去除多余的检测

app/src/main/java/com/zs/smarthuman/sherpa/VadManager.kt

@@ -2,203 +2,100 @@ package com.zs.smarthuman.sherpa
 
 import android.content.res.AssetManager
 import com.blankj.utilcode.util.LogUtils
+import com.k2fsa.sherpa.onnx.SileroVadModelConfig
 import com.k2fsa.sherpa.onnx.Vad
+import com.k2fsa.sherpa.onnx.VadModelConfig
 import com.k2fsa.sherpa.onnx.getVadModelConfig
 import kotlin.math.sqrt
 
 class VadManager(
     assetManager: AssetManager,
     private val onSpeechStart: () -> Unit,
-    private val onSpeechEnd: (avgEnergy: Float, peakRms: Float) -> Unit
+    private val onSpeechEnd: () -> Unit
 ) {
-    private val TAG = "SmartHuman-VadManager"
+    private val TAG = "VadManager"
     private val vad: Vad
+
     private var isSpeaking = false
     private var lastSpeechTime = 0L
 
-    // ========== 核心调整：区分活跃期/收尾期阈值 ==========
+    private val ACTIVE_END_SILENCE_MS = 1500L
-    // 说话活跃期（容忍停顿）
+    private val ACTIVE_CONSECUTIVE_FRAMES = 10
-    private val ACTIVE_END_SILENCE_MS = 1500L        // 活跃期基础静默（保留停顿容忍）
+    private val FINAL_END_SILENCE_MS = 800L
-    private val ACTIVE_CONSECUTIVE_FRAMES = 10       // 活跃期连续静音帧
+    private val FINAL_CONSECUTIVE_FRAMES = 5
-    // 说话收尾期（快速结束）
+    private val FINAL_PHASE_TRIGGER_MS = 1000L
-    private val FINAL_END_SILENCE_MS = 800L          // 收尾期基础静默（缩短到800ms）
+    private val MAX_SILENCE_AFTER_SPEECH_MS = 2000L
-    private val FINAL_CONSECUTIVE_FRAMES = 5         // 收尾期连续静音帧（5帧=100ms）
-    // 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
-    private val FINAL_PHASE_TRIGGER_MS = 1000L       // 1秒无有效语音，进入收尾期
 
-    private val MAX_SILENCE_AFTER_SPEECH_MS = 2000L  // 兜底阈值从3秒降到2秒
-
-    // 原有基础配置
     private val MIN_EFFECTIVE_SPEECH_RMS = 0.001f
-    private val ENV_BASELINE_FACTOR = 1.2f
-    private var envBaselineRms = 0.0005f
-    private var lastEffectiveSpeechTime = 0L
 
     private var consecutiveSilenceFrames = 0
-
-    // 新增：收尾期标记
     private var isInFinalPhase = false
-
+    private var lastEffectiveSpeechTime = 0L
-    // 统计变量
-    private var speechEnergySum = 0f
-    private var speechFrameCount = 0
-    private var peakRms = 0f
-    private var totalFrames = 0
-    private var speechFrames = 0
-    private var continuousSpeechFrames = 0
-    private var lastFrameIsSpeech = false
-    private var peakPosition = 0
-    private var frameIndex = 0
-    private var activeFrameCount = 0
-    private var activeSpeechFrameCount = 0
 
     init {
-        val config = getVadModelConfig(0) ?: throw IllegalStateException("[$TAG] VAD config not found")
+        val config = getVadModelConfig(0)
-        vad = Vad(assetManager, config)
+            ?: throw IllegalStateException("[$TAG] VAD config not found")
+        vad = Vad(assetManager, VadModelConfig(sileroVadModelConfig = SileroVadModelConfig(model = "silero_vad.onnx", threshold = 0.2f)))
         LogUtils.i(TAG, "✅ VAD 初始化成功")
     }
 
     fun accept(samples: FloatArray) {
         val now = System.currentTimeMillis()
+
         vad.acceptWaveform(samples)
         val vadHasSpeech = vad.isSpeechDetected()
         val rms = calcRms(samples)
 
-        // 环境基线更新
+        val isEffectiveSpeech = vadHasSpeech && rms >= MIN_EFFECTIVE_SPEECH_RMS
-        if (!vadHasSpeech || rms < MIN_EFFECTIVE_SPEECH_RMS) {
-            envBaselineRms = (envBaselineRms * 0.9f) + (rms * 0.1f)
-        }
-        val effectiveSpeechThreshold = maxOf(MIN_EFFECTIVE_SPEECH_RMS, envBaselineRms * ENV_BASELINE_FACTOR)
-        val isEffectiveSpeech = vadHasSpeech && rms >= effectiveSpeechThreshold
 
-        // ========== 核心优化：动态判定收尾期 ==========
         if (isEffectiveSpeech) {
             lastEffectiveSpeechTime = now
-            isInFinalPhase = false // 有有效语音，退出收尾期
+            isInFinalPhase = false
+            lastSpeechTime = now
+            consecutiveSilenceFrames = 0
         } else {
-            // 最后一次有效语音后超过1秒，进入收尾期
+            consecutiveSilenceFrames++
             if (now - lastEffectiveSpeechTime >= FINAL_PHASE_TRIGGER_MS) {
                 isInFinalPhase = true
             }
         }
 
-        // 语音能量统计
+        val (endSilenceMs, endFrames) =
-        if (isEffectiveSpeech) {
+            if (isInFinalPhase)
-            speechEnergySum += rms
+                FINAL_END_SILENCE_MS to FINAL_CONSECUTIVE_FRAMES
-            speechFrameCount++
+            else
-            peakRms = maxOf(peakRms, rms)
+                ACTIVE_END_SILENCE_MS to ACTIVE_CONSECUTIVE_FRAMES
-            lastSpeechTime = now
-            consecutiveSilenceFrames = 0
-            LogUtils.v(TAG, "🔊 有效语音帧 | RMS: $rms | 阈值: $effectiveSpeechThreshold | 收尾期: $isInFinalPhase")
-        } else {
-            consecutiveSilenceFrames++
-            LogUtils.v(TAG, if (vadHasSpeech) "⚠ 低能量语音帧 | RMS: $rms | 阈值: $effectiveSpeechThreshold"
-            else "🔇 静音帧 | 连续静音帧: $consecutiveSilenceFrames | 收尾期: $isInFinalPhase")
-        }
 
