705 lines
28 KiB
Python
705 lines
28 KiB
Python
"""Full duplex audio pipeline for AI voice conversation.
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This module implements the core duplex pipeline that orchestrates:
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- VAD (Voice Activity Detection)
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- EOU (End of Utterance) Detection
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- ASR (Automatic Speech Recognition) - optional
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- LLM (Language Model)
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- TTS (Text-to-Speech)
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Inspired by pipecat's frame-based architecture and active-call's
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event-driven design.
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"""
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import asyncio
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import time
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from typing import Optional, Callable, Awaitable
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from loguru import logger
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from core.transports import BaseTransport
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from core.conversation import ConversationManager, ConversationState
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from core.events import get_event_bus
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from processors.vad import VADProcessor, SileroVAD
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from processors.eou import EouDetector
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from services.base import BaseLLMService, BaseTTSService, BaseASRService
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from services.llm import OpenAILLMService, MockLLMService
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from services.tts import EdgeTTSService, MockTTSService
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from services.asr import BufferedASRService
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from services.siliconflow_tts import SiliconFlowTTSService
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from services.siliconflow_asr import SiliconFlowASRService
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from app.config import settings
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class DuplexPipeline:
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"""
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Full duplex audio pipeline for AI voice conversation.
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Handles bidirectional audio flow with:
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- User speech detection and transcription
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- AI response generation
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- Text-to-speech synthesis
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- Barge-in (interruption) support
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Architecture (inspired by pipecat):
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User Audio → VAD → EOU → [ASR] → LLM → TTS → Audio Out
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↓
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Barge-in Detection → Interrupt
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"""
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def __init__(
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self,
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transport: BaseTransport,
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session_id: str,
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llm_service: Optional[BaseLLMService] = None,
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tts_service: Optional[BaseTTSService] = None,
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asr_service: Optional[BaseASRService] = None,
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system_prompt: Optional[str] = None,
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greeting: Optional[str] = None
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):
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"""
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Initialize duplex pipeline.
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Args:
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transport: Transport for sending audio/events
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session_id: Session identifier
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llm_service: LLM service (defaults to OpenAI)
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tts_service: TTS service (defaults to EdgeTTS)
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asr_service: ASR service (optional)
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system_prompt: System prompt for LLM
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greeting: Optional greeting to speak on start
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"""
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self.transport = transport
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self.session_id = session_id
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self.event_bus = get_event_bus()
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# Initialize VAD
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self.vad_model = SileroVAD(
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model_path=settings.vad_model_path,
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sample_rate=settings.sample_rate
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)
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self.vad_processor = VADProcessor(
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vad_model=self.vad_model,
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threshold=settings.vad_threshold
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)
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# Initialize EOU detector
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self.eou_detector = EouDetector(
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silence_threshold_ms=settings.vad_eou_threshold_ms,
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min_speech_duration_ms=settings.vad_min_speech_duration_ms
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)
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# Initialize services
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self.llm_service = llm_service
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self.tts_service = tts_service
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self.asr_service = asr_service # Will be initialized in start()
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# Track last sent transcript to avoid duplicates
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self._last_sent_transcript = ""
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# Conversation manager
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self.conversation = ConversationManager(
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system_prompt=system_prompt,
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greeting=greeting
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)
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# State
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self._running = True
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self._is_bot_speaking = False
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self._current_turn_task: Optional[asyncio.Task] = None
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self._audio_buffer: bytes = b""
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self._last_vad_status: str = "Silence"
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# Interruption handling
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self._interrupt_event = asyncio.Event()
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# Latency tracking - TTFB (Time to First Byte)
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self._turn_start_time: Optional[float] = None
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self._first_audio_sent: bool = False
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# Barge-in filtering - require minimum speech duration to interrupt
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self._barge_in_speech_start_time: Optional[float] = None
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self._barge_in_min_duration_ms: int = settings.barge_in_min_duration_ms if hasattr(settings, 'barge_in_min_duration_ms') else 50
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self._barge_in_speech_frames: int = 0 # Count speech frames
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self._barge_in_silence_frames: int = 0 # Count silence frames during potential barge-in
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self._barge_in_silence_tolerance: int = 3 # Allow up to 3 silence frames (60ms at 20ms chunks)
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logger.info(f"DuplexPipeline initialized for session {session_id}")
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async def start(self) -> None:
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"""Start the pipeline and connect services."""
