添加动态阈值
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@ -1,6 +1,7 @@
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package com.zs.smarthuman.sherpa
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import android.content.res.AssetManager
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import com.blankj.utilcode.util.LogUtils
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import com.k2fsa.sherpa.onnx.Vad
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import com.k2fsa.sherpa.onnx.getVadModelConfig
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import kotlin.math.sqrt
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@ -10,44 +11,81 @@ class VadManager(
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private val onSpeechStart: () -> Unit,
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private val onSpeechEnd: (avgEnergy: Float, peakRms: Float) -> Unit
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) {
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private val TAG = "SmartHuman-VadManager"
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private val vad: Vad
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private var isSpeaking = false
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private var lastSpeechTime = 0L
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private val END_SILENCE_MS = 800L
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// 基础统计变量
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private var activeFrameCount = 0
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private var activeSpeechFrameCount = 0
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private var speechEnergySum = 0f
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private var speechFrameCount = 0
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private var peakRms = 0f
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// ========== 新增：连续性检测核心变量 ==========
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private var totalFrames = 0 // 总处理帧数
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private var speechFrames = 0 // 语音帧总数
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private var continuousSpeechFrames = 0 // 连续语音帧数
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private var lastFrameIsSpeech = false // 上一帧是否为语音
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private var peakPosition = 0 // 峰值所在帧位置
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private var frameIndex = 0 // 当前帧索引
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init {
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val config = getVadModelConfig(0) ?: throw IllegalStateException("VAD config not found")
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val config = getVadModelConfig(0) ?: throw IllegalStateException("[$TAG] VAD config not found")
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vad = Vad(assetManager, config)
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LogUtils.i(TAG, "✅ VAD 初始化成功")
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}
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/**
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* 接收音频数据并进行VAD检测
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* @param samples 音频采样数据（float数组）
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*/
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fun accept(samples: FloatArray) {
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val now = System.currentTimeMillis()
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vad.acceptWaveform(samples)
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val hasSpeech = vad.isSpeechDetected()
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// RMS & peak 统计
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val rms = calcRms(samples)
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// ========== 1. 语音能量统计 ==========
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if (hasSpeech) {
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speechEnergySum += rms
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speechFrameCount++
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peakRms = maxOf(peakRms, rms)
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LogUtils.v(TAG, "🔊 检测到语音帧 | RMS: $rms | 累计峰值: $peakRms")
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} else {
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LogUtils.v(TAG, "🔇 检测到静音帧 | RMS: $rms")
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}
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// VAD逻辑
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// ========== 2. 新增：帧统计与连续性计算 ==========
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totalFrames++
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frameIndex++
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if (hasSpeech) {
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speechFrames++
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// 连续语音帧计数
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continuousSpeechFrames = if (lastFrameIsSpeech) {
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continuousSpeechFrames + 1
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} else {