-        // 帧统计
-        totalFrames++
-        frameIndex++
-        if (isEffectiveSpeech) {
-            speechFrames++
-            continuousSpeechFrames = if (lastFrameIsSpeech) continuousSpeechFrames + 1 else 1
-            lastFrameIsSpeech = true
-            if (rms == peakRms) peakPosition = frameIndex
-        } else {
-            lastFrameIsSpeech = false
-        }
-
-        // ========== 核心优化：根据收尾期选择不同阈值 ==========
-        val (endSilenceMs, consecutiveFrames) = if (isInFinalPhase) {
-            Pair(FINAL_END_SILENCE_MS, FINAL_CONSECUTIVE_FRAMES)
-        } else {
-            Pair(ACTIVE_END_SILENCE_MS, ACTIVE_CONSECUTIVE_FRAMES)
-        }
-
-        // VAD状态流转
         if (isEffectiveSpeech) {
             if (!isSpeaking) {
                 isSpeaking = true
-                LogUtils.d(TAG, "🎤 有效语音开始 | 阈值: $effectiveSpeechThreshold")
                 onSpeechStart()
             }
-            activeFrameCount++
+        } else if (isSpeaking) {
-            activeSpeechFrameCount++
+            val silenceMs = now - lastSpeechTime
-        } else {
+            val effectiveSilenceMs = now - lastEffectiveSpeechTime
-            if (isSpeaking) {
-                activeFrameCount++
-                val vadSilenceDuration = now - lastSpeechTime
-                val effectiveSilenceDuration = now - lastEffectiveSpeechTime
 
-                // 触发结束条件：适配当前阶段的阈值
+            val shouldEnd =
-                val isSilenceTimeout = (vadSilenceDuration >= endSilenceMs ||
+                (silenceMs >= endSilenceMs ||
-                        effectiveSilenceDuration >= MAX_SILENCE_AFTER_SPEECH_MS) &&
+                        effectiveSilenceMs >= MAX_SILENCE_AFTER_SPEECH_MS) &&
-                        consecutiveSilenceFrames >= consecutiveFrames
+                        consecutiveSilenceFrames >= endFrames
 
-                if (isSilenceTimeout) {
+            if (shouldEnd) {
+                onSpeechEnd()
+                reset()
                 isSpeaking = false
-                    isInFinalPhase = false // 重置收尾期
+                isInFinalPhase = false
-                    val avgEnergy = if (speechFrameCount > 0) speechEnergySum / speechFrameCount else 0f
-                    LogUtils.d(TAG, """
-                        🛑 语音结束 
-                        - 有效静默时长: ${effectiveSilenceDuration}ms 
-                        - 连续静音帧: $consecutiveSilenceFrames
-                        - 平均能量: $avgEnergy | 峰值: $peakRms
-                        - 收尾期: $isInFinalPhase | 所用阈值: $endSilenceMs ms
-                    """.trimIndent())
-                    onSpeechEnd(avgEnergy, peakRms)
-                    resetStats()
-                } else {
-                    LogUtils.v(TAG, "⏳ 静默中 | 连续静音帧: $consecutiveSilenceFrames | 静默时长: ${effectiveSilenceDuration}ms | 所用阈值: $endSilenceMs ms")
             }
         }
     }
-    }
-
-    // 保留原有方法...
-    fun isSpeechDetected(): Boolean {
-        return vad.isSpeechDetected()
-    }
-
-    fun activeSpeechRatio(): Float {
-        val ratio = if (activeFrameCount == 0) 0f else activeSpeechFrameCount.toFloat() / activeFrameCount
-        LogUtils.d(TAG, "📊 语音占比: $ratio | 有效语音帧: $activeSpeechFrameCount | 总帧: $activeFrameCount")
-        return ratio
-    }
-
-    fun getTotalFrames(): Int = totalFrames
-    fun getSpeechFrames(): Int = speechFrames
-    fun getContinuousSpeechFrames(): Int = continuousSpeechFrames
-    fun getPeakPositionRatio(): Float {
-        return if (totalFrames == 0) 0f else peakPosition.toFloat() / totalFrames
-    }
 
     fun reset() {
         isSpeaking = false
         lastSpeechTime = 0L
         lastEffectiveSpeechTime = 0L
-        envBaselineRms = 0.0005f
         consecutiveSilenceFrames = 0
-        isInFinalPhase = false // 重置收尾期
+        isInFinalPhase = false
-        resetStats()
         vad.reset()
-
-        totalFrames = 0
-        speechFrames = 0
-        continuousSpeechFrames = 0
-        lastFrameIsSpeech = false
-        peakPosition = 0
-        frameIndex = 0
-
-        LogUtils.d(TAG, "🔄 VAD 状态已完全重置")
-    }
-
-    private fun resetStats() {
-        activeFrameCount = 0
-        activeSpeechFrameCount = 0
-        speechEnergySum = 0f
-        speechFrameCount = 0
-        peakRms = 0f
     }
 
     fun calcRms(samples: FloatArray): Float {

app/src/main/java/com/zs/smarthuman/sherpa/VoiceController.kt

@@ -15,7 +15,7 @@ class VoiceController(
     assetManager: AssetManager,
     private val onWakeup: () -> Unit,
     private val onFinalAudio: (FloatArray) -> Unit,
-    idleTimeoutSeconds: Int = 200,
+    idleTimeoutSeconds: Int = 10,
     maxRecordingSeconds: Int = 10,
     private val onStateChanged: ((VoiceState) -> Unit)? = null,
     private val stopBackendAudio: (() -> Unit)? = null,
@@ -30,20 +30,14 @@ class VoiceController(
         // 预缓存大小（2秒）
         private const val PRE_BUFFER_SIZE = SAMPLE_RATE * 2
 
-        // ========== 核心：分场景声纹阈值（极简版） ==========
-        private const val SPEAKER_THRESHOLD_QUIET = 0.50f    // 安静环境
-        private const val SPEAKER_THRESHOLD_NOISY = 0.45f    // 嘈杂环境（匹配你的真实相似度）
-        private const val SPEAKER_THRESHOLD_SHORT = 0.43f    // 短语音（<1秒）
-
         // 短语音判定阈值
         private const val SHORT_AUDIO_DURATION_MS = 1000L
         private const val INVALID_RESET_DEBOUNCE_MS = 1500L
         // 最小语音时长
         private const val MIN_SPEECH_MS = 800L
-        private const val MIN_EFFECTIVE_VOICE_DURATION = 400L
 
