稳定版参数
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@ -1,7 +1,6 @@
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package com.zs.smarthuman.sherpa
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import android.content.res.AssetManager
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import android.text.TextUtils
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import com.blankj.utilcode.util.LogUtils
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import com.k2fsa.sherpa.onnx.OnlineStream
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import com.k2fsa.sherpa.onnx.SpeakerRecognition
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@ -16,7 +15,7 @@ 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,
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idleTimeoutSeconds: Int = 10,
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idleTimeoutSeconds: Int = 200,
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maxRecordingSeconds: Int = 10,
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private val onStateChanged: ((VoiceState) -> Unit)? = null,
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private val stopBackendAudio: (() -> Unit)? = null,
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@ -30,15 +29,21 @@ class VoiceController(
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private const val SAMPLE_RATE = 16000
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// 预缓存大小（2秒）
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private const val PRE_BUFFER_SIZE = SAMPLE_RATE * 2
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// 声纹验证阈值
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private const val SPEAKER_VERIFY_THRESHOLD_NORMAL = 0.25f
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private const val SPEAKER_VERIFY_THRESHOLD_SHORT = 0.20f
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private const val SHORT_AUDIO_THRESHOLD = SAMPLE_RATE * 0.5f // 0.5秒音频长度
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// 防抖时间
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// ========== 核心：分场景声纹阈值（极简版） ==========
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private const val SPEAKER_THRESHOLD_QUIET = 0.50f // 安静环境
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private const val SPEAKER_THRESHOLD_NOISY = 0.43f // 嘈杂环境（匹配你的真实相似度）
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private const val SPEAKER_THRESHOLD_SHORT = 0.40f // 短语音（<1秒）
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// 短语音判定阈值
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private const val SHORT_AUDIO_DURATION_MS = 1000L
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private const val INVALID_RESET_DEBOUNCE_MS = 1500L
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// 最小语音时长
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private const val MIN_SPEECH_MS = 800L
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private const val MIN_EFFECTIVE_VOICE_DURATION = 400L
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// 噪音场景判定阈值
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private const val NOISE_BASELINE_THRESHOLD = 0.01f
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}
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var state: VoiceState = VoiceState.WAIT_WAKEUP
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@ -48,23 +53,21 @@ class VoiceController(
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onStateChanged?.invoke(value)
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}
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// ========== 缺失变量补充：实时能量与帧统计变量 ==========
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// 实时能量统计
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// 实时能量与帧统计变量
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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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// 实时帧统计
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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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// 多人对话检测标记
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private var isMultiPersonDialogueDetected = false
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// 防抖重置标记
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private var lastInvalidResetMs = 0L
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// 声纹管理器锁（解决并发问题）
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private val speakerManagerLock = ReentrantLock()
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// 环境噪音状态标记
