diff --git a/app/src/main/AndroidManifest.xml b/app/src/main/AndroidManifest.xml
index 83c55d3..d64e047 100644
--- a/app/src/main/AndroidManifest.xml
+++ b/app/src/main/AndroidManifest.xml
@@ -52,7 +52,7 @@
tools:targetApi="31">
@@ -66,9 +66,9 @@
-
+ android:screenOrientation="portrait"/>
diff --git a/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/README.md b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/README.md
new file mode 100644
index 0000000..e54b99f
--- /dev/null
+++ b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/README.md
@@ -0,0 +1,4 @@
+# Introduction
+
+Model in this directory is converted from
+https://huggingface.co/ASLP-lab/WSYue-ASR/tree/main/u2pp_conformer_yue
diff --git a/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/model.int8.onnx b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/model.int8.onnx
new file mode 100644
index 0000000..6bbac08
Binary files /dev/null and b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/model.int8.onnx differ
diff --git a/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/tokens.txt b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/tokens.txt
new file mode 100644
index 0000000..de9835d
--- /dev/null
+++ b/app/src/main/assets/sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/tokens.txt
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+▁TRA 1844
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+▁TRANS 1846
+▁TRAVEL 1847
+▁TRE 1848
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diff --git a/app/src/main/java/com/zs/smarthuman/sherpa/VoiceController.kt b/app/src/main/java/com/zs/smarthuman/sherpa/VoiceController.kt
index c5fb4db..6db6dda 100644
--- a/app/src/main/java/com/zs/smarthuman/sherpa/VoiceController.kt
+++ b/app/src/main/java/com/zs/smarthuman/sherpa/VoiceController.kt
@@ -2,10 +2,6 @@ package com.zs.smarthuman.sherpa
import android.content.res.AssetManager
import com.blankj.utilcode.util.LogUtils
-import kotlinx.coroutines.GlobalScope
-import kotlinx.coroutines.Job
-import kotlinx.coroutines.delay
-import kotlinx.coroutines.launch
import java.util.ArrayDeque
class VoiceController(
@@ -29,6 +25,22 @@ class VoiceController(
onStateChanged?.invoke(value)
}
+ // ========== 缺失变量补充：实时能量与帧统计变量 ==========
+ // 实时能量统计
+ private var realtimeEnergySum = 0f
+ private var realtimeEnergyCount = 0
+ private var realtimePeakRms = 0f
+ // 实时帧统计
+ private var realtimeTotalFrames = 0
+ private var realtimeSpeechFrames = 0
+ private var realtimeContinuousSpeechFrames = 0
+ private var realtimeLastFrameIsSpeech = false
+ // 多人对话检测标记
+ private var isMultiPersonDialogueDetected = false
+ // 防抖重置标记
+ private var lastInvalidResetMs = 0L
+ private val INVALID_RESET_DEBOUNCE_MS = 1500L
+
private val wakeupManager = WakeupManager(assetManager, onWakeup)
private val vadManager = VadManager(
assetManager,
@@ -55,69 +67,56 @@ class VoiceController(
private val idleTimeoutMs = idleTimeoutSeconds * 1000L
private val maxRecordingMs = maxRecordingSeconds * 1000L
- // ================= 保留分场景动态系数 + 强制兜底配置 =================
+ // ================= 保留分场景动态系数 + 强制兜底配置（近距离优化版） =================
private val BASELINE_WINDOW_SIZE = 50
private val envNoiseBuffer = ArrayDeque(BASELINE_WINDOW_SIZE)
private var currentEnvBaseline = 0.001f
- // 强制兜底：正常语音最低门槛
- private val MIN_NORMAL_VOICE_ENERGY = 0.06f
- private val MIN_NORMAL_VOICE_VAD_RATIO = 0.3f
+ // 强制兜底：正常语音最低门槛（近距离场景大幅降低）
+ private val MIN_NORMAL_VOICE_ENERGY = 0.03f
+ private val MIN_NORMAL_VOICE_VAD_RATIO = 0.2f
- // 分场景动态系数（安静环境系数极低）
- private val BASELINE_QUIET_THRESHOLD = 0.005f // 安静环境基线阈值
- private val SHORT_SPEECH_ENERGY_COEFF_QUIET = 2.0f // 安静环境短语音系数