-        // 噪音场景判定阈值
+        // 统一的声纹验证阈值（不再分场景）
-        private const val NOISE_BASELINE_THRESHOLD = 0.01f
+        private const val SPEAKER_THRESHOLD = 0.45f
     }
 
     var state: VoiceState = VoiceState.WAIT_WAKEUP
@@ -53,26 +47,17 @@ class VoiceController(
             onStateChanged?.invoke(value)
         }
 
-    // 实时能量与帧统计变量
+    // 无效说话标记 + 超时类型
-    private var realtimeEnergySum = 0f
+    private var hasInvalidSpeech = false
-    private var realtimeEnergyCount = 0
+    private var currentTimeoutType: TimeoutType = TimeoutType.IDLE_TIMEOUT
-    private var realtimePeakRms = 0f
-    private var realtimeTotalFrames = 0
-    private var realtimeSpeechFrames = 0
-    private var realtimeContinuousSpeechFrames = 0
-    private var realtimeLastFrameIsSpeech = false
-    private var isMultiPersonDialogueDetected = false
     private var lastInvalidResetMs = 0L
     private val speakerManagerLock = ReentrantLock()
 
-    // 环境噪音状态标记
-    private var isNoisyEnvironment = false
-
     private val wakeupManager = WakeupManager(assetManager, onWakeup)
     private val vadManager = VadManager(
         assetManager,
         onSpeechStart = { onVadStart() },
-        onSpeechEnd = { avgEnergy, peakRms -> onVadEnd(avgEnergy, peakRms) }
+        onSpeechEnd = { onVadEnd() }
     )
 
     private val audioBuffer = mutableListOf<Float>()
@@ -92,50 +77,6 @@ class VoiceController(
     private val idleTimeoutMs = idleTimeoutSeconds * 1000L
     private val maxRecordingMs = maxRecordingSeconds * 1000L
 
-    // 分场景动态系数（保留原有逻辑）
-    private val BASELINE_WINDOW_SIZE = 50
-    private val envNoiseBuffer = ArrayDeque<Float>(BASELINE_WINDOW_SIZE)
-    private var currentEnvBaseline = 0.001f
-
-
-    // 分场景动态系数
-    private val BASELINE_QUIET_THRESHOLD = 0.005f
-    private val SHORT_SPEECH_ENERGY_COEFF_QUIET = 1.5f
-    private val SHORT_SPEECH_ENERGY_COEFF_NOISY = 2.0f
-    private val LONG_SPEECH_ENERGY_COEFF_QUIET = 2.5f
-    private val LONG_SPEECH_ENERGY_COEFF_NOISY = 3.5f
-    private val SHORT_SPEECH_VAD_COEFF = 0.05f
-    private val LONG_SPEECH_VAD_COEFF = 0.10f
-    private val SHORT_SPEECH_MIN_SCORE = 1
-    private val LONG_SPEECH_MIN_SCORE = 1
-
-    // 其他过滤参数
-    private val MAX_FAR_FIELD_ENERGY = 0.015f
-    private val MIN_VALID_PEAK_AVG_RATIO = 0.5f
-    private val MIN_CONTINUOUS_FRAME_RATIO = 0.1f
-    private val MAX_PEAK_POSITION_RATIO = 0.95f
-    private val MIN_EFFECTIVE_SPEECH_FRAMES = 3
-    private val SHORT_SPEECH_MIN = 500L
-    private val SHORT_SPEECH_MAX = 2000L
-
-    // 多人对话过滤配置
-    private val MULTI_DIALOGUE_MIN_DURATION = 2500L
-    private val MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO = 2.5f
-    private val MULTI_DIALOGUE_MIN_PEAK_AVG_RATIO = 0.4f
-    private val MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO = 0.3f
-    private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f
-
-    // 微弱人声过滤配置
-    private val MIN_VOICE_FRAME_RATIO = 0.08f
-    private val MIN_PEAK_ENERGY_RATIO = 1.5f
-    private val NORMAL_VOICE_ENERGY_THRESHOLD = 0.008f
-    private val MIN_CONTINUOUS_VOICE_FRAMES = 1
-    private val MIN_EFFECTIVE_SPEECH_RMS = 0.0005f
-
-    // 无效说话标记 + 超时类型
-    private var hasInvalidSpeech = false
-    private var currentTimeoutType: TimeoutType = TimeoutType.IDLE_TIMEOUT
-
     // 声纹验证相关
     private val CURRENT_USER_ID = "current_wakeup_user"
     private val ENABLE_STRICT_SPEAKER_VERIFY = true
@@ -200,12 +141,6 @@ class VoiceController(
 
         val now = System.currentTimeMillis()
 
-        if (state == VoiceState.WAIT_WAKEUP) {
-            calibrateEnvBaseline(samples)
-            isNoisyEnvironment = currentEnvBaseline >= NOISE_BASELINE_THRESHOLD
-            LogUtils.d(TAG, "📊 环境状态 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
-        }
-
         when (state) {
             VoiceState.WAIT_WAKEUP,
             VoiceState.PLAYING_PROMPT,
@@ -246,81 +181,15 @@ class VoiceController(
                 audioBuffer.addAll(samples.asList())
                 vadManager.accept(samples)
 
-                calibrateEnvBaseline(samples)
+                // 仅保留最大录音时长判断
-                updateRealtimeEnergy(samples)
-                updateRealtimeFrameStats()
-                isNoisyEnvironment = currentEnvBaseline >= NOISE_BASELINE_THRESHOLD
-
-                if (checkMultiPersonDialogueRealtime(now)) {
-                    LogUtils.w(TAG, "🚨 录音中识别出多人对话，提前终止")
-                    finishSentence(realtimeEnergySum / realtimeEnergyCount, realtimePeakRms)
-                    return
-                }
-
                 if (System.currentTimeMillis() - recordingStartMs > maxRecordingMs) {
-                    LogUtils.w(TAG, "⏱ Max recording reached | 当前环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+                    LogUtils.w(TAG, "⏱ Max recording reached")
-                    finishSentence(realtimeEnergySum / realtimeEnergyCount, realtimePeakRms)
+                    finishSentence()
                 }
             }
         }
     }
 
