.__class__.__name__) + return False +Layer.dump_layer = dump_layer_ignore + +def dump_sparse_gru(self, f, hf): + global max_rnn_neurons + name = 'sparse_' + self.name + print("printing layer " + name + " of type sparse " + self.__class__.__name__) + weights = self.get_weights() + qweights = printSparseVector(f, weights[1], name + '_recurrent_weights') + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = max(max_rnn_neurons, neurons) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + model_struct.write(' SparseGRULayer {};\n'.format(name)); + model_init.write(' if (sparse_gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_recurrent_weights_diag", "{}_recurrent_weights", "{}_recurrent_weights_idx", {}, ACTIVATION_{}, {})) return 1;\n' + .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after)) + return True + +def dump_grub(self, f, hf, gru_a_size): + global max_rnn_neurons + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False) + + f.write('#ifdef DOT_PROD\n') + qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127) + printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + printVector(f, weights[1], name + '_recurrent_weights') + f.write('#endif /*DOT_PROD*/\n') + + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0) + subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = max(max_rnn_neurons, neurons) + model_struct.write(' GRULayer {};\n'.format(name)); + model_init.write(' if (gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_weights", "{}_weights_idx", "{}_recurrent_weights", {}, {}, ACTIVATION_{}, {})) return 1;\n' + .format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after)) + return True + +def dump_gru_layer_dummy(self, f, hf): + name = self.name + weights = self.get_weights() + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + return True; + +GRU.dump_layer = dump_gru_layer_dummy + +def dump_dense_layer_impl(name, weights, bias, activation, f, hf): + printVector(f, weights, name + '_weights') + printVector(f, bias, name + '_bias') + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1])) + model_struct.write(' DenseLayer {};\n'.format(name)); + model_init.write(' if (dense_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, ACTIVATION_{})) return 1;\n' + .format(name, name, name, weights.shape[0], weights.shape[1], activation)) + +def dump_dense_layer(self, f, hf): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + activation = self.activation.__name__.upper() + dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf) + return False + +Dense.dump_layer = dump_dense_layer + +def dump_mdense_layer(self, f, hf): + global max_mdense_tmp + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + printVector(f, np.transpose(weights[0], (0, 2, 1)), name + '_weights') + printVector(f, np.transpose(weights[1], (1, 0)), name + '_bias') + printVector(f, np.transpose(weights[2], (1, 0)), name + '_factor') + activation = self.activation.__name__.upper() + max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2]) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0])) + model_struct.write(' MDenseLayer {};\n'.format(name)); + model_init.write(' if (mdense_init(&model->{}, arrays, "{}_bias", "{}_weights", "{}_factor", {}, {}, {}, ACTIVATION_{})) return 1;\n' + .format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation)) + return False +MDense.dump_layer = dump_mdense_layer + +def dump_conv1d_layer(self, f, hf): + global max_conv_inputs + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + printVector(f, weights[0], name + '_weights') + printVector(f, weights[-1], name + '_bias') + activation = self.activation.__name__.upper() + max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0]) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2])) + hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1))) + hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2)) + model_struct.write(' Conv1DLayer {};\n'.format(name)); + model_init.write(' if (conv1d_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, {}, ACTIVATION_{})) return 1;\n' + .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation)) + return True +Conv1D.dump_layer = dump_conv1d_layer + + +def dump_embedding_layer_impl(name, weights, f, hf): + printVector(f, weights, name + '_weights') + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1])) + model_struct.write(' EmbeddingLayer {};\n'.format(name)); + model_init.write(' if (embedding_init(&model->{}, arrays, "{}_weights", {}, {})) return 1;\n' + .format(name, name, weights.shape[0], weights.shape[1])) + +def dump_embedding_layer(self, f, hf): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights()[0] + dump_embedding_layer_impl(name, weights, f, hf) + return False +Embedding.dump_layer = dump_embedding_layer +diff_Embed.dump_layer = dump_embedding_layer + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('model_file', type=str, help='model weight h5 file') + parser.add_argument('--nnet-header', type=str, help='name of c header file for dumped model', default='nnet_data.h') + parser.add_argument('--nnet-source', type=str, help='name of c source file for dumped model', default='nnet_data.c') + parser.add_argument('--lpc-gamma', type=float, help='LPC weighting factor. If not specified I will attempt to read it from the model file with 1 as default', default=None) + parser.add_argument('--lookahead', type=float, help='Features lookahead. If not specified I will attempt to read it from the model file with 2 as default', default=None) + + args = parser.parse_args() + + filename = args.model_file + with h5py.File(filename, "r") as f: + units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape) + units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape) + cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape) + e2e = 'rc2lpc' in f['model_weights'] + + model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size) + model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) + #model.summary() + + model.load_weights(filename, by_name=True) + + cfile = args.nnet_source + hfile = args.nnet_header + + f = open(cfile, 'w') + hf = open(hfile, 'w') + model_struct = io.StringIO() + model_init = io.StringIO() + model_struct.write('typedef struct {\n') + model_init.write('#ifndef DUMP_BINARY_WEIGHTS\n') + model_init.write('int init_lpcnet_model(LPCNetModel *model, const WeightArray *arrays) {\n') + array_list = [] + + f.write('/*This file is automatically generated from a Keras model*/\n') + f.write('/*based on model {}*/\n\n'.format(sys.argv[1])) + f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile)) + + hf.write('/*This file is automatically generated from a Keras model*/\n\n') + hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "nnet.h"\n\n') + + if e2e: + hf.write('/* This is an end-to-end model */\n') + hf.write('#define END2END\n\n') + else: + hf.write('/* This is *not* an end-to-end model */\n') + hf.write('/* #define END2END */\n\n') + + # LPC weighting factor + if type(args.lpc_gamma) == type(None): + lpc_gamma = get_parameter(model, 'lpc_gamma', 1) + else: + lpc_gamma = args.lpc_gamma + + hf.write('/* LPC weighting factor */\n') + hf.write('#define LPC_GAMMA ' + str(lpc_gamma) +'f\n\n') + + # look-ahead + if type(args.lookahead) == type(None): + lookahead = get_parameter(model, 'lookahead', 2) + else: + lookahead = args.lookahead + + hf.write('/* Features look-ahead */\n') + hf.write('#define FEATURES_DELAY ' + str(lookahead) +'\n\n') + + embed_size = lpcnet.embed_size + + E = model.get_layer('embed_sig').get_weights()[0] + W = model.get_layer('gru_a').get_weights()[0][:embed_size,:] + dump_embedding_layer_impl('gru_a_embed_sig', np.dot(E, W), f, hf) + W = model.get_layer('gru_a').get_weights()[0][embed_size:2*embed_size,:] + dump_embedding_layer_impl('gru_a_embed_pred', np.dot(E, W), f, hf) + W = model.get_layer('gru_a').get_weights()[0][2*embed_size:3*embed_size,:] + dump_embedding_layer_impl('gru_a_embed_exc', np.dot(E, W), f, hf) + W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:] + #FIXME: dump only half the biases + b = model.get_layer('gru_a').get_weights()[2] + dump_dense_layer_impl('gru_a_dense_feature', W, b[:len(b)//2], 'LINEAR', f, hf) + + W = model.get_layer('gru_b').get_weights()[0][model.rnn_units1:,:] + b = model.get_layer('gru_b').get_weights()[2] + # Set biases to zero because they'll be included in the GRU input part + # (we need regular and SU biases) + dump_dense_layer_impl('gru_b_dense_feature', W, 0*b[:len(b)//2], 'LINEAR', f, hf) + dump_grub(model.get_layer('gru_b'), f, hf, model.rnn_units1) + + layer_list = [] + for i, layer in enumerate(model.layers): + if layer.dump_layer(f, hf): + layer_list.append(layer.name) + + dump_sparse_gru(model.get_layer('gru_a'), f, hf) + + f.write('#ifndef USE_WEIGHTS_FILE\n') + f.write('const WeightArray lpcnet_arrays[] = {\n') + for name in array_list: + f.write('#ifdef WEIGHTS_{}_DEFINED\n'.format(name)) + f.write(' {{"{}", WEIGHTS_{}_TYPE, sizeof({}), {}}},\n'.format(name, name, name, name)) + f.write('#endif\n') + f.write(' {NULL, 0, 0, NULL}\n};\n') + f.write('#endif\n') + + model_init.write(' return 0;\n}\n') + model_init.write('#endif\n') + f.write(model_init.getvalue()) + + hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons)) + hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs)) + hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp)) + + + hf.write('typedef struct {\n') + for i, name in enumerate(layer_list): + hf.write(' float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper())) + hf.write('} NNetState;\n\n') + + model_struct.write('} LPCNetModel;\n\n') + hf.write(model_struct.getvalue()) + hf.write('int init_lpcnet_model(LPCNetModel *model, const WeightArray *arrays);\n\n') + hf.write('\n\n#endif\n') + + f.close() + hf.close() diff --git a/managed_components/78__esp-opus/dnn/training_tf2/dump_plc.py b/managed_components/78__esp-opus/dnn/training_tf2/dump_plc.py new file mode 100644 index 0000000..a490ade --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/dump_plc.py @@ -0,0 +1,296 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + Copyright (c) 2017-2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import lpcnet_plc +import io +import sys +import numpy as np +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding +import h5py +import re + +# Flag for dumping e2e (differentiable lpc) network weights +flag_e2e = False + +max_rnn_neurons = 1 +max_conv_inputs = 1 + +def printVector(f, vector, name, dtype='float', dotp=False): + global array_list + if dotp: + vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8)) + vector = vector.transpose((2, 0, 3, 1)) + v = np.reshape(vector, (-1)); + #print('static const float ', name, '[', len(v), '] = \n', file=f) + if name not in array_list: + array_list.append(name) + f.write('#ifndef USE_WEIGHTS_FILE\n') + f.write('#define WEIGHTS_{}_DEFINED\n'.format(name)) + f.write('#define WEIGHTS_{}_TYPE WEIGHT_TYPE_{}\n'.format(name, dtype)) + f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v))) + for i in range(0, len(v)): + f.write('{}'.format(v[i])) + if (i!=len(v)-1): + f.write(',') + else: + break; + if (i%8==7): + f.write("\n ") + else: + f.write(" ") + #print(v, file=f) + f.write('\n};\n') + f.write('#endif\n\n') + return; + +def printSparseVector(f, A, name, have_diag=True): + N = A.shape[0] + M = A.shape[1] + W = np.zeros((0,), dtype='int') + W0 = np.zeros((0,)) + if have_diag: + diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])]) + A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N])) + A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N])) + A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:])) + printVector(f, diag, name + '_diag') + AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int') + idx = np.zeros((0,), dtype='int') + for i in range(M//8): + pos = idx.shape[0] + idx = np.append(idx, -1) + nb_nonzero = 0 + for j in range(N//4): + block = A[j*4:(j+1)*4, i*8:(i+1)*8] + qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8] + if np.sum(np.abs(block)) > 1e-10: + nb_nonzero = nb_nonzero + 1 + idx = np.append(idx, j*4) + vblock = qblock.transpose((1,0)).reshape((-1,)) + W0 = np.concatenate([W0, block.reshape((-1,))]) + W = np.concatenate([W, vblock]) + idx[pos] = nb_nonzero + f.write('#ifdef DOT_PROD\n') + printVector(f, W, name, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + printVector(f, W0, name, dtype='qweight') + f.write('#endif /*DOT_PROD*/\n') + #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16) + printVector(f, idx, name + '_idx', dtype='int') + return AQ + +def dump_layer_ignore(self, f, hf): + print("ignoring layer " + self.name + " of type " + self.__class__.__name__) + return False +Layer.dump_layer = dump_layer_ignore + +def dump_sparse_gru(self, f, hf): + global max_rnn_neurons + name = 'sparse_' + self.name + print("printing layer " + name + " of type sparse " + self.__class__.__name__) + weights = self.get_weights() + qweights = printSparseVector(f, weights[1], name + '_recurrent_weights') + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = max(max_rnn_neurons, neurons) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + model_struct.write(' SparseGRULayer {};\n'.format(name)); + model_init.write(' if (sparse_gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_recurrent_weights_diag", "{}_recurrent_weights", "{}_recurrent_weights_idx", {}, ACTIVATION_{}, {})) return 1;\n' + .