- private val SHORT_SPEECH_ENERGY_COEFF_NOISY = 3.0f // 嘈杂环境短语音系数
- private val LONG_SPEECH_ENERGY_COEFF_QUIET = 4.0f // 安静环境长语音系数
- private val LONG_SPEECH_ENERGY_COEFF_NOISY = 6.0f // 嘈杂环境长语音系数
- private val SHORT_SPEECH_VAD_COEFF = 0.08f
- private val LONG_SPEECH_VAD_COEFF = 0.15f
+ // 分场景动态系数（安静环境系数极低，适配近距离轻声）
+ 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 = 2
+ private val LONG_SPEECH_MIN_SCORE = 1
- // 其他过滤参数
- private val MAX_FAR_FIELD_ENERGY = 0.03f
- private val MIN_VALID_PEAK_AVG_RATIO = 0.8f
- private val MIN_CONTINUOUS_FRAME_RATIO = 0.2f
+ // 其他过滤参数（近距离场景放宽）
+ 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 = 5
+ private val MIN_EFFECTIVE_SPEECH_FRAMES = 3
private val SHORT_SPEECH_MIN = 500L
private val SHORT_SPEECH_MAX = 2000L
- // ========== 核心修改：多人对话过滤配置（适配2人以上场景） ==========
- private val MULTI_DIALOGUE_MIN_DURATION = 2500L // 多人对话最小时长（2.5秒）
- private val MULTI_DIALOGUE_MAX_PEAK_AVG_RATIO = 2.5f // 多人对话峰均比范围
+ // ========== 核心修改：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 // 多人对话连续帧占比
- private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f // 多人对话有效帧占比
+ private val MULTI_DIALOGUE_MAX_CONTINUOUS_RATIO = 0.3f
+ private val MULTI_DIALOGUE_MIN_VAD_RATIO = 0.55f
- // ========== 新增：录音过程中实时统计的变量 ==========
- // 能量统计
- private var realtimeEnergySum = 0f
- private var realtimeEnergyCount = 0
- private var realtimePeakRms = 0f
- // 帧统计（实时累加）
- private var realtimeTotalFrames = 0
- private var realtimeSpeechFrames = 0
- private var realtimeContinuousSpeechFrames = 0
- private var realtimeLastFrameIsSpeech = false
- // 多人对话实时判定标记
- private var isMultiPersonDialogueDetected = false
- // 防抖变量
- private var lastInvalidResetMs = 0L
- private val INVALID_RESET_DEBOUNCE_MS = 1500L // 1.5秒内不重复重置
+ // ========== 核心调整：近距离场景 微弱人声过滤配置（重点优化） ==========
+ private val MIN_EFFECTIVE_VOICE_DURATION = 400L
+ 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
- // ========== 核心新增：区分超时类型的标记 ==========
- private var hasInvalidSpeech = false // 是否有过无效说话行为
- private var currentTimeoutType: TimeoutType = TimeoutType.IDLE_TIMEOUT // 当前超时类型
-
- // ========== 补充：MIN_EFFECTIVE_SPEECH_RMS 常量（和VadManager对齐） ==========
- private val MIN_EFFECTIVE_SPEECH_RMS = 0.001f
+ // ========== 核心新增：MIN_EFFECTIVE_SPEECH_RMS 常量 ==========
+ private val MIN_EFFECTIVE_SPEECH_RMS = 0.0005f
- //播放等待超时
- private val PLAY_WAIT_TIMEOUT_MS = 3000L
- private var playWaitJob: Job? = null
+ // ========== 核心新增：无效说话标记 + 超时类型 ==========
+ private var hasInvalidSpeech = false
+ private var currentTimeoutType: TimeoutType = TimeoutType.IDLE_TIMEOUT
/* ================= 音频入口 ================= */
fun acceptAudio(samples: FloatArray) {
@@ -153,14 +152,12 @@ class VoiceController(
if ((waitSpeechStartMs > 0 && now - waitSpeechStartMs >= idleTimeoutMs) ||
(waitSpeechFailStartMs > 0 && now - waitSpeechFailStartMs >= idleTimeoutMs)
) {
- // 核心修改：超时前先判定超时类型
currentTimeoutType = if (hasInvalidSpeech) {
TimeoutType.INVALID_SPEECH_TIMEOUT
} else {
TimeoutType.IDLE_TIMEOUT
}
LogUtils.d(TAG, "⏱ WAIT_SPEECH timeout → WAIT_WAKEUP | 超时类型: $currentTimeoutType")
- // 触发超时提示语回调
onTimeoutTip?.invoke(currentTimeoutType)
resetAll()
return
@@ -177,20 +174,15 @@ class VoiceController(
vadManager.accept(samples)
// ========== 核心优化：录音过程中实时计算 ==========
- // 1. 实时校准环境基线（适配录音中环境变化）
calibrateEnvBaseline(samples)
- // 2. 实时计算能量/峰值
updateRealtimeEnergy(samples)
- // 3. 实时更新帧统计
updateRealtimeFrameStats()
- // 4. 实时判定是否为多人对话，若是则立即终止录音
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
}
- // 原有最大录音时长判断
if (System.currentTimeMillis() - recordingStartMs > maxRecordingMs) {
LogUtils.w(TAG, "⏱ Max recording reached | 当前环境基线: $currentEnvBaseline")