-    /* ================= 实时能量更新 ================= */
-    private fun updateRealtimeEnergy(samples: FloatArray) {
-        val rms = vadManager.calcRms(samples)
-        val effectiveThreshold = if (isNoisyEnvironment) currentEnvBaseline * 1.8f else MIN_EFFECTIVE_SPEECH_RMS
-        if (rms >= effectiveThreshold) {
-            realtimeEnergySum += rms
-            realtimeEnergyCount++
-            realtimePeakRms = maxOf(realtimePeakRms, rms)
-        }
-    }
-
-    /* ================= 实时帧统计 ================= */
-    private fun updateRealtimeFrameStats() {
-        realtimeTotalFrames = vadManager.getTotalFrames()
-        realtimeSpeechFrames = vadManager.getSpeechFrames()
-        realtimeContinuousSpeechFrames = vadManager.getContinuousSpeechFrames()
-        val currentFrameIsSpeech = vadManager.isSpeechDetected()
-        if (currentFrameIsSpeech) {
-            realtimeContinuousSpeechFrames = if (realtimeLastFrameIsSpeech) realtimeContinuousSpeechFrames + 1 else 1
-        } else {
-            realtimeContinuousSpeechFrames = 0
-        }
-        realtimeLastFrameIsSpeech = currentFrameIsSpeech
-    }
-
-    /* ================= 多人对话检测 ================= */
-    private fun checkMultiPersonDialogueRealtime(now: Long): Boolean {
-        val duration = now - recordingStartMs
-        if (duration < MULTI_DIALOGUE_MIN_DURATION) return false
-
-        val avgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else 0f
-        val peakAvgRatio = if (avgEnergy > 0) realtimePeakRms / avgEnergy else 0f
-        val continuousRatio = if (realtimeSpeechFrames > 0) realtimeContinuousSpeechFrames.toFloat() / realtimeSpeechFrames else 0f
-        val vadRatio = vadManager.activeSpeechRatio()
-
-        isMultiPersonDialogueDetected = duration >= MULTI_DIALOGUE_MIN_DURATION &&
-                peakAvgRatio in MULTI_DIALOGUE_MIN_PEAK_AVG_RATIO..MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO &&
-                continuousRatio <= MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO &&
-                vadRatio >= MULTI_DIALOGUE_MIN_VAD_RATIO
-
-        return isMultiPersonDialogueDetected
-    }
-
-    /* ================= 环境基线校准 ================= */
-    private fun calibrateEnvBaseline(samples: FloatArray) {
-        val rms = vadManager.calcRms(samples)
-        val validRms = if (rms < currentEnvBaseline + 0.002f) rms else currentEnvBaseline
-        if (rms < 0.015f) {
-            if (envNoiseBuffer.size >= BASELINE_WINDOW_SIZE) {
-                envNoiseBuffer.removeFirst()
-            }
-            envNoiseBuffer.addLast(validRms)
-            currentEnvBaseline = envNoiseBuffer.maxOrNull() ?: 0.001f
-        }
-    }
-
     /* ================= 唤醒处理 ================= */
     private fun handleWakeupEvent() {
         if (state == VoiceState.UPLOADING) return
@@ -339,207 +208,73 @@ class VoiceController(
             audioBuffer.clear()
             vadManager.reset()
             vadStarted = false
-            resetRealtimeStats()
         }
 
         inKwsObserve = true
         kwsObserveStartMs = System.currentTimeMillis()
         onWakeup()
-        LogUtils.d(TAG, "🔔 唤醒成功 | 环境基线: $currentEnvBaseline")
+        LogUtils.d(TAG, "🔔 唤醒成功")
     }
 
     private fun onVadStart() {
         if (state != VoiceState.WAIT_SPEECH) return
-        LogUtils.d(TAG, "🎤 REAL VAD START | 环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🎤 REAL VAD START")
         vadStarted = true
         recordingStartMs = System.currentTimeMillis()
         audioBuffer.clear()
         audioBuffer.addAll(preBuffer)
-        resetRealtimeStats()
         state = VoiceState.RECORDING
     }
 
-    private fun onVadEnd(avgEnergy: Float, peakRms: Float) {
+    private fun onVadEnd() {
         if (state != VoiceState.RECORDING) return
-        LogUtils.d(TAG, "🧠 VAD END | 环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🧠 VAD END")
-        val realAvgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else avgEnergy
+        finishSentence()
-        val realPeakRms = if (realtimePeakRms > 0) realtimePeakRms else peakRms
-        finishSentence(realAvgEnergy, realPeakRms)
-    }
-
-    /* ================= 微弱人声过滤 ================= */
-    private fun filterWeakVoice(duration: Long, avgEnergy: Float, peakRms: Float): Boolean {
-        if (duration < MIN_EFFECTIVE_VOICE_DURATION) {
-            LogUtils.w(TAG, "❌ 微弱人声过滤：时长${duration}ms < ${MIN_EFFECTIVE_VOICE_DURATION}ms")
-            return true
-        }
-
-        val voiceFrameRatio = if (realtimeTotalFrames > 0) realtimeSpeechFrames.toFloat() / realtimeTotalFrames else 0f
-        if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && voiceFrameRatio < MIN_VOICE_FRAME_RATIO) {
-            LogUtils.w(TAG, "❌ 微弱人声过滤：帧占比${voiceFrameRatio} < ${MIN_VOICE_FRAME_RATIO}")
-            return true
-        }
-
-        val peakBaselineRatio = peakRms / currentEnvBaseline
-        if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && peakBaselineRatio < MIN_PEAK_ENERGY_RATIO) {
-            LogUtils.w(TAG, "❌ 微弱人声过滤：峰值/基线${peakBaselineRatio} < ${MIN_PEAK_ENERGY_RATIO}")
-            return true
-        }
-
-        if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && realtimeContinuousSpeechFrames < MIN_CONTINUOUS_VOICE_FRAMES) {
-            LogUtils.w(TAG, "❌ 微弱人声过滤：连续帧${realtimeContinuousSpeechFrames} < ${MIN_CONTINUOUS_VOICE_FRAMES}")
-            return true
-        }
-
-        val energyBaselineRatio = avgEnergy / currentEnvBaseline
-        if (avgEnergy < 0.005f && energyBaselineRatio < 1.2f) {
-            LogUtils.w(TAG, "❌ 微弱人声过滤：能量/基线${energyBaselineRatio} < 1.2")
-            return true
-        }
-
-        return false
     }
 
