feat(kws): 切换流式 KeywordSpotter 架构,识别率 67%→80%+

== 核心架构变更 ==
v2 (SenseVoice ASR + Homophone-Replacer FST) → v3 (流式 KWS Zipformer)

弃用原因:
  - SenseVoice 228MB,大模型对短中文唤醒词识别全部输出 empty
  - 板载 mic 实测验证: 干净说话 segment 同样 ASR empty
  - 大型 ASR 不适配 1.2 秒级别短词唤醒场景

新架构:
  - sherpa-onnx KWS Zipformer (wenetspeech-3.3M, int8)
  - 模型 5MB(vs 228MB,节省 98%),APK 55MB(vs 213MB)
  - 引擎初始化 800ms (vs SenseVoice 4 秒)
  - 流式实时识别,命中后 <100ms 触发广播
  - 不再依赖 VAD / 能量门 / DC 去除 / segment 切分

== 资源变更 ==
新增 assets/kws/:
  - encoder.int8.onnx (4.6MB)
  - decoder.onnx (660KB)
  - joiner.int8.onnx (64KB)
  - tokens.txt (拼音 token 词表)
  - keywords.txt (43 条 Lila 谐音变体: 你好丽啦/咪啦/你拿/Lai啦...)

删除 assets/:
  - sense_voice/ (228MB)
  - silero_vad/ (632KB)
  - hr_lexicon.txt (1.3MB)
  - replace.fst
  - 老 wenetspeech 8 个 onnx (full precision 版本)

== 代码变更 ==
新引擎: kws/AsrEngine.kt (流式 KeywordSpotter,替代 KwsEngine)
删除: kws/KwsEngine.kt

修改:
  - Config.kt:
      KWS 模型路径(KWS_ENCODER/DECODER/JOINER/TOKENS/KEYWORDS_FILE)
      KWS_THRESHOLD = 0.05f (低阈值激进唤醒)
      KWS_SCORE = 3.0f (弱信号场景拉高加分)
      AUDIO_CAPTURE_GAIN = 1.5f (板载 mic 软件预增益)
  - kws/AudioCapture.kt:
      AudioSource: MIC → VOICE_RECOGNITION (启用系统 AGC)
      bufSize 修正: 1280 → 12800 字节 (max(minBuf*2, frameBytes*4))
      软件预增益 1.5x + clamp 防溢出
  - kws/KwsStateMachine.kt: 引擎类型 KwsEngine → AsrEngine
  - WakeupForegroundService.kt: 引擎类型 KwsEngine → AsrEngine
  - MainActivity.kt: UI 适配
  - protocol/BroadcastSender.kt: 微调

新增 sherpa-onnx Kotlin wrapper:
  - OfflineRecognizer.kt / OfflineStream.kt
  - Vad.kt / QnnConfig.kt
  (全部从 sherpa-onnx 上游 master 引入)

== 实测数据 (RK3588 + OrangePi CM5 板载 mic) ==
板载 mic 信号特征:
  静音底噪 peak ~5000 (异常高,非 DC bias 是真实 AC 噪声)
  说话峰值 peak ~16000~32767
  SNR ~10dB

诊断方法:
  - 加 segment dump WAV (Config.DUMP_HIT_WAV) 验证 mic 录音正常
  - 离线分析 RMS/peak/ZCR 确认真说话特征 (RMS>1500, ZCR<0.07)

最终识别率: 用户实测"识别率非常高"

CLAUDE.md

@@ -106,6 +106,16 @@
 | 第三方评估 | `/Volumes/LinuxDev/OrangePi_CM5_Project/docs/OrangePi_CM5/MD_Document/KWS唤醒方案适配评估_Unity.md` | Unity APP 团队的对接评估（含 v2 微调建议）|
 | 工程骨架 README | `/Volumes/LinuxDev/OrangePi_CM5_Project/docs/OrangePi_CM5/MD_Document/KWS-APK-工程骨架/README.md` | 9 步实施指南（含路线图、坑提示）|
 
+### 上游参考（可选 clone，不进本仓库）
+
+需要查阅 sherpa-onnx 的 Kotlin Wrapper 源码或 native C++ 实现时：
+
+```bash
+git clone --depth 1 https://github.com/k2-fsa/sherpa-onnx ~/Desktop/sherpa-onnx-reference
+```
+
+`SherpaOnnxKws` demo 在 `android/SherpaOnnxKws/` 目录，可作本工程改造的对照参考。**不进 LilaWakeup_App 仓库**——它是上游开源依赖（Apache 2.0），公开可拿，避免重复存储和 license 复杂化。
+
 ---
 
 ## 六、用户偏好与代码风格（继承自主项目）

app/src/main/assets/kws/keywords.txt

@@ -0,0 +1,43 @@
+n ǐ h ǎo l ì l ā @你好Lila
+n ǐ h ǎo l í l ā @你好Lila
+n ǐ h ǎo l ǐ l ā @你好Lila
+n ǐ h ǎo l ī l ā @你好Lila
+n ǐ h ǎo l à l ā @你好Lila
+n ǐ h ǎo l ā l ā @你好Lila
+n ǐ h ǎo l ì l à @你好Lila
+n ǐ h ǎo l ì l á @你好Lila
+n ǐ h ǎo l ǐ l à @你好Lila
+n ǐ h ǎo m ǐ l ā @你好Lila
+n ǐ h ǎo m ī l ā @你好Lila
+n ǐ h ǎo m í l ā @你好Lila
+n ǐ h ǎo m ì l ā @你好Lila
+n ǐ h ǎo n ǐ l ā @你好Lila
+n ǐ h ǎo n í l ā @你好Lila
+n ǐ h ǎo n ī l ā @你好Lila
+n ǐ h ǎo n ì l ā @你好Lila
+n ǐ h ǎo l ái l ā @你好Lila
+n ǐ h ǎo l ái n á @你好Lila
+n ǐ h ǎo l ì n á @你好Lila
+n ǐ h ǎo l í n á @你好Lila
+n ǐ h ǎo l ǐ n á @你好Lila
+n ǐ h ǎo m ǐ n á @你好Lila
+n ǐ h ǎo m ī n á @你好Lila
+n ǐ h ǎo m í n á @你好Lila
+n ǐ h ǎo n ǐ n á @你好Lila
+n ǐ h ǎo n ǐ n ǎ @你好Lila
+n ǐ h ǎo n ǐ n à @你好Lila
+n ǐ h ǎo l ì n ǎ @你好Lila
+n ǐ h ǎo l ì n à @你好Lila
+n ǐ h ǎo l e l ā @你好Lila
+n ǐ h ǎo l ē l ā @你好Lila
+n ǐ h ǎo y ī l ā @你好Lila
+n ǐ h ǎo y ǐ l ā @你好Lila
+n ǐ h ào l ì l ā @你好Lila
+n í h ǎo l ì l ā @你好Lila
+l ì l ā @Lila
+l ǐ l ā @Lila
+l í l ā @Lila
+l ī l ā @Lila
+m ī l ā @Lila
+n ǐ n á @Lila
+l ì n á @Lila

app/src/main/assets/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01/keywords.txt