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1 // 重置连续计数
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}
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lastFrameIsSpeech = true
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// 更新峰值位置（仅当当前RMS为新峰值时）
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if (rms == peakRms) {
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peakPosition = frameIndex
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}
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} else {
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lastFrameIsSpeech = false
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}
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// ========== 3. VAD核心状态流转 ==========
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if (hasSpeech) {
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lastSpeechTime = now
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if (!isSpeaking) {
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isSpeaking = true
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LogUtils.d(TAG, "🎤 语音开始")
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onSpeechStart()
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}
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activeFrameCount++
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@ -55,36 +93,85 @@ class VadManager(
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} else {
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if (isSpeaking) {
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activeFrameCount++
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if (now - lastSpeechTime >= END_SILENCE_MS) {
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val silenceDuration = now - lastSpeechTime
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if (silenceDuration >= END_SILENCE_MS) {
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isSpeaking = false
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val avgEnergy = if (speechFrameCount > 0) speechEnergySum / speechFrameCount else 0f
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val peak = peakRms
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onSpeechEnd(avgEnergy, peak)
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LogUtils.d(TAG, "🛑 语音结束 | 静音时长: ${silenceDuration}ms | 平均能量: $avgEnergy | 峰值: $peakRms")
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onSpeechEnd(avgEnergy, peakRms)
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resetStats() // 重置基础统计
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} else {
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LogUtils.v(TAG, "⏳ 静音中，时长: ${silenceDuration}ms (阈值: ${END_SILENCE_MS}ms)")
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}
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}
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}
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}
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fun activeSpeechRatio(): Float = if (activeFrameCount == 0) 0f else activeSpeechFrameCount.toFloat() / activeFrameCount
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/**
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* 计算语音占比（活跃语音帧 / 总活跃帧）
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* @return 语音占比（0~1）
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*/
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fun activeSpeechRatio(): Float {
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val ratio = if (activeFrameCount == 0) 0f else activeSpeechFrameCount.toFloat() / activeFrameCount
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LogUtils.d(TAG, "📊 语音占比: $ratio | 语音帧: $activeSpeechFrameCount | 总帧: $activeFrameCount")
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return ratio
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}
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// ========== 新增：帧统计获取方法（给VoiceController调用） ==========
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/** 获取总处理帧数 */
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fun getTotalFrames(): Int = totalFrames
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/** 获取语音帧总数 */
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fun getSpeechFrames(): Int = speechFrames
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/** 获取连续语音帧数 */
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fun getContinuousSpeechFrames(): Int = continuousSpeechFrames
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/** 获取峰值位置占比（峰值帧索引/总帧数） */
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fun getPeakPositionRatio(): Float {
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return if (totalFrames == 0) 0f else peakPosition.toFloat() / totalFrames
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}
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/**
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* 重置VAD状态（保留核心对象，清空统计数据）
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*/
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fun reset() {
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// 基础状态重置
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isSpeaking = false
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lastSpeechTime = 0L