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after)) + return True + +def dump_gru_layer(self, f, hf): + global max_rnn_neurons + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False) + + f.write('#ifdef DOT_PROD\n') + qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127) + printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + printVector(f, weights[1], name + '_recurrent_weights') + f.write('#endif /*DOT_PROD*/\n') + + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0) + subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = max(max_rnn_neurons, neurons) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + model_struct.write(' GRULayer {};\n'.format(name)); + model_init.write(' if (gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_weights", "{}_weights_idx", "{}_recurrent_weights", {}, {}, ACTIVATION_{}, {})) return 1;\n' + .format(name, name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after)) + return True +GRU.dump_layer = dump_gru_layer + +def dump_gru_layer_dummy(self, f, hf): + name = self.name + weights = self.get_weights() + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + return True; + +#GRU.dump_layer = dump_gru_layer_dummy + +def dump_dense_layer_impl(name, weights, bias, activation, f, hf): + printVector(f, weights, name + '_weights') + printVector(f, bias, name + '_bias') + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1])) + model_struct.write(' DenseLayer {};\n'.format(name)); + model_init.write(' if (dense_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, ACTIVATION_{})) return 1;\n' + .format(name, name, name, weights.shape[0], weights.shape[1], activation)) + +def dump_dense_layer(self, f, hf): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + activation = self.activation.__name__.upper() + dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf) + return False + +Dense.dump_layer = dump_dense_layer + +def dump_conv1d_layer(self, f, hf): + global max_conv_inputs + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + printVector(f, weights[0], name + '_weights') + printVector(f, weights[-1], name + '_bias') + activation = self.activation.__name__.upper() + max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0]) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2])) + hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1))) + hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2)) + model_struct.write(' Conv1DLayer {};\n'.format(name)); + model_init.write(' if (conv1d_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, {}, ACTIVATION_{})) return 1;\n' + .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation)) + return True +Conv1D.dump_layer = dump_conv1d_layer + + + +filename = sys.argv[1] +with h5py.File(filename, "r") as f: + units = min(f['model_weights']['plc_gru1']['plc_gru1']['recurrent_kernel:0'].shape) + units2 = min(f['model_weights']['plc_gru2']['plc_gru2']['recurrent_kernel:0'].shape) + cond_size = f['model_weights']['plc_dense1']['plc_dense1']['kernel:0'].shape[1] + +model = lpcnet_plc.new_lpcnet_plc_model(rnn_units=units, cond_size=cond_size) +model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) +#model.summary() + +model.load_weights(filename, by_name=True) + +if len(sys.argv) > 2: + cfile = sys.argv[2]; + hfile = sys.argv[3]; +else: + cfile = 'plc_data.c' + hfile = 'plc_data.h' + + +f = open(cfile, 'w') +hf = open(hfile, 'w') +model_struct = io.StringIO() +model_init = io.StringIO() +model_struct.write('typedef struct {\n') +model_init.write('#ifndef DUMP_BINARY_WEIGHTS\n') +model_init.write('int init_plc_model(PLCModel *model, const WeightArray *arrays) {\n') +array_list = [] + + +f.write('/*This file is automatically generated from a Keras model*/\n') +f.write('/*based on model {}*/\n\n'.format(sys.argv[1])) +f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile)) + +hf.write('/*This file is automatically generated from a Keras model*/\n\n') +hf.write('#ifndef PLC_DATA_H\n#define PLC_DATA_H\n\n#include "nnet.h"\n\n') + +layer_list = [] +for i, layer in enumerate(model.layers): + if layer.dump_layer(f, hf): + layer_list.append(layer.name) + +#dump_sparse_gru(model.get_layer('gru_a'), f, hf) +f.write('#ifndef USE_WEIGHTS_FILE\n') +f.write('const WeightArray lpcnet_plc_arrays[] = {\n') +for name in array_list: + f.write('#ifdef WEIGHTS_{}_DEFINED\n'.format(name)) + f.write(' {{"{}", WEIGHTS_{}_TYPE, sizeof({}), {}}},\n'.format(name, name, name, name)) + f.write('#endif\n') +f.write(' {NULL, 0, 0, NULL}\n};\n') +f.write('#endif\n') + +model_init.write(' return 0;\n}\n') +model_init.write('#endif\n') +f.write(model_init.getvalue()) + + +hf.write('#define PLC_MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons)) +#hf.write('#define PLC_MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs)) + +hf.write('typedef struct {\n') +for i, name in enumerate(layer_list): + hf.write(' float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper())) +hf.write('} PLCNetState;\n\n') + +model_struct.write('} PLCModel;\n\n') +hf.write(model_struct.getvalue()) +hf.write('int init_plc_model(PLCModel *model, const WeightArray *arrays);\n\n') + +hf.write('\n\n#endif\n') + +f.close() +hf.close() diff --git a/managed_components/78__esp-opus/dnn/training_tf2/dump_rdovae.py b/managed_components/78__esp-opus/dnn/training_tf2/dump_rdovae.py new file mode 100644 index 0000000..1858c8a --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/dump_rdovae.py @@ -0,0 +1,306 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import argparse +from ftplib import parse150 +import os + +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +parser = argparse.ArgumentParser() + +parser.add_argument('weights', metavar="", type=str, help='model weight file in hdf5 format') +parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256) +parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80) +parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16) + +args = parser.parse_args() + +# now import the heavy stuff +import tensorflow as tf +import numpy as np +from keraslayerdump import dump_conv1d_layer, dump_dense_layer, dump_gru_layer, printVector +from rdovae import new_rdovae_model + +def start_header(header_fid, header_name): + header_guard = os.path.basename(header_name)[:-2].upper() + "_H" + header_fid.write( +f""" +#ifndef {header_guard} +#define {header_guard} + +""" + ) + +def finish_header(header_fid): + header_fid.write( +""" +#endif + +""" + ) + +def start_source(source_fid, header_name, weight_file): + source_fid.write( +f""" +/* this source file was automatically generated from weight file {weight_file} */ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "{header_name}" + +""" + ) + +def finish_source(source_fid): + pass + + +def dump_statistical_model(qembedding, f, fh): + w = qembedding.weights[0].numpy() + levels, dim = w.shape + N = dim // 6 + + print("dumping statistical model") + quant_scales = tf.math.softplus(w[:, : N]).numpy() + dead_zone = 0.05 * tf.math.softplus(w[:, N : 2 * N]).numpy() + r = tf.math.sigmoid(w[:, 5 * N : 6 * N]).numpy() + p0 = tf.math.sigmoid(w[:, 4 * N : 5 * N]).numpy() + p0 = 1 - r ** (0.5 + 0.5 * p0) + + quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16) + dead_zone_q10 = np.round(dead_zone * 2**10).astype(np.uint16) + r_q15 = np.round(r * 2**15).astype(np.uint16) + p0_q15 = np.round(p0 * 2**15).astype(np.uint16) + + printVector(f, quant_scales_q8, 'dred_quant_scales_q8', dtype='opus_uint16', static=False) + printVector(f, dead_zone_q10, 'dred_dead_zone_q10', dtype='opus_uint16', static=False) + printVector(f, r_q15, 'dred_r_q15', dtype='opus_uint16', static=False) + printVector(f, p0_q15, 'dred_p0_q15', dtype='opus_uint16', static=False) + + fh.write( +f""" +extern const opus_uint16 dred_quant_scales_q8[{levels * N}]; +extern const opus_uint16 dred_dead_zone_q10[{levels * N}]; +extern const opus_uint16 dred_r_q15[{levels * N}]; +extern const opus_uint16 dred_p0_q15[{levels * N}]; + +""" + ) + +if __name__ == "__main__": + + model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels) + model.load_weights(args.weights) + + + + + # encoder + encoder_dense_names = [ + 'enc_dense1', + 'enc_dense3', + 'enc_dense5', + 'enc_dense7', + 'enc_dense8', + 'gdense1', + 'gdense2' + ] + + encoder_gru_names = [ + 'enc_dense2', + 'enc_dense4', + 'enc_dense6' + ] + + encoder_conv1d_names = [ + 'bits_dense' + ] + + source_fid = open("dred_rdovae_enc_data.c", 'w') + header_fid = open("dred_rdovae_enc_data.h", 'w') + + start_header(header_fid, "dred_rdovae_enc_data.h") + start_source(source_fid, "dred_rdovae_enc_data.h", os.path.basename(args.weights)) + + header_fid.write( +f""" +#include "dred_rdovae_constants.h" + +#include "nnet.h" +""" + ) + + # dump GRUs + max_rnn_neurons_enc = max( + [ + dump_gru_layer(encoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True) + for name in encoder_gru_names + ] + ) + + # dump conv layers + max_conv_inputs = max( + [ + dump_conv1d_layer(encoder.get_layer(name), source_fid, header_fid) + for name in encoder_conv1d_names + ] + ) + + # dump Dense layers + for name in encoder_dense_names: + layer = encoder.get_layer(name) + dump_dense_layer(layer, source_fid, header_fid) + + # some global constants + header_fid.write( +f""" + +#define DRED_ENC_MAX_RNN_NEURONS {max_rnn_neurons_enc} + +#define DRED_ENC_MAX_CONV_INPUTS {max_conv_inputs} + +""" + ) + + finish_header(header_fid) + finish_source(source_fid) + + header_fid.close() + source_fid.close() + + # statistical model + source_fid = open("dred_rdovae_stats_data.c", 'w') + header_fid = open("dred_rdovae_stats_data.h", 'w') + + start_header(header_fid, "dred_rdovae_stats_data.h") + start_source(source_fid, "dred_rdovae_stats_data.h", os.path.basename(args.weights)) + + header_fid.write( +""" + +#include "opus_types.h" + +""" + ) + + dump_statistical_model(qembedding, source_fid, header_fid) + + finish_header(header_fid) + finish_source(source_fid) + + header_fid.close() + source_fid.close() + + # decoder + decoder_dense_names = [ + 'state1', + 'state2', + 'state3', + 'dec_dense1', + 'dec_dense3', + 'dec_dense5', + 'dec_dense7', + 'dec_dense8', + 'dec_final' + ] + + decoder_gru_names = [ + 'dec_dense2', + 'dec_dense4', + 'dec_dense6' + ] + + source_fid = open("dred_rdovae_dec_data.c", 'w') + header_fid = open("dred_rdovae_dec_data.h", 'w') + + start_header(header_fid, "dred_rdovae_dec_data.h") + start_source(source_fid, "dred_rdovae_dec_data.h", os.path.basename(args.weights)) + + header_fid.write( +f""" +#include "dred_rdovae_constants.h" + +#include "nnet.h" +""" + ) + + + # dump GRUs + max_rnn_neurons_dec = max( + [ + dump_gru_layer(decoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True) + for name in decoder_gru_names + ] + ) + + # dump Dense layers + for name in decoder_dense_names: + layer = decoder.get_layer(name) + dump_dense_layer(layer, source_fid, header_fid) + + # some global constants + header_fid.write( +f""" + +#define DRED_DEC_MAX_RNN_NEURONS {max_rnn_neurons_dec} + +""" + ) + + finish_header(header_fid) + finish_source(source_fid) + + header_fid.close() + source_fid.close() + + # common constants + header_fid = open("dred_rdovae_constants.h", 'w') + start_header(header_fid, "dred_rdovae_constants.h") + + header_fid.write( +f""" +#define DRED_NUM_FEATURES 20 + +#define DRED_LATENT_DIM {args.latent_dim} + +#define DRED_STATE_DIM {24} + +#define DRED_NUM_QUANTIZATION_LEVELS {qembedding.weights[0].shape[0]} + +#define DRED_MAX_RNN_NEURONS {max(max_rnn_neurons_enc, max_rnn_neurons_dec)} + +#define DRED_MAX_CONV_INPUTS {max_conv_inputs} +""" + ) + + finish_header(header_fid) \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/training_tf2/encode_rdovae.py b/managed_components/78__esp-opus/dnn/training_tf2/encode_rdovae.py new file mode 100644 index 0000000..f144fc4 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/encode_rdovae.py @@ -0,0 +1,125 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + Copyright (c) 2018-2019 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +# Train an LPCNet model + +import argparse +#from plc_loader import PLCLoader + +parser = argparse.ArgumentParser(description='Train a PLC model') + +parser.add_argument('features', metavar='', help='binary features file (float32)') +parser.add_argument('output', metavar='', help='trained model file (.h5)') +parser.add_argument('--model', metavar='', default='rdovae', help='PLC model python definition (without .py)') +group1 = parser.add_mutually_exclusive_group() +group1.add_argument('--weights', metavar='', help='model weights') +parser.add_argument('--cond-size', metavar='', default=1024, type=int, help='number of units in conditioning network (default 1024)') +parser.add_argument('--batch-size', metavar='', default=1, type=int, help='batch size to use (default 128)') +parser.add_argument('--seq-length', metavar='', default=1000, type=int, help='sequence length to use (default 1000)') + + +args = parser.parse_args() + +import importlib +rdovae = importlib.import_module(args.model) + +from rdovae import apply_dead_zone + +import sys +import numpy as np +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger +import tensorflow.keras.backend as K +import h5py + +import tensorflow as tf +from rdovae import pvq_quantize + +# Try reducing batch_size if you run out of memory on your GPU +batch_size = args.batch_size + +model, encoder, decoder, qembedding = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size) +model.load_weights(args.weights) + +lpc_order = 16 + +feature_file = args.features +nb_features = model.nb_used_features + lpc_order +nb_used_features = model.nb_used_features +sequence_size = args.seq_length + +# u for unquantised, load 16 bit PCM samples and convert to mu-law + + +features = np.memmap(feature_file, dtype='float32', mode='r') +nb_sequences = len(features)//(nb_features*sequence_size)//batch_size*batch_size +features = features[:nb_sequences*sequence_size*nb_features] + +features = np.reshape(features, (nb_sequences, sequence_size, nb_features)) +print(features.shape) +features = features[:, :, :nb_used_features] +#features = np.random.randn(73600, 1000, 17) + + +bits, gru_state_dec = encoder.predict([features], batch_size=batch_size) +(gru_state_dec).astype('float32').tofile(args.output + "-state.f32") + + +#dist = rdovae.feat_dist_loss(features, quant_out) +#rate = rdovae.sq1_rate_loss(features, model_bits) +#rate2 = rdovae.sq_rate_metric(features, model_bits) +#print(dist, rate, rate2) + +print("shapes are:") +print(bits.shape) +print(gru_state_dec.shape) + +features.astype('float32').tofile(args.output + "-input.f32") +#quant_out.astype('float32').tofile(args.output + "-enc_dec.f32") +nbits=80 +bits.astype('float32').tofile(args.output + "-syms.f32") + +lambda_val = 0.0002 * np.ones((nb_sequences, sequence_size//2, 1)) +quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16') +quant_id = quant_id[:,:,0] +quant_embed = qembedding(quant_id) +quant_scale = tf.math.softplus(quant_embed[:,:,:nbits]) +dead_zone = tf.math.softplus(quant_embed[:, :, nbits : 2 * nbits]) + +bits = bits*quant_scale +bits = np.round(apply_dead_zone([bits, dead_zone]).numpy()) +bits = bits/quant_scale + +gru_state_dec = pvq_quantize(gru_state_dec, 82) +#gru_state_dec = gru_state_dec/(1e-15+tf.norm(gru_state_dec, axis=-1,keepdims=True)) +gru_state_dec = gru_state_dec[:,-1,:] +dec_out = decoder([bits[:,1::2,:], gru_state_dec]) + +print(dec_out.shape) + +dec_out.numpy().astype('float32').tofile(args.output + "-quant_out.f32") diff --git a/managed_components/78__esp-opus/dnn/training_tf2/fec_encoder.py b/managed_components/78__esp-opus/dnn/training_tf2/fec_encoder.py new file mode 100644 index 0000000..15ef12b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/fec_encoder.py @@ -0,0 +1,256 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe and Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" +import os +import subprocess +import argparse + + +import numpy as np +from scipy.io import wavfile +import tensorflow as tf + +from rdovae import new_rdovae_model, pvq_quantize, apply_dead_zone, sq_rate_metric +from fec_packets import write_fec_packets, read_fec_packets + + +debug = False + +if debug: + args = type('dummy', (object,), + { + 'input' : 'item1.wav', + 'weights' : 'testout/rdovae_alignment_fix_1024_120.h5', + 'enc_lambda' : 0.0007, + 'output' : "test_0007.fec", + 'cond_size' : 1024, + 'num_redundancy_frames' : 64, + 'extra_delay' : 0, + 'dump_data' : './dump_data' + })() + os.environ['CUDA_VISIBLE_DEVICES']="" +else: + parser = argparse.ArgumentParser(description='Encode redundancy for Opus neural FEC. Designed for use with voip application and 20ms frames') + + parser.add_argument('input', metavar='', help='audio input (.wav or .raw or .pcm as int16)') + parser.add_argument('weights', metavar='', help='trained model file (.h5)') +# parser.add_argument('enc_lambda', metavar='', type=float, help='lambda for controlling encoder rate') + parser.add_argument('output', type=str, help='output file (will be extended with .fec)') + + parser.add_argument('--dump-data', type=str, default='./dump_data', help='path to dump data executable (default ./dump_data)') + parser.add_argument('--cond-size', metavar='', default=1024, type=int, help='number of units in conditioning network (default 1024)') + parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 40)", default=40) + parser.add_argument('--num-redundancy-frames', default=64, type=int, help='number of redundancy frames (20ms) per packet (default 64)') + parser.add_argument('--extra-delay', default=0, type=int, help="last features in packet are calculated with the decoder aligned samples, use this option to add extra delay (in samples at 16kHz)") + parser.add_argument('--lossfile', type=str, help='file containing loss trace (0 for frame received, 1 for lost)') + + parser.add_argument('--debug-output', action='store_true', help='if set, differently assembled features are written to disk') + + args = parser.parse_args() + +model, encoder, decoder, qembedding = new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=1, nb_quant=args.quant_levels, cond_size=args.cond_size) +model.load_weights(args.weights) + +lpc_order = 16 + +## prepare input signal +# SILK frame size is 20ms and LPCNet subframes are 10ms +subframe_size = 160 +frame_size = 2 * subframe_size + +# 91 samples delay to align with SILK decoded frames +silk_delay = 91 + +# prepend zeros to have enough history to produce the first package +zero_history = (args.num_redundancy_frames - 1) * frame_size + +# dump data has a (feature) delay of 10ms +dump_data_delay = 160 + +total_delay = silk_delay + zero_history + args.extra_delay - dump_data_delay + +# load signal +if args.input.endswith('.raw') or args.input.endswith('.pcm') or args.input.endswith('.sw'): + signal = np.fromfile(args.input, dtype='int16') + +elif args.input.endswith('.wav'): + fs, signal = wavfile.read(args.input) +else: + raise ValueError(f'unknown input signal format: {args.input}') + +# fill up last frame with zeros +padded_signal_length = len(signal) + total_delay +tail = padded_signal_length % frame_size +right_padding = (frame_size - tail) % frame_size + +signal = np.concatenate((np.zeros(total_delay, dtype=np.int16), signal, np.zeros(right_padding, dtype=np.int16))) + +padded_signal_file = os.path.splitext(args.input)[0] + '_padded.raw' +signal.tofile(padded_signal_file) + +# write signal and call dump_data to create features + +feature_file = os.path.splitext(args.input)[0] + '_features.f32' +command = f"{args.dump_data} -test {padded_signal_file} {feature_file}" +r = subprocess.run(command, shell=True) +if r.returncode != 0: + raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}") + +# load features +nb_features = model.nb_used_features + lpc_order +nb_used_features = model.nb_used_features + +# load features +features = np.fromfile(feature_file, dtype='float32') +num_subframes = len(features) // nb_features +num_subframes = 2 * (num_subframes // 2) +num_frames = num_subframes // 2 + +features = np.reshape(features, (1, -1, nb_features)) +features = features[:, :, :nb_used_features] +features = features[:, :num_subframes, :] + +#variable quantizer depending on the delay +q0 = 3 +q1 = 15 +quant_id = np.round(q1 + (q0-q1)*np.arange(args.num_redundancy_frames//2)/args.num_redundancy_frames).astype('int16') +#print(quant_id) + +quant_embed = qembedding(quant_id) + +# run encoder +print("running fec encoder...") +symbols, gru_state_dec = encoder.predict(features) + +# apply quantization +nsymbols = 80 +quant_scale = tf.math.softplus(quant_embed[:, :nsymbols]).numpy() +dead_zone = tf.math.softplus(quant_embed[:, nsymbols : 2 * nsymbols]).numpy() +#symbols = apply_dead_zone([symbols, dead_zone]).numpy() +#qsymbols = np.round(symbols) +quant_gru_state_dec = pvq_quantize(gru_state_dec, 82) + +# rate estimate +hard_distr_embed = tf.math.sigmoid(quant_embed[:, 4 * nsymbols : ]).numpy() +#rate_input = np.concatenate((qsymbols, hard_distr_embed, enc_lambda), axis=-1) +#rates = sq_rate_metric(None, rate_input, reduce=False).numpy() + +# run decoder +input_length = args.num_redundancy_frames // 2 +offset = args.num_redundancy_frames - 1 + +packets = [] +packet_sizes = [] + +sym_batch = np.zeros((num_frames-offset, args.num_redundancy_frames//2, nsymbols), dtype='float32') +quant_state = quant_gru_state_dec[0, offset:num_frames, :] +#pack symbols for batch processing +for i in range(offset, num_frames): + sym_batch[i-offset, :, :] = symbols[0, i - 2 * input_length + 2 : i + 1 : 2, :] + +#quantize symbols +sym_batch = sym_batch * quant_scale +sym_batch = apply_dead_zone([sym_batch, dead_zone]).numpy() +sym_batch = np.round(sym_batch) + +hard_distr_embed = np.broadcast_to(hard_distr_embed, (sym_batch.shape[0], sym_batch.shape[1], 2*sym_batch.shape[2])) +fake_lambda = np.ones((sym_batch.shape[0], sym_batch.shape[1], 1), dtype='float32') +rate_input = np.concatenate((sym_batch, hard_distr_embed, fake_lambda), axis=-1) +rates = sq_rate_metric(None, rate_input, reduce=False).numpy() +#print(rates.shape) +print("average rate = ", np.mean(rates[args.num_redundancy_frames:,:])) + +#sym_batch.tofile('qsyms.f32') + +sym_batch = sym_batch / quant_scale +#print(sym_batch.shape, quant_state.shape) +#features = decoder.predict([sym_batch, quant_state]) +features = decoder([sym_batch, quant_state]) + +#for i in range(offset, num_frames): +# print(f"processing frame {i - offset}...") +# features = decoder.predict([qsymbols[:, i - 2 * input_length + 2 : i + 1 : 2, :], quant_embed_dec[:, i - 2 * input_length + 2 : i + 1 : 2, :], quant_gru_state_dec[:, i, :]]) +# packets.append(features) +# packet_size = 8 * int((np.sum(rates[:, i - 2 * input_length + 2 : i + 1 : 2]) + 7) / 8) + 64 +# packet_sizes.append(packet_size) + + +# write packets +packet_file = args.output + '.fec' if not args.output.endswith('.fec') else args.output +#write_fec_packets(packet_file, packets, packet_sizes) + + +#print(f"average redundancy rate: {int(round(sum(packet_sizes) / len(packet_sizes) * 50 / 1000))} kbps") + +if args.lossfile != None: + loss = np.loadtxt(args.lossfile, dtype='int16') + fec_out = np.zeros((features.shape[0]*2, features.shape[-1]), dtype='float32') + foffset = -2 + ptr = 0; + count = 2; + for i in range(features.shape[0]): + if (loss[i] == 0) or (i == features.shape[0]-1): + fec_out[ptr:ptr+count,:] = features[i, foffset:, :] + #print("filled ", count) + foffset = -2 + ptr = ptr+count + count = 2 + else: + count = count + 2 + foffset = foffset - 2 + + fec_out_full = np.zeros((fec_out.shape[0], nb_features), dtype=np.float32) + fec_out_full[:, :nb_used_features] = fec_out + + fec_out_full.tofile(packet_file[:-4] + f'_fec.f32') + + +#create packets array like in the original version for debugging purposes +for i in range(offset, num_frames): + packets.append(features[i-offset:i-offset+1, :, :]) + +if args.debug_output: + import itertools + + #batches = [2, 4] + batches = [4] + #offsets = [0, 4, 20] + offsets = [0, (args.num_redundancy_frames - 2)*2] + # sanity checks + # 1. concatenate features at offset 0 + for batch, offset in itertools.product(batches, offsets): + + stop = packets[0].shape[1] - offset + print(batch, offset, stop) + test_features = np.concatenate([packet[:,stop - batch: stop, :] for packet in packets[::batch//2]], axis=1) + + test_features_full = np.zeros((test_features.shape[1], nb_features), dtype=np.float32) + test_features_full[:, :nb_used_features] = test_features[0, :, :] + + print(f"writing debug output {packet_file[:-4] + f'_tf_batch{batch}_offset{offset}.f32'}") + test_features_full.tofile(packet_file[:-4] + f'_tf_batch{batch}_offset{offset}.f32') diff --git a/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.c b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.c new file mode 100644 index 0000000..ee08ba9 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.c @@ -0,0 +1,142 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include +#include + +#include "fec_packets.h" + +int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index) +{ + + int16_t version; + int16_t header_size; + int16_t num_packets; + int16_t packet_size; + int16_t subframe_size; + int16_t subframes_per_packet; + int16_t num_features; + long offset; + + FILE *fid = fopen(filename, "rb"); + + /* read header */ + if (fread(&version, sizeof(version), 1, fid) != 1) goto error; + if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; + if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; + if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; + if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; + if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; + if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; + + /* check if indices are valid */ + if (packet_index >= num_packets || subframe_index >= subframes_per_packet) + { + fprintf(stderr, "get_fec_frame: index out of bounds\n"); + goto error; + } + + /* calculate offset in file (+ 2 is for rate) */ + offset = header_size + packet_index * packet_size + 2 + subframe_index * subframe_size; + fseek(fid, offset, SEEK_SET); + + /* read features */ + if (fread(features, sizeof(*features), num_features, fid) != num_features) goto error; + + fclose(fid); + return 0; + +error: + fclose(fid); + return 1; +} + +int get_fec_rate(const char * const filename, int packet_index) +{ + int16_t version; + int16_t header_size; + int16_t num_packets; + int16_t packet_size; + int16_t subframe_size; + int16_t subframes_per_packet; + int16_t num_features; + long offset; + int16_t rate; + + FILE *fid = fopen(filename, "rb"); + + /* read header */ + if (fread(&version, sizeof(version), 1, fid) != 1) goto error; + if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; + if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; + if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; + if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; + if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; + if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; + + /* check