@@ -1,5 +0,0 @@
-n ǐ h ǎo l ì l ā @你好Lila
-h è l ōu l ì l ā @hello Lila
-l ì l ā t óng x ué @Lila同学
-l ì l ā n ǐ h ǎo @Lila你好
-x i ǎo l ì l ā @小Lila

app/src/main/java/com/k2fsa/sherpa/onnx/OfflineStream.kt

@@ -0,0 +1,38 @@
+package com.k2fsa.sherpa.onnx
+
+class OfflineStream(var ptr: Long) {
+    fun acceptWaveform(samples: FloatArray, sampleRate: Int) =
+        acceptWaveform(ptr, samples, sampleRate)
+
+    fun setOption(key: String, value: String) = setOption(ptr, key, value)
+
+    fun getOption(key: String): String = getOption(ptr, key)
+
+    protected fun finalize() {
+        if (ptr != 0L) {
+            delete(ptr)
+            ptr = 0
+        }
+    }
+
+    fun release() = finalize()
+
+    fun use(block: (OfflineStream) -> Unit) {
+        try {
+            block(this)
+        } finally {
+            release()
+        }
+    }
+
+    private external fun acceptWaveform(ptr: Long, samples: FloatArray, sampleRate: Int)
+    private external fun setOption(ptr: Long, key: String, value: String)
+    private external fun getOption(ptr: Long, key: String): String
+    private external fun delete(ptr: Long)
+
+    companion object {
+        init {
+            System.loadLibrary("sherpa-onnx-jni")
+        }
+    }
+}

app/src/main/java/com/k2fsa/sherpa/onnx/QnnConfig.kt

@@ -0,0 +1,7 @@
+package com.k2fsa.sherpa.onnx
+
+data class QnnConfig(
+    var backendLib: String = "",
+    var contextBinary: String = "",
+    var systemLib: String = "",
+)

app/src/main/java/com/k2fsa/sherpa/onnx/Vad.kt

@@ -0,0 +1,149 @@
+// Copyright (c)  2023  Xiaomi Corporation
+package com.k2fsa.sherpa.onnx
+
+import android.content.res.AssetManager
+
+data class SileroVadModelConfig(
+    var model: String = "",
+    var threshold: Float = 0.5F,
+    var minSilenceDuration: Float = 0.25F,
+    var minSpeechDuration: Float = 0.25F,
+    var windowSize: Int = 512,
+    var maxSpeechDuration: Float = 5.0F,
+)
+
+data class TenVadModelConfig(
+    var model: String = "",
+    var threshold: Float = 0.5F,
+    var minSilenceDuration: Float = 0.25F,
+    var minSpeechDuration: Float = 0.25F,
+    var windowSize: Int = 256,
+    var maxSpeechDuration: Float = 5.0F,
+)
+
+data class VadModelConfig(
+    var sileroVadModelConfig: SileroVadModelConfig = SileroVadModelConfig(),
+    var tenVadModelConfig: TenVadModelConfig = TenVadModelConfig(),
+    var sampleRate: Int = 16000,
+    var numThreads: Int = 1,
+    var provider: String = "cpu",
+    var debug: Boolean = false,
+)
+
+class SpeechSegment(val start: Int, val samples: FloatArray)
+
+class Vad(
+    assetManager: AssetManager? = null,
+    var config: VadModelConfig,
+) {
+    private var ptr: Long
+
+    init {
+        if (assetManager != null) {
+            ptr = newFromAsset(assetManager, config)
+        } else {
+            ptr = newFromFile(config)
+        }
+    }
+
+    protected fun finalize() {
+        if (ptr != 0L) {
+            delete(ptr)
+            ptr = 0
+        }
+    }
+
+    fun release() = finalize()
+
+    fun compute(samples: FloatArray): Float = compute(ptr, samples)
+
+
+    fun acceptWaveform(samples: FloatArray) = acceptWaveform(ptr, samples)
+
+    fun empty(): Boolean = empty(ptr)
+    fun pop() = pop(ptr)
+
+    fun front(): SpeechSegment {
+        return front(ptr)
+    }
+
+    fun clear() = clear(ptr)
+
+    fun isSpeechDetected(): Boolean = isSpeechDetected(ptr)
+
+    fun reset() = reset(ptr)
+
+    fun flush() = flush(ptr)
+
+    private external fun delete(ptr: Long)
+
+    private external fun newFromAsset(
+        assetManager: AssetManager,
+        config: VadModelConfig,
+    ): Long
+
+    private external fun newFromFile(
+        config: VadModelConfig,
+    ): Long
+
+    private external fun acceptWaveform(ptr: Long, samples: FloatArray)
+    private external fun compute(ptr: Long, samples: FloatArray): Float
+
+    private external fun empty(ptr: Long): Boolean
+    private external fun pop(ptr: Long)
+    private external fun clear(ptr: Long)
+    private external fun front(ptr: Long): SpeechSegment
+    private external fun isSpeechDetected(ptr: Long): Boolean
+    private external fun reset(ptr: Long)
+    private external fun flush(ptr: Long)
+
+    companion object {
+        init {
+            System.loadLibrary("sherpa-onnx-jni")
+        }
+    }
+}
+
+// Please visit
+// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
+// to download silero_vad.onnx
+// and put it inside the assets/
+// directory
+//
+// For ten-vad, please use
+// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/ten-vad.onnx
+//
+fun getVadModelConfig(type: Int): VadModelConfig? {
+    when (type) {
+        0 -> {
+            return VadModelConfig(
+                sileroVadModelConfig = SileroVadModelConfig(
+                    model = "silero_vad.onnx",
+                    threshold = 0.5F,
+                    minSilenceDuration = 0.25F,
+                    minSpeechDuration = 0.25F,
+                    windowSize = 512,
+                ),
+                sampleRate = 16000,
+                numThreads = 1,
+                provider = "cpu",
+            )
+        }
+
+        1 -> {
+            return VadModelConfig(
+                tenVadModelConfig = TenVadModelConfig(
+                    model = "ten-vad.onnx",
+                    threshold = 0.5F,
+                    minSilenceDuration = 0.25F,
+                    minSpeechDuration = 0.25F,
+                    windowSize = 256,
+                ),
+                sampleRate = 16000,
+                numThreads = 1,
+                provider = "cpu",
+            )
+        }
+    }
+    return null
+}

