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Fix Race risks if process_audio is called concurrently.

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This commit is contained in:
Xin Wang
2026-02-06 08:26:56 +08:00
parent 26458faa6c
commit 0576231d8d
1 changed files with 47 additions and 45 deletions
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92
core/duplex_pipeline.py
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@@ -111,6 +111,7 @@ class DuplexPipeline:
max_buffer_seconds = settings.max_audio_buffer_seconds if hasattr(settings, "max_audio_buffer_seconds") else 30
self._max_audio_buffer_bytes = int(settings.sample_rate * 2 * max_buffer_seconds)
self._last_vad_status: str = "Silence"
self._process_lock = asyncio.Lock()
# Interruption handling
self._interrupt_event = asyncio.Event()
@@ -208,8 +209,9 @@ class DuplexPipeline:
return
try:
# 1. Process through VAD
vad_result = self.vad_processor.process(pcm_bytes, settings.chunk_size_ms)
async with self._process_lock:
# 1. Process through VAD
vad_result = self.vad_processor.process(pcm_bytes, settings.chunk_size_ms)
vad_status = "Silence"
if vad_result:
@@ -231,51 +233,51 @@ class DuplexPipeline:
self._last_vad_status = vad_status
# 2. Check for barge-in (user speaking while bot speaking)
# Filter false interruptions by requiring minimum speech duration
if self._is_bot_speaking:
if vad_status == "Speech":
# User is speaking while bot is speaking
self._barge_in_silence_frames = 0 # Reset silence counter
if self._barge_in_speech_start_time is None:
# Start tracking speech duration
self._barge_in_speech_start_time = time.time()
self._barge_in_speech_frames = 1
logger.debug("Potential barge-in detected, tracking duration...")
# 2. Check for barge-in (user speaking while bot speaking)
# Filter false interruptions by requiring minimum speech duration
if self._is_bot_speaking:
if vad_status == "Speech":
# User is speaking while bot is speaking
self._barge_in_silence_frames = 0 # Reset silence counter
if self._barge_in_speech_start_time is None:
# Start tracking speech duration
self._barge_in_speech_start_time = time.time()
self._barge_in_speech_frames = 1
logger.debug("Potential barge-in detected, tracking duration...")
else:
self._barge_in_speech_frames += 1
# Check if speech duration exceeds threshold
speech_duration_ms = (time.time() - self._barge_in_speech_start_time) * 1000
if speech_duration_ms >= self._barge_in_min_duration_ms:
logger.info(f"Barge-in confirmed after {speech_duration_ms:.0f}ms of speech ({self._barge_in_speech_frames} frames)")
await self._handle_barge_in()
else:
self._barge_in_speech_frames += 1
# Check if speech duration exceeds threshold
speech_duration_ms = (time.time() - self._barge_in_speech_start_time) * 1000
if speech_duration_ms >= self._barge_in_min_duration_ms:
logger.info(f"Barge-in confirmed after {speech_duration_ms:.0f}ms of speech ({self._barge_in_speech_frames} frames)")
await self._handle_barge_in()
else:
# Silence frame during potential barge-in
if self._barge_in_speech_start_time is not None:
self._barge_in_silence_frames += 1
# Allow brief silence gaps (VAD flickering)
if self._barge_in_silence_frames > self._barge_in_silence_tolerance:
# Too much silence - reset barge-in tracking
logger.debug(f"Barge-in cancelled after {self._barge_in_silence_frames} silence frames")
self._barge_in_speech_start_time = None
self._barge_in_speech_frames = 0
self._barge_in_silence_frames = 0
# 3. Buffer audio for ASR
if vad_status == "Speech" or self.conversation.state == ConversationState.LISTENING:
self._audio_buffer += pcm_bytes
if len(self._audio_buffer) > self._max_audio_buffer_bytes:
# Keep only the most recent audio to cap memory usage
self._audio_buffer = self._audio_buffer[-self._max_audio_buffer_bytes:]
await self.asr_service.send_audio(pcm_bytes)
# Silence frame during potential barge-in
if self._barge_in_speech_start_time is not None:
self._barge_in_silence_frames += 1
# Allow brief silence gaps (VAD flickering)
if self._barge_in_silence_frames > self._barge_in_silence_tolerance:
# Too much silence - reset barge-in tracking
logger.debug(f"Barge-in cancelled after {self._barge_in_silence_frames} silence frames")
self._barge_in_speech_start_time = None
self._barge_in_speech_frames = 0
self._barge_in_silence_frames = 0
# For SiliconFlow ASR, trigger interim transcription periodically
# The service handles timing internally via start_interim_transcription()
# 4. Check for End of Utterance - this triggers LLM response
if self.eou_detector.process(vad_status):
await self._on_end_of_utterance()
# 3. Buffer audio for ASR
if vad_status == "Speech" or self.conversation.state == ConversationState.LISTENING:
self._audio_buffer += pcm_bytes
if len(self._audio_buffer) > self._max_audio_buffer_bytes:
# Keep only the most recent audio to cap memory usage
self._audio_buffer = self._audio_buffer[-self._max_audio_buffer_bytes:]
await self.asr_service.send_audio(pcm_bytes)
# For SiliconFlow ASR, trigger interim transcription periodically
# The service handles timing internally via start_interim_transcription()
# 4. Check for End of Utterance - this triggers LLM response
if self.eou_detector.process(vad_status):
await self._on_end_of_utterance()
except Exception as e:
logger.error(f"Pipeline audio processing error: {e}", exc_info=True)