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private var isNoisyEnvironment = false
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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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@ -89,16 +92,16 @@ class VoiceController(
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private val idleTimeoutMs = idleTimeoutSeconds * 1000L
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private val maxRecordingMs = maxRecordingSeconds * 1000L
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// ================= 保留分场景动态系数 + 强制兜底配置（近距离优化版） =================
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// 分场景动态系数（保留原有逻辑）
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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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// 强制兜底：正常语音最低门槛（近距离场景大幅降低）
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// 强制兜底：正常语音最低门槛
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private val MIN_NORMAL_VOICE_ENERGY = 0.03f
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private val MIN_NORMAL_VOICE_VAD_RATIO = 0.2f
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// 分场景动态系数（安静环境系数极低，适配近距离轻声）
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// 分场景动态系数
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private val BASELINE_QUIET_THRESHOLD = 0.005f
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private val SHORT_SPEECH_ENERGY_COEFF_QUIET = 1.5f
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private val SHORT_SPEECH_ENERGY_COEFF_NOISY = 2.0f
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@ -109,7 +112,7 @@ class VoiceController(
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private val SHORT_SPEECH_MIN_SCORE = 1
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private val LONG_SPEECH_MIN_SCORE = 1
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// 其他过滤参数（近距离场景放宽）
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// 其他过滤参数
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private val MAX_FAR_FIELD_ENERGY = 0.015f
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private val MIN_VALID_PEAK_AVG_RATIO = 0.5f
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private val MIN_CONTINUOUS_FRAME_RATIO = 0.1f
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@ -118,40 +121,32 @@ class VoiceController(
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private val SHORT_SPEECH_MIN = 500L
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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 ==========
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// 多人对话过滤配置
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private val MULTI_DIALOGUE_MIN_DURATION = 2500L
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private val MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO = 2.5f
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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
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private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f
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// ========== 核心调整：近距离场景 微弱人声过滤配置（重点优化） ==========
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// 微弱人声过滤配置
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private val MIN_VOICE_FRAME_RATIO = 0.08f
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private val MIN_PEAK_ENERGY_RATIO = 1.5f
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private val NORMAL_VOICE_ENERGY_THRESHOLD = 0.008f
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private val MIN_CONTINUOUS_VOICE_FRAMES = 1
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// ========== 核心新增：MIN_EFFECTIVE_SPEECH_RMS 常量 ==========
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private val MIN_EFFECTIVE_SPEECH_RMS = 0.0005f
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// ========== 核心新增：无效说话标记 + 超时类型 ==========
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// 无效说话标记 + 超时类型
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private var hasInvalidSpeech = false
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private var currentTimeoutType: TimeoutType = TimeoutType.IDLE_TIMEOUT
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// ========== 核心配置：声纹验证相关 ==========
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private val CURRENT_USER_ID = "current_wakeup_user" // 当前唤醒用户唯一标识
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private val ENABLE_STRICT_SPEAKER_VERIFY = true // 严格验证开关
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// 声纹验证相关