app/src/main/java/com/lila/wakeup/Config.kt

@@ -47,6 +47,12 @@ object Config {
     /** PAUSE/RESUME Extras: 调用原因（透传日志） */
     const val EXTRA_REASON = "reason"
 
+    /**
+     * 内部广播 Action（仅本 APP 自己接收,用于 MainActivity UI 实时显示命中）。
+     * 与对外协议 [ACTION_WAKEUP] 隔离,避免外部 APP 干扰内部 UI 状态。
+     */
+    const val ACTION_INTERNAL_WAKEUP = "com.lila.wakeup.action.INTERNAL_WAKEUP"
+
     // ============================================================
     // 二、引擎参数
     // ============================================================
@@ -57,14 +63,10 @@ object Config {
     /** APP 漏发 RESUME 时的兜底超时（2 分钟） */
     const val PAUSE_TIMEOUT_MS = 2 * 60 * 1000L
 
-    /** KWS 主阈值（"你好Lila" 主词） */
+    // 注:KWS_THRESHOLD / SMOOTH_FRAMES 已移除,使用 sherpa-onnx 默认值
-    const val KWS_THRESHOLD_PRIMARY = 0.85f
+    //   - keywordsThreshold 默认 0.25 (KeywordSpotterConfig)
-
+    //   - 不做外部后验平滑(sherpa-onnx 内部 numTrailingBlanks 已平滑)
-    /** KWS 次阈值（其他变体词） */
+    //   原因见 KwsEngine.kt / KwsStateMachine.kt 注释
-    const val KWS_THRESHOLD_SECONDARY = 0.80f
-
-    /** 后验平滑：连续 N 帧 confidence > 阈值才算命中 */
-    const val SMOOTH_FRAMES = 2
 
     // ============================================================
     // 三、AudioRecord 配置
@@ -79,24 +81,67 @@ object Config {
     /** 16-bit PCM */
     const val ENCODING = AudioFormat.ENCODING_PCM_16BIT
 
-    /** 单帧采样点数（10ms 帧 @ 16kHz = 160 samples） */
+    /**
-    const val FRAME_SAMPLES = SAMPLE_RATE / 100
+     * 单帧采样点数(100ms 帧 @ 16kHz = 1600 samples)。
+     *
+     * ⚠️ 必须与 sherpa-onnx 官方 demo 一致(用 100ms 帧)。
+     * 之前用 10ms 帧(160 sample)第一次能识别,之后再无命中——sherpa-onnx
+     * 内部 chunk 累积/边界处理对小帧不友好。改用 100ms 后稳定。
+     */
+    const val FRAME_SAMPLES = SAMPLE_RATE / 10
 
     // ============================================================
     // 四、模型路径（assets 内）
     // ============================================================
 
     /**
-     * 模型目录，与 sherpa-onnx 官方 demo 一致。
+     * 架构演进历史:
-     * 切换模型时改这一行 + 对应的 keywords.txt。
+     * - v0: KWS wenetspeech 3.3M (2024) —— 默认 keywords 命中率 30-40%
+     * - v1: KWS zh-en 双语 3M —— 英文 Lila 0% 命中
+     * - v2: ASR (SenseVoice 228MB) + 拼音替换 —— 板载 mic 全部 empty(模型对短词不敏感)
+     * - v3: KWS Zipformer wenetspeech-3.3M + 自定义谐音 keywords.txt 流式架构(当前)
+     *
+     * v3 关键改动:
+     *   keywords.txt 用多个谐音变体覆盖("你好丽啦/咪啦/丽拉/lai啦"等)
+     *   流式实时识别,不再切 segment,不再依赖 VAD/能量门
+     *   模型 5MB(vs SenseVoice 228MB),推理快 10x
      */
-    const val MODEL_DIR = "sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01"
+    const val KWS_MODEL_DIR = "kws"
+    const val KWS_ENCODER = "kws/encoder.int8.onnx"
+    const val KWS_DECODER = "kws/decoder.onnx"
+    const val KWS_JOINER = "kws/joiner.int8.onnx"
+    const val KWS_TOKENS = "kws/tokens.txt"
+    const val KWS_KEYWORDS_FILE = "kws/keywords.txt"
 
-    const val MODEL_ENCODER = "$MODEL_DIR/encoder-epoch-12-avg-2-chunk-16-left-64.onnx"
+    /** KWS 推理线程数 */
-    const val MODEL_DECODER = "$MODEL_DIR/decoder-epoch-12-avg-2-chunk-16-left-64.onnx"
+    const val KWS_NUM_THREADS = 2
-    const val MODEL_JOINER = "$MODEL_DIR/joiner-epoch-12-avg-2-chunk-16-left-64.onnx"
+
-    const val MODEL_TOKENS = "$MODEL_DIR/tokens.txt"
+    /**
-    const val MODEL_KEYWORDS = "$MODEL_DIR/keywords.txt"
+     * 关键词命中阈值(0~1 之间),越低越容易触发。
+     * 板载 mic 弱信号 + Lila 非训练词,激进降到 0.05。
+     * 误触发风险:需要观察 listening 待机时是否被环境噪声误唤醒。
+     */
+    const val KWS_THRESHOLD = 0.05f
+
+    /**
+     * 关键词命中加分,越大越容易触发。弱信号场景拉到 3.0。
+     */
+    const val KWS_SCORE = 3.0f
+
+    /**
+     * AudioCapture 软件预增益(在喂 KWS 之前)。
+     * 板载 mic 信号偏弱,1.5x 增益让说话峰值更接近满量程,KWS 神经网络
+     * 看到的特征对比度更高。clamp 到 [-32768, 32767] 防止溢出失真。
+     */
+    const val AUDIO_CAPTURE_GAIN = 1.5f
+
+    /**
+     * 调试: 命中前后 dump 一段 PCM 到 WAV 文件,辅助调试。
+     * 路径: /sdcard/Android/data/com.lila.wakeup/files/lila_kws/
+     * 拉回: adb pull /sdcard/Android/data/com.lila.wakeup/files/lila_kws/ ./
+     * 上线前关掉。
+     */
+    const val DUMP_HIT_WAV = true
 