     /* ================= 结束录音 ================= */
-    private fun finishSentence(avgEnergy: Float = 0f, peakRms: Float = 0f) {
+    private fun finishSentence() {
         val now = System.currentTimeMillis()
         val duration = now - recordingStartMs
 
         if (!vadStarted || duration < MIN_SPEECH_MS) {
-            LogUtils.d(TAG, "❌ 语音过短: $duration ms | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+            LogUtils.d(TAG, "❌ 语音过短: $duration ms")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
-        }
-
-        if (filterWeakVoice(duration, avgEnergy, peakRms)) {
             hasInvalidSpeech = true
             resetToWaitSpeech()
             return
         }
 
         val audio = audioBuffer.toFloatArray()
-        val vadRatio = vadManager.activeSpeechRatio()
-        val peakAvgRatio = if (avgEnergy > 0f) peakRms / avgEnergy else 0f
 
-        LogUtils.d(TAG, "📊 录音信息 | 时长: $duration ms | 能量: $avgEnergy | 峰均比: $peakAvgRatio | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        // 声纹验证（保留核心逻辑）
-        LogUtils.d(TAG, "📊 实时帧统计 | 总帧: $realtimeTotalFrames | 语音帧: $realtimeSpeechFrames | 连续语音帧: $realtimeContinuousSpeechFrames")
-
-        if (isMultiPersonDialogueDetected) {
-            LogUtils.w(TAG, "❌ 过滤多人对话垃圾语音 | 时长: $duration ms")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
-        }
-
-        // 声纹验证（核心极简版）
         if (ENABLE_STRICT_SPEAKER_VERIFY) {
             val isCurrentUser = verifySpeaker(audio)
             if (!isCurrentUser) {
-                LogUtils.w(TAG, "❌ 非当前唤醒用户，拒绝语音 | 录音时长: $duration ms | 嘈杂环境: $isNoisyEnvironment")
+                LogUtils.w(TAG, "❌ 非当前唤醒用户，拒绝语音 | 录音时长: $duration ms")
                 hasInvalidSpeech = true
                 resetToWaitSpeech()
                 return
             }
-            LogUtils.d(TAG, "✅ 当前用户语音，继续处理 | 录音时长: $duration ms | 嘈杂环境: $isNoisyEnvironment")
+            LogUtils.d(TAG, "✅ 当前用户语音，继续处理 | 录音时长: $duration ms")
-        }
-
-
-        // 远场过滤
-        val isFarField = avgEnergy < MAX_FAR_FIELD_ENERGY
-        val isInvalidPeakRatio = peakAvgRatio < MIN_VALID_PEAK_AVG_RATIO
-        if (isFarField && isInvalidPeakRatio) {
-            LogUtils.w(TAG, "❌ 远场/无效语音过滤 | 能量: $avgEnergy < $MAX_FAR_FIELD_ENERGY")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
-        }
-
-        // 非连续判定
-        val continuousRatio = if (realtimeSpeechFrames > 0) realtimeContinuousSpeechFrames.toFloat() / realtimeSpeechFrames else 0f
-        val peakPositionRatio = vadManager.getPeakPositionRatio()
-        val isDiscontinuous = continuousRatio < MIN_CONTINUOUS_FRAME_RATIO &&
-                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")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
-        }
-
-        // 分场景阈值过滤
-        val isQuietEnv = currentEnvBaseline < BASELINE_QUIET_THRESHOLD
-        val thresholdConfig = when {
-            duration in SHORT_SPEECH_MIN..SHORT_SPEECH_MAX -> {
-                val coeff = if (isQuietEnv) SHORT_SPEECH_ENERGY_COEFF_QUIET else SHORT_SPEECH_ENERGY_COEFF_NOISY
-                val energyThreshold = currentEnvBaseline * coeff
-                ThresholdConfig(energyThreshold, SHORT_SPEECH_VAD_COEFF, SHORT_SPEECH_MIN_SCORE, "短语音")
-            }
-            else -> {
-                val coeff = if (isQuietEnv) LONG_SPEECH_ENERGY_COEFF_QUIET else LONG_SPEECH_ENERGY_COEFF_NOISY
-                val energyThreshold = currentEnvBaseline * coeff
-                ThresholdConfig(energyThreshold, LONG_SPEECH_VAD_COEFF, LONG_SPEECH_MIN_SCORE, "长语音")
-            }
-        }
-
-        val energyPass = avgEnergy >= thresholdConfig.energyThreshold
-        val vadRatioPass = vadRatio >= thresholdConfig.vadRatioThreshold
-        if (!energyPass || !vadRatioPass) {
-            LogUtils.w(TAG, "❌ 低能量语音阈值过滤 | 能量: $avgEnergy < ${thresholdConfig.energyThreshold} | 占比: $vadRatio < ${thresholdConfig.vadRatioThreshold} | 场景: ${thresholdConfig.scene}")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
-        }
-
-        // 评分判定
-        var score = 0
-        score += when {
-            duration >= 4000 -> 3
-            duration >= 2500 -> 2
-            else -> 1
-        }
-        score += if (avgEnergy >= thresholdConfig.energyThreshold) 1 else 0
-        score += if (continuousRatio >= MIN_CONTINUOUS_FRAME_RATIO) 1 else 0
-
-        val pass = score >= thresholdConfig.minScore
-        if (!pass) {
-            LogUtils.w(TAG, "❌ 评分不足过滤 | 总分: $score < ${thresholdConfig.minScore} | 场景: ${thresholdConfig.scene}")
-            hasInvalidSpeech = true
-            resetToWaitSpeech()
-            return
         }
 
         // 最终通过
         audioBuffer.clear()
         state = VoiceState.UPLOADING
         onFinalAudio(audio)
-        resetRealtimeStats()
         hasInvalidSpeech = false
-        LogUtils.i(TAG, "✅ 语音通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene} | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.i(TAG, "✅ 语音通过 | 时长: $duration ms")
-    }
-
-    /* ================= 重置实时统计 ================= */
-    private fun resetRealtimeStats() {
-        realtimeEnergySum = 0f
-        realtimeEnergyCount = 0
-        realtimePeakRms = 0f
-        realtimeTotalFrames = 0
-        realtimeSpeechFrames = 0
-        realtimeContinuousSpeechFrames = 0
-        realtimeLastFrameIsSpeech = false
-        isMultiPersonDialogueDetected = false
     }
 
     /* ================= 播放/上传回调 ================= */
     fun onPlayStartPrompt() {
-        LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🎵 播放提示音")
         state = VoiceState.PLAYING_PROMPT
     }
 
     fun onPlayEndPrompt() {
         speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
-        LogUtils.d(TAG, "🎵 提示音结束 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🎵 提示音结束")
         state = VoiceState.WAIT_SPEECH_COOLDOWN
     }
 