finishSentence(realtimeEnergySum / realtimeEnergyCount, realtimePeakRms)
@@ -199,10 +191,10 @@ class VoiceController(
}
}
- /* ================= 新增：录音中实时更新能量统计 ================= */
+ /* ================= 新增：录音中实时更新能量统计（适配近距离轻声） ================= */
private fun updateRealtimeEnergy(samples: FloatArray) {
val rms = vadManager.calcRms(samples)
- // 仅统计有效语音帧的能量
+ // 仅统计有效语音帧的能量（阈值降低）
if (rms >= MIN_EFFECTIVE_SPEECH_RMS) {
realtimeEnergySum += rms
realtimeEnergyCount++
@@ -212,12 +204,10 @@ class VoiceController(
/* ================= 新增：录音中实时更新帧统计 ================= */
private fun updateRealtimeFrameStats() {
- // 从VADManager获取最新帧状态
realtimeTotalFrames = vadManager.getTotalFrames()
realtimeSpeechFrames = vadManager.getSpeechFrames()
realtimeContinuousSpeechFrames = vadManager.getContinuousSpeechFrames()
- // 实时更新连续帧标记
- val currentFrameIsSpeech = vadManager.isSpeechDetected() // 需给VadManager新增isSpeechDetected()方法
+ val currentFrameIsSpeech = vadManager.isSpeechDetected()
if (currentFrameIsSpeech) {
realtimeContinuousSpeechFrames = if (realtimeLastFrameIsSpeech) realtimeContinuousSpeechFrames + 1 else 1
} else {
@@ -228,17 +218,14 @@ class VoiceController(
/* ================= 新增：录音中实时判定多人对话 ================= */
private fun checkMultiPersonDialogueRealtime(now: Long): Boolean {
- // 还没到多人对话最小时长，不判定
val duration = now - recordingStartMs
if (duration < MULTI_DIALOGUE_MIN_DURATION) return false
- // 实时计算特征值
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()
- // 多人对话判定逻辑（和原逻辑一致，但实时执行）
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 &&
@@ -247,21 +234,21 @@ class VoiceController(
return isMultiPersonDialogueDetected
}
- /* ================= 环境基线校准（保留，录音中也会调用） ================= */
+ /* ================= 环境基线校准（适配近距离场景，降低噪音敏感度） ================= */
private fun calibrateEnvBaseline(samples: FloatArray) {
val rms = vadManager.calcRms(samples)
- // 新增：只保留低于基线+阈值的有效值，过滤突发噪音
- val validRms = if (rms < currentEnvBaseline + 0.005f) rms else currentEnvBaseline
- if (rms < 0.03f) {
+ // 只保留低于基线+阈值的有效值，过滤突发噪音（阈值降低）
+ 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) // 用过滤后的有效值更新
+ envNoiseBuffer.addLast(validRms)
currentEnvBaseline = envNoiseBuffer.maxOrNull() ?: 0.001f
}
}
- /* ================= 唤醒 ================= */
+ /* ================= 唤醒相关方法 ================= */
private fun handleWakeupEvent() {
if (state == VoiceState.UPLOADING) return
stopBackendAudio?.invoke()
@@ -272,7 +259,6 @@ class VoiceController(
waitSpeechFailStartMs = System.currentTimeMillis()
waitSpeechStartMs = System.currentTimeMillis()
- // 核心新增：唤醒时重置无效说话标记（每次唤醒都是新的会话）
hasInvalidSpeech = false
currentTimeoutType = TimeoutType.IDLE_TIMEOUT
@@ -280,7 +266,6 @@ class VoiceController(
audioBuffer.clear()
vadManager.reset()
vadStarted = false
- // 重置实时统计变量
resetRealtimeStats()
}
@@ -297,7 +282,6 @@ class VoiceController(
recordingStartMs = System.currentTimeMillis()
audioBuffer.clear()
audioBuffer.addAll(preBuffer)
- // 初始化实时统计变量
resetRealtimeStats()
state = VoiceState.RECORDING
}
@@ -305,20 +289,65 @@ class VoiceController(
private fun onVadEnd(avgEnergy: Float, peakRms: Float) {
if (state != VoiceState.RECORDING) return
LogUtils.d(TAG, "🧠 VAD END | 环境基线: $currentEnvBaseline")
- // 优先使用实时统计的能量值，避免重复计算
val realAvgEnergy = if (realtimeEnergyCount > 0) realtimeEnergySum / realtimeEnergyCount else avgEnergy
val realPeakRms = if (realtimePeakRms > 0) realtimePeakRms else peakRms
finishSentence(realAvgEnergy, realPeakRms)
}
- /* ================= 结束录音（核心：复用实时计算结果） ================= */
+ /* ================= 核心优化：近距离场景 微弱人声过滤方法 ================= */
+ private fun filterWeakVoice(duration: Long, avgEnergy: Float, peakRms: Float): Boolean {
