判断提前做
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@ -16,39 +16,41 @@ class VadManager(
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private var isSpeaking = false
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private var lastSpeechTime = 0L
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// ========== 核心调整：适配人类正常说话停顿 ==========
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private val END_SILENCE_MS = 1500L // 基础静默阈值（1.5秒）
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private val MAX_SILENCE_AFTER_SPEECH_MS = 3000L// 兜底静默阈值（3秒）
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private val SPEECH_ACTIVE_DURATION = 5000L // 语音活跃期（5秒内容忍更长停顿）
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// ========== 核心调整：区分活跃期/收尾期阈值 ==========
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// 说话活跃期（容忍停顿）
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private val ACTIVE_END_SILENCE_MS = 1500L // 活跃期基础静默（保留停顿容忍）
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private val ACTIVE_CONSECUTIVE_FRAMES = 10 // 活跃期连续静音帧
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// 说话收尾期（快速结束）
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private val FINAL_END_SILENCE_MS = 800L // 收尾期基础静默（缩短到800ms）
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private val FINAL_CONSECUTIVE_FRAMES = 5 // 收尾期连续静音帧（5帧=100ms）
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// 收尾期触发条件：最后一次有效语音后超过X秒，判定为进入收尾期
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private val FINAL_PHASE_TRIGGER_MS = 1000L // 1秒无有效语音，进入收尾期
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// ========== 核心调整：降低有效语音阈值 ==========
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private val MIN_EFFECTIVE_SPEECH_RMS = 0.001f // 有效语音最小RMS
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private val ENV_BASELINE_FACTOR = 1.2f // 环境基线倍数
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private var envBaselineRms = 0.0005f // 初始环境基线
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private var lastEffectiveSpeechTime = 0L // 最后一次有效语音时间戳
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private val MAX_SILENCE_AFTER_SPEECH_MS = 2000L // 兜底阈值从3秒降到2秒
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// ========== 新增：语音活跃期变量 ==========
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private var isSpeechActive = false // 语音活跃期标记
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private var speechActiveStartMs = 0L // 活跃期开始时间
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// 原有基础配置
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private val MIN_EFFECTIVE_SPEECH_RMS = 0.001f
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private val ENV_BASELINE_FACTOR = 1.2f
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private var envBaselineRms = 0.0005f
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private var lastEffectiveSpeechTime = 0L
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// ========== 连续静音帧校验（放宽阈值） ==========
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private var consecutiveSilenceFrames = 0
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private val CONSECUTIVE_SILENCE_FRAME_THRESHOLD = 10 // 连续10帧静音（200ms）
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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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// 新增：收尾期标记
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private var isInFinalPhase = false
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// 统计变量
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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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// 连续性检测核心变量
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private var totalFrames = 0
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private var speechFrames = 0
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private var continuousSpeechFrames = 0
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private var lastFrameIsSpeech = false
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private var peakPosition = 0
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private var frameIndex = 0
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private var activeFrameCount = 0
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private var activeSpeechFrameCount = 0
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init {
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val config = getVadModelConfig(0) ?: throw IllegalStateException("[$TAG] VAD config not found")
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@ -62,42 +64,39 @@ class VadManager(
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val vadHasSpeech = vad.isSpeechDetected()
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val rms = calcRms(samples)
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// 环境基线更新（滑动平均，适配背景噪音）
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// 环境基线更新
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if (!vadHasSpeech || rms < MIN_EFFECTIVE_SPEECH_RMS) {
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envBaselineRms = (envBaselineRms * 0.9f) + (rms * 0.1f)
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}
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// 有效语音判定：阈值更低，更容易触发
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val effectiveSpeechThreshold = maxOf(MIN_EFFECTIVE_SPEECH_RMS, envBaselineRms * ENV_BASELINE_FACTOR)
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val isEffectiveSpeech = vadHasSpeech && rms >= effectiveSpeechThreshold
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// ========== 核心优化：语音活跃期逻辑 ==========
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// ========== 核心优化：动态判定收尾期 ==========
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if (isEffectiveSpeech) {
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isSpeechActive = true
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speechActiveStartMs = now // 重置活跃期
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}
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// 活跃期内（5秒）用2秒静默阈值，活跃期外用1.5秒
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val dynamicEndSilenceMs = if (isSpeechActive && (now - speechActiveStartMs) < SPEECH_ACTIVE_DURATION) {
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2000L
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lastEffectiveSpeechTime = now
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isInFinalPhase = false // 有有效语音，退出收尾期
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} else {