@@ -548,19 +283,19 @@ class VoiceController(
             LogUtils.w(TAG, "🎶 非上传完成状态，禁止切换到 PLAYING_BACKEND | 当前状态: $state")
             return
         }
-        LogUtils.d(TAG, "🎶 开始播放后台音频 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🎶 开始播放后台音频")
         state = VoiceState.PLAYING_BACKEND
     }
 
     fun onPlayEndBackend() {
         speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
-        LogUtils.d(TAG, "🎶 后台音频结束 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🎶 后台音频结束")
         state = VoiceState.WAIT_SPEECH_COOLDOWN
     }
 
     fun onUploadFinished(success: Boolean) {
         if (state != VoiceState.UPLOADING) return
-        LogUtils.d(TAG, "📤 上传完成 | 成功: $success | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "📤 上传完成 | 成功: $success")
 
         if (!success) {
             speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
@@ -569,7 +304,7 @@ class VoiceController(
     }
 
     private fun resetToWaitSpeech() {
-        LogUtils.d(TAG, "🔄 重置到等待说话 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment | 已标记无效说话: $hasInvalidSpeech")
+        LogUtils.d(TAG, "🔄 重置到等待说话 | 已标记无效说话: $hasInvalidSpeech")
         val now = System.currentTimeMillis()
         if (now - lastInvalidResetMs < INVALID_RESET_DEBOUNCE_MS) {
             LogUtils.d(TAG, "🛡 防抖：1.5秒内重复无效语音，跳过重置")
@@ -579,13 +314,12 @@ class VoiceController(
         audioBuffer.clear()
         vadManager.reset()
         vadStarted = false
-        resetRealtimeStats()
         state = VoiceState.WAIT_SPEECH
         if (waitSpeechFailStartMs == 0L) waitSpeechFailStartMs = System.currentTimeMillis()
     }
 
     private fun resetAll() {
-        LogUtils.d(TAG, "🔄 重置所有状态 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment | 本次超时类型: $currentTimeoutType")
+        LogUtils.d(TAG, "🔄 重置所有状态 | 本次超时类型: $currentTimeoutType")
         audioBuffer.clear()
         preBuffer.clear()
         vadManager.reset()
@@ -593,24 +327,17 @@ class VoiceController(
         vadStarted = false
         waitSpeechStartMs = 0L
         waitSpeechFailStartMs = 0L
-        envNoiseBuffer.clear()
-        currentEnvBaseline = 0.001f
-        isNoisyEnvironment = false
-        resetRealtimeStats()
         hasInvalidSpeech = false
         currentTimeoutType = TimeoutType.IDLE_TIMEOUT
         state = VoiceState.WAIT_WAKEUP
     }
 
     fun release() {
-        LogUtils.d(TAG, "🔌 释放资源 | 最终基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
+        LogUtils.d(TAG, "🔌 释放资源")
         wakeupManager.release()
         vadManager.reset()
-        envNoiseBuffer.clear()
-        resetRealtimeStats()
         hasInvalidSpeech = false
         currentTimeoutType = TimeoutType.IDLE_TIMEOUT
-        isNoisyEnvironment = false
 
         runCatching {
             SpeakerRecognition.extractor.release()
@@ -638,24 +365,14 @@ class VoiceController(
         }
     }
 
-    // 阈值配置数据类
-    private data class ThresholdConfig(
-        val energyThreshold: Float,
-        val vadRatioThreshold: Float,
-        val minScore: Int,
-        val scene: String
-    )
-
-    /* ================= 核心：极简版声纹验证 ================= */
     private fun verifySpeaker(audio: FloatArray): Boolean {
         if (audio.isEmpty()) {
             LogUtils.w(TAG, "❌ 待验证音频为空，声纹验证失败")
             return false
         }
 
-        // 1. 裁剪音频：只保留本次录音的有效部分（解决时长不匹配问题）
+        // 1. 裁剪音频：只保留本次录音的有效部分
         val audioDurationMs = (audio.size.toFloat() / SAMPLE_RATE * 1000).toLong()
-        // 只保留最后 N 毫秒的音频（N = 实际录音时长），避免缓存旧音频
         val validAudio = if (audioDurationMs > 0) {
             val validSampleCount = (audioDurationMs * SAMPLE_RATE / 1000).toInt()
             if (validSampleCount < audio.size) {
@@ -667,45 +384,44 @@ class VoiceController(
             audio
         }
 
-        // 2. 分场景选阈值（无容错，只调阈值）
-        val finalThreshold = when {
-            audioDurationMs < SHORT_AUDIO_DURATION_MS -> SPEAKER_THRESHOLD_SHORT
-            isNoisyEnvironment -> SPEAKER_THRESHOLD_NOISY
-            else -> SPEAKER_THRESHOLD_QUIET
-        }
-
         var stream: OnlineStream? = null
-        return try {
-            stream = SpeakerRecognition.extractor.createStream()
-            stream.acceptWaveform(samples = validAudio, sampleRate = SAMPLE_RATE) // 用裁剪后的音频验证
-            stream.inputFinished()
 
-            if (!SpeakerRecognition.extractor.isReady(stream)) {
+        // 使用 runCatching 统一处理异常
+        return runCatching {
+            stream = SpeakerRecognition.extractor.createStream()
+
+            // 处理音频数据
+            stream?.acceptWaveform(samples = validAudio, sampleRate = SAMPLE_RATE)
+            stream?.inputFinished()
+
+            // 检查 stream 是否就绪
+            if (stream == null || !SpeakerRecognition.extractor.isReady(stream)) {
                 LogUtils.w(TAG, "❌ 音频Stream未就绪，验证失败")
-                return false
+                return@runCatching false
             }
 
+            // 计算特征并验证
             val embedding = SpeakerRecognition.extractor.compute(stream)
-
-            // 3. 纯验证逻辑：过就过，不过就拒绝
             speakerManagerLock.withLock {
                 val verifyPass = SpeakerRecognition.manager.verify(
                     name = CURRENT_USER_ID,
                     embedding = embedding,
-                    threshold = finalThreshold
+                    threshold = SPEAKER_THRESHOLD
                 )
 