+ // 1. 时长过滤：<400ms的极短杂音才过滤
+ if (duration < MIN_EFFECTIVE_VOICE_DURATION) {
+ LogUtils.w(TAG, "❌ 微弱人声过滤：时长${duration}ms < ${MIN_EFFECTIVE_VOICE_DURATION}ms")
+ return true
+ }
+
+ // 2. 帧占比过滤：仅对极低能量语音生效
+ val voiceFrameRatio = if (realtimeTotalFrames > 0) realtimeSpeechFrames.toFloat() / realtimeTotalFrames else 0f
+ if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && voiceFrameRatio < MIN_VOICE_FRAME_RATIO) {
+ LogUtils.w(TAG, "❌ 微弱人声过滤：帧占比${voiceFrameRatio} < ${MIN_VOICE_FRAME_RATIO}（极低能量）")
+ return true
+ }
+
+ // 3. 峰值能量过滤：仅对极低能量语音生效，且阈值大幅降低
+ val peakBaselineRatio = peakRms / currentEnvBaseline
+ if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && peakBaselineRatio < MIN_PEAK_ENERGY_RATIO) {
+ LogUtils.w(TAG, "❌ 微弱人声过滤：峰值/基线${peakBaselineRatio} < ${MIN_PEAK_ENERGY_RATIO}（极低能量）")
+ return true
+ }
+
+ // 4. 连续帧过滤：仅对极低能量语音生效，且阈值降到1
+ if (avgEnergy < NORMAL_VOICE_ENERGY_THRESHOLD && realtimeContinuousSpeechFrames < MIN_CONTINUOUS_VOICE_FRAMES) {
+ LogUtils.w(TAG, "❌ 微弱人声过滤：连续帧${realtimeContinuousSpeechFrames} < ${MIN_CONTINUOUS_VOICE_FRAMES}（极低能量）")
+ return true
+ }
+
+ // 5. 平均能量过滤：仅对极极低能量语音生效
+ val energyBaselineRatio = avgEnergy / currentEnvBaseline
+ if (avgEnergy < 0.005f && energyBaselineRatio < 1.2f) {
+ LogUtils.w(TAG, "❌ 微弱人声过滤：能量/基线${energyBaselineRatio} < 1.2（极极低能量）")
+ 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） ================= */
private fun finishSentence(avgEnergy: Float = 0f, peakRms: Float = 0f) {
val now = System.currentTimeMillis()
val duration = now - recordingStartMs
+ // ========== 第一步：基础过滤（语音过短） ==========
if (!vadStarted || duration < MIN_SPEECH_MS) {
LogUtils.d(TAG, "❌ 语音过短: $duration ms | 基线: $currentEnvBaseline")
- // 核心新增：无效说话（语音过短），标记hasInvalidSpeech为true
+ 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()
return
@@ -328,44 +357,40 @@ class VoiceController(
val vadRatio = vadManager.activeSpeechRatio()
val peakAvgRatio = if (avgEnergy > 0f) peakRms / avgEnergy else 0f
- // 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, "📊 实时帧统计 | 总帧: $realtimeTotalFrames | 语音帧: $realtimeSpeechFrames | 连续语音帧: $realtimeContinuousSpeechFrames")
- // 若录音中已识别出多人对话，直接过滤
+ // 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")
- // 核心新增：无效说话（多人对话），标记hasInvalidSpeech为true
hasInvalidSpeech = true
resetToWaitSpeech()
return
}
- // ========== 1. 强制兜底：正常语音直接通过 ==========
+ // ========== 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() // 重置实时统计
- // 核心新增：有效语音通过后，重置无效说话标记（后续超时重新判定）
+ resetRealtimeStats()
hasInvalidSpeech = false
return
}
- // ========== 2. 远场过滤：只过滤极低能量 ==========
+ // ========== 2. 远场过滤（近距离场景几乎不生效） ==========
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")
- // 核心新增：无效说话（远场），标记hasInvalidSpeech为true
hasInvalidSpeech = true
resetToWaitSpeech()
return
}
- // ========== 3. 非连续判定：极度宽松 ==========
+ // ========== 3. 非连续判定（大幅放宽） ==========
val continuousRatio = if (realtimeSpeechFrames > 0) realtimeContinuousSpeechFrames.toFloat() / realtimeSpeechFrames else 0f
val peakPositionRatio = vadManager.getPeakPositionRatio()
val isDiscontinuous = continuousRatio < MIN_CONTINUOUS_FRAME_RATIO &&
@@ -373,13 +398,12 @@ class VoiceController(
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")
- // 核心新增：无效说话（非连续杂音），标记hasInvalidSpeech为true
hasInvalidSpeech = true
resetToWaitSpeech()
return
}
- // ========== 4. 分场景动态阈值计算（保留核心逻辑） ==========
+ // ========== 4. 分场景动态阈值计算（系数大幅降低） ==========
val isQuietEnv = currentEnvBaseline < BASELINE_QUIET_THRESHOLD
val thresholdConfig = when {
duration in SHORT_SPEECH_MIN..SHORT_SPEECH_MAX -> {
@@ -406,18 +430,17 @@ class VoiceController(
}
}
- // ========== 5. 分场景阈值过滤 ==========