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try:
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# Connect LLM service
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if not self.llm_service:
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if settings.openai_api_key:
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self.llm_service = OpenAILLMService(
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api_key=settings.openai_api_key,
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base_url=settings.openai_api_url,
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model=settings.llm_model
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)
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else:
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logger.warning("No OpenAI API key - using mock LLM")
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self.llm_service = MockLLMService()
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await self.llm_service.connect()
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# Connect TTS service
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if not self.tts_service:
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if settings.tts_provider == "siliconflow" and settings.siliconflow_api_key:
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self.tts_service = SiliconFlowTTSService(
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api_key=settings.siliconflow_api_key,
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voice=settings.tts_voice,
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model=settings.siliconflow_tts_model,
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sample_rate=settings.sample_rate,
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speed=settings.tts_speed
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)
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logger.info("Using SiliconFlow TTS service")
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else:
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self.tts_service = EdgeTTSService(
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voice=settings.tts_voice,
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sample_rate=settings.sample_rate
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)
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logger.info("Using Edge TTS service")
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await self.tts_service.connect()
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# Connect ASR service
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if not self.asr_service:
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if settings.asr_provider == "siliconflow" and settings.siliconflow_api_key:
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self.asr_service = SiliconFlowASRService(
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api_key=settings.siliconflow_api_key,
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model=settings.siliconflow_asr_model,
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sample_rate=settings.sample_rate,
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interim_interval_ms=settings.asr_interim_interval_ms,
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min_audio_for_interim_ms=settings.asr_min_audio_ms,
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on_transcript=self._on_transcript_callback
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)
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logger.info("Using SiliconFlow ASR service")
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else:
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self.asr_service = BufferedASRService(
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sample_rate=settings.sample_rate
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)
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logger.info("Using Buffered ASR service (no real transcription)")
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await self.asr_service.connect()
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logger.info("DuplexPipeline services connected")
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# Speak greeting if configured
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if self.conversation.greeting:
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await self._speak(self.conversation.greeting)
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except Exception as e:
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logger.error(f"Failed to start pipeline: {e}")
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raise
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async def process_audio(self, pcm_bytes: bytes) -> None:
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"""
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Process incoming audio chunk.
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This is the main entry point for audio from the user.
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Args:
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pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
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"""
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if not self._running:
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return
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try:
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# 1. Process through VAD
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vad_result = self.vad_processor.process(pcm_bytes, settings.chunk_size_ms)
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vad_status = "Silence"
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if vad_result:
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event_type, probability = vad_result
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vad_status = "Speech" if event_type == "speaking" else "Silence"
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# Emit VAD event
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await self.event_bus.publish(event_type, {
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"trackId": self.session_id,
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"probability": probability
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})
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else:
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# No state change - keep previous status
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vad_status = self._last_vad_status
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# Update state based on VAD
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if vad_status == "Speech" and self._last_vad_status != "Speech":
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await self._on_speech_start()
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self._last_vad_status = vad_status
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# 2. Check for barge-in (user speaking while bot speaking)
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# Filter false interruptions by requiring minimum speech duration
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if self._is_bot_speaking:
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if vad_status == "Speech":
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# User is speaking while bot is speaking
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self._barge_in_silence_frames = 0 # Reset silence counter
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if self._barge_in_speech_start_time is None:
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# Start tracking speech duration
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self._barge_in_speech_start_time = time.time()
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self._barge_in_speech_frames = 1
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logger.debug("Potential barge-in detected, tracking duration...")