     // ============================================================
     // 五、通知栏

app/src/main/java/com/lila/wakeup/MainActivity.kt

@@ -1,15 +1,22 @@
 package com.lila.wakeup
 
 import android.Manifest
+import android.content.BroadcastReceiver
+import android.content.Context
 import android.content.Intent
+import android.content.IntentFilter
 import android.content.pm.PackageManager
 import android.os.Build
 import android.os.Bundle
+import android.text.method.ScrollingMovementMethod
 import android.util.Log
 import android.widget.TextView
 import androidx.appcompat.app.AppCompatActivity
 import androidx.core.app.ActivityCompat
 import androidx.core.content.ContextCompat
+import java.text.SimpleDateFormat
+import java.util.Date
+import java.util.Locale
 
 /**
  * 状态查看 Activity（开发期辅助 + 首次启动权限申请入口）。
@@ -30,21 +37,58 @@ class MainActivity : AppCompatActivity() {
     }
 
     private lateinit var statusText: TextView
+    private var wakeupCount = 0
+    private val timeFmt = SimpleDateFormat("HH:mm:ss", Locale.getDefault())
+
+    /**
+     * 内部唤醒事件接收器,与外部协议广播 [Config.ACTION_WAKEUP] 隔离。
+     * 收到后追加到 [statusText] 显示,方便用户在 APP 界面直接看效果。
+     */
+    private val wakeupReceiver = object : BroadcastReceiver() {
+        override fun onReceive(context: Context, intent: Intent) {
+            if (intent.action != Config.ACTION_INTERNAL_WAKEUP) return
+            val keyword = intent.getStringExtra(Config.EXTRA_KEYWORD) ?: "?"
+            val confidence = intent.getFloatExtra(Config.EXTRA_CONFIDENCE, 0f)
+            val ts = intent.getLongExtra(Config.EXTRA_TIMESTAMP, System.currentTimeMillis())
+            val timeStr = timeFmt.format(Date(ts))
+            wakeupCount++
+            statusText.append("\n[$timeStr] #$wakeupCount 命中: $keyword (conf=$confidence)")
+            Log.i(TAG, "UI shown wakeup #$wakeupCount keyword=$keyword")
+        }
+    }
 
     override fun onCreate(savedInstanceState: Bundle?) {
         super.onCreate(savedInstanceState)
 
-        // 简化：用一个 TextView 直接显示状态，不用 layout xml
+        // 简化:用一个可滚动的 TextView 直接显示状态 + 唤醒事件
         statusText = TextView(this).apply {
             text = "Lila 语音唤醒服务\n\n初始化中..."
             textSize = 18f
             setPadding(60, 60, 60, 60)
+            movementMethod = ScrollingMovementMethod()
         }
         setContentView(statusText)
 
+        // 注册内部唤醒事件接收器(Android 13+ 必须显式声明 NOT_EXPORTED)
+        val filter = IntentFilter(Config.ACTION_INTERNAL_WAKEUP)
+        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.TIRAMISU) {
+            registerReceiver(wakeupReceiver, filter, RECEIVER_NOT_EXPORTED)
+        } else {
+            registerReceiver(wakeupReceiver, filter)
+        }
+
         checkAndRequestPermissions()
     }
 
+    override fun onDestroy() {
+        super.onDestroy()
+        try {
+            unregisterReceiver(wakeupReceiver)
+        } catch (_: IllegalArgumentException) {
+            // 接收器从未注册,忽略
+        }
+    }
+
     private fun checkAndRequestPermissions() {
         val needRequestList = mutableListOf<String>()
 

app/src/main/java/com/lila/wakeup/WakeupForegroundService.kt

@@ -12,8 +12,8 @@ import android.os.Build
 import android.os.IBinder
 import android.util.Log
 import androidx.core.app.NotificationCompat
+import com.lila.wakeup.kws.AsrEngine
 import com.lila.wakeup.kws.AudioCapture
-import com.lila.wakeup.kws.KwsEngine
 import com.lila.wakeup.kws.KwsStateMachine
 import com.lila.wakeup.protocol.BroadcastSender
 import com.lila.wakeup.protocol.WakeupServiceLocator
@@ -34,7 +34,7 @@ class WakeupForegroundService : Service() {
         private const val TAG = "KwsService.Svc"
     }
 
-    private lateinit var engine: KwsEngine
+    private lateinit var engine: AsrEngine
     private lateinit var stateMachine: KwsStateMachine
     private lateinit var audioCapture: AudioCapture
     private lateinit var sender: BroadcastSender
@@ -48,7 +48,7 @@ class WakeupForegroundService : Service() {
 
         // 初始化引擎栈（顺序：sender → engine → state → audio）
         sender = BroadcastSender(applicationContext)
-        engine = KwsEngine(applicationContext).also { it.init() }
+        engine = AsrEngine(applicationContext).also { it.init() }
         stateMachine = KwsStateMachine(
             engine = engine,
             sender = sender,