+ // ========== 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) {
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}")
- // 核心新增：无效说话（低能量），标记hasInvalidSpeech为true
hasInvalidSpeech = true
resetToWaitSpeech()
return
}
- // ========== 6. 评分判定：极度宽松 ==========
+ // ========== 6. 评分判定（门槛降低到1） ==========
var score = 0
score += when {
duration >= 4000 -> 3
@@ -430,7 +453,6 @@ class VoiceController(
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}")
- // 核心新增：无效说话（评分不足），标记hasInvalidSpeech为true
hasInvalidSpeech = true
resetToWaitSpeech()
return
@@ -440,13 +462,12 @@ class VoiceController(
audioBuffer.clear()
state = VoiceState.UPLOADING
onFinalAudio(audio)
- resetRealtimeStats() // 重置实时统计
- // 核心新增：有效语音通过后，重置无效说话标记
+ resetRealtimeStats()
hasInvalidSpeech = false
- LogUtils.i(TAG, "✅ 低能量语音通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene}")
+ LogUtils.i(TAG, "✅ 近距离轻声通过 | 时长: $duration ms | 能量: $avgEnergy | 场景: ${thresholdConfig.scene}")
}
- /* ================= 新增：重置实时统计变量 ================= */
+ /* ================= 重置实时统计变量 ================= */
private fun resetRealtimeStats() {
realtimeEnergySum = 0f
realtimeEnergyCount = 0
@@ -458,6 +479,7 @@ class VoiceController(
isMultiPersonDialogueDetected = false
}
+
/* ================= 播放/上传/Reset 回调 ================= */
fun onPlayStartPrompt() {
LogUtils.d(TAG, "🎵 播放提示音 | 基线: $currentEnvBaseline")
@@ -471,7 +493,6 @@ class VoiceController(
}
fun onPlayStartBackend() {
- // 仅当上传完成（成功）且状态为 UPLOADING 时，才切换状态
if (state != VoiceState.UPLOADING) {
LogUtils.w(TAG, "🎶 非上传完成状态，禁止切换到 PLAYING_BACKEND | 当前状态: $state")
return
@@ -490,42 +511,12 @@ class VoiceController(
if (state != VoiceState.UPLOADING) return
LogUtils.d(TAG, "📤 上传完成 | 成功: $success | 基线: $currentEnvBaseline")
- if (success) {
- // 上传成功：启动协程超时任务
- startPlayWaitTimer()
- } else {
- // 上传失败：取消超时任务，重置状态
- cancelPlayWaitTimer()
+ if (!success) {
speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
state = VoiceState.WAIT_SPEECH_COOLDOWN
}
}
- private fun startPlayWaitTimer() {
- // 先取消旧任务，避免重复
- cancelPlayWaitTimer()
-
- // 启动协程超时任务（Dispatchers.Main保证状态修改在主线程）
- playWaitJob = GlobalScope.launch {
- delay(PLAY_WAIT_TIMEOUT_MS) // 挂起3秒，无线程阻塞
- LogUtils.w(TAG, "⏱ 播放等待超时（${PLAY_WAIT_TIMEOUT_MS}ms），自动重置状态")
-
- // 超时后重置状态（加同步锁，避免多线程冲突）
- synchronized(this@VoiceController) {
- speechEnableAtMs = System.currentTimeMillis() + SPEECH_COOLDOWN_MS
- state = VoiceState.WAIT_SPEECH_COOLDOWN
- }
- }
- }
-
- // ================= 替换：取消协程任务 =================
- private fun cancelPlayWaitTimer() {
- playWaitJob?.cancel() // 取消协程（挂起函数会立即停止）
- playWaitJob = null
- LogUtils.d(TAG, "🔄 播放等待协程已取消")
- }
-
-
private fun resetToWaitSpeech() {
LogUtils.d(TAG, "🔄 重置到等待说话 | 基线: $currentEnvBaseline | 已标记无效说话: $hasInvalidSpeech")
val now = System.currentTimeMillis()
@@ -537,7 +528,7 @@ class VoiceController(
audioBuffer.clear()
vadManager.reset()
vadStarted = false
- resetRealtimeStats() // 重置实时统计
+ resetRealtimeStats()
state = VoiceState.WAIT_SPEECH
if (waitSpeechFailStartMs == 0L) waitSpeechFailStartMs = System.currentTimeMillis()
}
@@ -553,13 +544,11 @@ class VoiceController(
waitSpeechFailStartMs = 0L
envNoiseBuffer.clear()
currentEnvBaseline = 0.001f
- resetRealtimeStats() // 重置实时统计
- // 核心新增：重置所有状态时，同时重置无效说话标记和超时类型
+ 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
- cancelPlayWaitTimer()
}
fun release() {
@@ -567,11 +556,9 @@ class VoiceController(
wakeupManager.release()
vadManager.reset()
envNoiseBuffer.clear()
- resetRealtimeStats() // 重置实时统计
- // 核心新增：释放资源时重置标记
+ resetRealtimeStats()
hasInvalidSpeech = false
currentTimeoutType = TimeoutType.IDLE_TIMEOUT
- cancelPlayWaitTimer()
}
private fun cachePreBuffer(samples: FloatArray) {