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private val CURRENT_USER_ID = "current_wakeup_user"
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private val ENABLE_STRICT_SPEAKER_VERIFY = true
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init {
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// 参数校验
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require(idleTimeoutSeconds > 0) { "idleTimeoutSeconds 必须大于0" }
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require(maxRecordingSeconds > 0) { "maxRecordingSeconds 必须大于0" }
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require(maxRecordingSeconds >= idleTimeoutSeconds) { "maxRecordingSeconds 必须大于等于 idleTimeoutSeconds" }
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// 初始化声纹识别器（适配你提供的API）
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try {
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SpeakerRecognition.initExtractor(assetManager) // 对齐原生API
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LogUtils.d(TAG, "✅ 声纹识别器初始化成功（原生Stream版本）")
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SpeakerRecognition.initExtractor(assetManager)
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LogUtils.d(TAG, "✅ 声纹识别器初始化成功")
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} catch (e: Exception) {
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LogUtils.e(TAG, "❌ 声纹识别器初始化失败", e)
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throw RuntimeException("声纹识别初始化失败", e)
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@ -163,8 +158,8 @@ class VoiceController(
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cachePreBuffer(samples)
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wakeupManager.acceptAudio(samples)
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if (wakeupManager.consumeWakeupFlag()) {
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handleWakeupEvent() // 仅调用一次
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// 注册唤醒用户特征（异步执行）
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handleWakeupEvent()
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// 注册唤醒用户特征
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CoroutineScope(Dispatchers.IO).launch {
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var stream: OnlineStream? = null
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runCatching {
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@ -174,18 +169,14 @@ class VoiceController(
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return@launch
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}
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// 创建原生Stream
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stream = SpeakerRecognition.extractor.createStream()
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stream.acceptWaveform(samples = wakeupAudio, sampleRate = SAMPLE_RATE)
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stream.inputFinished()
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stream?.acceptWaveform(samples = wakeupAudio, sampleRate = SAMPLE_RATE)
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stream?.inputFinished()
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// 计算特征并注册（仅当前用户）
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if (SpeakerRecognition.extractor.isReady(stream)) {
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if (stream != null && SpeakerRecognition.extractor.isReady(stream)) {
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val embedding = SpeakerRecognition.extractor.compute(stream)
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// 加锁保护 manager 操作
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speakerManagerLock.withLock {
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SpeakerRecognition.manager.remove(CURRENT_USER_ID)
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// 注册当前唤醒用户
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val embeddingList = mutableListOf(embedding)
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val ok = SpeakerRecognition.manager.add(
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name = CURRENT_USER_ID,
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@ -194,7 +185,7 @@ class VoiceController(
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if (ok) {
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LogUtils.d(TAG, "✅ 注册当前唤醒用户特征成功 | 特征长度: ${embedding.size}")
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} else {
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LogUtils.w(TAG, "❌ 注册当前唤醒用户特征失败（manager.add返回false）")