if indices are valid */ + if (packet_index >= num_packets) + { + fprintf(stderr, "get_fec_rate: index out of bounds\n"); + goto error; + } + + /* calculate offset in file (+ 2 is for rate) */ + offset = header_size + packet_index * packet_size; + fseek(fid, offset, SEEK_SET); + + /* read rate */ + if (fread(&rate, sizeof(rate), 1, fid) != 1) goto error; + + fclose(fid); + return (int) rate; + +error: + fclose(fid); + return -1; +} + +#if 0 +int main() +{ + float features[20]; + int i; + + if (get_fec_frame("../test.fec", &features[0], 0, 127)) + { + return 1; + } + + for (i = 0; i < 20; i ++) + { + printf("%d %f\n", i, features[i]); + } + + printf("rate: %d\n", get_fec_rate("../test.fec", 0)); + +} +#endif \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.h b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.h new file mode 100644 index 0000000..01b128b --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.h @@ -0,0 +1,34 @@ +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef FEC_PACKETS_H +#define FEC_PACKETS_H + +int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index); +int get_fec_rate(const char * const filename, int packet_index); + +#endif diff --git a/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.py b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.py new file mode 100644 index 0000000..6acbe9d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/fec_packets.py @@ -0,0 +1,108 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + +import numpy as np + + + +def write_fec_packets(filename, packets, rates=None): + """ writes packets in binary format """ + + assert np.dtype(np.float32).itemsize == 4 + assert np.dtype(np.int16).itemsize == 2 + + # derive some sizes + num_packets = len(packets) + subframes_per_packet = packets[0].shape[-2] + num_features = packets[0].shape[-1] + + # size of float is 4 + subframe_size = num_features * 4 + packet_size = subframe_size * subframes_per_packet + 2 # two bytes for rate + + version = 1 + # header size (version, header_size, num_packets, packet_size, subframe_size, subrames_per_packet, num_features) + header_size = 14 + + with open(filename, 'wb') as f: + + # header + f.write(np.int16(version).tobytes()) + f.write(np.int16(header_size).tobytes()) + f.write(np.int16(num_packets).tobytes()) + f.write(np.int16(packet_size).tobytes()) + f.write(np.int16(subframe_size).tobytes()) + f.write(np.int16(subframes_per_packet).tobytes()) + f.write(np.int16(num_features).tobytes()) + + # packets + for i, packet in enumerate(packets): + if type(rates) == type(None): + rate = 0 + else: + rate = rates[i] + + f.write(np.int16(rate).tobytes()) + + features = np.flip(packet, axis=-2) + f.write(features.astype(np.float32).tobytes()) + + +def read_fec_packets(filename): + """ reads packets from binary format """ + + assert np.dtype(np.float32).itemsize == 4 + assert np.dtype(np.int16).itemsize == 2 + + with open(filename, 'rb') as f: + + # header + version = np.frombuffer(f.read(2), dtype=np.int16).item() + header_size = np.frombuffer(f.read(2), dtype=np.int16).item() + num_packets = np.frombuffer(f.read(2), dtype=np.int16).item() + packet_size = np.frombuffer(f.read(2), dtype=np.int16).item() + subframe_size = np.frombuffer(f.read(2), dtype=np.int16).item() + subframes_per_packet = np.frombuffer(f.read(2), dtype=np.int16).item() + num_features = np.frombuffer(f.read(2), dtype=np.int16).item() + + dummy_features = np.zeros((1, subframes_per_packet, num_features), dtype=np.float32) + + # packets + rates = [] + packets = [] + for i in range(num_packets): + + rate = np.frombuffer(f.read(2), dtype=np.int16).item + rates.append(rate) + + features = np.reshape(np.frombuffer(f.read(subframe_size * subframes_per_packet), dtype=np.float32), dummy_features.shape) + packet = np.flip(features, axis=-2) + packets.append(packet) + + return packets \ No newline at end of file diff --git a/managed_components/78__esp-opus/dnn/training_tf2/keraslayerdump.py b/managed_components/78__esp-opus/dnn/training_tf2/keraslayerdump.py new file mode 100644 index 0000000..5abc148 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/keraslayerdump.py @@ -0,0 +1,189 @@ +'''Copyright (c) 2017-2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +""" helper functions for dumping some Keras layers to C files """ + +import numpy as np + + +def printVector(f, vector, name, dtype='float', dotp=False, static=True): + """ prints vector as one-dimensional C array """ + if dotp: + vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8)) + vector = vector.transpose((2, 0, 3, 1)) + v = np.reshape(vector, (-1)) + if static: + f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v))) + else: + f.write('const {} {}[{}] = {{\n '.format(dtype, name, len(v))) + for i in range(0, len(v)): + f.write('{}'.format(v[i])) + if (i!=len(v)-1): + f.write(',') + else: + break; + if (i%8==7): + f.write("\n ") + else: + f.write(" ") + f.write('\n};\n\n') + return vector + +def printSparseVector(f, A, name, have_diag=True): + N = A.shape[0] + M = A.shape[1] + W = np.zeros((0,), dtype='int') + W0 = np.zeros((0,)) + if have_diag: + diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])]) + A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N])) + A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N])) + A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:])) + printVector(f, diag, name + '_diag') + AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int') + idx = np.zeros((0,), dtype='int') + for i in range(M//8): + pos = idx.shape[0] + idx = np.append(idx, -1) + nb_nonzero = 0 + for j in range(N//4): + block = A[j*4:(j+1)*4, i*8:(i+1)*8] + qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8] + if np.sum(np.abs(block)) > 1e-10: + nb_nonzero = nb_nonzero + 1 + idx = np.append(idx, j*4) + vblock = qblock.transpose((1,0)).reshape((-1,)) + W0 = np.concatenate([W0, block.reshape((-1,))]) + W = np.concatenate([W, vblock]) + idx[pos] = nb_nonzero + f.write('#ifdef DOT_PROD\n') + printVector(f, W, name, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + printVector(f, W0, name, dtype='qweight') + f.write('#endif /*DOT_PROD*/\n') + printVector(f, idx, name + '_idx', dtype='int') + return AQ + +def dump_sparse_gru(self, f, hf): + name = 'sparse_' + self.name + print("printing layer " + name + " of type sparse " + self.__class__.__name__) + weights = self.get_weights() + qweights = printSparseVector(f, weights[1], name + '_recurrent_weights') + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = neurons + f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n' + .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after)) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('extern const SparseGRULayer {};\n\n'.format(name)); + return max_rnn_neurons + +def dump_gru_layer(self, f, hf, dotp=False, sparse=False): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + if sparse: + qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False) + else: + qweight = printVector(f, weights[0], name + '_weights') + + if dotp: + f.write('#ifdef DOT_PROD\n') + qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127) + printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight') + f.write('#else /*DOT_PROD*/\n') + else: + qweight2 = weights[1] + + printVector(f, weights[1], name + '_recurrent_weights') + if dotp: + f.write('#endif /*DOT_PROD*/\n') + + printVector(f, weights[-1], name + '_bias') + subias = weights[-1].copy() + subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0) + subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0) + printVector(f, subias, name + '_subias') + if hasattr(self, 'activation'): + activation = self.activation.__name__.upper() + else: + activation = 'TANH' + if hasattr(self, 'reset_after') and not self.reset_after: + reset_after = 0 + else: + reset_after = 1 + neurons = weights[0].shape[1]//3 + max_rnn_neurons = neurons + f.write('const GRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_weights,\n {},\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n' + .format(name, name, name, name, name + "_weights_idx" if sparse else "NULL", name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after)) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) + hf.write('extern const GRULayer {};\n\n'.format(name)); + return max_rnn_neurons + +def dump_dense_layer_impl(name, weights, bias, activation, f, hf): + printVector(f, weights, name + '_weights') + printVector(f, bias, name + '_bias') + f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' + .format(name, name, name, weights.shape[0], weights.shape[1], activation)) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1])) + hf.write('extern const DenseLayer {};\n\n'.format(name)); + +def dump_dense_layer(self, f, hf): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + activation = self.activation.__name__.upper() + dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf) + return False + +def dump_conv1d_layer(self, f, hf): + name = self.name + print("printing layer " + name + " of type " + self.__class__.__name__) + weights = self.get_weights() + printVector(f, weights[0], name + '_weights') + printVector(f, weights[-1], name + '_bias') + activation = self.activation.__name__.upper() + max_conv_inputs = weights[0].shape[1]*weights[0].shape[0] + f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n' + .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation)) + hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2])) + hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1))) + hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2)) + hf.write('extern const Conv1DLayer {};\n\n'.format(name)); + return max_conv_inputs diff --git a/managed_components/78__esp-opus/dnn/training_tf2/lossfuncs.py b/managed_components/78__esp-opus/dnn/training_tf2/lossfuncs.py new file mode 100644 index 0000000..78be1fd --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/lossfuncs.py @@ -0,0 +1,99 @@ +""" +Custom Loss functions and metrics for training/analysis +""" + +from tf_funcs import * +import tensorflow as tf + +# The following loss functions all expect the lpcnet model to output the lpc prediction + +# Computing the excitation by subtracting the lpc prediction from the target, followed by minimizing the cross entropy +def res_from_sigloss(): + def loss(y_true,y_pred): + p = y_pred[:,:,0:1] + model_out = y_pred[:,:,2:] + e_gt = tf_l2u(y_true - p) + e_gt = tf.round(e_gt) + e_gt = tf.cast(e_gt,'int32') + sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,model_out) + return sparse_cel + return loss + +# Interpolated and Compensated Loss (In case of end to end lpcnet) +# Interpolates between adjacent embeddings based on the fractional value of the excitation computed (similar to the embedding interpolation) +# Also adds a probability compensation (to account for matching cross entropy in the linear domain), weighted by gamma +def interp_mulaw(gamma = 1): + def loss(y_true,y_pred): + y_true = tf.cast(y_true, 'float32') + p = y_pred[:,:,0:1] + real_p = y_pred[:,:,1:2] + model_out = y_pred[:,:,2:] + e_gt = tf_l2u(y_true - p) + exc_gt = tf_l2u(y_true - real_p) + prob_compensation = tf.squeeze((K.abs(e_gt - 128)/128.0)*K.log(256.0)) + regularization = tf.squeeze((K.abs(exc_gt - 128)/128.0)*K.log(256.0)) + alpha = e_gt - tf.math.floor(e_gt) + alpha = tf.tile(alpha,[1,1,256]) + e_gt = tf.cast(e_gt,'int32') + e_gt = tf.clip_by_value(e_gt,0,254) + interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1) + sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab) + loss_mod = sparse_cel + prob_compensation + gamma*regularization + return loss_mod + return loss + +# Same as above, except a metric +def metric_oginterploss(y_true,y_pred): + p = y_pred[:,:,0:1] + model_out = y_pred[:,:,2:] + e_gt = tf_l2u(y_true - p) + prob_compensation = tf.squeeze((K.abs(e_gt - 128)/128.0)*K.log(256.0)) + alpha = e_gt - tf.math.floor(e_gt) + alpha = tf.tile(alpha,[1,1,256]) + e_gt = tf.cast(e_gt,'int32') + e_gt = tf.clip_by_value(e_gt,0,254) + interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1) + sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab) + loss_mod = sparse_cel + prob_compensation + return loss_mod + +# Interpolated cross entropy loss metric +def metric_icel(y_true, y_pred): + p = y_pred[:,:,0:1] + model_out = y_pred[:,:,2:] + e_gt = tf_l2u(y_true - p) + alpha = e_gt - tf.math.floor(e_gt) + alpha = tf.tile(alpha,[1,1,256]) + e_gt = tf.cast(e_gt,'int32') + e_gt = tf.clip_by_value(e_gt,0,254) #Check direction + interp_probab = (1 - alpha)*model_out + alpha*tf.roll(model_out,shift = -1,axis = -1) + sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,interp_probab) + return sparse_cel + +# Non-interpolated (rounded) cross entropy loss metric +def metric_cel(y_true, y_pred): + y_true = tf.cast(y_true, 'float32') + p = y_pred[:,:,0:1] + model_out = y_pred[:,:,2:] + e_gt = tf_l2u(y_true - p) + e_gt = tf.round(e_gt) + e_gt = tf.cast(e_gt,'int32') + e_gt = tf.clip_by_value(e_gt,0,255) + sparse_cel = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)(e_gt,model_out) + return sparse_cel + +# Variance metric of the output excitation +def metric_exc_sd(y_true,y_pred): + p = y_pred[:,:,0:1] + e_gt = tf_l2u(y_true - p) + sd_egt = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)(e_gt,128) + return sd_egt + +def loss_matchlar(): + def loss(y_true,y_pred): + model_rc = y_pred[:,:,:16] + #y_true = lpc2rc(y_true) + loss_lar_diff = K.log((1.01 + model_rc)/(1.01 - model_rc)) - K.log((1.01 + y_true)/(1.01 - y_true)) + loss_lar_diff = tf.square(loss_lar_diff) + return tf.reduce_mean(loss_lar_diff, axis=-1) + return loss diff --git a/managed_components/78__esp-opus/dnn/training_tf2/lpcnet.py b/managed_components/78__esp-opus/dnn/training_tf2/lpcnet.py new file mode 100644 index 0000000..497f757 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/lpcnet.py @@ -0,0 +1,339 @@ +#!