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END_SILENCE_MS
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// 最后一次有效语音后超过1秒，进入收尾期
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if (now - lastEffectiveSpeechTime >= FINAL_PHASE_TRIGGER_MS) {
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isInFinalPhase = true
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}
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}
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// 语音能量统计（仅有效语音）
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// 语音能量统计
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if (isEffectiveSpeech) {
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speechEnergySum += rms
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speechFrameCount++
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peakRms = maxOf(peakRms, rms)
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lastEffectiveSpeechTime = now
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lastSpeechTime = now
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consecutiveSilenceFrames = 0 // 重置连续静音帧
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LogUtils.v(TAG, "🔊 有效语音帧 | RMS: $rms | 阈值: $effectiveSpeechThreshold")
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consecutiveSilenceFrames = 0
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LogUtils.v(TAG, "🔊 有效语音帧 | RMS: $rms | 阈值: $effectiveSpeechThreshold | 收尾期: $isInFinalPhase")
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} else {
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consecutiveSilenceFrames++ // 累计连续静音帧
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consecutiveSilenceFrames++
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LogUtils.v(TAG, if (vadHasSpeech) "⚠ 低能量语音帧 | RMS: $rms | 阈值: $effectiveSpeechThreshold"
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else "🔇 静音帧 | 连续静音帧: $consecutiveSilenceFrames")
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else "🔇 静音帧 | 连续静音帧: $consecutiveSilenceFrames | 收尾期: $isInFinalPhase")
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}
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// 帧统计与连续性计算
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// 帧统计
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totalFrames++
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frameIndex++
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if (isEffectiveSpeech) {
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@ -109,7 +108,14 @@ class VadManager(
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lastFrameIsSpeech = false
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}
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// VAD核心状态流转（使用动态静默阈值）
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// ========== 核心优化：根据收尾期选择不同阈值 ==========
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val (endSilenceMs, consecutiveFrames) = if (isInFinalPhase) {
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Pair(FINAL_END_SILENCE_MS, FINAL_CONSECUTIVE_FRAMES)
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} else {
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Pair(ACTIVE_END_SILENCE_MS, ACTIVE_CONSECUTIVE_FRAMES)
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}
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// VAD状态流转
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if (isEffectiveSpeech) {
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if (!isSpeaking) {
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isSpeaking = true
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@ -124,32 +130,36 @@ class VadManager(
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val vadSilenceDuration = now - lastSpeechTime
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val effectiveSilenceDuration = now - lastEffectiveSpeechTime
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// 触发条件：动态静默时长 + 连续静音帧达标
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val isSilenceTimeout = (vadSilenceDuration >= dynamicEndSilenceMs ||
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// 触发结束条件：适配当前阶段的阈值
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val isSilenceTimeout = (vadSilenceDuration >= endSilenceMs ||
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effectiveSilenceDuration >= MAX_SILENCE_AFTER_SPEECH_MS) &&
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consecutiveSilenceFrames >= CONSECUTIVE_SILENCE_FRAME_THRESHOLD
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consecutiveSilenceFrames >= consecutiveFrames
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if (isSilenceTimeout) {
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isSpeaking = false
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isSpeechActive = false // 结束活跃期
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isInFinalPhase = false // 重置收尾期
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val avgEnergy = if (speechFrameCount > 0) speechEnergySum / speechFrameCount else 0f
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LogUtils.d(TAG, """
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🛑 语音结束
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- 有效静默时长: ${effectiveSilenceDuration}ms
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- 连续静音帧: $consecutiveSilenceFrames
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- 平均能量: $avgEnergy | 峰值: $peakRms
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- 活跃期: ${if (isSpeechActive) "是" else "否"}
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- 收尾期: $isInFinalPhase | 所用阈值: $endSilenceMs ms
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""".trimIndent())
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onSpeechEnd(avgEnergy, peakRms)
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resetStats()
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} else {
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LogUtils.v(TAG, "⏳ 静默中（停顿容忍） | 连续静音帧: $consecutiveSilenceFrames | 静默时长: ${effectiveSilenceDuration}ms | 动态阈值: $dynamicEndSilenceMs")
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LogUtils.v(TAG, "⏳ 静默中 | 连续静音帧: $consecutiveSilenceFrames | 静默时长: ${effectiveSilenceDuration}ms | 所用阈值: $endSilenceMs ms")
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}
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}
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}
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}
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// ========== 保留原有方法 ==========
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// 保留原有方法...