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resetStats()
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vad.reset()
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// 新增：连续性统计重置
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totalFrames = 0
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speechFrames = 0
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continuousSpeechFrames = 0
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lastFrameIsSpeech = false
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peakPosition = 0
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frameIndex = 0
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LogUtils.d(TAG, "🔄 VAD 状态已完全重置")
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}
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/**
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* 重置统计数据（内部使用）
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*/
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private fun resetStats() {
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activeFrameCount = 0
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activeSpeechFrameCount = 0
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speechEnergySum = 0f
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speechFrameCount = 0
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peakRms = 0f
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vad.reset()
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}
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private fun calcRms(samples: FloatArray): Float {
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/**
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* 计算音频采样的RMS（均方根）能量
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* @param samples 音频采样数据
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* @return RMS值
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*/
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fun calcRms(samples: FloatArray): Float {
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var sum = 0f
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var peak = 0f
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for (v in samples) {
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sum += v * v
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peak = maxOf(peak, kotlin.math.abs(v))
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}
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for (v in samples) sum += v * v
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return sqrt(sum / samples.size)
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}
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}
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@ -1,13 +1,13 @@
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package com.zs.smarthuman.sherpa
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import android.content.res.AssetManager
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import android.util.Log
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import com.blankj.utilcode.util.LogUtils
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import java.util.ArrayDeque
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class VoiceController(
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assetManager: AssetManager,
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private val onWakeup: () -> Unit,
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private val onFinalAudio: (FloatArray) -> Unit, // 修改：传回识别结果文本
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private val onFinalAudio: (FloatArray) -> Unit,
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private val idleTimeoutSeconds: Int = 10,
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private val maxRecordingSeconds: Int = 10,
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private val onStateChanged: ((VoiceState) -> Unit)? = null,
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@ -20,14 +20,16 @@ class VoiceController(
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var state: VoiceState = VoiceState.WAIT_WAKEUP
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private set(value) {
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field = value
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Log.d(TAG, "➡ State = $value")
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LogUtils.d(TAG, "➡ State = $value")
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onStateChanged?.invoke(value)
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}
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private val wakeupManager = WakeupManager(assetManager) {
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Log.d(TAG, "🔥 WakeWord detected")
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handleWakeupEvent()
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}
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private val wakeupManager = WakeupManager(assetManager, onWakeup)
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private val vadManager = VadManager(
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assetManager,
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onSpeechStart = { onVadStart() },