/usr/bin/python3 +'''Copyright (c) 2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import math +import tensorflow as tf +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise +from tensorflow.compat.v1.keras.layers import CuDNNGRU +from tensorflow.keras import backend as K +from tensorflow.keras.constraints import Constraint +from tensorflow.keras.initializers import Initializer +from tensorflow.keras.callbacks import Callback +from mdense import MDense +import numpy as np +import h5py +import sys +from tf_funcs import * +from diffembed import diff_Embed +from parameters import set_parameter + +frame_size = 160 +pcm_bits = 8 +embed_size = 128 +pcm_levels = 2**pcm_bits + +def interleave(p, samples): + p2=tf.expand_dims(p, 3) + nb_repeats = pcm_levels//(2*p.shape[2]) + p3 = tf.reshape(tf.repeat(tf.concat([1-p2, p2], 3), nb_repeats), (-1, samples, pcm_levels)) + return p3 + +def tree_to_pdf(p, samples): + return interleave(p[:,:,1:2], samples) * interleave(p[:,:,2:4], samples) * interleave(p[:,:,4:8], samples) * interleave(p[:,:,8:16], samples) \ + * interleave(p[:,:,16:32], samples) * interleave(p[:,:,32:64], samples) * interleave(p[:,:,64:128], samples) * interleave(p[:,:,128:256], samples) + +def tree_to_pdf_train(p): + #FIXME: try not to hardcode the 2400 samples (15 frames * 160 samples/frame) + return tree_to_pdf(p, 2400) + +def tree_to_pdf_infer(p): + return tree_to_pdf(p, 1) + +def quant_regularizer(x): + Q = 128 + Q_1 = 1./Q + #return .01 * tf.reduce_mean(1 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))) + return .01 * tf.reduce_mean(K.sqrt(K.sqrt(1.0001 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))))) + +class Sparsify(Callback): + def __init__(self, t_start, t_end, interval, density, quantize=False): + super(Sparsify, self).__init__() + self.batch = 0 + self.t_start = t_start + self.t_end = t_end + self.interval = interval + self.final_density = density + self.quantize = quantize + + def on_batch_end(self, batch, logs=None): + #print("batch number", self.batch) + self.batch += 1 + if self.quantize or (self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end: + #print("constrain"); + layer = self.model.get_layer('gru_a') + w = layer.get_weights() + p = w[1] + nb = p.shape[1]//p.shape[0] + N = p.shape[0] + #print("nb = ", nb, ", N = ", N); + #print(p.shape) + #print ("density = ", density) + for k in range(nb): + density = self.final_density[k] + if self.batch < self.t_end and not self.quantize: + r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start) + density = 1 - (1-self.final_density[k])*(1 - r*r*r) + A = p[:, k*N:(k+1)*N] + A = A - np.diag(np.diag(A)) + #This is needed because of the CuDNNGRU strange weight ordering + A = np.transpose(A, (1, 0)) + L=np.reshape(A, (N//4, 4, N//8, 8)) + S=np.sum(L*L, axis=-1) + S=np.sum(S, axis=1) + SS=np.sort(np.reshape(S, (-1,))) + thresh = SS[round(N*N//32*(1-density))] + mask = (S>=thresh).astype('float32') + mask = np.repeat(mask, 4, axis=0) + mask = np.repeat(mask, 8, axis=1) + mask = np.minimum(1, mask + np.diag(np.ones((N,)))) + #This is needed because of the CuDNNGRU strange weight ordering + mask = np.transpose(mask, (1, 0)) + p[:, k*N:(k+1)*N] = p[:, k*N:(k+1)*N]*mask + #print(thresh, np.mean(mask)) + if self.quantize and ((self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end): + if self.batch < self.t_end: + threshold = .5*(self.batch - self.t_start)/(self.t_end - self.t_start) + else: + threshold = .5 + quant = np.round(p*128.) + res = p*128.-quant + mask = (np.abs(res) <= threshold).astype('float32') + p = mask/128.*quant + (1-mask)*p + + w[1] = p + layer.set_weights(w) + +class SparsifyGRUB(Callback): + def __init__(self, t_start, t_end, interval, grua_units, density, quantize=False): + super(SparsifyGRUB, self).__init__() + self.batch = 0 + self.t_start = t_start + self.t_end = t_end + self.interval = interval + self.final_density = density + self.grua_units = grua_units + self.quantize = quantize + + def on_batch_end(self, batch, logs=None): + #print("batch number", self.batch) + self.batch += 1 + if self.quantize or (self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end: + #print("constrain"); + layer = self.model.get_layer('gru_b') + w = layer.get_weights() + p = w[0] + N = p.shape[0] + M = p.shape[1]//3 + for k in range(3): + density = self.final_density[k] + if self.batch < self.t_end and not self.quantize: + r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start) + density = 1 - (1-self.final_density[k])*(1 - r*r*r) + A = p[:, k*M:(k+1)*M] + #This is needed because of the CuDNNGRU strange weight ordering + A = np.reshape(A, (M, N)) + A = np.transpose(A, (1, 0)) + N2 = self.grua_units + A2 = A[:N2, :] + L=np.reshape(A2, (N2//4, 4, M//8, 8)) + S=np.sum(L*L, axis=-1) + S=np.sum(S, axis=1) + SS=np.sort(np.reshape(S, (-1,))) + thresh = SS[round(M*N2//32*(1-density))] + mask = (S>=thresh).astype('float32') + mask = np.repeat(mask, 4, axis=0) + mask = np.repeat(mask, 8, axis=1) + A = np.concatenate([A2*mask, A[N2:,:]], axis=0) + #This is needed because of the CuDNNGRU strange weight ordering + A = np.transpose(A, (1, 0)) + A = np.reshape(A, (N, M)) + p[:, k*M:(k+1)*M] = A + #print(thresh, np.mean(mask)) + if self.quantize and ((self.batch > self.t_start and (self.batch-self.t_start) % self.interval == 0) or self.batch >= self.t_end): + if self.batch < self.t_end: + threshold = .5*(self.batch - self.t_start)/(self.t_end - self.t_start) + else: + threshold = .5 + quant = np.round(p*128.) + res = p*128.-quant + mask = (np.abs(res) <= threshold).astype('float32') + p = mask/128.*quant + (1-mask)*p + + w[0] = p + layer.set_weights(w) + + +class PCMInit(Initializer): + def __init__(self, gain=.1, seed=None): + self.gain = gain + self.seed = seed + + def __call__(self, shape, dtype=None): + num_rows = 1 + for dim in shape[:-1]: + num_rows *= dim + num_cols = shape[-1] + flat_shape = (num_rows, num_cols) + if self.seed is not None: + np.random.seed(self.seed) + a = np.random.uniform(-1.7321, 1.7321, flat_shape) + #a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows + #a[:,1] = .5*a[:,0]*a[:,0]*a[:,0] + a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1)) + return self.gain * a.astype("float32") + + def get_config(self): + return { + 'gain': self.gain, + 'seed': self.seed + } + +class WeightClip(Constraint): + '''Clips the weights incident to each hidden unit to be inside a range + ''' + def __init__(self, c=2): + self.c = c + + def __call__(self, p): + # Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of + # saturation when implementing dot products with SSSE3 or AVX2. + return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1)) + #return K.clip(p, -self.c, self.c) + + def get_config(self): + return {'name': self.__class__.__name__, + 'c': self.c} + +constraint = WeightClip(0.992) + +def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features=20, batch_size=128, training=False, adaptation=False, quantize=False, flag_e2e = False, cond_size=128, lpc_order=16, lpc_gamma=1., lookahead=2): + pcm = Input(shape=(None, 1), batch_size=batch_size) + dpcm = Input(shape=(None, 3), batch_size=batch_size) + feat = Input(shape=(None, nb_used_features), batch_size=batch_size) + pitch = Input(shape=(None, 1), batch_size=batch_size) + dec_feat = Input(shape=(None, cond_size)) + dec_state1 = Input(shape=(rnn_units1,)) + dec_state2 = Input(shape=(rnn_units2,)) + + padding = 'valid' if training else 'same' + fconv1 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv1') + fconv2 = Conv1D(cond_size, 3, padding=padding, activation='tanh', name='feature_conv2') + pembed = Embedding(256, 64, name='embed_pitch') + cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))]) + + cfeat = fconv2(fconv1(cat_feat)) + + fdense1 = Dense(cond_size, activation='tanh', name='feature_dense1') + fdense2 = Dense(cond_size, activation='tanh', name='feature_dense2') + + if flag_e2e and quantize: + fconv1.trainable = False + fconv2.trainable = False + fdense1.trainable = False + fdense2.trainable = False + + cfeat = fdense2(fdense1(cfeat)) + + error_calc = Lambda(lambda x: tf_l2u(x[0] - tf.roll(x[1],1,axis = 1))) + if flag_e2e: + lpcoeffs = diff_rc2lpc(name = "rc2lpc")(cfeat) + else: + lpcoeffs = Input(shape=(None, lpc_order), batch_size=batch_size) + + real_preds = diff_pred(name = "real_lpc2preds")([pcm,lpcoeffs]) + weighting = lpc_gamma ** np.arange(1, 17).astype('float32') + weighted_lpcoeffs = Lambda(lambda x: x[0]*x[1])([lpcoeffs, weighting]) + tensor_preds = diff_pred(name = "lpc2preds")([pcm,weighted_lpcoeffs]) + past_errors = error_calc([pcm,tensor_preds]) + + embed = diff_Embed(name='embed_sig',initializer = PCMInit()) + cpcm = Concatenate()([tf_l2u(pcm),tf_l2u(tensor_preds),past_errors]) + cpcm = GaussianNoise(.3)(cpcm) + cpcm = Reshape((-1, embed_size*3))(embed(cpcm)) + cpcm_decoder = Reshape((-1, embed_size*3))(embed(dpcm)) + + + rep = Lambda(lambda x: K.repeat_elements(x, frame_size, 1)) + + quant = quant_regularizer if quantize else None + + if training: + rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a', stateful=True, + recurrent_constraint = constraint, recurrent_regularizer=quant) + rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + else: + rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a', stateful=True, + recurrent_constraint = constraint, recurrent_regularizer=quant) + rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + + rnn_in = Concatenate()([cpcm, rep(cfeat)]) + md = MDense(pcm_levels, activation='sigmoid', name='dual_fc') + gru_out1, _ = rnn(rnn_in) + gru_out1 = GaussianNoise(.005)(gru_out1) + gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)])) + ulaw_prob = Lambda(tree_to_pdf_train)(md(gru_out2)) + + if adaptation: + rnn.trainable=False + rnn2.trainable=False + md.trainable=False + embed.Trainable=False + + m_out = Concatenate(name='pdf')([tensor_preds,real_preds,ulaw_prob]) + if not flag_e2e: + model = Model([pcm, feat, pitch, lpcoeffs], m_out) + else: + model = Model([pcm, feat, pitch], [m_out, cfeat]) + model.rnn_units1 = rnn_units1 + model.rnn_units2 = rnn_units2 + model.nb_used_features = nb_used_features + model.frame_size = frame_size + + if not flag_e2e: + encoder = Model([feat, pitch], cfeat) + dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat]) + else: + encoder = Model([feat, pitch], [cfeat,lpcoeffs]) + dec_rnn_in = Concatenate()([cpcm_decoder, dec_feat]) + dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1) + dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]), initial_state=dec_state2) + dec_ulaw_prob = Lambda(tree_to_pdf_infer)(md(dec_gru_out2)) + + if flag_e2e: + decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2]) + else: + decoder = Model([dpcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2]) + + # add parameters to model + set_parameter(model, 'lpc_gamma', lpc_gamma, dtype='float64') + set_parameter(model, 'flag_e2e', flag_e2e, dtype='bool') + set_parameter(model, 'lookahead', lookahead, dtype='int32') + + return model, encoder, decoder diff --git a/managed_components/78__esp-opus/dnn/training_tf2/lpcnet_plc.py b/managed_components/78__esp-opus/dnn/training_tf2/lpcnet_plc.py new file mode 100644 index 0000000..618e008 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/lpcnet_plc.py @@ -0,0 +1,101 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + Copyright (c) 2018-2019 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import math +import tensorflow as tf +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise +from tensorflow.compat.v1.keras.layers import CuDNNGRU +from tensorflow.keras import backend as K +from tensorflow.keras.constraints import Constraint +from tensorflow.keras.initializers import Initializer +from tensorflow.keras.callbacks import Callback +import numpy as np + +def quant_regularizer(x): + Q = 128 + Q_1 = 1./Q + #return .01 * tf.reduce_mean(1 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))) + return .01 * tf.reduce_mean(K.sqrt(K.sqrt(1.0001 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))))) + + +class WeightClip(Constraint): + '''Clips the weights incident to each hidden unit to be inside a range + ''' + def __init__(self, c=2): + self.c = c + + def __call__(self, p): + # Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of + # saturation when implementing dot products with SSSE3 or AVX2. + return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1)) + #return K.clip(p, -self.c, self.c) + + def get_config(self): + return {'name': self.__class__.