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fun isSpeechDetected(): Boolean {
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return vad.isSpeechDetected()
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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 | 有效语音帧: $activeSpeechFrameCount | 总帧: $activeFrameCount")
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@ -169,10 +179,7 @@ class VadManager(
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lastEffectiveSpeechTime = 0L
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envBaselineRms = 0.0005f
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consecutiveSilenceFrames = 0
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// 重置活跃期
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isSpeechActive = false
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speechActiveStartMs = 0L
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// 重置统计
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isInFinalPhase = false // 重置收尾期
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resetStats()
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vad.reset()
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@ -80,11 +80,27 @@ class VoiceController(
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private val SHORT_SPEECH_MAX = 2000L
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// ========== 核心修改：多人对话过滤配置（适配2人以上场景） ==========
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private val MULTI_DIALOGUE_MIN_DURATION = 2500L // 多人对话最小时长（2.5秒，比两人更短也能判定）
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private val MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO = 2.5f // 多人对话峰均比范围更大（多人音量差异更大）
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private val MULTI_DIALOGUE_MIN_DURATION = 2500L // 多人对话最小时长（2.5秒）
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private val MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO = 2.5f // 多人对话峰均比范围
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private val MULTI_DIALOGUE_MIN_PEAK_AVG_RATIO = 0.4f
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private val MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO = 0.3f // 多人对话连续帧占比更低（轮流说话，断层更多）
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private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f // 多人对话有效帧占比要求稍低（避免漏过滤）
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private val MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO = 0.3f // 多人对话连续帧占比
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private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f // 多人对话有效帧占比
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// ========== 新增：录音过程中实时统计的变量 ==========
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// 能量统计
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private var realtimeEnergySum = 0f
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private var realtimeEnergyCount = 0
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private var realtimePeakRms = 0f
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// 帧统计（实时累加）
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private var realtimeTotalFrames = 0
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private var realtimeSpeechFrames = 0
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private var realtimeContinuousSpeechFrames = 0
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private var realtimeLastFrameIsSpeech = false
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// 多人对话实时判定标记
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private var isMultiPersonDialogueDetected = false
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// 防抖变量
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private var lastInvalidResetMs = 0L
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private val INVALID_RESET_DEBOUNCE_MS = 1500L // 1.5秒内不重复重置
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// 阈值配置数据类
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private data class ThresholdConfig(
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@ -143,15 +159,78 @@ class VoiceController(
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audioBuffer.addAll(samples.asList())
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vadManager.accept(samples)
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// ========== 核心优化：录音过程中实时计算 ==========
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// 1. 实时校准环境基线（适配录音中环境变化）
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calibrateEnvBaseline(samples)