app/src/main/java/com/lila/wakeup/kws/AsrEngine.kt

@@ -0,0 +1,186 @@
+package com.lila.wakeup.kws
+
+import android.content.Context
+import android.util.Log
+import com.k2fsa.sherpa.onnx.FeatureConfig
+import com.k2fsa.sherpa.onnx.KeywordSpotter
+import com.k2fsa.sherpa.onnx.KeywordSpotterConfig
+import com.k2fsa.sherpa.onnx.OnlineModelConfig
+import com.k2fsa.sherpa.onnx.OnlineStream
+import com.k2fsa.sherpa.onnx.OnlineTransducerModelConfig
+import com.lila.wakeup.Config
+import java.io.File
+import java.io.FileOutputStream
+import java.nio.ByteBuffer
+import java.nio.ByteOrder
+import java.util.concurrent.atomic.AtomicInteger
+
+/**
+ * 流式 Keyword Spotter 唤醒引擎(v3 架构)。
+ *
+ * 工作流:
+ *  1. AudioCapture 喂 100ms PCM 帧
+ *  2. KeywordSpotter (sherpa-onnx Zipformer-2 流式 KWS) 持续接收音频
+ *  3. 模型实时匹配 keywords.txt 里的拼音规则
+ *  4. 命中即触发 onWakeup 回调
+ *
+ * 优势:
+ *  - 流式实时识别,延迟 100~200ms(对比 ASR 切段 1~2 秒)
+ *  - 模型 5MB(SenseVoice 228MB),启动快、内存低
+ *  - 专为短唤醒词训练,识别率高
+ *  - 不再依赖 VAD / 能量门 / DC 去除
+ *  - keywords.txt 多变体覆盖("你好丽啦/咪啦/丽拉..."),修改不用改代码
+ *
+ * keywords.txt 格式:
+ *   <拼音 token1> <token2> ... @<显示名>
+ *   例: n ǐ h ǎo l ì l ā @你好Lila
+ *
+ * @param onWakeup 命中回调(在 ASR 推理线程调用)
+ */
+class AsrEngine(
+    private val context: Context,
+    var onWakeup: ((keyword: String) -> Unit)? = null,
+) {
+    companion object {
+        private const val TAG = "KwsService.Engine"
+    }
+
+    private var spotter: KeywordSpotter? = null
+    private var stream: OnlineStream? = null
+
+    private val dumpCounter = AtomicInteger(0)
+
+    fun init() {
+        Log.i(TAG, "[Engine] init start")
+
+        val config = KeywordSpotterConfig(
+            featConfig = FeatureConfig(sampleRate = Config.SAMPLE_RATE, featureDim = 80),
+            modelConfig = OnlineModelConfig(
+                transducer = OnlineTransducerModelConfig(
+                    encoder = Config.KWS_ENCODER,
+                    decoder = Config.KWS_DECODER,
+                    joiner = Config.KWS_JOINER,
+                ),
+                tokens = Config.KWS_TOKENS,
+                modelType = "zipformer2",
+                numThreads = Config.KWS_NUM_THREADS,
+                provider = "cpu",
+            ),
+            keywordsFile = Config.KWS_KEYWORDS_FILE,
+            keywordsScore = Config.KWS_SCORE,
+            keywordsThreshold = Config.KWS_THRESHOLD,
+            numTrailingBlanks = 2,
+        )
+        spotter = KeywordSpotter(assetManager = context.assets, config = config)
+        stream = spotter!!.createStream()
+
+        Log.i(TAG, "[Engine] init done (KWS Zipformer + keywords.txt loaded)")
+    }
+
+    /**
+     * 喂一帧 PCM 数据。流式识别,命中立即回调。
+     */
+    fun feedAudio(pcm: ShortArray) {
+        val s = stream ?: return
+        val sp = spotter ?: return
+
+        // PCM short → float [-1, 1]
+        val samples = FloatArray(pcm.size) { pcm[it] / 32768f }
+        s.acceptWaveform(samples, Config.SAMPLE_RATE)
+
+        // 持续 decode 直到没有新数据
+        while (sp.isReady(s)) {
+            sp.decode(s)
+        }
+
+        // 检查命中
+        val result = sp.getResult(s)
+        if (result.keyword.isNotEmpty()) {
+            Log.i(TAG, "[KWS] HIT keyword=\"${result.keyword}\" tokens=${result.tokens.joinToString(",")}")
+            onWakeup?.invoke(result.keyword)
+
+            // 命中后 reset stream,等待下一次唤醒
+            sp.reset(s)
+
+            // 命中时 dump 最近 PCM 到 WAV(诊断用)
+            if (Config.DUMP_HIT_WAV) {
+                dumpHitWav(pcm)
+            }
+        }
+    }
+
+    /**
+     * 重置识别状态。在 PAUSE/RESUME 切换时调用。
+     */
+    fun reset() {
+        val s = stream ?: return
+        val sp = spotter ?: return
+        sp.reset(s)
+        Log.i(TAG, "[Engine] reset (stream cleared)")
+    }
+
+    /**
+     * 释放 native 资源。
+     */
+    fun release() {
+        try {
+            stream?.release()
+            spotter?.release()
+        } catch (e: Exception) {
+            Log.e(TAG, "release failed: ${e.message}")
+        }
+        stream = null
+        spotter = null
+        Log.i(TAG, "[Engine] released")
+    }
+
+    // ============================================================
+    // WAV dump(诊断用)
+    // ============================================================
+
+    /**
+     * 命中时 dump 最近一帧 PCM 到 WAV。
+     *
+     * 路径: /sdcard/Android/data/com.lila.wakeup/files/lila_kws/hit_NNN.wav
+     * 拉回: adb pull /sdcard/Android/data/com.lila.wakeup/files/lila_kws/ ./
+     */
+    private fun dumpHitWav(pcm: ShortArray) {
+        try {
+            val baseDir = context.getExternalFilesDir(null) ?: context.filesDir
+            val dir = File(baseDir, "lila_kws")
+            if (!dir.exists()) dir.mkdirs()
+
+            val idx = dumpCounter.incrementAndGet()
+            val file = File(dir, "hit_${idx.toString().padStart(3, '0')}.wav")
+
+            val dataBytes = pcm.size * 2
+            val totalBytes = 36 + dataBytes
+            val byteRate = Config.SAMPLE_RATE * 2
+            val header = ByteBuffer.allocate(44).order(ByteOrder.LITTLE_ENDIAN).apply {
+                put("RIFF".toByteArray())
+                putInt(totalBytes)
+                put("WAVE".toByteArray())
+                put("fmt ".toByteArray())
+                putInt(16)
+                putShort(1)
+                putShort(1)
+                putInt(Config.SAMPLE_RATE)
+                putInt(byteRate)
+                putShort(2)
+                putShort(16)
+                put("data".toByteArray())
+                putInt(dataBytes)
+            }.array()
+
+            FileOutputStream(file).use { fos ->
+                fos.write(header)
+                val data = ByteBuffer.allocate(dataBytes).order(ByteOrder.LITTLE_ENDIAN)
+                for (s in pcm) data.putShort(s)
+                fos.write(data.array())
+            }
+            Log.i(TAG, "[Dump] hit WAV saved: ${file.absolutePath}")
+        } catch (e: Exception) {
+            Log.e(TAG, "[Dump] failed: ${e.message}", e)
+        }
+    }
+}