__name__, + 'c': self.c} + +constraint = WeightClip(0.992) + +def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, nb_burg_features=36, batch_size=128, training=False, adaptation=False, quantize=False, cond_size=128): + feat = Input(shape=(None, nb_used_features+nb_burg_features), batch_size=batch_size) + lost = Input(shape=(None, 1), batch_size=batch_size) + + fdense1 = Dense(cond_size, activation='tanh', name='plc_dense1') + + cfeat = Concatenate()([feat, lost]) + cfeat = fdense1(cfeat) + #cfeat = Conv1D(cond_size, 3, padding='causal', activation='tanh', name='plc_conv1')(cfeat) + + quant = quant_regularizer if quantize else None + + if training: + rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru1', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + rnn2 = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru2', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + else: + rnn = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru1', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + rnn2 = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru2', stateful=True, + kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) + + gru_out1, _ = rnn(cfeat) + gru_out1 = GaussianNoise(.005)(gru_out1) + gru_out2, _ = rnn2(gru_out1) + + out_dense = Dense(nb_used_features, activation='linear', name='plc_out') + plc_out = out_dense(gru_out2) + + model = Model([feat, lost], plc_out) + model.rnn_units = rnn_units + model.cond_size = cond_size + model.nb_used_features = nb_used_features + model.nb_burg_features = nb_burg_features + + return model diff --git a/managed_components/78__esp-opus/dnn/training_tf2/mdense.py b/managed_components/78__esp-opus/dnn/training_tf2/mdense.py new file mode 100644 index 0000000..844ae23 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/mdense.py @@ -0,0 +1,95 @@ +from tensorflow.keras import backend as K +from tensorflow.keras.layers import Layer, InputSpec +from tensorflow.keras import activations +from tensorflow.keras import initializers, regularizers, constraints +import numpy as np +import math + +class MDense(Layer): + + def __init__(self, outputs, + channels=2, + activation=None, + use_bias=True, + kernel_initializer='glorot_uniform', + bias_initializer='zeros', + kernel_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + kernel_constraint=None, + bias_constraint=None, + **kwargs): + if 'input_shape' not in kwargs and 'input_dim' in kwargs: + kwargs['input_shape'] = (kwargs.pop('input_dim'),) + super(MDense, self).__init__(**kwargs) + self.units = outputs + self.channels = channels + self.activation = activations.get(activation) + self.use_bias = use_bias + self.kernel_initializer = initializers.get(kernel_initializer) + self.bias_initializer = initializers.get(bias_initializer) + self.kernel_regularizer = regularizers.get(kernel_regularizer) + self.bias_regularizer = regularizers.get(bias_regularizer) + self.activity_regularizer = regularizers.get(activity_regularizer) + self.kernel_constraint = constraints.get(kernel_constraint) + self.bias_constraint = constraints.get(bias_constraint) + self.input_spec = InputSpec(min_ndim=2) + self.supports_masking = True + + def build(self, input_shape): + assert len(input_shape) >= 2 + input_dim = input_shape[-1] + + self.kernel = self.add_weight(shape=(self.units, input_dim, self.channels), + initializer=self.kernel_initializer, + name='kernel', + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint) + if self.use_bias: + self.bias = self.add_weight(shape=(self.units, self.channels), + initializer=self.bias_initializer, + name='bias', + regularizer=self.bias_regularizer, + constraint=self.bias_constraint) + else: + self.bias = None + self.factor = self.add_weight(shape=(self.units, self.channels), + initializer='ones', + name='factor', + regularizer=self.bias_regularizer, + constraint=self.bias_constraint) + self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim}) + self.built = True + + def call(self, inputs): + output = K.dot(inputs, self.kernel) + if self.use_bias: + output = output + self.bias + output = K.tanh(output) * self.factor + output = K.sum(output, axis=-1) + if self.activation is not None: + output = self.activation(output) + return output + + def compute_output_shape(self, input_shape): + assert input_shape and len(input_shape) >= 2 + assert input_shape[-1] + output_shape = list(input_shape) + output_shape[-1] = self.units + return tuple(output_shape) + + def get_config(self): + config = { + 'units': self.units, + 'activation': activations.serialize(self.activation), + 'use_bias': self.use_bias, + 'kernel_initializer': initializers.serialize(self.kernel_initializer), + 'bias_initializer': initializers.serialize(self.bias_initializer), + 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), + 'bias_regularizer': regularizers.serialize(self.bias_regularizer), + 'activity_regularizer': regularizers.serialize(self.activity_regularizer), + 'kernel_constraint': constraints.serialize(self.kernel_constraint), + 'bias_constraint': constraints.serialize(self.bias_constraint) + } + base_config = super(MDense, self).get_config() + return dict(list(base_config.items()) + list(config.items())) diff --git a/managed_components/78__esp-opus/dnn/training_tf2/pade.py b/managed_components/78__esp-opus/dnn/training_tf2/pade.py new file mode 100644 index 0000000..f88f425 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/pade.py @@ -0,0 +1,70 @@ +# Optimizing a rational function to optimize a tanh() approximation + +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation +import tensorflow.keras.backend as K +from tensorflow.keras.optimizers import Adam, SGD + +def my_loss1(y_true, y_pred): + return 1*K.mean(K.square(y_true-y_pred)) + 1*K.max(K.square(y_true-y_pred), axis=1) + +def my_loss2(y_true, y_pred): + return .1*K.mean(K.square(y_true-y_pred)) + 1*K.max(K.square(y_true-y_pred), axis=1) + +def my_loss3(y_true, y_pred): + return .01*K.mean(K.square(y_true-y_pred)) + 1*K.max(K.square(y_true-y_pred), axis=1) + +# Using these initializers to seed the approximation +# with a reasonable starting point +def num_init(shape, dtype=None): + rr = tf.constant([[945], [105], [1]], dtype=dtype) + #rr = tf.constant([[946.56757], [98.01368], [0.66841]], dtype=dtype) + print(rr) + return rr + +def den_init(shape, dtype=None): + rr = tf.constant([[945], [420], [15]], dtype=dtype) + #rr = tf.constant([[946.604], [413.342], [12.465]], dtype=dtype) + print(rr) + return rr + + +x = np.arange(-10, 10, .01) +N = len(x) +x = np.reshape(x, (1, -1, 1)) +x2 = x*x + +x2in = np.concatenate([x2*0 + 1, x2, x2*x2], axis=2) +yout = np.tanh(x) + + +model_x = Input(shape=(None, 1,)) +model_x2 = Input(shape=(None, 3,)) + +num = Dense(1, name='num', use_bias=False, kernel_initializer=num_init) +den = Dense(1, name='den', use_bias=False, kernel_initializer=den_init) + +def ratio(x): + return tf.minimum(1., tf.maximum(-1., x[0]*x[1]/x[2])) + +out_layer = Lambda(ratio) +output = out_layer([model_x, num(model_x2), den(model_x2)]) + +model = Model([model_x, model_x2], output) +model.summary() + +model.compile(Adam(0.05, beta_1=0.9, beta_2=0.9, decay=2e-5), loss='mean_squared_error') +model.fit([x, x2in], yout, batch_size=1, epochs=500000, validation_split=0.0) + +model.compile(Adam(0.001, beta_2=0.9, decay=1e-4), loss=my_loss1) +model.fit([x, x2in], yout, batch_size=1, epochs=50000, validation_split=0.0) + +model.compile(Adam(0.0001, beta_2=0.9, decay=1e-4), loss=my_loss2) +model.fit([x, x2in], yout, batch_size=1, epochs=50000, validation_split=0.0) + +model.compile(Adam(0.00001, beta_2=0.9, decay=1e-4), loss=my_loss3) +model.fit([x, x2in], yout, batch_size=1, epochs=50000, validation_split=0.0) + +model.save_weights('tanh.h5') diff --git a/managed_components/78__esp-opus/dnn/training_tf2/parameters.py b/managed_components/78__esp-opus/dnn/training_tf2/parameters.py new file mode 100644 index 0000000..3621a4e --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/parameters.py @@ -0,0 +1,29 @@ +""" module for handling extra model parameters for tf.keras models """ + +import tensorflow as tf + + +def set_parameter(model, parameter_name, parameter_value, dtype='float32'): + """ stores parameter_value as non-trainable weight with name parameter_name:0 """ + + weights = [weight for weight in model.weights if weight.name == (parameter_name + ":0")] + + if len(weights) == 0: + model.add_weight(parameter_name, trainable=False, initializer=tf.keras.initializers.Constant(parameter_value), dtype=dtype) + elif len(weights) == 1: + weights[0].assign(parameter_value) + else: + raise ValueError(f"more than one weight starting with {parameter_name}:0 in model") + + +def get_parameter(model, parameter_name, default=None): + """ returns parameter value if parameter is present in model and otherwise default """ + + weights = [weight for weight in model.weights if weight.name == (parameter_name + ":0")] + + if len(weights) == 0: + return default + elif len(weights) > 1: + raise ValueError(f"more than one weight starting with {parameter_name}:0 in model") + else: + return weights[0].numpy().item() diff --git a/managed_components/78__esp-opus/dnn/training_tf2/plc_loader.py b/managed_components/78__esp-opus/dnn/training_tf2/plc_loader.py new file mode 100644 index 0000000..a9bd41d --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/plc_loader.py @@ -0,0 +1,73 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import numpy as np +from tensorflow.keras.utils import Sequence + +class PLCLoader(Sequence): + def __init__(self, features, lost, nb_burg_features, batch_size): + self.batch_size = batch_size + self.nb_batches = features.shape[0]//self.batch_size + self.features = features[:self.nb_batches*self.batch_size, :, :] + self.lost = lost.astype('float') + self.lost = self.lost[:(len(self.lost)//features.shape[1]-1)*features.shape[1]] + self.nb_burg_features = nb_burg_features + self.on_epoch_end() + + def on_epoch_end(self): + self.indices = np.arange(self.nb_batches*self.batch_size) + np.random.shuffle(self.indices) + offset = np.random.randint(0, high=self.features.shape[1]) + self.lost_offset = np.reshape(self.lost[offset:-self.features.shape[1]+offset], (-1, self.features.shape[1])) + self.lost_indices = np.random.randint(0, high=self.lost_offset.shape[0], size=self.nb_batches*self.batch_size) + + def __getitem__(self, index): + features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :] + burg_lost = (np.random.rand(features.shape[0], features.shape[1]) > .1).astype('float') + burg_lost = np.reshape(burg_lost, (features.shape[0], features.shape[1], 1)) + burg_mask = np.tile(burg_lost, (1,1,self.nb_burg_features)) + + lost = self.lost_offset[self.lost_indices[index*self.batch_size:(index+1)*self.batch_size], :] + lost = np.reshape(lost, (features.shape[0], features.shape[1], 1)) + lost_mask = np.tile(lost, (1,1,features.shape[2])) + in_features = features*lost_mask + in_features[:,:,:self.nb_burg_features] = in_features[:,:,:self.nb_burg_features]*burg_mask + + #For the first frame after a loss, we don't have valid features, but the Burg estimate is valid. + #in_features[:,1:,self.nb_burg_features:] = in_features[:,1:,self.nb_burg_features:]*lost_mask[:,:-1,self.nb_burg_features:] + out_lost = np.copy(lost) + #out_lost[:,1:,:] = out_lost[:,1:,:]*out_lost[:,:-1,:] + + out_features = np.concatenate([features[:,:,self.nb_burg_features:], 1.-out_lost], axis=-1) + burg_sign = 2*burg_lost - 1 + # last dim is 1 for received packet, 0 for lost packet, and -1 when just the Burg info is missing + inputs = [in_features*lost_mask, lost*burg_sign] + outputs = [out_features] + return (inputs, outputs) + + def __len__(self): + return self.nb_batches diff --git a/managed_components/78__esp-opus/dnn/training_tf2/rdovae.py b/managed_components/78__esp-opus/dnn/training_tf2/rdovae.py new file mode 100644 index 0000000..6240120 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/rdovae.py @@ -0,0 +1,372 @@ +#!/usr/bin/python3 +'''Copyright (c) 2022 Amazon + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +import math +import tensorflow as tf +from tensorflow.keras.models import Model +from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise, AveragePooling1D, RepeatVector +from tensorflow.compat.v1.keras.layers import CuDNNGRU +from tensorflow.keras import backend as K +from tensorflow.keras.constraints import Constraint +from tensorflow.keras.initializers import Initializer +from tensorflow.keras.callbacks import Callback +from tensorflow.keras.regularizers import l1 +import numpy as np +import h5py +from uniform_noise import UniformNoise + +class WeightClip(Constraint): + '''Clips the weights incident to each hidden unit to be inside a range + ''' + def __init__(self, c=2): + self.c = c + + def __call__(self, p): + # Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of + # saturation when implementing dot products with SSSE3 or AVX2. + return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1)) + #return K.clip(p, -self.c, self.c) + + def get_config(self): + return {'name': self.__class__.__name__, + 'c': self.c} + +constraint = WeightClip(0.496) + +def soft_quantize(x): + #x = 4*x + #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) + #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) + #x = x - (.25/np.math.pi)*tf.math.sin(2*np.math.pi*x) + return x + +def noise_quantize(x): + return soft_quantize(x + (K.random_uniform((128, 16, 80))-.5) ) + +def hard_quantize(x): + x = soft_quantize(x) + quantized = tf.round(x) + return x + tf.stop_gradient(quantized - x) + +def apply_dead_zone(x): + d = x[1]*.05 + x = x[0] + y = x - d*tf.math.tanh(x/(.1+d)) + return y + +def rate_loss(y_true,y_pred): + log2_e = 1.4427 + n = y_pred.shape[-1] + C = n - log2_e*np.math.log(np.math.gamma(n)) + k = K.sum(K.abs(y_pred), axis=-1) + p = 1.5 + #rate = C + (n-1)*log2_e*tf.math.log((k**p + (n/5)**p)**(1/p)) + rate = C + (n-1)*log2_e*tf.math.log(k + .112*n**2/(n/1.8+k) ) + return K.mean(rate) + +eps=1e-6 +def safelog2(x): + log2_e = 1.4427 + return log2_e*tf.math.log(eps+x) + +def feat_dist_loss(y_true,y_pred): + lambda_1 = 1./K.sqrt(y_pred[:,:,:,-1]) + y_pred = y_pred[:,:,:,:-1] + ceps = y_pred[:,:,:,:18] - y_true[:,:,:18] + pitch = 2*(y_pred[:,:,:,18:19] - y_true[:,:,18:19])/(y_true[:,:,18:19] + 2) + corr = y_pred[:,:,:,19:] - y_true[:,:,19:] + pitch_weight = K.square(K.maximum(0., y_true[:,:,19:]+.5)) + return K.mean(lambda_1*K.mean(K.square(ceps) + 10*(1/18.)