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LogUtils.w(TAG, "❌ 注册当前唤醒用户特征失败")
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}
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}
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} else {
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@ -203,9 +194,7 @@ class VoiceController(
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}.onFailure {
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LogUtils.e(TAG, "❌ 唤醒用户特征注册失败", it)
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}.also {
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// 释放Stream
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stream?.release()
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LogUtils.d(TAG, "🔄 唤醒注册Stream已释放")
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}
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}
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return
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@ -215,6 +204,8 @@ class VoiceController(
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if (state == VoiceState.WAIT_WAKEUP) {
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calibrateEnvBaseline(samples)
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isNoisyEnvironment = currentEnvBaseline >= NOISE_BASELINE_THRESHOLD
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LogUtils.d(TAG, "📊 环境状态 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
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}
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when (state) {
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@ -257,10 +248,11 @@ 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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calibrateEnvBaseline(samples)
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updateRealtimeEnergy(samples)
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updateRealtimeFrameStats()
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isNoisyEnvironment = currentEnvBaseline >= NOISE_BASELINE_THRESHOLD
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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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@ -268,25 +260,25 @@ class VoiceController(
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}
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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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LogUtils.w(TAG, "⏱ Max recording reached | 当前环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
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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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/* ================= 新增：录音中实时更新能量统计（适配近距离轻声） ================= */
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/* ================= 实时能量更新 ================= */
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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（阈值降低）
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if (rms >= MIN_EFFECTIVE_SPEECH_RMS) {
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val effectiveThreshold = if (isNoisyEnvironment) currentEnvBaseline * 1.8f else MIN_EFFECTIVE_SPEECH_RMS
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if (rms >= effectiveThreshold) {
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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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/* ================= 实时帧统计 ================= */
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private fun updateRealtimeFrameStats() {
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realtimeTotalFrames = vadManager.getTotalFrames()
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realtimeSpeechFrames = vadManager.getSpeechFrames()
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@ -300,7 +292,7 @@ class VoiceController(
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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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/* ================= 多人对话检测 ================= */
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private fun checkMultiPersonDialogueRealtime(now: Long): Boolean {
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val duration = now - recordingStartMs
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if (duration < MULTI_DIALOGUE_MIN_DURATION) return false
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@ -318,10 +310,9 @@ class VoiceController(