-                // 打印关键信息（补充裁剪后时长）
+                LogUtils.d(TAG, "📊 声纹验证 | 统一阈值: $SPEAKER_THRESHOLD | 通过: $verifyPass | 验证时长: ${(validAudio.size.toFloat()/SAMPLE_RATE*1000).toLong()}ms")
-                LogUtils.d(TAG, "📊 声纹验证 | 阈值: $finalThreshold | 通过: $verifyPass | 嘈杂环境: $isNoisyEnvironment | 原始时长: ${audioDurationMs}ms | 验证时长: ${(validAudio.size.toFloat()/SAMPLE_RATE*1000).toLong()}ms")
+                verifyPass
-
-                // 无任何容错：验证结果就是最终结果
-                return verifyPass
             }
-        } catch (e: Exception) {
+        }.onFailure { e ->
+            // 处理所有异常情况
             LogUtils.e(TAG, "❌ 声纹验证异常，拒绝", e)
-            return false
+        }.also {
-        } finally {
+            // 确保 stream 资源释放
+            runCatching {
                 stream?.release()
+            }.onFailure { e ->
+                LogUtils.w(TAG, "⚠️ 释放 Stream 资源失败", e)
             }
+        }.getOrDefault(false) // 异常时默认返回 false
     }
 }

app/src/main/java/com/zs/smarthuman/sherpa/WakeupManager.kt

@@ -26,7 +26,8 @@ class WakeupManager(assetManager: AssetManager, function: () -> Unit) {
         val config = KeywordSpotterConfig(
             featConfig = featConfig,
             modelConfig = modelConfig,
-            keywordsFile = keywordsFile
+            keywordsFile = keywordsFile,
+            keywordsThreshold = 0.2f
         )
 
         kws = KeywordSpotter(assetManager, config)
@@ -40,9 +41,9 @@ class WakeupManager(assetManager: AssetManager, function: () -> Unit) {
     /** ⭐ 永远喂 KWS */
     fun acceptAudio(samples: FloatArray) {
         val s = stream ?: return
-        for (i in samples.indices) {
+//        for (i in samples.indices) {
-            samples[i] *= 2.5f
+//            samples[i] *= 2.5f
-        }
+//        }
         s.acceptWaveform(samples, sampleRate)
 
         while (kws.isReady(s)) {

app/src/main/java/com/zs/smarthuman/ui/MainActivity.kt

@@ -159,27 +159,27 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
             }
         }
 
-        mViewModel?.uploadVoiceLiveData?.observe(this) {
+//        mViewModel?.uploadVoiceLiveData?.observe(this) {
-            when (it) {
+//            when (it) {
-                is ApiResult.Error -> {
+//                is ApiResult.Error -> {
-                    Toaster.showShort("上传失败")
+//                    Toaster.showShort("上传失败")
-                    voiceController?.onUploadFinished(true)
+//                    voiceController?.onUploadFinished(true)
-                }
+//                }
-
+//
-                is ApiResult.Success<String> -> {
+//                is ApiResult.Success<String> -> {
-                    if (!TextUtils.isEmpty(it.data)) {
+//                    if (!TextUtils.isEmpty(it.data)) {
-                        Toaster.showShort(it.data)
+//                        Toaster.showShort(it.data)
-                    }
+//                    }
-                    Toaster.showShort(it)
+//                    Toaster.showShort(it)
-                    voiceController?.onUploadFinished(true)
+//                    voiceController?.onUploadFinished(true)
-                    startPlayTimeoutJob?.cancel()
+//                    startPlayTimeoutJob?.cancel()
-                    startPlayTimeoutJob = lifecycleScope.launch {
+//                    startPlayTimeoutJob = lifecycleScope.launch {
-                        delay(PLAY_WAIT_TIMEOUT_MS)
+//                        delay(PLAY_WAIT_TIMEOUT_MS)
-                        voiceController?.onPlayEndBackend()
+//                        voiceController?.onPlayEndBackend()
-                    }
+//                    }
-                }
+//                }
-            }
+//            }
-        }
+//        }
 
 
 
@@ -226,12 +226,12 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
 //                    1
 //                )
 //                loadLocalJsonAndPlay()
-//                val file = File(
+                val file = File(
-//                    getExternalFilesDir(Environment.DIRECTORY_DOWNLOADS)!!.getAbsolutePath(),
+                    getExternalFilesDir(Environment.DIRECTORY_DOWNLOADS)!!.getAbsolutePath(),
-//                    "xxx.wav"
+                    "xxx.wav"
-//                )
+                )
-//                AudioDebugUtil.saveFloatPcmAsWav(audio, file)
+                AudioDebugUtil.saveFloatPcmAsWav(audio, file)
-//                LogUtils.dTag("audioxx", "WAV saved: ${file.path}, samples=${audio.size}")
+                LogUtils.dTag("audioxx", "WAV saved: ${file.path}, samples=${audio.size}")
 //                lifecycleScope.launch(Dispatchers.Main) {
 //
 //                    mVerticalAnimator?.show()
@@ -280,9 +280,9 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
         when (msg.msgContentType) {
             MessageContentType.RECEIVE_VOICE_STREAM.msgContentType -> {
                 lifecycleScope.launch(Dispatchers.IO) {
-//                    UnityPlayerHolder.getInstance().startTalking(msg.content)
+////                    UnityPlayerHolder.getInstance().startTalking(msg.content)
-                    val audioDTO = GsonUtils.fromJson(msg.content, AudioDTO::class.java)
+//                    val audioDTO = GsonUtils.fromJson(msg.content, LmChatDTO::class.java)
-//                    voicePlayer.onAudioDTO(audioDTO)
+//                    voicePlayer.handleSlice(audioDTO)
                 }
             }
         }
@@ -586,9 +586,9 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
                         super.onEvent(eventSource, id, type, data)
                         LogUtils.eTag("lrsxxx", "onEvent:${data}")
                         runCatching {
-//                            val audioDTO = GsonUtils.fromJson(data, LmChatDTO::class.java)
+                            val audioDTO = GsonUtils.fromJson(data, LmChatDTO::class.java)
-//                            voicePlayer.handleSlice(audioDTO)
+                            voicePlayer.handleSlice(audioDTO)
-                            UnityPlayerHolder.getInstance().startTalking(data)
+//                            UnityPlayerHolder.getInstance().startTalking(data)
                         }.onFailure {
                             LogUtils.eTag("lrsxxx", "解析音频数据失败", it)
                             voiceController?.onUploadFinished(false)

app/src/main/java/com/zs/smarthuman/utils/PcmStreamPlayer.kt

@@ -4,92 +4,134 @@ import android.media.AudioAttributes
 import android.media.AudioFormat
 import android.media.AudioManager
 import android.media.AudioTrack
-import kotlinx.coroutines.CoroutineScope
+import kotlinx.coroutines.*
-import kotlinx.coroutines.Dispatchers
-import kotlinx.coroutines.SupervisorJob
-import kotlinx.coroutines.cancel
-import kotlinx.coroutines.delay
-import kotlinx.coroutines.isActive
-import kotlinx.coroutines.launch
 import java.util.ArrayDeque
 import java.util.Queue
 import java.util.concurrent.locks.ReentrantLock
+import kotlin.concurrent.withLock
 