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// 2. 实时计算能量/峰值
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updateRealtimeEnergy(samples)
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// 3. 实时更新帧统计
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updateRealtimeFrameStats()
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// 4. 实时判定是否为多人对话，若是则立即终止录音
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if (checkMultiPersonDialogueRealtime(now)) {
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LogUtils.w(TAG, "🚨 录音中识别出多人对话，提前终止")
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finishSentence(realtimeEnergySum / realtimeEnergyCount, realtimePeakRms)
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return
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}
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// 原有最大录音时长判断
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if (System.currentTimeMillis() - recordingStartMs > maxRecordingMs) {
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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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finishSentence(realtimeEnergySum / realtimeEnergyCount, realtimePeakRms)
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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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/* ================= 新增：录音中实时更新能量统计 ================= */
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private fun updateRealtimeEnergy(samples: FloatArray) {
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val rms = vadManager.calcRms(samples)
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// 仅统计有效语音帧的能量
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if (rms >= MIN_EFFECTIVE_SPEECH_RMS) {
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realtimeEnergySum += rms
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realtimeEnergyCount++
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realtimePeakRms = maxOf(realtimePeakRms, rms)
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}
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}
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/* ================= 新增：录音中实时更新帧统计 ================= */
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private fun updateRealtimeFrameStats() {
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// 从VADManager获取最新帧状态
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realtimeTotalFrames = vadManager.getTotalFrames()
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realtimeSpeechFrames = vadManager.getSpeechFrames()
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realtimeContinuousSpeechFrames = vadManager.getContinuousSpeechFrames()
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// 实时更新连续帧标记
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val currentFrameIsSpeech = vadManager.isSpeechDetected() // 需给VadManager新增isSpeechDetected()方法
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if (currentFrameIsSpeech) {
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realtimeContinuousSpeechFrames = if (realtimeLastFrameIsSpeech) realtimeContinuousSpeechFrames + 1 else 1
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} else {
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realtimeContinuousSpeechFrames = 0
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}
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realtimeLastFrameIsSpeech = currentFrameIsSpeech
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}
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/* ================= 新增：录音中实时判定多人对话 ================= */
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private fun checkMultiPersonDialogueRealtime(now: Long): Boolean {
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// 还没到多人对话最小时长，不判定
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val duration = now - recordingStartMs
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if (duration < MULTI_DIALOGUE_MIN_DURATION) return false
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// 实时计算特征值
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val avgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else 0f
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val peakAvgRatio = if (avgEnergy > 0) realtimePeakRms / avgEnergy else 0f
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val continuousRatio = if (realtimeSpeechFrames > 0) realtimeContinuousSpeechFrames.toFloat() / realtimeSpeechFrames else 0f