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return isMultiPersonDialogueDetected
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}
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/* ================= 环境基线校准（适配近距离场景，降低噪音敏感度） ================= */
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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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// 只保留低于基线+阈值的有效值，过滤突发噪音（阈值降低）
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val validRms = if (rms < currentEnvBaseline + 0.002f) rms else currentEnvBaseline
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if (rms < 0.015f) {
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if (envNoiseBuffer.size >= BASELINE_WINDOW_SIZE) {
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@ -332,7 +323,7 @@ class VoiceController(
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}
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}
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/* ================= 唤醒相关方法 ================= */
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/* ================= 唤醒处理 ================= */
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private fun handleWakeupEvent() {
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if (state == VoiceState.UPLOADING) return
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stopBackendAudio?.invoke()
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@ -361,7 +352,7 @@ class VoiceController(
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private fun onVadStart() {
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if (state != VoiceState.WAIT_SPEECH) return
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LogUtils.d(TAG, "🎤 REAL VAD START | 环境基线: $currentEnvBaseline")
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LogUtils.d(TAG, "🎤 REAL VAD START | 环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
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vadStarted = true
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recordingStartMs = System.currentTimeMillis()
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audioBuffer.clear()
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@ -372,64 +363,57 @@ 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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LogUtils.d(TAG, "🧠 VAD END | 环境基线: $currentEnvBaseline")
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LogUtils.d(TAG, "🧠 VAD END | 环境基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
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val realAvgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else avgEnergy
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val realPeakRms = if (realtimePeakRms > 0) realtimePeakRms else peakRms
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finishSentence(realAvgEnergy, realPeakRms)
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}
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/* ================= 核心优化：近距离场景 微弱人声过滤方法 ================= */
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/* ================= 微弱人声过滤 ================= */
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private fun filterWeakVoice(duration: Long, avgEnergy: Float, peakRms: Float): Boolean {
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// 1. 时长过滤：<400ms的极短杂音才过滤
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if (duration < MIN_EFFECTIVE_VOICE_DURATION) {
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LogUtils.w(TAG, "❌ 微弱人声过滤：时长${duration}ms < ${MIN_EFFECTIVE_VOICE_DURATION}ms")
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return true
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}
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// 2. 帧占比过滤：仅对极低能量语音生效
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val voiceFrameRatio = if (realtimeTotalFrames > 0) realtimeSpeechFrames.toFloat() / realtimeTotalFrames else 0f
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if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && voiceFrameRatio < MIN_VOICE_FRAME_RATIO) {
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LogUtils.w(TAG, "❌ 微弱人声过滤：帧占比${voiceFrameRatio} < ${MIN_VOICE_FRAME_RATIO}（极低能量）")
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LogUtils.w(TAG, "❌ 微弱人声过滤：帧占比${voiceFrameRatio} < ${MIN_VOICE_FRAME_RATIO}")
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return true
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}
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// 3. 峰值能量过滤：仅对极低能量语音生效，且阈值大幅降低