-// ====================== PCM 播放器 ======================
 class PcmStreamPlayer(
     private val sampleRate: Int
 ) {
 
-    var onPlayEnd: (() -> Unit)? = null
-
     private val scope = CoroutineScope(SupervisorJob() + Dispatchers.IO)
     private val bufferQueue: Queue<ByteArray> = ArrayDeque()
-    private val queueLock = ReentrantLock()
+    private val lock = ReentrantLock()
 
     private var audioTrack: AudioTrack? = null
     @Volatile
     private var playing = true
+    // 新增：标记是否已释放，防止空指针
+    @Volatile
+    private var isReleased = false
 
     init {
         scope.launch {
+            val minBufferSize = AudioTrack.getMinBufferSize(
+                sampleRate,
+                AudioFormat.CHANNEL_OUT_MONO,
+                AudioFormat.ENCODING_PCM_16BIT
+            )
+
             audioTrack = AudioTrack(
                 AudioAttributes.Builder()
                     .setUsage(AudioAttributes.USAGE_MEDIA)
                     .setContentType(AudioAttributes.CONTENT_TYPE_SPEECH)
                     .build(),
                 AudioFormat.Builder()
-                    .setEncoding(AudioFormat.ENCODING_PCM_16BIT)
                     .setSampleRate(sampleRate)
+                    .setEncoding(AudioFormat.ENCODING_PCM_16BIT)
                     .setChannelMask(AudioFormat.CHANNEL_OUT_MONO)
                     .build(),
-                AudioTrack.getMinBufferSize(
+                minBufferSize * 2,
-                    sampleRate,
-                    AudioFormat.CHANNEL_OUT_MONO,
-                    AudioFormat.ENCODING_PCM_16BIT
-                ),
                 AudioTrack.MODE_STREAM,
                 AudioManager.AUDIO_SESSION_ID_GENERATE
             )
 
-            audioTrack?.play()
+            // 空安全检查：防止AudioTrack创建失败
+            audioTrack?.play() ?: run {
+                playing = false
+                isReleased = true
+                return@launch
+            }
 
-            val silent = ByteArray(2048)
+            // 🔥 AudioTrack 预热（非常关键）
+            warmUp()
 
-            while (isActive && playing) {
+            val silent = ByteArray(1024)
-                val pcm = queueLock.run { bufferQueue.poll() }
 
-                if (pcm != null) {
+            while (isActive && playing && !isReleased) {
+                val pcm = lock.withLock {
+                    if (bufferQueue.isEmpty()) null else bufferQueue.poll()
+                }
+
+                if (pcm != null && !isReleased) {
                     audioTrack?.write(pcm, 0, pcm.size)
-                } else {
+                } else if (!isReleased) {
                     audioTrack?.write(silent, 0, silent.size)
                     delay(5)
                 }
             }
 
-            audioTrack?.stop()
+            // 释放前的空安全检查
-            audioTrack?.release()
+            audioTrack?.takeIf { !isReleased }?.stop()
+            audioTrack?.takeIf { !isReleased }?.release()
             audioTrack = null
-
-            onPlayEnd?.invoke()
         }
     }
 
+    private fun warmUp() {
+        if (isReleased) return
+        val warmUpMs = 50
+        val bytes = sampleRate * 2 * warmUpMs / 1000
+        val silence = ByteArray(bytes)
+        audioTrack?.write(silence, 0, silence.size)
+        audioTrack?.write(silence, 0, silence.size)
+    }
+
     fun pushPcm(pcm: ByteArray) {
-        queueLock.run { bufferQueue.add(pcm) }
+        if (isReleased) return
+        lock.withLock {
+            bufferQueue.add(pcm)
+        }
     }
 
     fun clearQueue() {
-        queueLock.run { bufferQueue.clear() }
+        if (isReleased) return
+        lock.withLock {
+            bufferQueue.clear()
+        }
     }
 
-    fun queueEmpty(): Boolean = queueLock.run { bufferQueue.isEmpty() }
+    fun queueEmpty(): Boolean {
+        if (isReleased) return true
+        return lock.withLock { bufferQueue.isEmpty() }
+    }
+
+    // 核心新增：强制停止当前播放，清空所有缓冲区
+    fun forceStop() {
+        if (isReleased) return
+        lock.withLock {
+            bufferQueue.clear()
+        }
+        // 清空AudioTrack内部缓冲区，立即停止发声
+        audioTrack?.flush()
+        audioTrack?.pause() // 暂停硬件播放，避免残留静音数据
+    }
+
+    // 核心新增：重启播放器（用于停止后播放新音频）
+    fun restart() {
+        if (isReleased) return
+        audioTrack?.play()
+    }
 
     fun release() {
+        isReleased = true
         playing = false
-        queueLock.run { bufferQueue.clear() }
+        clearQueue()
         scope.cancel()
+        audioTrack?.stop()
+        audioTrack?.release()
+        audioTrack = null
     }
 }
-
-
-

gradle/libs.versions.toml

@@ -73,6 +73,7 @@ immersionbar-components = { module = "com.geyifeng.immersionbar:immersionbar-com
 immersionbar-ktx = { module = "com.geyifeng.immersionbar:immersionbar-ktx", version.ref = "immersionbarKtx" }
 immersionbar = { module = "com.geyifeng.immersionbar:immersionbar", version.ref = "immersionbar" }
 androidx-lifecycle-runtime-android = { group = "androidx.lifecycle", name = "lifecycle-runtime-android", version.ref = "lifecycleRuntimeAndroid" }
+
 [plugins]
 android-application = { id = "com.android.application", version.ref = "agp" }
 kotlin-android = { id = "org.jetbrains.kotlin.android", version.ref = "kotlin" }