diff --git a/app/src/main/java/com/zs/smarthuman/ui/MainActivity.kt b/app/src/main/java/com/zs/smarthuman/ui/MainActivity.kt
index 1a7539b..a8daa05 100644
--- a/app/src/main/java/com/zs/smarthuman/ui/MainActivity.kt
+++ b/app/src/main/java/com/zs/smarthuman/ui/MainActivity.kt
@@ -54,6 +54,7 @@ import com.zs.smarthuman.utils.AudioDebugUtil
import com.zs.smarthuman.utils.AudioPcmUtil
import com.zs.smarthuman.utils.DangerousUtils
import com.zs.smarthuman.utils.LogFileUtils
+import com.zs.smarthuman.utils.SimulateStreamingAsr
import com.zs.smarthuman.utils.UnityPlayerHolder
import com.zs.smarthuman.utils.ViewSlideAnimator
@@ -86,6 +87,9 @@ class MainActivity : BaseViewModelActivity()
private var versionUpdateDialog: VersionUpdateDialog? = null
+ private val PLAY_WAIT_TIMEOUT_MS = 2000L // 统一2秒超时阈值
+ private var startPlayTimeoutJob: Job? = null // 统一管理所有播放场景的超时Job
+
override fun getViewBinding(): ActivityMainBinding = ActivityMainBinding.inflate(layoutInflater)
override fun initView() {
UnityPlayerHolder.getInstance().initialize(this)
@@ -97,6 +101,7 @@ class MainActivity : BaseViewModelActivity()
}
override fun initData() {
+ initAsrModel()
PermissionUtils.permissionGroup(PermissionConstants.MICROPHONE)
.callback(object : PermissionUtils.FullCallback {
override fun onGranted(granted: List) {
@@ -154,6 +159,11 @@ class MainActivity : BaseViewModelActivity()
}
Toaster.showShort(it)
voiceController?.onUploadFinished(true)
+ startPlayTimeoutJob?.cancel()
+ startPlayTimeoutJob = lifecycleScope.launch {
+ delay(PLAY_WAIT_TIMEOUT_MS)
+ voiceController?.onPlayEndBackend()
+ }
}
}
}
@@ -181,38 +191,42 @@ class MainActivity : BaseViewModelActivity()
voiceController = VoiceController(
assetManager = assets,
onWakeup = {
- Log.d("lrs", "当前状态: 唤醒成功wakeup")
//每次唤醒前都要把前面的音频停掉
-// UnityPlayerHolder.getInstance().cancelPCM()
+ UnityPlayerHolder.getInstance().cancelPCM()
UnityPlayerHolder.getInstance()
.sendVoiceToUnity(
voiceInfo = mutableListOf().apply {
add(
VoiceBeanResp(
- audioUrl = UserInfoManager.userInfo?.wakeUpAudioUrl
- ?: "https://static.seerteach.net/aidialogue/systemVoice/aliyun-nv.mp3"
+ audioUrl = "https://static.seerteach.net/aidialogue/systemVoice/aliyun-nv.mp3"
)
)
}
)
+
+ startPlayTimeoutJob?.cancel()
+ startPlayTimeoutJob = lifecycleScope.launch {
+ delay(PLAY_WAIT_TIMEOUT_MS)
+ voiceController?.onPlayEndPrompt()
+ }
},
onFinalAudio = { audio ->
Log.d("lrsxx", "检测到语音，长度=${audio.size}")
- mViewModel?.uploadVoice(
- AudioPcmUtil.pcm16ToBase64(AudioPcmUtil.floatToPcm16(audio)),
- 1
- )
-// loadLocalJsonAndPlay()
+// mViewModel?.uploadVoice(
+// AudioPcmUtil.pcm16ToBase64(AudioPcmUtil.floatToPcm16(audio)),
+// 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}")
-// lifecycleScope.launch(Dispatchers.Main) {
-//
-// mVerticalAnimator?.show()
-// }
+ lifecycleScope.launch(Dispatchers.Main) {
+
+ mVerticalAnimator?.show()
+ }
},
onStateChanged = { state ->
@@ -233,7 +247,7 @@ class MainActivity : BaseViewModelActivity()
voiceInfo = mutableListOf().apply {
add(
VoiceBeanResp(
- audioUrl = "https://static.seerteach.net/aidialogue/userWakeUpAudio/ttsmaker-file-2025-12-31-16-2-51.mp3"
+ audioUrl = UserInfoManager.userInfo?.endAudioUrl?:""
)
)
}
@@ -244,6 +258,11 @@ class MainActivity : BaseViewModelActivity()
)
}
+ private fun initAsrModel(){
+ lifecycleScope.launch(Dispatchers.IO){
+ SimulateStreamingAsr.initOfflineRecognizer(App.getInstance())
+ }
+ }
override fun receivedIMMsg(msg: SingleMessage) {
when (msg.msgContentType) {
MessageContentType.RECEIVE_VOICE_STREAM.msgContentType -> {
@@ -415,9 +434,6 @@ class MainActivity : BaseViewModelActivity()
private var promptPlaying = false