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val peakBaselineRatio = peakRms / currentEnvBaseline
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if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && peakBaselineRatio < MIN_PEAK_ENERGY_RATIO) {
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LogUtils.w(TAG, "❌ 微弱人声过滤：峰值/基线${peakBaselineRatio} < ${MIN_PEAK_ENERGY_RATIO}（极低能量）")
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LogUtils.w(TAG, "❌ 微弱人声过滤：峰值/基线${peakBaselineRatio} < ${MIN_PEAK_ENERGY_RATIO}")
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return true
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}
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// 4. 连续帧过滤：仅对极低能量语音生效，且阈值降到1
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if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && realtimeContinuousSpeechFrames < MIN_CONTINUOUS_VOICE_FRAMES) {
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LogUtils.w(TAG, "❌ 微弱人声过滤：连续帧${realtimeContinuousSpeechFrames} < ${MIN_CONTINUOUS_VOICE_FRAMES}（极低能量）")
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LogUtils.w(TAG, "❌ 微弱人声过滤：连续帧${realtimeContinuousSpeechFrames} < ${MIN_CONTINUOUS_VOICE_FRAMES}")
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return true
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}
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// 5. 平均能量过滤：仅对极极低能量语音生效
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val energyBaselineRatio = avgEnergy / currentEnvBaseline
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if (avgEnergy < 0.005f && energyBaselineRatio < 1.2f) {
|
||||
LogUtils.w(TAG, "❌ 微弱人声过滤：能量/基线${energyBaselineRatio} < 1.2（极极低能量）")
|
||||
LogUtils.w(TAG, "❌ 微弱人声过滤：能量/基线${energyBaselineRatio} < 1.2")
|
||||
return true
|
||||
}
|
||||
|
||||
// 正常语音（包括近距离轻声）直接通过
|
||||
return false
|
||||
}
|
||||
|
||||
/* ================= 结束录音（核心：适配近距离轻声） ================= */
|
||||
/* ================= 结束录音 ================= */
|
||||
private fun finishSentence(avgEnergy: Float = 0f, peakRms: Float = 0f) {
|
||||
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")
|
||||
LogUtils.d(TAG, "❌ 语音过短: $duration ms | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
hasInvalidSpeech = true
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 第二步：微弱人声专项过滤（仅过滤极微弱杂音） ==========
|
||||
if (filterWeakVoice(duration, avgEnergy, peakRms)) {
|
||||
hasInvalidSpeech = true
|
||||
resetToWaitSpeech()
|
||||
@ -440,42 +424,30 @@ class VoiceController(
|
||||
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")
|
||||
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")
|
||||
LogUtils.d(TAG, "📊 实时帧统计 | 总帧: $realtimeTotalFrames | 语音帧: $realtimeSpeechFrames | 连续语音帧: $realtimeContinuousSpeechFrames")
|
||||
|
||||
// 多人对话过滤
|
||||
if (isMultiPersonDialogueDetected) {
|
||||
LogUtils.w(TAG, "❌ 过滤多人对话垃圾语音（实时识别） | 时长: $duration ms")
|
||||
LogUtils.w(TAG, "❌ 过滤多人对话垃圾语音 | 时长: $duration ms")
|
||||
hasInvalidSpeech = true
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 步骤1：优先声纹验证（核心！仅当前用户可通过） ==========
|
||||
// 声纹验证（核心极简版）
|
||||
if (ENABLE_STRICT_SPEAKER_VERIFY) {
|
||||
val isCurrentUser = verifySpeaker(audioBuffer.toFloatArray())
|
||||
val isCurrentUser = verifySpeaker(audio)
|
||||
if (!isCurrentUser) {
|
||||
LogUtils.w(TAG, "❌ 非当前唤醒用户，直接拒绝语音 | 录音时长: $duration ms")
|
||||
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")
|
||||
hasInvalidSpeech = true
|
||||
resetToWaitSpeech()
|
||||
return
|
||||
}
|
||||
LogUtils.d(TAG, "✅ 当前用户语音，继续处理 | 录音时长: $duration ms")
|
||||
LogUtils.d(TAG, "✅ 当前用户语音，继续处理 | 录音时长: $duration ms | 嘈杂环境: $isNoisyEnvironment")
|
||||
}
|
||||
|
||||
// ========== 1. 强制兜底：正常语音直接通过（阈值降低） ==========
|
||||
val isNormalVoice = avgEnergy >= MIN_NORMAL_VOICE_ENERGY && vadRatio >= MIN_NORMAL_VOICE_VAD_RATIO
|
||||
if (isNormalVoice) {
|
||||
LogUtils.i(TAG, "✅ 正常语音强制通过 | 能量: $avgEnergy ≥ $MIN_NORMAL_VOICE_ENERGY | 占比: $vadRatio ≥ $MIN_NORMAL_VOICE_VAD_RATIO")
|
||||
audioBuffer.clear()
|
||||
state = VoiceState.UPLOADING
|
||||
onFinalAudio(audio)
|
||||
resetRealtimeStats()
|
||||
hasInvalidSpeech = false
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 2. 远场过滤（近距离场景几乎不生效） ==========
|
||||
// 远场过滤
|
||||
val isFarField = avgEnergy < MAX_FAR_FIELD_ENERGY
|
||||
val isInvalidPeakRatio = peakAvgRatio < MIN_VALID_PEAK_AVG_RATIO
|
||||
if (isFarField && isInvalidPeakRatio) {
|
||||
@ -485,7 +457,7 @@ class VoiceController(
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 3. 非连续判定（大幅放宽） ==========
|
||||
// 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 &&
|
||||
@ -498,34 +470,21 @@ class VoiceController(