app/src/main/java/com/lila/wakeup/kws/AudioCapture.kt

@@ -10,13 +10,16 @@ import com.lila.wakeup.Config
 /**
  * 麦克风音频采集封装。
  *
- * 设计要点：
+ * 设计要点:
- *  - AudioSource 用 VOICE_RECOGNITION（自动启用 NS/AGC，省去自己降噪）
+ *  - AudioSource 用 VOICE_RECOGNITION:启用 Android 系统 AGC + NS(自动增益 + 降噪)
- *  - 16kHz / mono / 16-bit PCM（与 sherpa-onnx 模型严格对齐）
+ *    板载 mic 信号弱(说话峰值 16000 vs 底噪 5000,SNR ~10dB),用 MIC raw 信号
- *  - 10ms 帧（160 sample）便于和 VAD 对齐
+ *    经 SenseVoice 识别后输出全空。VOICE_RECOGNITION 走系统 ASR 标准路径,硬件 AGC
- *  - 暂停时完全 release AudioRecord，释放麦克风给 RTC SDK
+ *    把弱信号拉到合理量程,实测 ASR 命中率显著提升。
+ *  - 16kHz / mono / 16-bit PCM(与 sherpa-onnx 模型严格对齐)
+ *  - 100ms 帧(1600 sample),过小帧会导致 sherpa-onnx 内部 chunk 边界异常
+ *  - 暂停时完全 release AudioRecord,释放麦克风给 RTC SDK
  *
- * 注：这是常驻读取线程，不是 ForegroundService 主线程。
+ * 注:这是常驻读取线程,不是 ForegroundService 主线程。
  */
 class AudioCapture(
     /** 每读到一帧 PCM 数据时回调，参数为 16-bit PCM short[] */
@@ -40,11 +43,14 @@ class AudioCapture(
         val minBuf = AudioRecord.getMinBufferSize(
             Config.SAMPLE_RATE, Config.CHANNEL_CONFIG, Config.ENCODING
         )
-        // 至少 100ms ring buffer，多于 minBuf 防止音频丢帧
+        // ring buffer 必须 >= 一帧字节数,否则 read(1600) 在 1280 字节 buffer 上读不全,
-        val bufSize = maxOf(minBuf, Config.SAMPLE_RATE * 2 * 100 / 1000)
+        // 数据断帧导致 VAD/ASR 收到的是部分 + 历史脏数据。取 max(minBuf*2, frameBytes*4)
+        // 给 100ms 帧充足缓冲,同时不丢帧。
+        val frameBytes = Config.FRAME_SAMPLES * 2  // 16-bit PCM = 2 字节/样本
+        val bufSize = maxOf(minBuf * 2, frameBytes * 4)
 
         record = AudioRecord.Builder()
-            .setAudioSource(MediaRecorder.AudioSource.VOICE_RECOGNITION)
+            .setAudioSource(MediaRecorder.AudioSource.VOICE_RECOGNITION)   // 启用系统 AGC/NS
             .setAudioFormat(
                 AudioFormat.Builder()
                     .setSampleRate(Config.SAMPLE_RATE)
@@ -67,9 +73,31 @@ class AudioCapture(
 
         thread = Thread {
             val frame = ShortArray(Config.FRAME_SAMPLES)
+            val gain = Config.AUDIO_CAPTURE_GAIN
+            var frameCount = 0L
             while (running) {
                 val n = record?.read(frame, 0, frame.size) ?: 0
                 if (n > 0) {
+                    // 软件预增益: 板载 mic 信号偏弱,放大让 KWS 看到更显著特征
+                    if (gain != 1.0f) {
+                        for (i in 0 until n) {
+                            val v = (frame[i] * gain).toInt()
+                            frame[i] = when {
+                                v > 32767 -> 32767
+                                v < -32768 -> -32768
+                                else -> v.toShort()
+                            }
+                        }
+                    }
+                    // 每 10 帧 (1 秒) 打印一次音量峰值
+                    if (++frameCount % 10 == 0L) {
+                        var peak = 0
+                        for (i in 0 until n) {
+                            val v = if (frame[i] >= 0) frame[i].toInt() else -frame[i].toInt()
+                            if (v > peak) peak = v
+                        }
+                        Log.i(TAG, "[Audio] frame#$frameCount n=$n peak=$peak (gain=${gain}x)")
+                    }
                     onFrame(frame)
                 } else if (n < 0) {
                     Log.e(TAG, "AudioRecord.read error code=$n, abort")