private var backPlaying = false
- private var promptTimeoutJob: Job? = null
- private val PROMPT_PLAY_TIMEOUT_MS = 3000L // 3 秒
-
fun onAudioProgressUpdated( // Unity 调用此方法传递音频进度
progress: Float,
@@ -427,7 +443,6 @@ class MainActivity : BaseViewModelActivity()
audioUrl: String
) {
val wakeupUrl = UserInfoManager.userInfo?.wakeUpAudioUrl
- ?: "https://static.seerteach.net/aidialogue/systemVoice/aliyun-nv.mp3"
if (audioUrl != wakeupUrl) return
@@ -436,13 +451,8 @@ class MainActivity : BaseViewModelActivity()
if (!promptPlaying) {
promptPlaying = true
voiceController?.onPlayStartPrompt()
-
- promptTimeoutJob = lifecycleScope.launch {
- delay(PROMPT_PLAY_TIMEOUT_MS)
- promptPlaying = false
- voiceController?.onPlayEndPrompt()
- promptTimeoutJob?.cancel()
- }
+ startPlayTimeoutJob?.cancel()
+ LogUtils.eTag("MainActivity","等待超时")
}
}
@@ -450,7 +460,6 @@ class MainActivity : BaseViewModelActivity()
if (promptPlaying) {
promptPlaying = false
voiceController?.onPlayEndPrompt()
- promptTimeoutJob?.cancel()
}
}
}
@@ -464,14 +473,15 @@ class MainActivity : BaseViewModelActivity()
isFinal: Boolean
) {
when (state) {
- 1 -> { // play
+ 1 -> {
if (!backPlaying) {
backPlaying = true
voiceController?.onPlayStartBackend()
+ startPlayTimeoutJob?.cancel()
}
}
- 3 -> { // complete
+ 3 -> {
if (backPlaying) {
backPlaying = false
voiceController?.onPlayEndBackend()
diff --git a/app/src/main/java/com/zs/smarthuman/utils/SimulateStreamingAsr.kt b/app/src/main/java/com/zs/smarthuman/utils/SimulateStreamingAsr.kt
new file mode 100644
index 0000000..cb10e37
--- /dev/null
+++ b/app/src/main/java/com/zs/smarthuman/utils/SimulateStreamingAsr.kt
@@ -0,0 +1,155 @@
+package com.zs.smarthuman.utils
+
+import android.content.Context
+import android.content.res.AssetManager
+import com.blankj.utilcode.util.LogUtils
+
+import com.k2fsa.sherpa.onnx.OfflineModelConfig
+import com.k2fsa.sherpa.onnx.OfflineRecognizer
+import com.k2fsa.sherpa.onnx.OfflineRecognizerConfig
+import com.k2fsa.sherpa.onnx.OfflineWenetCtcModelConfig
+import com.k2fsa.sherpa.onnx.Vad
+import com.k2fsa.sherpa.onnx.getVadModelConfig
+import java.io.File
+import java.io.FileOutputStream
+import java.io.InputStream
+import java.io.OutputStream
+
+
+fun assetExists(assetManager: AssetManager, path: String): Boolean {
+ val dir = path.substringBeforeLast('/', "")
+ val fileName = path.substringAfterLast('/')
+
+ val files = assetManager.list(dir) ?: return false
+ return files.contains(fileName)
+}
+
+fun copyAssetToInternalStorage(path: String, context: Context): String {
+ val targetRoot = context.filesDir
+ val outFile = File(targetRoot, path)
+
+ if (!assetExists(context.assets, path = path)) {
+ // for context binary, if it is does not exist, we return a path
+ // that can be written to
+ outFile.parentFile?.mkdirs()
+ LogUtils.i("VoiceController", "$path does not exist, return ${outFile.absolutePath}")
+ return outFile.absolutePath
+ }
+
+ if (outFile.exists()) {
+ val assetSize = context.assets.open(path).use { it.available() }
+ if (outFile.length() == assetSize.toLong()) {
+ LogUtils.i("VoiceController", "$targetRoot/$path already exists, skip copying, return $targetRoot/$path")
+
+ return "$targetRoot/$path"
+ }
+ }
+
+ outFile.parentFile?.mkdirs()
+
+ context.assets.open(path).use { input: InputStream ->
+ FileOutputStream(outFile).use { output: OutputStream ->
+ input.copyTo(output)
+ }
+ }
+ LogUtils.i("VoiceController", "Copied $path to $targetRoot/$path")
+
+ return outFile.absolutePath
+}
+
+
+object SimulateStreamingAsr {
+ private var _recognizer: OfflineRecognizer? = null
+ val recognizer: OfflineRecognizer
+ get() {
+ return _recognizer!!