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 4. 分场景动态阈值计算（系数大幅降低） ==========
|
||||
// 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
|
||||
LogUtils.d(TAG, "📏 短语音阈值 | 场景: ${if (isQuietEnv) "安静" else "嘈杂"} | 系数: $coeff | 阈值: $energyThreshold")
|
||||
ThresholdConfig(
|
||||
energyThreshold = energyThreshold,
|
||||
vadRatioThreshold = SHORT_SPEECH_VAD_COEFF,
|
||||
minScore = SHORT_SPEECH_MIN_SCORE,
|
||||
scene = "短语音"
|
||||
)
|
||||
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
|
||||
LogUtils.d(TAG, "📏 长语音阈值 | 场景: ${if (isQuietEnv) "安静" else "嘈杂"} | 系数: $coeff | 阈值: $energyThreshold")
|
||||
ThresholdConfig(
|
||||
energyThreshold = energyThreshold,
|
||||
vadRatioThreshold = LONG_SPEECH_VAD_COEFF,
|
||||
minScore = LONG_SPEECH_MIN_SCORE,
|
||||
scene = "长语音"
|
||||
)
|
||||
ThresholdConfig(energyThreshold, LONG_SPEECH_VAD_COEFF, LONG_SPEECH_MIN_SCORE, "长语音")
|
||||
}
|
||||
}
|
||||
|
||||
// ========== 5. 分场景阈值过滤（阈值降低） ==========
|
||||
val energyPass = avgEnergy >= thresholdConfig.energyThreshold
|
||||
val vadRatioPass = vadRatio >= thresholdConfig.vadRatioThreshold
|
||||
if (!energyPass || !vadRatioPass) {
|
||||
@ -535,7 +494,7 @@ class VoiceController(
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 6. 评分判定（门槛降低到1） ==========
|
||||
// 评分判定
|
||||
var score = 0
|
||||
score += when {
|
||||
duration >= 4000 -> 3
|
||||
@ -553,16 +512,16 @@ class VoiceController(
|
||||
return
|
||||
}
|
||||
|
||||
// ========== 最终通过 ==========
|
||||
// 最终通过
|
||||
audioBuffer.clear()
|
||||
state = VoiceState.UPLOADING
|
||||
onFinalAudio(audio)
|
||||
resetRealtimeStats()
|
||||
hasInvalidSpeech = false
|
||||
LogUtils.i(TAG, "✅ 近距离轻声通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene}")
|
||||
LogUtils.i(TAG, "✅ 语音通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene} | 嘈杂环境: $isNoisyEnvironment")
|
||||
}
|
||||
|
||||
/* ================= 重置实时统计变量 ================= */
|
||||
/* ================= 重置实时统计 ================= */
|
||||
private fun resetRealtimeStats() {
|
||||
realtimeEnergySum = 0f
|
||||
realtimeEnergyCount = 0
|
||||
@ -574,15 +533,15 @@ class VoiceController(
|
||||
isMultiPersonDialogueDetected = false
|
||||
}
|
||||
|
||||
/* ================= 播放/上传/Reset 回调 ================= */
|
||||
/* ================= 播放/上传回调 ================= */
|
||||
fun onPlayStartPrompt() {
|
||||
LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline")
|
||||
LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
state = VoiceState.PLAYING_PROMPT
|
||||
}
|
||||
|
||||
fun onPlayEndPrompt() {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
LogUtils.d(TAG, "🎵 提示音结束 | 基线: $currentEnvBaseline")
|
||||
LogUtils.d(TAG, "🎵 提示音结束 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
state = VoiceState.WAIT_SPEECH_COOLDOWN
|
||||
}
|
||||
|
||||
@ -591,19 +550,19 @@ 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")
|
||||
LogUtils.d(TAG, "🎶 开始播放后台音频 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
state = VoiceState.PLAYING_BACKEND
|
||||
}
|
||||
|
||||
fun onPlayEndBackend() {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
LogUtils.d(TAG, "🎶 后台音频结束 | 基线: $currentEnvBaseline")
|
||||
LogUtils.d(TAG, "🎶 后台音频结束 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
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")
|
||||
LogUtils.d(TAG, "📤 上传完成 | 成功: $success | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
|
||||
if (!success) {
|
||||
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
|
||||
@ -612,7 +571,7 @@ class VoiceController(
|
||||
}
|
||||
|
||||
private fun resetToWaitSpeech() {
|
||||
LogUtils.d(TAG, "🔄 重置到等待说话 | 基线: $currentEnvBaseline | 已标记无效说话: $hasInvalidSpeech")
|
||||
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")
|
||||
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")
|
||||
@ -628,7 +587,7 @@ class VoiceController(
|
||||
}
|
||||
|
||||
private fun resetAll() {
|
||||
LogUtils.d(TAG, "🔄 重置所有状态 | 基线: $currentEnvBaseline | 本次超时类型: $currentTimeoutType")
|
||||
LogUtils.d(TAG, "🔄 重置所有状态 | 基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment | 本次超时类型: $currentTimeoutType")
|
||||
audioBuffer.clear()
|
||||
preBuffer.clear()
|
||||
vadManager.reset()
|
||||
@ -638,23 +597,23 @@ class VoiceController(
|
||||
waitSpeechFailStartMs = 0L
|
||||
envNoiseBuffer.clear()
|
||||
currentEnvBaseline = 0.001f
|
||||
isNoisyEnvironment = false
|
||||
resetRealtimeStats()
|
||||
hasInvalidSpeech = false
|
||||
currentTimeoutType = TimeoutType.IDLE_TIMEOUT
|
||||
LogUtils.d(TAG, "🔄 环境基线已重置 | 新基线: $currentEnvBaseline | 无效说话标记已重置")
|
||||
state = VoiceState.WAIT_WAKEUP
|
||||
}
|
||||
|
||||