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val vadRatio = vadManager.activeSpeechRatio()
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// 多人对话判定逻辑（和原逻辑一致，但实时执行）
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isMultiPersonDialogueDetected = duration >= MULTI_DIALOGUE_MIN_DURATION &&
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peakAvgRatio in MULTI_DIALOGUE_MIN_PEAK_AVG_RATIO..MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO &&
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continuousRatio <= MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO &&
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vadRatio >= MULTI_DIALOGUE_MIN_VAD_RATIO
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return isMultiPersonDialogueDetected
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}
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/* ================= 环境基线校准（保留，录音中也会调用） ================= */
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private fun calibrateEnvBaseline(samples: FloatArray) {
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val rms = vadManager.calcRms(samples)
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// 新增：只保留低于基线+阈值的有效值，过滤突发噪音
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@ -160,7 +239,7 @@ class VoiceController(
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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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envNoiseBuffer.addLast(validRms) // 用过滤后的有效值更新
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currentEnvBaseline = envNoiseBuffer.maxOrNull() ?: 0.001f
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}
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}
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@ -180,6 +259,8 @@ class VoiceController(
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audioBuffer.clear()
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vadManager.reset()
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vadStarted = false
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// 重置实时统计变量
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resetRealtimeStats()
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}
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inKwsObserve = true
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@ -195,16 +276,21 @@ class VoiceController(
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recordingStartMs = System.currentTimeMillis()
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audioBuffer.clear()
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audioBuffer.addAll(preBuffer)
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// 初始化实时统计变量
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resetRealtimeStats()
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state = VoiceState.RECORDING
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}
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private fun onVadEnd(avgEnergy: Float, peakRms: Float) {
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if (state != VoiceState.RECORDING) return
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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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// 优先使用实时统计的能量值，避免重复计算
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val realAvgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else avgEnergy
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val realPeakRms = if (realtimePeakRms > 0) realtimePeakRms else peakRms
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finishSentence(realAvgEnergy, realPeakRms)
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}
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/* ================= 结束录音（核心：多人对话过滤） ================= */
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/* ================= 结束录音（核心：复用实时计算结果） ================= */
|
||||
private fun finishSentence(avgEnergy: Float = 0f, peakRms: Float = 0f) {
|
||||
val now = System.currentTimeMillis()
|
||||
val duration = now - recordingStartMs
|
||||
@ -219,24 +305,13 @@ class VoiceController(
|
||||
val vadRatio = vadManager.activeSpeechRatio()
|
||||
val peakAvgRatio = if (avgEnergy > 0f) peakRms / avgEnergy else 0f
|
||||
|
||||
// 获取VAD帧统计
|
||||
val totalFrames = vadManager.getTotalFrames()
|
||||
val speechFrames = vadManager.getSpeechFrames()
|
||||
val continuousSpeechFrames = vadManager.getContinuousSpeechFrames()
|
||||