*K.abs(pitch)*pitch_weight + (1/18.)*K.square(corr), axis=-1)) + +def sq1_rate_loss(y_true,y_pred): + lambda_val = K.sqrt(y_pred[:,:,-1]) + y_pred = y_pred[:,:,:-1] + log2_e = 1.4427 + n = y_pred.shape[-1]//3 + r = (y_pred[:,:,2*n:]) + p0 = (y_pred[:,:,n:2*n]) + p0 = 1-r**(.5+.5*p0) + y_pred = y_pred[:,:,:n] + y_pred = soft_quantize(y_pred) + + y0 = K.maximum(0., 1. - K.abs(y_pred))**2 + rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1)) + rate = -safelog2(-.5*tf.math.log(r)*r**K.abs(y_pred)) + rate = -safelog2((1-r)/(1+r)*r**K.abs(y_pred)) + #rate = -safelog2(- tf.math.sinh(.5*tf.math.log(r))* r**K.abs(y_pred) - tf.math.cosh(K.maximum(0., .5 - K.abs(y_pred))*tf.math.log(r)) + 1) + rate = lambda_val*K.sum(rate, axis=-1) + return K.mean(rate) + +def sq2_rate_loss(y_true,y_pred): + lambda_val = K.sqrt(y_pred[:,:,-1]) + y_pred = y_pred[:,:,:-1] + log2_e = 1.4427 + n = y_pred.shape[-1]//3 + r = y_pred[:,:,2*n:] + p0 = y_pred[:,:,n:2*n] + p0 = 1-r**(.5+.5*p0) + #theta = K.minimum(1., .5 + 0*p0 - 0.04*tf.math.log(r)) + #p0 = 1-r**theta + y_pred = tf.round(y_pred[:,:,:n]) + y0 = K.maximum(0., 1. - K.abs(y_pred))**2 + rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1)) + rate = lambda_val*K.sum(rate, axis=-1) + return K.mean(rate) + +def sq_rate_metric(y_true,y_pred, reduce=True): + y_pred = y_pred[:,:,:-1] + log2_e = 1.4427 + n = y_pred.shape[-1]//3 + r = y_pred[:,:,2*n:] + p0 = y_pred[:,:,n:2*n] + p0 = 1-r**(.5+.5*p0) + #theta = K.minimum(1., .5 + 0*p0 - 0.04*tf.math.log(r)) + #p0 = 1-r**theta + y_pred = tf.round(y_pred[:,:,:n]) + y0 = K.maximum(0., 1. - K.abs(y_pred))**2 + rate = -y0*safelog2(p0*r**K.abs(y_pred)) - (1-y0)*safelog2(.5*(1-p0)*(1-r)*r**(K.abs(y_pred)-1)) + rate = K.sum(rate, axis=-1) + if reduce: + rate = K.mean(rate) + return rate + +def pvq_quant_search(x, k): + x = x/tf.reduce_sum(tf.abs(x), axis=-1, keepdims=True) + kx = k*x + y = tf.round(kx) + newk = k + + for j in range(10): + #print("y = ", y) + #print("iteration ", j) + abs_y = tf.abs(y) + abs_kx = tf.abs(kx) + kk=tf.reduce_sum(abs_y, axis=-1) + #print("sums = ", kk) + plus = 1.000001*tf.reduce_min((abs_y+.5)/(abs_kx+1e-15), axis=-1) + minus = .999999*tf.reduce_max((abs_y-.5)/(abs_kx+1e-15), axis=-1) + #print("plus = ", plus) + #print("minus = ", minus) + factor = tf.where(kk>k, minus, plus) + factor = tf.where(kk==k, tf.ones_like(factor), factor) + #print("scale = ", factor) + factor = tf.expand_dims(factor, axis=-1) + #newk = newk * (k/kk)**.2 + newk = newk*factor + kx = newk*x + #print("newk = ", newk) + #print("unquantized = ", newk*x) + y = tf.round(kx) + + #print(y) + #print(K.mean(K.sum(K.abs(y), axis=-1))) + return y + +def pvq_quantize(x, k): + x = x/(1e-15+tf.norm(x, axis=-1,keepdims=True)) + quantized = pvq_quant_search(x, k) + quantized = quantized/(1e-15+tf.norm(quantized, axis=-1,keepdims=True)) + return x + tf.stop_gradient(quantized - x) + + +def var_repeat(x): + return tf.repeat(tf.expand_dims(x[0], 1), K.shape(x[1])[1], axis=1) + +nb_state_dim = 24 + +def new_rdovae_encoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False): + feat = Input(shape=(None, nb_used_features), batch_size=batch_size) + + gru = CuDNNGRU if training else GRU + enc_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense1') + enc_dense2 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense2') + enc_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense3') + enc_dense4 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense4') + enc_dense5 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense5') + enc_dense6 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense6') + enc_dense7 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense7') + enc_dense8 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense8') + + #bits_dense = Dense(nb_bits, activation='linear', name='bits_dense') + bits_dense = Conv1D(nb_bits, 4, padding='causal', activation='linear', name='bits_dense') + + zero_out = Lambda(lambda x: 0*x) + inputs = Reshape((-1, 2*nb_used_features))(feat) + d1 = enc_dense1(inputs) + d2 = enc_dense2(d1) + d3 = enc_dense3(d2) + d4 = enc_dense4(d3) + d5 = enc_dense5(d4) + d6 = enc_dense6(d5) + d7 = enc_dense7(d6) + d8 = enc_dense8(d7) + pre_out = Concatenate()([d1, d2, d3, d4, d5, d6, d7, d8]) + enc_out = bits_dense(pre_out) + global_dense1 = Dense(128, activation='tanh', name='gdense1') + global_dense2 = Dense(nb_state_dim, activation='tanh', name='gdense2') + global_bits = global_dense2(global_dense1(pre_out)) + + encoder = Model([feat], [enc_out, global_bits], name='encoder') + return encoder + +def new_rdovae_decoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False): + bits_input = Input(shape=(None, nb_bits), batch_size=batch_size, name="dec_bits") + gru_state_input = Input(shape=(nb_state_dim,), batch_size=batch_size, name="dec_state") + + + gru = CuDNNGRU if training else GRU + dec_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense1') + dec_dense2 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense2') + dec_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense3') + dec_dense4 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense4') + dec_dense5 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense5') + dec_dense6 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense6') + dec_dense7 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='dec_dense7') + dec_dense8 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='dec_dense8') + + dec_final = Dense(bunch*nb_used_features, activation='linear', name='dec_final') + + time_reverse = Lambda(lambda x: K.reverse(x, 1)) + #time_reverse = Lambda(lambda x: x) + #gru_state_rep = RepeatVector(64//bunch)(gru_state_input) + + #gru_state_rep = Lambda(var_repeat, output_shape=(None, nb_state_dim)) ([gru_state_input, bits_input]) + gru_state1 = Dense(cond_size, name="state1", activation='tanh')(gru_state_input) + gru_state2 = Dense(cond_size, name="state2", activation='tanh')(gru_state_input) + gru_state3 = Dense(cond_size, name="state3", activation='tanh')(gru_state_input) + + dec1 = dec_dense1(time_reverse(bits_input)) + dec2 = dec_dense2(dec1, initial_state=gru_state1) + dec3 = dec_dense3(dec2) + dec4 = dec_dense4(dec3, initial_state=gru_state2) + dec5 = dec_dense5(dec4) + dec6 = dec_dense6(dec5, initial_state=gru_state3) + dec7 = dec_dense7(dec6) + dec8 = dec_dense8(dec7) + output = Reshape((-1, nb_used_features))(dec_final(Concatenate()([dec1, dec2, dec3, dec4, dec5, dec6, dec7, dec8]))) + decoder = Model([bits_input, gru_state_input], time_reverse(output), name='decoder') + decoder.nb_bits = nb_bits + decoder.bunch = bunch + return decoder + +def new_split_decoder(decoder): + nb_bits = decoder.nb_bits + bunch = decoder.bunch + bits_input = Input(shape=(None, nb_bits), name="split_bits") + gru_state_input = Input(shape=(None,nb_state_dim), name="split_state") + + range_select = Lambda(lambda x: x[0][:,x[1]:x[2],:]) + elem_select = Lambda(lambda x: x[0][:,x[1],:]) + points = [0, 100, 200, 300, 400] + outputs = [] + for i in range(len(points)-1): + begin = points[i]//bunch + end = points[i+1]//bunch + state = elem_select([gru_state_input, end-1]) + bits = range_select([bits_input, begin, end]) + outputs.append(decoder([bits, state])) + output = Concatenate(axis=1)(outputs) + split = Model([bits_input, gru_state_input], output, name="split") + return split + +def tensor_concat(x): + #n = x[1]//2 + #x = x[0] + n=2 + y = [] + for i in range(n-1): + offset = 2 * (n-1-i) + tmp = K.concatenate([x[i][:, offset:, :], x[-1][:, -offset:, :]], axis=-2) + y.append(tf.expand_dims(tmp, axis=0)) + y.append(tf.expand_dims(x[-1], axis=0)) + return Concatenate(axis=0)(y) + + +def new_rdovae_model(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False): + + feat = Input(shape=(None, nb_used_features), batch_size=batch_size) + quant_id = Input(shape=(None,), batch_size=batch_size) + lambda_val = Input(shape=(None, 1), batch_size=batch_size) + lambda_bunched = AveragePooling1D(pool_size=bunch//2, strides=bunch//2, padding="valid")(lambda_val) + lambda_up = Lambda(lambda x: K.repeat_elements(x, 2, axis=-2))(lambda_val) + + qembedding = Embedding(nb_quant, 6*nb_bits, name='quant_embed', embeddings_initializer='zeros') + quant_embed_dec = qembedding(quant_id) + quant_scale = Activation('softplus')(Lambda(lambda x: x[:,:,:nb_bits], name='quant_scale_embed')(quant_embed_dec)) + + encoder = new_rdovae_encoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2, training=training) + ze, gru_state_dec = encoder([feat]) + ze = Multiply()([ze, quant_scale]) + + decoder = new_rdovae_decoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2, training=training) + split_decoder = new_split_decoder(decoder) + + dead_zone = Activation('softplus')(Lambda(lambda x: x[:,:,nb_bits:2*nb_bits], name='dead_zone_embed')(quant_embed_dec)) + soft_distr_embed = Activation('sigmoid')(Lambda(lambda x: x[:,:,2*nb_bits:4*nb_bits], name='soft_distr_embed')(quant_embed_dec)) + hard_distr_embed = Activation('sigmoid')(Lambda(lambda x: x[:,:,4*nb_bits:], name='hard_distr_embed')(quant_embed_dec)) + + noisequant = UniformNoise() + hardquant = Lambda(hard_quantize) + dzone = Lambda(apply_dead_zone) + dze = dzone([ze,dead_zone]) + ndze = noisequant(dze) + dze_quant = hardquant(dze) + + div = Lambda(lambda x: x[0]/x[1]) + dze_quant = div([dze_quant,quant_scale]) + ndze_unquant = div([ndze,quant_scale]) + + mod_select = Lambda(lambda x: x[0][:,x[1]::bunch//2,:]) + gru_state_dec = Lambda(lambda x: pvq_quantize(x, 82))(gru_state_dec) + combined_output = [] + unquantized_output = [] + cat = Concatenate(name="out_cat") + for i in range(bunch//2): + dze_select = mod_select([dze_quant, i]) + ndze_select = mod_select([ndze_unquant, i]) + state_select = mod_select([gru_state_dec, i]) + + tmp = split_decoder([dze_select, state_select]) + tmp = cat([tmp, lambda_up]) + combined_output.append(tmp) + + tmp = split_decoder([ndze_select, state_select]) + tmp = cat([tmp, lambda_up]) + unquantized_output.append(tmp) + + concat = Lambda(tensor_concat, name="output") + combined_output = concat(combined_output) + unquantized_output = concat(unquantized_output) + + e2 = Concatenate(name="hard_bits")([dze, hard_distr_embed, lambda_val]) + e = Concatenate(name="soft_bits")([dze, soft_distr_embed, lambda_val]) + + + model = Model([feat, quant_id, lambda_val], [combined_output, unquantized_output, e, e2], name="end2end") + model.nb_used_features = nb_used_features + + return model, encoder, decoder, qembedding diff --git a/managed_components/78__esp-opus/dnn/training_tf2/rdovae_exchange.py b/managed_components/78__esp-opus/dnn/training_tf2/rdovae_exchange.py new file mode 100644 index 0000000..3249677 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/rdovae_exchange.py @@ -0,0 +1,138 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import argparse +import os +import sys + +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +parser = argparse.ArgumentParser() + +parser.add_argument('weights', metavar="", type=str, help='model weight file in hdf5 format') +parser.add_argument('output', metavar="", type=str, help='output exchange folder') +parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256) +parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80) +parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16) + +args = parser.parse_args() + +# now import the heavy stuff +from rdovae import new_rdovae_model +from wexchange.tf import dump_tf_weights, load_tf_weights + + +exchange_name = { + 'enc_dense1' : 'encoder_stack_layer1_dense', + 'enc_dense3' : 'encoder_stack_layer3_dense', + 'enc_dense5' : 'encoder_stack_layer5_dense', + 'enc_dense7' : 'encoder_stack_layer7_dense', + 'enc_dense8' : 'encoder_stack_layer8_dense', + 'gdense1' : 'encoder_state_layer1_dense', + 'gdense2' : 'encoder_state_layer2_dense', + 'enc_dense2' : 'encoder_stack_layer2_gru', + 'enc_dense4' : 'encoder_stack_layer4_gru', + 'enc_dense6' : 'encoder_stack_layer6_gru', + 'bits_dense' : 'encoder_stack_layer9_conv', + 'qembedding' : 'statistical_model_embedding', + 'state1' : 'decoder_state1_dense', + 'state2' : 'decoder_state2_dense', + 'state3' : 'decoder_state3_dense', + 'dec_dense1' : 'decoder_stack_layer1_dense', + 'dec_dense3' : 'decoder_stack_layer3_dense', + 'dec_dense5' : 'decoder_stack_layer5_dense', + 'dec_dense7' : 'decoder_stack_layer7_dense', + 'dec_dense8' : 'decoder_stack_layer8_dense', + 'dec_final' : 'decoder_stack_layer9_dense', + 'dec_dense2' : 'decoder_stack_layer2_gru', + 'dec_dense4' : 'decoder_stack_layer4_gru', + 'dec_dense6' : 'decoder_stack_layer6_gru' +} + + +if __name__ == "__main__": + + model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels) + model.load_weights(args.weights) + + os.makedirs(args.output, exist_ok=True) + + # encoder + encoder_dense_names = [ + 'enc_dense1', + 'enc_dense3', + 'enc_dense5', + 'enc_dense7', + 'enc_dense8', + 'gdense1', + 'gdense2' + ] + + encoder_gru_names = [ + 