fun release() {
|
||||
LogUtils.d(TAG, "🔌 释放资源 | 最终基线: $currentEnvBaseline")
|
||||
LogUtils.d(TAG, "🔌 释放资源 | 最终基线: $currentEnvBaseline | 嘈杂环境: $isNoisyEnvironment")
|
||||
wakeupManager.release()
|
||||
vadManager.reset()
|
||||
envNoiseBuffer.clear()
|
||||
resetRealtimeStats()
|
||||
hasInvalidSpeech = false
|
||||
currentTimeoutType = TimeoutType.IDLE_TIMEOUT
|
||||
isNoisyEnvironment = false
|
||||
|
||||
// 释放声纹识别器资源
|
||||
runCatching {
|
||||
SpeakerRecognition.extractor.release()
|
||||
speakerManagerLock.withLock {
|
||||
@ -666,7 +625,6 @@ class VoiceController(
|
||||
}
|
||||
}
|
||||
|
||||
// 兜底释放（防止未调用release）
|
||||
protected fun finalize() {
|
||||
runCatching {
|
||||
release()
|
||||
@ -690,60 +648,66 @@ class VoiceController(
|
||||
val scene: String
|
||||
)
|
||||
|
||||
/* ================= 核心：原生Stream声纹验证（仅当前用户有效） ================= */
|
||||
/**
|
||||
* 验证语音是否属于当前唤醒用户（完全适配你提供的API）
|
||||
* @param audio 待验证的语音数据
|
||||
* @return true=是当前用户，false=非当前用户
|
||||
*/
|
||||
/* ================= 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）
|
||||
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) {
|
||||
audio.copyOfRange(audio.size - validSampleCount, audio.size)
|
||||
} else {
|
||||
audio
|
||||
}
|
||||
} else {
|
||||
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 = audio, sampleRate = SAMPLE_RATE)
|
||||
stream.acceptWaveform(samples = validAudio, sampleRate = SAMPLE_RATE) // 用裁剪后的音频验证
|
||||
stream.inputFinished()
|
||||
|
||||
if (!SpeakerRecognition.extractor.isReady(stream)) {
|
||||
LogUtils.w(TAG, "❌ 验证音频Stream未就绪，验证失败")
|
||||
LogUtils.w(TAG, "❌ 音频Stream未就绪，验证失败")
|
||||
return false
|
||||
}
|
||||
|
||||
val embedding = SpeakerRecognition.extractor.compute(stream)
|
||||
// 动态选择阈值
|
||||
val threshold = if (audio.size < SHORT_AUDIO_THRESHOLD) {
|
||||
LogUtils.d(TAG, "📢 检测到短速语音，使用放宽阈值: $SPEAKER_VERIFY_THRESHOLD_SHORT")
|
||||
SPEAKER_VERIFY_THRESHOLD_SHORT
|
||||
} else {
|
||||
SPEAKER_VERIFY_THRESHOLD_NORMAL
|
||||
}
|
||||
|
||||
// 加锁验证
|
||||
// 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 = threshold
|
||||
threshold = finalThreshold
|
||||
)
|
||||
if (verifyPass) {
|
||||
LogUtils.d(TAG, "✅ 声纹验证通过 | 阈值: $threshold")
|
||||
} else {
|
||||
LogUtils.w(TAG, "❌ 声纹验证失败 | 阈值: $threshold")
|
||||
}
|
||||
|
||||
// 打印关键信息（补充裁剪后时长）
|
||||
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")
|
||||
|
||||
// 无任何容错：验证结果就是最终结果
|
||||
return verifyPass
|
||||
}
|
||||
} catch (e: Exception) {
|
||||
LogUtils.e(TAG, "❌ 声纹验证异常", e)
|
||||
false
|
||||
LogUtils.e(TAG, "❌ 声纹验证异常，拒绝", e)
|
||||
return false
|
||||
} finally {
|
||||
// 释放Stream
|
||||
stream?.release()
|
||||
LogUtils.d(TAG, "🔄 验证Stream已释放")
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
@ -49,6 +49,7 @@ import com.zs.smarthuman.kt.releaseIM
|
||||
import com.zs.smarthuman.sherpa.TimeoutType
|
||||
import com.zs.smarthuman.sherpa.VoiceController
|
||||
import com.zs.smarthuman.toast.Toaster
|
||||
import com.zs.smarthuman.utils.AudioDebugUtil
|
||||
import com.zs.smarthuman.utils.AudioPcmUtil
|
||||
import com.zs.smarthuman.utils.DangerousUtils
|
||||
import com.zs.smarthuman.utils.LogFileUtils
|
||||
@ -213,12 +214,12 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
|
||||
1
|
||||
)
|
||||
// loadLocalJsonAndPlay()
|
||||
// val file = File(
|
||||
// getExternalFilesDir(Environment.DIRECTORY_DOWNLOADS)!!.getAbsolutePath(),
|
||||
// "xxx.wav"
|
||||
// )
|
||||
// AudioDebugUtil.saveFloatPcmAsWav(audio, file)
|
||||
// LogUtils.dTag("audioxx", "WAV saved: ${file.path}, samples=${audio.size}")
|
||||
val file = File(
|
||||
getExternalFilesDir(Environment.DIRECTORY_DOWNLOADS)!!.getAbsolutePath(),
|
||||
"xxx.wav"
|
||||
)
|
||||
AudioDebugUtil.saveFloatPcmAsWav(audio, file)
|
||||
LogUtils.dTag("audioxx", "WAV saved: ${file.path}, samples=${audio.size}")
|
||||
lifecycleScope.launch(Dispatchers.Main) {
|
||||
|
||||
mVerticalAnimator?.show()
|
||||
@ -291,7 +292,7 @@ class MainActivity : BaseViewModelActivity<ActivityMainBinding, MainViewModel>()
|
||||
|
||||
override fun onPause() {
|
||||
super.onPause()
|
||||
stopRecording()
|
||||
// stopRecording()
|
||||
UnityPlayerHolder.getInstance().pause()
|
||||
}
|
||||
|
||||
|
||||
@ -41,6 +41,9 @@ class MainViewModel: BaseViewModel() {
|
||||
RxHttp.postJson(ApiService.UPLOAD_RECORD_VOICE_URL)
|
||||
.add("sessionCode",sessionCode)
|
||||
.add("audio", audioVoice)
|
||||
.readTimeout(3000L)
|
||||
.writeTimeout(3000L)
|
||||
.connectTimeout(3000L)
|
||||
.toAwaitResponse<String>()
|
||||
.awaitResult()
|
||||
.getOrThrow()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user