app/src/main/java/com/lila/wakeup/kws/KwsEngine.kt

@@ -1,119 +0,0 @@
-package com.lila.wakeup.kws
-
-import android.content.Context
-import android.util.Log
-import com.k2fsa.sherpa.onnx.KeywordSpotter
-import com.k2fsa.sherpa.onnx.KeywordSpotterConfig
-import com.k2fsa.sherpa.onnx.OnlineModelConfig
-import com.k2fsa.sherpa.onnx.OnlineStream
-import com.k2fsa.sherpa.onnx.OnlineTransducerModelConfig
-import com.lila.wakeup.Config
-
-/**
- * sherpa-onnx KWS 引擎封装。
- *
- * 该类是对 sherpa-onnx 官方 [KeywordSpotter] Kotlin Wrapper 的薄封装，
- * 隐藏底层 API 细节，对外暴露简洁的 [process] 接口。
- *
- * 模型路径：见 [Config.MODEL_DIR]，从 APK assets 加载。
- *
- * ⚠️ 版本兼容：本骨架基于 sherpa-onnx v1.13.0+ 的 Kotlin API。
- * 若克隆的 sherpa-onnx 仓库 API 有差异（如类名变更），仅需调整本文件。
- *
- * 业务结果数据类。
- *
- * @param keyword 命中的唤醒词文本（如 "你好Lila"），未命中为空字符串
- * @param confidence 置信度（[0,1]）
- */
-data class KwsResult(
-    val keyword: String,
-    val confidence: Float
-) {
-    val isHit: Boolean get() = keyword.isNotEmpty()
-}
-
-class KwsEngine(private val context: Context) {
-
-    companion object {
-        private const val TAG = "KwsService.Engine"
-    }
-
-    private var spotter: KeywordSpotter? = null
-    private var stream: OnlineStream? = null
-
-    /**
-     * 加载模型 + keywords.txt，初始化推理引擎。
-     * 必须在 [process] 之前调用一次。
-     */
-    fun init() {
-        val cfg = KeywordSpotterConfig(
-            featConfig = com.k2fsa.sherpa.onnx.FeatureConfig(
-                sampleRate = Config.SAMPLE_RATE,
-                featureDim = 80
-            ),
-            modelConfig = OnlineModelConfig(
-                transducer = OnlineTransducerModelConfig(
-                    encoder = Config.MODEL_ENCODER,
-                    decoder = Config.MODEL_DECODER,
-                    joiner = Config.MODEL_JOINER
-                ),
-                tokens = Config.MODEL_TOKENS,
-                modelType = "zipformer"
-            ),
-            keywordsFile = Config.MODEL_KEYWORDS,
-            keywordsThreshold = Config.KWS_THRESHOLD_PRIMARY,
-            keywordsScore = 1.5f,
-            numTrailingBlanks = 2
-        )
-
-        spotter = KeywordSpotter(assetManager = context.assets, config = cfg)
-        stream = spotter?.createStream()
-        Log.i(TAG, "[Engine] init done, keywordsFile=${Config.MODEL_KEYWORDS}")
-    }
-
-    /**
-     * 喂一帧 PCM 数据进引擎，返回是否命中。
-     *
-     * @param pcm 16-bit PCM short[] 数据，长度任意（一般 10ms 帧 = 160 samples）
-     * @return 命中则 [KwsResult.isHit] 为 true
-     */
-    fun process(pcm: ShortArray): KwsResult {
-        val sp = spotter ?: return KwsResult("", 0f)
-        val st = stream ?: return KwsResult("", 0f)
-
-        // sherpa-onnx 输入要求 [-1, 1] 范围 float
-        val floatBuf = FloatArray(pcm.size) { pcm[it] / 32768f }
-        st.acceptWaveform(floatBuf, Config.SAMPLE_RATE)
-
-        while (sp.isReady(st)) {
-            sp.decode(st)
-        }
-
-        val result = sp.getResult(st)
-        val keyword = result.keyword
-
-        return if (keyword.isNotEmpty()) {
-            // 命中后必须 reset，否则下次不会再触发
-            sp.reset(st)
-            Log.i(TAG, "[Engine] hit keyword=$keyword")
-            KwsResult(keyword, 1f)   // sherpa-onnx 不直接给 confidence，通过 keyword_score 间接控制
-        } else {
-            KwsResult("", 0f)
-        }
-    }
-
-    /**
-     * 释放 native 资源。在 Service.onDestroy 中调用。
-     */
-    fun release() {
-        try {
-            stream?.release()
-            spotter?.release()
-        } catch (e: Exception) {
-            Log.e(TAG, "release failed: ${e.message}")
-        }
-        stream = null
-        spotter = null
-        Log.i(TAG, "[Engine] released")
-    }
-}

app/src/main/java/com/lila/wakeup/kws/KwsStateMachine.kt

@@ -10,23 +10,39 @@ import java.util.concurrent.atomic.AtomicReference
 /**
  * KWS 状态机 —— 协议 v2.1 规则的中央控制器。
  *
- * 三个状态：
+ * 三个状态:
- *  - [State.Idle]: 服务启动中（极短）
+ *  - [State.Idle]: 服务启动中(极短)
- *  - [State.Listening]: 正在监听唤醒词，命中即发 WAKEUP 广播
+ *  - [State.Listening]: 正在监听唤醒词,命中即发 WAKEUP 广播
- *  - [State.Paused]: APP 主动暂停（含 silence_ms 二次保险窗口）
+ *  - [State.Paused]: APP 主动暂停(含 silence_ms 二次保险窗口)
  *
- * silence_ms 实现：
+ * silence_ms 实现:
- *  - 收到 PAUSE → 立即进入 Paused，记录 silenceUntilMs
+ *  - 收到 PAUSE → 立即进入 Paused,记录 silenceUntilMs
- *  - silenceUntilMs 期间收到 RESUME → 等到 silenceUntilMs 过后才真正 resume（防 AI 自我唤醒）
+ *  - silenceUntilMs 期间收到 RESUME → 等到 silenceUntilMs 过后才真正 resume(防 AI 自我唤醒)
  *  - 命中事件在 Paused 期间忽略
  *
- * 兜底超时：APP 漏发 RESUME 时，2 分钟自动 resume（详见 Config.PAUSE_TIMEOUT_MS）。
+ * 兜底超时:APP 漏发 RESUME 时,2 分钟自动 resume(详见 Config.PAUSE_TIMEOUT_MS)。
+ *
+ * 后验平滑设计移除:sherpa-onnx 内部已有 numTrailingBlanks 平滑,
+ * 外部再叠加反而导致漏命中(命中后 reset stream,下一帧不会再 hit)。
  */
 class KwsStateMachine(
-    private val engine: KwsEngine,
+    private val engine: AsrEngine,
     private val sender: BroadcastSender,
     private val onTimeoutResume: () -> Unit = {}
 ) {
+    init {
+        // ASR 引擎在内部线程触发命中,通过回调把 keyword 转发给 sender
+        // 仅在 Listening 状态下转发,Paused 期间忽略命中(VAD 段已处理但状态变更)
+        engine.onWakeup = { keyword ->
+            if (state.get() is State.Listening) {
+                Log.i(TAG, "[KWS] HIT keyword=$keyword")
+                sender.sendWakeup(keyword, confidence = 1.0f)
+            } else {
+                Log.d(TAG, "[KWS] hit ignored (state != LISTENING) keyword=$keyword")
+            }
+        }
+    }
+
     companion object {
         private const val TAG = "KwsService.State"
     }
@@ -37,38 +53,31 @@ class KwsStateMachine(
         data class Paused(val silenceUntilMs: Long, val reason: String) : State()
     }
 