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onSpeechEnd = { avgEnergy, peakRms -> onVadEnd(avgEnergy, peakRms) }
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)
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private val audioBuffer = mutableListOf<Float>()
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private val preBuffer = ArrayDeque<Float>()
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@ -48,10 +50,36 @@ class VoiceController(
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private val idleTimeoutMs = idleTimeoutSeconds * 1000L
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private val maxRecordingMs = maxRecordingSeconds * 1000L
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private val vadManager = VadManager(
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assetManager,
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onSpeechStart = { onVadStart() },
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onSpeechEnd = { avgEnergy, peakRms -> onVadEnd(avgEnergy, peakRms) }
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// ================= 动态阈值核心配置（修复+调整） =================
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private val BASELINE_WINDOW_SIZE = 50
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private val envNoiseBuffer = ArrayDeque<Float>(BASELINE_WINDOW_SIZE)
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private var currentEnvBaseline = 0.001f
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// ========== 修复：远场过滤配置（关键调整） ==========
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private val MAX_FAR_FIELD_ENERGY = 0.05f // 远场能量上限（正常语音>0.05，远场<0.05）
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private val MIN_VALID_PEAK_AVG_RATIO = 1.5f // 有效峰均比下限（正常语音>1.5，咳嗽<1.5）
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private val BASELINE_QUIET_THRESHOLD = 0.002f
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private val MIN_CONTINUOUS_FRAME_RATIO = 0.4f // 连续帧占比下限（从0.6调低，兼容正常短语音）
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private val MAX_PEAK_POSITION_RATIO = 0.5f // 峰值位置上限（从0.3调高，兼容正常语音）
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private val MIN_EFFECTIVE_SPEECH_FRAMES = 3 // 最低有效帧数（从5调低）
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// 分场景动态系数（调整：降低安静环境系数）
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private val SHORT_SPEECH_ENERGY_COEFF_QUIET = 6.0f // 从8.0调低
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private val SHORT_SPEECH_ENERGY_COEFF_NOISY = 4.0f // 从5.0调低
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private val LONG_SPEECH_ENERGY_COEFF = 15.0f // 从20.0调低
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private val SHORT_SPEECH_VAD_COEFF = 0.15f // 从0.2调低
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private val LONG_SPEECH_VAD_COEFF = 0.3f // 从0.4调低
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private val SHORT_SPEECH_MIN_SCORE = 2 // 调回2分
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private val LONG_SPEECH_MIN_SCORE = 4 // 从5调低
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private val SHORT_SPEECH_MIN = 500L
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private val SHORT_SPEECH_MAX = 2000L
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// 阈值配置数据类
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private data class ThresholdConfig(
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val energyThreshold: Float,
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val vadRatioThreshold: Float,
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val minScore: Int,
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val scene: String
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)
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/* ================= 音频入口 ================= */
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@ -65,6 +93,10 @@ class VoiceController(
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val now = System.currentTimeMillis()
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if (state == VoiceState.WAIT_WAKEUP) {
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calibrateEnvBaseline(samples)
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}
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when (state) {
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VoiceState.WAIT_WAKEUP,
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VoiceState.PLAYING_PROMPT,
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@ -84,7 +116,7 @@ class VoiceController(
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if ((waitSpeechStartMs > 0 && now - waitSpeechStartMs >= idleTimeoutMs) ||
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(waitSpeechFailStartMs > 0 && now - waitSpeechFailStartMs >= idleTimeoutMs)
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) {
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Log.d(TAG, "⏱ WAIT_SPEECH idle timeout → WAIT_WAKEUP")
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LogUtils.d(TAG, "⏱ WAIT_SPEECH idle timeout → WAIT_WAKEUP")
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resetAll()
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return
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}