'enc_dense2', + 'enc_dense4', + 'enc_dense6' + ] + + encoder_conv1d_names = [ + 'bits_dense' + ] + + + for name in encoder_dense_names + encoder_gru_names + encoder_conv1d_names: + print(f"writing layer {exchange_name[name]}...") + dump_tf_weights(os.path.join(args.output, exchange_name[name]), encoder.get_layer(name)) + + # qembedding + print(f"writing layer {exchange_name['qembedding']}...") + dump_tf_weights(os.path.join(args.output, exchange_name['qembedding']), qembedding) + + # decoder + decoder_dense_names = [ + 'state1', + 'state2', + 'state3', + 'dec_dense1', + 'dec_dense3', + 'dec_dense5', + 'dec_dense7', + 'dec_dense8', + 'dec_final' + ] + + decoder_gru_names = [ + 'dec_dense2', + 'dec_dense4', + 'dec_dense6' + ] + + for name in decoder_dense_names + decoder_gru_names: + print(f"writing layer {exchange_name[name]}...") + dump_tf_weights(os.path.join(args.output, exchange_name[name]), decoder.get_layer(name)) diff --git a/managed_components/78__esp-opus/dnn/training_tf2/rdovae_import.py b/managed_components/78__esp-opus/dnn/training_tf2/rdovae_import.py new file mode 100644 index 0000000..bc8b460 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/rdovae_import.py @@ -0,0 +1,123 @@ +""" +/* Copyright (c) 2022 Amazon + Written by Jan Buethe */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER + OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ +""" + + +import argparse +import os +import sys + +os.environ['CUDA_VISIBLE_DEVICES'] = "" + +parser = argparse.ArgumentParser() + +parser.add_argument('input', metavar="", type=str, help='input exchange folder') +parser.add_argument('weights', metavar="", type=str, help='model weight file in hdf5 format') +parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256) +parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80) +parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16) + +args = parser.parse_args() + +# now import the heavy stuff +from rdovae import new_rdovae_model +from wexchange.tf import load_tf_weights + + +exchange_name = { + 'enc_dense1' : 'encoder_stack_layer1_dense', + 'enc_dense3' : 'encoder_stack_layer3_dense', + 'enc_dense5' : 'encoder_stack_layer5_dense', + 'enc_dense7' : 'encoder_stack_layer7_dense', + 'enc_dense8' : 'encoder_stack_layer8_dense', + 'gdense1' : 'encoder_state_layer1_dense', + 'gdense2' : 'encoder_state_layer2_dense', + 'enc_dense2' : 'encoder_stack_layer2_gru', + 'enc_dense4' : 'encoder_stack_layer4_gru', + 'enc_dense6' : 'encoder_stack_layer6_gru', + 'bits_dense' : 'encoder_stack_layer9_conv', + 'qembedding' : 'statistical_model_embedding', + 'state1' : 'decoder_state1_dense', + 'state2' : 'decoder_state2_dense', + 'state3' : 'decoder_state3_dense', + 'dec_dense1' : 'decoder_stack_layer1_dense', + 'dec_dense3' : 'decoder_stack_layer3_dense', + 'dec_dense5' : 'decoder_stack_layer5_dense', + 'dec_dense7' : 'decoder_stack_layer7_dense', + 'dec_dense8' : 'decoder_stack_layer8_dense', + 'dec_final' : 'decoder_stack_layer9_dense', + 'dec_dense2' : 'decoder_stack_layer2_gru', + 'dec_dense4' : 'decoder_stack_layer4_gru', + 'dec_dense6' : 'decoder_stack_layer6_gru' +} + +if __name__ == "__main__": + + model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels) + + encoder_layers = [ + 'enc_dense1', + 'enc_dense3', + 'enc_dense5', + 'enc_dense7', + 'enc_dense8', + 'gdense1', + 'gdense2', + 'enc_dense2', + 'enc_dense4', + 'enc_dense6', + 'bits_dense' + ] + + decoder_layers = [ + 'state1', + 'state2', + 'state3', + 'dec_dense1', + 'dec_dense3', + 'dec_dense5', + 'dec_dense7', + 'dec_dense8', + 'dec_final', + 'dec_dense2', + 'dec_dense4', + 'dec_dense6' + ] + + for name in encoder_layers: + print(f"loading weight for layer {name}...") + load_tf_weights(os.path.join(args.input, exchange_name[name]), encoder.get_layer(name)) + + print(f"loading weight for layer qembedding...") + load_tf_weights(os.path.join(args.input, exchange_name['qembedding']), qembedding) + + for name in decoder_layers: + print(f"loading weight for layer {name}...") + load_tf_weights(os.path.join(args.input, exchange_name[name]), decoder.get_layer(name)) + + model.save(args.weights) diff --git a/managed_components/78__esp-opus/dnn/training_tf2/test_lpcnet.py b/managed_components/78__esp-opus/dnn/training_tf2/test_lpcnet.py new file mode 100644 index 0000000..ca551e6 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/test_lpcnet.py @@ -0,0 +1,120 @@ +#!/usr/bin/python3 +'''Copyright (c) 2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' +import argparse +import sys + +import h5py +import numpy as np + +import lpcnet +from ulaw import ulaw2lin, lin2ulaw + + +parser = argparse.ArgumentParser() +parser.add_argument('model-file', type=str, help='model weight h5 file') +parser.add_argument('--lpc-gamma', type=float, help='LPC weighting factor. WARNING: giving an inconsistent value here will severely degrade performance', default=1) + +args = parser.parse_args() + +filename = args.model_file +with h5py.File(filename, "r") as f: + units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape) + units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape) + cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape) + e2e = 'rc2lpc' in f['model_weights'] + + +model, enc, dec = lpcnet.new_lpcnet_model(training = False, rnn_units1=units, rnn_units2=units2, flag_e2e = e2e, cond_size=cond_size, batch_size=1) + +model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) +#model.summary() + + +feature_file = sys.argv[2] +out_file = sys.argv[3] +frame_size = model.frame_size +nb_features = 36 +nb_used_features = model.nb_used_features + +features = np.fromfile(feature_file, dtype='float32') +features = np.resize(features, (-1, nb_features)) +nb_frames = 1 +feature_chunk_size = features.shape[0] +pcm_chunk_size = frame_size*feature_chunk_size + +features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features)) +periods = (.1 + 50*features[:,:,18:19]+100).astype('int16') + + + +model.load_weights(filename); + +order = 16 + +pcm = np.zeros((nb_frames*pcm_chunk_size, )) +fexc = np.zeros((1, 1, 3), dtype='int16')+128 +state1 = np.zeros((1, model.rnn_units1), dtype='float32') +state2 = np.zeros((1, model.rnn_units2), dtype='float32') + +mem = 0 +coef = 0.85 + +lpc_weights = np.array([args.lpc_gamma ** (i + 1) for i in range(16)]) + +fout = open(out_file, 'wb') + +skip = order + 1 +for c in range(0, nb_frames): + if not e2e: + cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]]) + else: + cfeat,lpcs = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]]) + for fr in range(0, feature_chunk_size): + f = c*feature_chunk_size + fr + if not e2e: + a = features[c, fr, nb_features-order:] * lpc_weights + else: + a = lpcs[c,fr] + for i in range(skip, frame_size): + pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1]) + fexc[0, 0, 1] = lin2ulaw(pred) + + p, state1, state2 = dec.predict([fexc, cfeat[:, fr:fr+1, :], state1, state2]) + #Lower the temperature for voiced frames to reduce noisiness + p *= np.power(p, np.maximum(0, 1.5*features[c, fr, 19] - .5)) + p = p/(1e-18 + np.sum(p)) + #Cut off the tail of the remaining distribution + p = np.maximum(p-0.002, 0).astype('float64') + p = p/(1e-8 + np.sum(p)) + + fexc[0, 0, 2] = np.argmax(np.random.multinomial(1, p[0,0,:], 1)) + pcm[f*frame_size + i] = pred + ulaw2lin(fexc[0, 0, 2]) + fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i]) + mem = coef*mem + pcm[f*frame_size + i] + #print(mem) + np.array([np.round(mem)], dtype='int16').tofile(fout) + skip = 0 diff --git a/managed_components/78__esp-opus/dnn/training_tf2/test_plc.py b/managed_components/78__esp-opus/dnn/training_tf2/test_plc.py new file mode 100644 index 0000000..0c0ac25 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/test_plc.py @@ -0,0 +1,92 @@ +#!/usr/bin/python3 +'''Copyright (c) 2021-2022 Amazon + Copyright (c) 2018-2019 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +# Train an LPCNet model + +import argparse +from plc_loader import PLCLoader + +parser = argparse.ArgumentParser(description='Test a PLC model') + +parser.add_argument('weights', metavar='', help='weights file (.h5)') +parser.add_argument('features', metavar='', help='binary features file (float32)') +parser.add_argument('output', metavar='', help='reconstructed file (float32)') +parser.add_argument('--model', metavar='', default='lpcnet_plc', help='PLC model python definition (without .py)') +group1 = parser.add_mutually_exclusive_group() + +parser.add_argument('--gru-size', metavar='', default=256, type=int, help='number of units in GRU (default 256)') +parser.add_argument('--cond-size', metavar='', default=128, type=int, help='number of units in conditioning network (default 128)') + + +args = parser.parse_args() + +import importlib +lpcnet = importlib.import_module(args.model) + +import sys +import numpy as np +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger +import tensorflow.keras.backend as K +import h5py + +import tensorflow as tf +#gpus = tf.config.experimental.list_physical_devices('GPU') +#if gpus: +# try: +# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)]) +# except RuntimeError as e: +# print(e) + +model = lpcnet.new_lpcnet_plc_model(rnn_units=args.gru_size, batch_size=1, training=False, quantize=False, cond_size=args.cond_size) +model.compile() + +lpc_order = 16 + +feature_file = args.features +nb_features = model.nb_used_features + lpc_order +nb_used_features = model.nb_used_features + +# u for unquantised, load 16 bit PCM samples and convert to mu-law + +features = np.loadtxt(feature_file) +print(features.shape) +sequence_size = features.shape[0] +lost = np.reshape(features[:,-1:], (1, sequence_size, 1)) +features = features[:,:nb_used_features] +features = np.reshape(features, (1, sequence_size, nb_used_features)) + + +model.load_weights(args.weights) + +features = features*lost +out = model.predict([features, lost]) + +out = features + (1-lost)*out + +np.savetxt(args.output, out[0,:,:]) diff --git a/managed_components/78__esp-opus/dnn/training_tf2/tf_funcs.py b/managed_components/78__esp-opus/dnn/training_tf2/tf_funcs.py new file mode 100644 index 0000000..b86f075 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/tf_funcs.py @@ -0,0 +1,70 @@ +""" +Tensorflow/Keras helper functions to do the following: + 1. \mu law <-> Linear domain conversion + 2. Differentiable prediction from the input signal and LP coefficients + 3. Differentiable transformations Reflection Coefficients (RCs) <-> LP Coefficients +""" +from tensorflow.keras.layers import Lambda, Multiply, Layer, Concatenate +from tensorflow.keras import backend as K +import tensorflow as tf + +# \mu law <-> Linear conversion functions +scale = 255.0/32768.0 +scale_1 = 32768.0/255.0 +def tf_l2u(x): + s = K.sign(x) + x = K.abs(x) + u = (s*(128*K.log(1+scale*x)/K.log(256.0))) + u = K.clip(128 + u, 0, 255) + return u + +def tf_u2l(u): + u = tf.cast(u,"float32") + u = u - 128.0 + s = K.sign(u) + u = K.abs(u) + return s*scale_1*(K.exp(u/128.*K.log(256.0))-1) + +# Differentiable Prediction Layer +# Computes the LP prediction from the input lag signal and the LP coefficients +# The inputs xt and lpc conform with the shapes in lpcnet.py (the '2400' is coded keeping this in mind) +class diff_pred(Layer): + def call(self, inputs, lpcoeffs_N = 16, frame_size = 160): + xt = inputs[0] + lpc = inputs[1] + + rept = Lambda(lambda x: K.repeat_elements(x , frame_size, 1)) + zpX = Lambda(lambda x: K.concatenate([0*x[:,0:lpcoeffs_N,:], x],axis = 1)) + cX = Lambda(lambda x: K.concatenate([x[:,(lpcoeffs_N - i):(lpcoeffs_N - i + 2400),:] for i in range(lpcoeffs_N)],axis = 2)) + + pred = -Multiply()([rept(lpc),cX(zpX(xt))]) + + return K.sum(pred,axis = 2,keepdims = True) + +# Differentiable Transformations (RC <-> LPC) computed using the Levinson Durbin Recursion +class diff_rc2lpc(Layer): + def call(self, inputs, lpcoeffs_N = 16): + def pred_lpc_recursive(input): + temp = (input[0] + K.repeat_elements(input[1],input[0].shape[2],2)*K.reverse(input[0],axes = 2)) + temp = Concatenate(axis = 2)([temp,input[1]]) + return temp + Llpc = Lambda(pred_lpc_recursive) + inputs = inputs[:,:,:lpcoeffs_N] + lpc_init = inputs + for i in range(1,lpcoeffs_N): + lpc_init = Llpc([lpc_init[:,:,:i],K.expand_dims(inputs[:,:,i],axis = -1)]) + return lpc_init + +class diff_lpc2rc(Layer): + def call(self, inputs, lpcoeffs_N = 16): + def pred_rc_recursive(input): + ki = K.repeat_elements(K.expand_dims(input[1][:,:,0],axis = -1),input[0].shape[2],2) + temp = (input[0] - ki*K.reverse(input[0],axes = 2))/(1 - ki*ki) + temp = Concatenate(axis = 2)([temp,input[1]]) + return temp + Lrc = Lambda(pred_rc_recursive) + rc_init = inputs + for i in range(1,lpcoeffs_N): + j = (lpcoeffs_N - i + 1) + rc_init = Lrc([rc_init[:,:,:(j - 1)],rc_init[:,:,(j - 1):]]) + return rc_init diff --git a/managed_components/78__esp-opus/dnn/training_tf2/train_lpcnet.py b/managed_components/78__esp-opus/dnn/training_tf2/train_lpcnet.py new file mode 100644 index 0000000..60e2b56 --- /dev/null +++ b/managed_components/78__esp-opus/dnn/training_tf2/train_lpcnet.py @@ -0,0 +1,214 @@ +#!/usr/bin/python3 +'''Copyright (c) 2018 Mozilla + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +''' + +# Train an LPCNet model + +import argparse +import os + +from dataloader import LPCNetLoader + +parser = argparse.ArgumentParser(description='Train an LPCNet model') + +parser.add_argument('features', metavar='', help='binary features file (float32)') +parser.add_argument('data', metavar='