+ }
+
+
+ fun initOfflineRecognizer(context: Context) {
+ synchronized(this) {
+ if (_recognizer != null) {
+ return
+ }
+
+ val wenetConfig = OfflineWenetCtcModelConfig(
+ model = "sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/model.int8.onnx",
+ )
+
+ val modelConfig = OfflineModelConfig(
+ wenetCtc = wenetConfig,
+ tokens = "sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10/tokens.txt"
+ )
+ val config = OfflineRecognizerConfig(
+ modelConfig = modelConfig,
+ )
+
+
+ var assetManager: AssetManager? = context.assets
+
+ if (config.modelConfig.provider == "qnn") {
+ // We assume you have copied files like libQnnHtpV81Skel.so to jniLibs/arm64-v8a
+ LogUtils.i("VoiceController", "nativelibdir: ${context.applicationInfo.nativeLibraryDir}")
+
+ // If we don't set the environment variable for ADSP_LIBRARY_PATH, we will see
+ // the error code 1008 from qnn_interface.deviceCreate()
+ // See also
+ // https://workbench.aihub.qualcomm.com/docs/hub/faq.html#why-am-i-seeing-error-1008-when-trying-to-use-htp
+ OfflineRecognizer.prependAdspLibraryPath(context.applicationInfo.nativeLibraryDir)
+
+ // for qnn, we need to copy *.so files from assets folder to sd card
+ if (config.modelConfig.senseVoice.qnnConfig.backendLib.isEmpty() && config.modelConfig.zipformerCtc.qnnConfig.backendLib.isEmpty()) {
+ LogUtils.i("VoiceController", "You should provide libQnnHtp.so for qnn")
+ throw IllegalArgumentException("You should provide libQnnHtp.so for qnn")
+ }
+ config.modelConfig.tokens =
+ copyAssetToInternalStorage(config.modelConfig.tokens, context)
+
+ if (config.modelConfig.senseVoice.model.isNotEmpty() || assetExists(
+ context.assets,
+ path = config.modelConfig.senseVoice.qnnConfig.contextBinary
+ )
+ ) {
+ if (config.modelConfig.senseVoice.model.isNotEmpty()) {
+ config.modelConfig.senseVoice.model =
+ copyAssetToInternalStorage(config.modelConfig.senseVoice.model, context)
+ }
+
+ config.modelConfig.senseVoice.qnnConfig.contextBinary =
+ copyAssetToInternalStorage(
+ config.modelConfig.senseVoice.qnnConfig.contextBinary,
+ context
+ )
+ } else if (config.modelConfig.zipformerCtc.model.isNotEmpty()) {
+ config.modelConfig.zipformerCtc.model =
+ copyAssetToInternalStorage(config.modelConfig.zipformerCtc.model, context)
+
+ config.modelConfig.zipformerCtc.qnnConfig.contextBinary =
+ copyAssetToInternalStorage(
+ config.modelConfig.zipformerCtc.qnnConfig.contextBinary,
+ context
+ )
+ }
+
+ if (config.hr.lexicon.isNotEmpty()) {
+ config.hr.lexicon = copyAssetToInternalStorage(config.hr.lexicon, context)
+ }
+
+ if (config.hr.ruleFsts.isNotEmpty()) {
+ // it assumes there is only one fst. otherwise, you need to copy each fst separately
+ config.hr.ruleFsts = copyAssetToInternalStorage(config.hr.ruleFsts, context)
+ }
+
+ assetManager = null
+ }
+
+ _recognizer = OfflineRecognizer(
+ assetManager = assetManager,
+ config = config,
+ )
+
+ LogUtils.i("VoiceController", "sherpa-onnx offline recognizer initialized")
+ }
+ }
+
+}
diff --git a/app/src/main/jniLibs/arm64-v8a/libandroidx.graphics.path.so b/app/src/main/jniLibs/arm64-v8a/libandroidx.graphics.path.so
new file mode 100644
index 0000000..5bf9a29
Binary files /dev/null and b/app/src/main/jniLibs/arm64-v8a/libandroidx.graphics.path.so differ