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@ -100,13 +132,28 @@ class VoiceController(
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vadManager.accept(samples)
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if (System.currentTimeMillis() - recordingStartMs > maxRecordingMs) {
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Log.w(TAG, "⏱ Max recording reached")
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finishSentence() // 超时也触发 finish
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LogUtils.w(TAG, "⏱ Max recording reached | 当前环境基线: $currentEnvBaseline")
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finishSentence()
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}
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}
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}
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}
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/* ================= 环境基线校准 ================= */
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private fun calibrateEnvBaseline(samples: FloatArray) {
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val rms = vadManager.calcRms(samples)
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if (rms < 0.01f) {
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if (envNoiseBuffer.size >= BASELINE_WINDOW_SIZE) {
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envNoiseBuffer.removeFirst()
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}
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envNoiseBuffer.addLast(rms)
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currentEnvBaseline = envNoiseBuffer.maxOrNull() ?: 0.001f
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LogUtils.d(TAG, "🌡 环境基线校准 | RMS: $rms | 基线: $currentEnvBaseline | 缓存数: ${envNoiseBuffer.size}")
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} else {
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LogUtils.v(TAG, "🔊 高能量音频跳过校准 | RMS: $rms | 基线: $currentEnvBaseline")
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}
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}
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/* ================= 唤醒 ================= */
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private fun handleWakeupEvent() {
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if (state == VoiceState.UPLOADING) return
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@ -127,11 +174,12 @@ class VoiceController(
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inKwsObserve = true
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kwsObserveStartMs = System.currentTimeMillis()
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onWakeup()
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LogUtils.d(TAG, "🔔 唤醒成功 | 环境基线: $currentEnvBaseline")
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}
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private fun onVadStart() {
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if (state != VoiceState.WAIT_SPEECH) return
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Log.d(TAG, "🎤 REAL VAD START")
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LogUtils.d(TAG, "🎤 REAL VAD START | 环境基线: $currentEnvBaseline")
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vadStarted = true
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recordingStartMs = System.currentTimeMillis()
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audioBuffer.clear()
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@ -141,17 +189,17 @@ class VoiceController(
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private fun onVadEnd(avgEnergy: Float, peakRms: Float) {
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if (state != VoiceState.RECORDING) return
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Log.d(TAG, "🧠 VAD END")
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LogUtils.d(TAG, "🧠 VAD END | 环境基线: $currentEnvBaseline")
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finishSentence(avgEnergy, peakRms)
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}
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/* ================= 结束录音 ================= */
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/* ================= 结束录音（修复远场过滤逻辑） ================= */
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private fun finishSentence(avgEnergy: Float = 0f, peakRms: Float = 0f) {
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val now = System.currentTimeMillis()
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val duration = now - recordingStartMs
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if (!vadStarted || duration < MIN_SPEECH_MS) {
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Log.d(TAG, "❌ Too short or no VAD start: $duration ms")
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LogUtils.d(TAG, "❌ 语音过短: $duration ms | 基线: $currentEnvBaseline")
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resetToWaitSpeech()
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return
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}
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@ -160,73 +208,148 @@ class VoiceController(
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val vadRatio = vadManager.activeSpeechRatio()