val peakPositionRatio = vadManager.getPeakPositionRatio()
|
||||
|
||||
// 直接复用实时统计的帧数据，无需重新获取
|
||||
LogUtils.d(TAG, "📊 录音信息 | 时长: $duration ms | 能量: $avgEnergy | 峰均比: $peakAvgRatio | 基线: $currentEnvBaseline")
|
||||
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")
|
||||
LogUtils.d(TAG, "📊 实时帧统计 | 总帧: $realtimeTotalFrames | 语音帧: $realtimeSpeechFrames | 连续语音帧: $realtimeContinuousSpeechFrames")
|
||||
|
||||
// ========== 核心修改：第一步过滤多人对话垃圾语音 ==========
|
||||
val continuousRatio = if (speechFrames > 0) continuousSpeechFrames.toFloat() / speechFrames else 0f
|
||||
val isMultiPersonDialogue = duration >= MULTI_DIALOGUE_MIN_DURATION && // 时长≥2.5秒
|
||||
peakAvgRatio in MULTI_DIALOGUE_MIN_PEAK_AVG_RATIO..MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO && // 峰均比0.4~2.5
|
||||
continuousRatio <= MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO && // 连续帧占比≤0.3（多人轮流说，断层多）
|
||||
vadRatio >= MULTI_DIALOGUE_MIN_VAD_RATIO // 有效帧占比≥0.55（整体语音占比高）
|
||||
|
||||
if (isMultiPersonDialogue) {
|
||||
LogUtils.w(TAG, "❌ 过滤多人对话垃圾语音 | 时长: $duration ms | 连续占比: $continuousRatio | 有效占比: $vadRatio | 峰均比: $peakAvgRatio")
|
||||
// 若录音中已识别出多人对话，直接过滤
|
||||
if (isMultiPersonDialogueDetected) {
|
||||
LogUtils.w(TAG, "❌ 过滤多人对话垃圾语音（实时识别） | 时长: $duration ms")
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
@ -248,6 +323,7 @@ class VoiceController(
|
||||
audioBuffer.clear()
|
||||
state = VoiceState.UPLOADING
|
||||
onFinalAudio(audio)
|
||||
resetRealtimeStats() // 重置实时统计
|
||||
return
|
||||
}
|
||||
|
||||
@ -261,8 +337,10 @@ class VoiceController(
|
||||
}
|
||||
|
||||
// ========== 3. 非连续判定：极度宽松 ==========
|
||||
val continuousRatio = if (realtimeSpeechFrames > 0) realtimeContinuousSpeechFrames.toFloat() / realtimeSpeechFrames else 0f
|
||||
val peakPositionRatio = vadManager.getPeakPositionRatio()
|
||||
val isDiscontinuous = continuousRatio < MIN_CONTINUOUS_FRAME_RATIO &&
|
||||
speechFrames < MIN_EFFECTIVE_SPEECH_FRAMES &&
|
||||
realtimeSpeechFrames < MIN_EFFECTIVE_SPEECH_FRAMES &&
|
||||
peakPositionRatio > MAX_PEAK_POSITION_RATIO
|
||||
if (isDiscontinuous) {
|
||||
LogUtils.w(TAG, "❌ 非连续杂音过滤 | 连续占比: $continuousRatio < $MIN_CONTINUOUS_FRAME_RATIO")
|
||||
@ -327,9 +405,22 @@ class VoiceController(
|
||||
audioBuffer.clear()
|
||||
state = VoiceState.UPLOADING
|
||||
onFinalAudio(audio)
|
||||
resetRealtimeStats() // 重置实时统计
|
||||
LogUtils.i(TAG, "✅ 低能量语音通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene}")
|
||||
}
|
||||
|
||||
/* ================= 新增：重置实时统计变量 ================= */
|
||||
private fun resetRealtimeStats() {
|
||||
realtimeEnergySum = 0f
|
||||
realtimeEnergyCount = 0
|
||||
realtimePeakRms = 0f
|
||||
realtimeTotalFrames = 0
|
||||
realtimeSpeechFrames = 0
|
||||
realtimeContinuousSpeechFrames = 0
|
||||
realtimeLastFrameIsSpeech = false
|
||||
isMultiPersonDialogueDetected = false
|
||||
}
|
||||
|
||||
/* ================= 播放/上传/Reset 回调 ================= */
|
||||
fun onPlayStartPrompt() {
|
||||
LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline")
|
||||
@ -362,8 +453,7 @@ class VoiceController(
|
||||
VoiceState.WAIT_SPEECH_COOLDOWN
|
||||
}
|
||||
}
|
||||
private var lastInvalidResetMs = 0L
|
||||
private val INVALID_RESET_DEBOUNCE_MS = 1500L // 1.5秒内不重复重置
|
||||
|
||||
private fun resetToWaitSpeech() {
|
||||
LogUtils.d(TAG, "🔄 重置到等待说话 | 基线: $currentEnvBaseline")
|
||||
val now = System.currentTimeMillis()
|
||||
@ -375,6 +465,7 @@ class VoiceController(
|
||||
audioBuffer.clear()
|
||||
vadManager.reset()
|
||||
vadStarted = false
|
||||
resetRealtimeStats() // 重置实时统计
|
||||
state = VoiceState.WAIT_SPEECH
|
||||
if (waitSpeechFailStartMs == 0L) waitSpeechFailStartMs = System.currentTimeMillis()
|
||||
}
|
||||
@ -390,6 +481,7 @@ class VoiceController(
|
||||
waitSpeechFailStartMs = 0L
|
||||
envNoiseBuffer.clear()
|
||||
currentEnvBaseline = 0.001f
|
||||
resetRealtimeStats() // 重置实时统计
|
||||
LogUtils.d(TAG, "🔄 环境基线已重置 | 新基线: $currentEnvBaseline")
|
||||
state = VoiceState.WAIT_WAKEUP
|
||||
}
|
||||
@ -399,6 +491,7 @@ class VoiceController(
|
||||
wakeupManager.release()
|
||||
vadManager.reset()
|
||||
envNoiseBuffer.clear()
|
||||
resetRealtimeStats() // 重置实时统计
|
||||
}
|
||||
|
||||
private fun cachePreBuffer(samples: FloatArray) {
|
||||
@ -407,4 +500,7 @@ class VoiceController(
|
||||
if (preBuffer.size > PRE_BUFFER_SIZE) preBuffer.removeFirst()
|
||||
}
|
||||
}
|
||||
|
||||
// ========== 补充：MIN_EFFECTIVE_SPEECH_RMS 常量（和VadManager对齐） ==========
|
||||
private val MIN_EFFECTIVE_SPEECH_RMS = 0.001f
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user