update:恢复vad判断

main/xiaozhi-server/config.yaml

@@ -454,7 +454,7 @@ VAD:
     threshold: 0.5
     threshold_low: 0.3
     model_dir: models/snakers4_silero-vad
-    min_silence_duration_ms: 200  # 如果说话停顿比较长，可以把这个值设置大一些，实际延迟为:min_silence_duration_ms // 32 * 32ms
+    min_silence_duration_ms: 200  # 如果说话停顿比较长，可以把这个值设置大一些
 
 LLM:
   # 所有openai类型均可以修改超参，以AliLLM为例

main/xiaozhi-server/core/connection.py

@@ -115,10 +115,10 @@ class ConnectionHandler:
         # vad相关变量
         self.client_audio_buffer = bytearray()
         self.client_have_voice = False
+        self.client_voice_window = deque(maxlen=5)
         self.last_activity_time = 0.0  # 统一的活动时间戳（毫秒）
         self.client_voice_stop = False
         self.last_is_voice = False
-        self.continuous_false_count = 0
 
         # asr相关变量
         # 因为实际部署时可能会用到公共的本地ASR，不能把变量暴露给公共ASR

main/xiaozhi-server/core/providers/vad/silero.py

@@ -1,6 +1,6 @@
 import time
-import torch
 import numpy as np
+import torch
 import opuslib_next
 from config.logger import setup_logging
 from core.providers.vad.base import VADProviderBase
@@ -26,12 +26,15 @@ class VADProvider(VADProviderBase):
         threshold_low = config.get("threshold_low", "0.2")
         min_silence_duration_ms = config.get("min_silence_duration_ms", "1000")
 
-        # 双阈值判断参数
         self.vad_threshold = float(threshold) if threshold else 0.5
         self.vad_threshold_low = float(threshold_low) if threshold_low else 0.2
 
-        # 静默阈值，连续多少帧低于最低值才判断为静音
+        self.silence_threshold_ms = (
-        self.stop_false_count = int(min_silence_duration_ms) // 32 if min_silence_duration_ms else 31
+            int(min_silence_duration_ms) if min_silence_duration_ms else 1000
+        )
+
+        # 至少要多少帧才算有语音
+        self.frame_window_threshold = 3
 
     def is_vad(self, conn, opus_packet):
         try:
@@ -56,24 +59,26 @@ class VADProvider(VADProviderBase):
 
                 # 双阈值判断
                 if speech_prob >= self.vad_threshold:
-                    client_have_voice = True
+                    is_voice = True
                 elif speech_prob <= self.vad_threshold_low:
-                    client_have_voice = False
+                    is_voice = False
                 else:
-                    client_have_voice = conn.last_is_voice
+                    is_voice = conn.last_is_voice
-
-                if not client_have_voice:
-                    conn.continuous_false_count += 1
-                else:
-                    conn.continuous_false_count = 0
 
                 # 声音没低于最低值则延续前一个状态，判断为有声音
-                conn.last_is_voice = client_have_voice
+                conn.last_is_voice = is_voice
 
-                # 如果之前有声音，且连续为静音帧，达到静默阈值要求帧数，则认为已经说完一句话
+                # 更新滑动窗口
-                if conn.client_have_voice and conn.continuous_false_count >= self.stop_false_count:
+                conn.client_voice_window.append(is_voice)
-                    conn.client_voice_stop = True
+                client_have_voice = (
+                    conn.client_voice_window.count(True) >= self.frame_window_threshold
+                )
 
+                # 如果之前有声音，但本次没有声音，且与上次有声音的时间差已经超过了静默阈值，则认为已经说完一句话
+                if conn.client_have_voice and not client_have_voice:
+                    stop_duration = time.time() * 1000 - conn.last_activity_time
+                    if stop_duration >= self.silence_threshold_ms:
+                        conn.client_voice_stop = True
                 if client_have_voice:
                     conn.client_have_voice = True
                     conn.last_activity_time = time.time() * 1000