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val peakAvgRatio = if (avgEnergy > 0f) peakRms / avgEnergy else 0f
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Log.d(TAG, "📊 Finish Sentence - duration: $duration ms, vadEnded: true")
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Log.d(
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TAG,
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"📊 vadRatio=$vadRatio, avgEnergy=$avgEnergy, peakRms=$peakRms, peakAvgRatio=$peakAvgRatio"
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)
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// 获取VAD帧统计
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val totalFrames = vadManager.getTotalFrames()
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val speechFrames = vadManager.getSpeechFrames()
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val continuousSpeechFrames = vadManager.getContinuousSpeechFrames()
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val peakPositionRatio = vadManager.getPeakPositionRatio()
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if (avgEnergy < 0.02f || peakAvgRatio < 1.2f || vadRatio < 0.4f) {
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Log.d(TAG, "❌ Sentence rejected")
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LogUtils.d(TAG, "📊 录音信息 | 时长: $duration ms | 能量: $avgEnergy | 峰均比: $peakAvgRatio | 基线: $currentEnvBaseline")
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LogUtils.d(TAG, "📊 帧统计 | 总帧: $totalFrames | 语音帧: $speechFrames | 连续语音帧: $continuousSpeechFrames | 峰值位置占比: $peakPositionRatio")
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// ========== 修复：远场语音过滤逻辑（核心） ==========
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// 正确逻辑：能量 < MAX_FAR_FIELD_ENERGY 才是远场；峰均比 < MIN_VALID_PEAK_AVG_RATIO 才是无效语音
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val isFarField = avgEnergy < MAX_FAR_FIELD_ENERGY // 修复：从 < MIN 改为 < MAX
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val isInvalidPeakRatio = peakAvgRatio < MIN_VALID_PEAK_AVG_RATIO // 降低阈值
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// 非连续特征（调整阈值后更宽松）
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val continuousRatio = if (speechFrames > 0) continuousSpeechFrames.toFloat() / speechFrames else 0f
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val isDiscontinuous = continuousRatio < MIN_CONTINUOUS_FRAME_RATIO ||
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speechFrames < MIN_EFFECTIVE_SPEECH_FRAMES ||
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peakPositionRatio < MAX_PEAK_POSITION_RATIO
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// 远场+无效语音过滤（仅过滤真正的远场/杂音）
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if (isFarField && isInvalidPeakRatio) { // 修复：同时满足才过滤
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LogUtils.w(TAG, "❌ 远场/无效语音过滤 | 能量: $avgEnergy < 远场上限: $MAX_FAR_FIELD_ENERGY | 峰均比: $peakAvgRatio < 有效下限: $MIN_VALID_PEAK_AVG_RATIO")
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
// 评分逻辑
|
||||
var score = 0
|
||||
when {
|
||||
duration >= 4000 -> score += 3
|
||||
duration >= 2500 -> score += 2
|
||||
duration >= 1500 -> score += 1
|
||||
// 非连续语音过滤（仅过滤真正的零散杂音）
|
||||
if (isDiscontinuous && isFarField) { // 修复：结合远场特征，避免过滤正常语音
|
||||
LogUtils.w(TAG, "❌ 非连续杂音过滤 | 连续占比: $continuousRatio < $MIN_CONTINUOUS_FRAME_RATIO | 语音帧: $speechFrames < $MIN_EFFECTIVE_SPEECH_FRAMES | 峰值位置: $peakPositionRatio < $MAX_PEAK_POSITION_RATIO")
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 动态阈值计算（调整后更宽松） ==========
|
||||
val thresholdConfig = when {
|
||||
duration in SHORT_SPEECH_MIN..SHORT_SPEECH_MAX -> {
|
||||
val coeff = if (currentEnvBaseline < BASELINE_QUIET_THRESHOLD) {
|
||||
SHORT_SPEECH_ENERGY_COEFF_QUIET
|
||||
} else {
|
||||
SHORT_SPEECH_ENERGY_COEFF_NOISY
|
||||
}
|
||||
val threshold = currentEnvBaseline * coeff
|
||||
LogUtils.d(TAG, "📏 短语音阈值 | 场景: ${if (currentEnvBaseline < BASELINE_QUIET_THRESHOLD) "安静" else "嘈杂"} | 系数: $coeff | 阈值: $threshold")
|
||||
ThresholdConfig(
|
||||
energyThreshold = threshold,
|
||||
vadRatioThreshold = SHORT_SPEECH_VAD_COEFF,
|
||||
minScore = SHORT_SPEECH_MIN_SCORE,
|
||||
scene = "短语音"
|
||||
)
|
||||
}
|
||||
else -> {
|
||||
val threshold = currentEnvBaseline * LONG_SPEECH_ENERGY_COEFF
|
||||
ThresholdConfig(
|
||||
energyThreshold = threshold,
|
||||
vadRatioThreshold = LONG_SPEECH_VAD_COEFF,
|
||||
minScore = LONG_SPEECH_MIN_SCORE,
|
||||
scene = "长语音"
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
LogUtils.d(TAG, "📊 动态阈值 | ${thresholdConfig.scene} | 能量阈值: ${thresholdConfig.energyThreshold} | 占比阈值: ${thresholdConfig.vadRatioThreshold} | 最低分: ${thresholdConfig.minScore}")
|
||||
|
||||
// 基础阈值过滤（调整后更宽松）
|
||||
if (avgEnergy < thresholdConfig.energyThreshold || vadRatio < thresholdConfig.vadRatioThreshold) {
|
||||
LogUtils.w(TAG, "❌ 阈值过滤 | 能量: $avgEnergy < ${thresholdConfig.energyThreshold} | 占比: $vadRatio < ${thresholdConfig.vadRatioThreshold}")
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
// 评分逻辑（恢复短语音保底分）
|
||||
var score = 0
|
||||
// 1. 时长评分（恢复短语音保底1分）
|
||||
score += when {
|
||||
duration >= 4000 -> 3
|
||||
duration >= 2500 -> 2
|
||||
duration >= 1500 -> 1
|
||||
duration >= SHORT_SPEECH_MIN -> 1 // 恢复保底分
|
||||
else -> 0
|
||||
}
|
||||
// 2. 能量评分
|
||||