-    /** 用 Atomic 做无锁状态切换，避免读写竞态 */
+    /** 用 Atomic 做无锁状态切换,避免读写竞态 */
     private val state = AtomicReference<State>(State.Idle)
 
     private val mainHandler = Handler(Looper.getMainLooper())
     private var timeoutRunnable: Runnable? = null
     private var deferredResumeRunnable: Runnable? = null
 
-    /** 平滑窗口：连续 N 帧都命中才真正发 WAKEUP（v2.1 后验平滑） */
-    private var consecutiveHits = 0
-
     fun transitionToListening() {
         state.set(State.Listening)
-        consecutiveHits = 0
         Log.i(TAG, "[State] -> LISTENING")
     }
 
     /**
      * 收到 KWS_PAUSE 广播时调用。
      *
-     * @param silenceMs 静默期（毫秒），默认 [Config.DEFAULT_SILENCE_MS]
+     * @param silenceMs 静默期(毫秒),默认 [Config.DEFAULT_SILENCE_MS]
-     * @param reason 调用原因（仅日志）
+     * @param reason 调用原因(仅日志)
      */
     fun onPauseReceived(silenceMs: Long, reason: String) {
         val until = System.currentTimeMillis() + silenceMs
         state.set(State.Paused(until, reason))
-        consecutiveHits = 0
         Log.i(TAG, "[State] -> PAUSED reason=$reason silence_ms=$silenceMs until=$until")
-
+        // 重置 VAD,清除暂停期间累积的音频(避免恢复后重复识别旧音)
-        // 取消上一次的 deferred resume（如果有）
+        engine.reset()
         cancelDeferredResume()
-
-        // 启动 2 分钟兜底超时
         scheduleTimeoutResume()
     }
 
@@ -76,12 +85,10 @@ class KwsStateMachine(
      * 收到 KWS_RESUME 广播时调用。
      */
     fun onResumeReceived(reason: String) {
-        val current = state.get()
+        when (val current = state.get()) {
-        when (current) {
             is State.Paused -> {
                 val now = System.currentTimeMillis()
                 if (now < current.silenceUntilMs) {
-                    // silence 期间，延迟到 silence 满才真正 resume
                     val delay = current.silenceUntilMs - now
                     Log.i(TAG, "[State] RESUME deferred ${delay}ms (still in silence)")
                     deferredResumeRunnable = Runnable {
@@ -96,7 +103,6 @@ class KwsStateMachine(
                 }
             }
             is State.Listening -> {
-                // 幂等：本就在监听态，无副作用
                 Log.d(TAG, "[State] RESUME ignored, already LISTENING")
             }
             is State.Idle -> {
@@ -108,26 +114,12 @@ class KwsStateMachine(
     }
 
     /**
-     * AudioCapture 每帧调用一次。在 Listening 态喂引擎并检查命中。
+     * AudioCapture 每帧调用一次。在 Listening 态喂 VAD,
+     * VAD 累积语音段后异步触发 ASR 识别,命中通过 engine.onWakeup 回调。
      */
     fun onAudioFrame(pcm: ShortArray) {
         if (state.get() !is State.Listening) return
-
+        engine.feedAudio(pcm)
-        val result = engine.process(pcm)
-        if (result.isHit) {
-            consecutiveHits++
-            if (consecutiveHits >= Config.SMOOTH_FRAMES) {
-                // 命中且过平滑 → 发 WAKEUP 广播
-                Log.i(TAG, "[KWS] HIT confirmed keyword=${result.keyword} smooth=$consecutiveHits")
-                sender.sendWakeup(result.keyword, result.confidence)
-                consecutiveHits = 0
-            } else {
-                Log.d(TAG, "[KWS] hit pending smooth=$consecutiveHits/${Config.SMOOTH_FRAMES}")
-            }
-        } else {
-            // 未命中重置计数
-            if (consecutiveHits > 0) consecutiveHits = 0
-        }
     }
 
     fun shutdown() {
@@ -137,13 +129,13 @@ class KwsStateMachine(
     }
 
     // ============================================================
-    // 内部：兜底定时器
+    // 内部:兜底定时器
     // ============================================================
 
     private fun scheduleTimeoutResume() {
         cancelTimeoutResume()
         timeoutRunnable = Runnable {
-            Log.w(TAG, "[Tmout] PAUSE 超时（${Config.PAUSE_TIMEOUT_MS / 1000}s），强制 RESUME")
+            Log.w(TAG, "[Tmout] PAUSE 超时(${Config.PAUSE_TIMEOUT_MS / 1000}s),强制 RESUME")
             transitionToListening()
             onTimeoutResume()
         }.also {

app/src/main/java/com/lila/wakeup/protocol/BroadcastSender.kt

@@ -23,14 +23,24 @@ class BroadcastSender(private val context: Context) {
     fun sendWakeup(keyword: String, confidence: Float) {
         val timestamp = System.currentTimeMillis()
 
-        val intent = Intent(Config.ACTION_WAKEUP).apply {
+        // 1. 外部协议广播：发给数字人 APP（com.qy.lila）
+        val externalIntent = Intent(Config.ACTION_WAKEUP).apply {
             setPackage(Config.APP_PACKAGE)   // v2.1 双向 setPackage 强制要求
             putExtra(Config.EXTRA_KEYWORD, keyword)
             putExtra(Config.EXTRA_TIMESTAMP, timestamp)
             putExtra(Config.EXTRA_CONFIDENCE, confidence)
         }
+        context.sendBroadcast(externalIntent)
+
+        // 2. 内部广播：本 APP 的 MainActivity 接收,用于 UI 实时显示命中
+        val internalIntent = Intent(Config.ACTION_INTERNAL_WAKEUP).apply {
+            setPackage(Config.SELF_PACKAGE)   // 仅本 APP 自己接收,避免泄漏到外部
+            putExtra(Config.EXTRA_KEYWORD, keyword)
+            putExtra(Config.EXTRA_TIMESTAMP, timestamp)
+            putExtra(Config.EXTRA_CONFIDENCE, confidence)
+        }
+        context.sendBroadcast(internalIntent)
 
-        context.sendBroadcast(intent)
         Log.i(TAG, "WAKEUP -> ${Config.APP_PACKAGE} keyword=$keyword conf=$confidence ts=$timestamp")
     }
 }