score += when {
|
||||
avgEnergy >= thresholdConfig.energyThreshold * 10 -> 3
|
||||
avgEnergy >= thresholdConfig.energyThreshold * 5 -> 2
|
||||
avgEnergy >= thresholdConfig.energyThreshold -> 1
|
||||
else -> 0
|
||||
}
|
||||
// 3. 占比+连续性评分（调整阈值）
|
||||
score += when {
|
||||
continuousRatio >= 0.7 -> 2 // 从0.8调低
|
||||
continuousRatio >= MIN_CONTINUOUS_FRAME_RATIO -> 1
|
||||
else -> 0
|
||||
}
|
||||
|
||||
LogUtils.d(TAG, "🏆 评分结果 | 总分: $score | 最低分: ${thresholdConfig.minScore} | 连续占比: $continuousRatio")
|
||||
|
||||
// 分场景判定（调整后更宽松）
|
||||
val pass = if (duration in SHORT_SPEECH_MIN..SHORT_SPEECH_MAX) {
|
||||
score >= thresholdConfig.minScore && continuousRatio >= 0.5 // 从0.7调低
|
||||
} else {
|
||||
score >= thresholdConfig.minScore || (score >= 2 && avgEnergy >= currentEnvBaseline * 4) // 从3→2，6→4
|
||||
}
|
||||
when {
|
||||
avgEnergy >= 0.10f -> score += 3
|
||||
avgEnergy >= 0.06f -> score += 2
|
||||
avgEnergy >= 0.02f -> score += 1
|
||||
}
|
||||
when {
|
||||
vadRatio >= 0.55f -> score += 2
|
||||
vadRatio >= 0.40f -> score += 1
|
||||
}
|
||||
Log.d(
|
||||
TAG,
|
||||
"📊 duration=$duration ms, vadRatio=$vadRatio, avgEnergy=$avgEnergy, score=$score"
|
||||
)
|
||||
|
||||
val pass = score >= 5 || (score == 3 && avgEnergy >= 0.06f)
|
||||
if (!pass) {
|
||||
Log.d(TAG, "❌ Sentence rejected (score=$score)")
|
||||
LogUtils.w(TAG, "❌ 评分/连续性不足过滤 | 总分: $score < ${thresholdConfig.minScore} | 连续占比: $continuousRatio")
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
audioBuffer.clear()
|
||||
state = VoiceState.UPLOADING
|
||||
onFinalAudio(audio) // 传递音频和识别文本
|
||||
onFinalAudio(audio)
|
||||
LogUtils.i(TAG, "✅ 录音通过 | 时长: $duration ms | 能量: $avgEnergy | 连续占比: $continuousRatio | 准备上传")
|
||||
}
|
||||
|
||||
/* ================= 播放回调 ================= */
|
||||
/* ================= 播放/上传/Reset 回调（无修改） ================= */
|
||||
fun onPlayStartPrompt() {
|
||||
LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline")
|
||||
state = VoiceState.PLAYING_PROMPT
|
||||
}
|
||||
|
||||
fun onPlayEndPrompt() {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
LogUtils.d(TAG, "🎵 提示音结束 | 基线: $currentEnvBaseline")
|
||||
state = VoiceState.WAIT_SPEECH_COOLDOWN
|
||||
}
|
||||
|
||||
fun onPlayStartBackend() {
|
||||
LogUtils.d(TAG, "🎶 播放后台音频 | 基线: $currentEnvBaseline")
|
||||
state = VoiceState.PLAYING_BACKEND
|
||||
}
|
||||
|
||||
fun onPlayEndBackend() {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
LogUtils.d(TAG, "🎶 后台音频结束 | 基线: $currentEnvBaseline")
|
||||
state = VoiceState.WAIT_SPEECH_COOLDOWN
|
||||
}
|
||||
|
||||
/* ================= 上传回调 ================= */
|
||||
fun onUploadFinished(success: Boolean) {
|
||||
if (state != VoiceState.UPLOADING) return
|
||||
LogUtils.d(TAG, "📤 上传完成 | 成功: $success | 基线: $currentEnvBaseline")
|
||||
state = if (success) VoiceState.PLAYING_BACKEND
|
||||
else {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
@ -234,8 +357,8 @@ class VoiceController(
|
||||
}
|
||||
}
|
||||
|
||||
/* ================= Reset ================= */
|
||||
private fun resetToWaitSpeech() {
|
||||
LogUtils.d(TAG, "🔄 重置到等待说话 | 基线: $currentEnvBaseline")
|
||||
audioBuffer.clear()
|
||||
vadManager.reset()
|
||||
vadStarted = false
|
||||
@ -244,21 +367,27 @@ class VoiceController(
|
||||
}
|
||||
|
||||
private fun resetAll() {
|
||||
LogUtils.d(TAG, "🔄 重置所有状态 | 基线: $currentEnvBaseline")
|
||||
audioBuffer.clear()
|
||||
preBuffer.clear()
|
||||
vadManager.reset()
|
||||
wakeupManager.reset()
|
||||
vadStarted = false
|
||||
waitSpeechStartMs = 0L
|
||||
waitSpeechFailStartMs = 0L
|
||||
envNoiseBuffer.clear()
|
||||
currentEnvBaseline = 0.001f
|
||||
LogUtils.d(TAG, "🔄 环境基线已重置 | 新基线: $currentEnvBaseline")
|
||||
state = VoiceState.WAIT_WAKEUP
|
||||
}
|
||||
|
||||
fun release() {
|
||||
LogUtils.d(TAG, "🔌 释放资源 | 最终基线: $currentEnvBaseline")
|
||||
wakeupManager.release()
|
||||
vadManager.reset()
|
||||
envNoiseBuffer.clear()
|
||||
}
|
||||
|
||||
/* ================= Utils ================= */
|
||||
private fun cachePreBuffer(samples: FloatArray) {
|
||||
for (s in samples) {
|
||||
preBuffer.addLast(s)
|
||||
|
||||
@ -2,6 +2,7 @@ package com.zs.smarthuman.sherpa
|
||||
|
||||
import android.content.res.AssetManager
|
||||
import android.util.Log
|
||||
import com.blankj.utilcode.util.LogUtils
|
||||
import com.k2fsa.sherpa.onnx.*
|
||||
|
||||
class WakeupManager(assetManager: AssetManager, function: () -> Unit) {
|
||||
@ -29,11 +30,11 @@ class WakeupManager(assetManager: AssetManager, function: () -> Unit) {
|
||||
)
|
||||
|
||||
kws = KeywordSpotter(assetManager, config)
|
||||
Log.d(TAG, "✅ KeywordSpotter initialized")
|
||||
LogUtils.d(TAG, "✅ KeywordSpotter initialized")
|
||||
|
||||
stream = kws.createStream()
|
||||
require(stream != null) { "Failed to create KWS stream" }
|
||||
Log.d(TAG, "✅ KWS stream created")
|
||||
LogUtils.d(TAG, "✅ KWS stream created")
|
||||
}
|
||||
|
||||
/** ⭐ 永远喂 KWS */
|
||||
@ -48,7 +49,7 @@ class WakeupManager(assetManager: AssetManager, function: () -> Unit) {
|
||||
kws.decode(s)
|
||||
val keyword = kws.getResult(s).keyword
|
||||
if (keyword.isNotBlank()) {
|
||||
Log.d(TAG, "🔥 KWS hit: $keyword")
|
||||
LogUtils.d(TAG, "🔥 KWS hit: $keyword")
|
||||
justWokeUp = true
|
||||
kws.reset(s)
|
||||
break
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user