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else:
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self._barge_in_speech_frames += 1
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# Check if speech duration exceeds threshold
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speech_duration_ms = (time.time() - self._barge_in_speech_start_time) * 1000
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if speech_duration_ms >= self._barge_in_min_duration_ms:
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logger.info(f"Barge-in confirmed after {speech_duration_ms:.0f}ms of speech ({self._barge_in_speech_frames} frames)")
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await self._handle_barge_in()
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else:
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# Silence frame during potential barge-in
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if self._barge_in_speech_start_time is not None:
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self._barge_in_silence_frames += 1
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# Allow brief silence gaps (VAD flickering)
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if self._barge_in_silence_frames > self._barge_in_silence_tolerance:
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# Too much silence - reset barge-in tracking
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logger.debug(f"Barge-in cancelled after {self._barge_in_silence_frames} silence frames")
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self._barge_in_speech_start_time = None
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self._barge_in_speech_frames = 0
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self._barge_in_silence_frames = 0
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# 3. Buffer audio for ASR
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if vad_status == "Speech" or self.conversation.state == ConversationState.LISTENING:
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self._audio_buffer += pcm_bytes
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await self.asr_service.send_audio(pcm_bytes)
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# For SiliconFlow ASR, trigger interim transcription periodically
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# The service handles timing internally via start_interim_transcription()
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# 4. Check for End of Utterance - this triggers LLM response
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if self.eou_detector.process(vad_status):
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await self._on_end_of_utterance()
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except Exception as e:
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logger.error(f"Pipeline audio processing error: {e}", exc_info=True)
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async def process_text(self, text: str) -> None:
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"""
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Process text input (chat command).
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Allows direct text input to bypass ASR.
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Args:
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text: User text input
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"""
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if not self._running:
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return
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logger.info(f"Processing text input: {text[:50]}...")
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# Cancel any current speaking
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await self._stop_current_speech()
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# Start new turn
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await self.conversation.end_user_turn(text)
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self._current_turn_task = asyncio.create_task(self._handle_turn(text))
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async def interrupt(self) -> None:
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"""Interrupt current bot speech (manual interrupt command)."""
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await self._handle_barge_in()
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async def _on_transcript_callback(self, text: str, is_final: bool) -> None:
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"""
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Callback for ASR transcription results.
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Streams transcription to client for display.
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Args:
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text: Transcribed text
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is_final: Whether this is the final transcription
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"""
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# Avoid sending duplicate transcripts
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if text == self._last_sent_transcript and not is_final:
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return
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self._last_sent_transcript = text
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# Send transcript event to client
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await self.transport.send_event({
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"event": "transcript",
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"trackId": self.session_id,
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"text": text,
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"isFinal": is_final,
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"timestamp": self._get_timestamp_ms()
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})
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logger.debug(f"Sent transcript ({'final' if is_final else 'interim'}): {text[:50]}...")
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async def _on_speech_start(self) -> None:
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"""Handle user starting to speak."""
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if self.conversation.state == ConversationState.IDLE:
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await self.conversation.start_user_turn()
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self._audio_buffer = b""
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self._last_sent_transcript = ""
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self.eou_detector.reset()
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# Clear ASR buffer and start interim transcriptions
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if hasattr(self.asr_service, 'clear_buffer'):
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self.asr_service.clear_buffer()
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if hasattr(self.asr_service, 'start_interim_transcription'):
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await self.asr_service.start_interim_transcription()
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logger.debug("User speech started")
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async def _on_end_of_utterance(self) -> None:
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"""Handle end of user utterance."""
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if self.conversation.state != ConversationState.LISTENING:
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return
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# Stop interim transcriptions
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if hasattr(self.asr_service, 'stop_interim_transcription'):
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await self.asr_service.stop_interim_transcription()
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# Get final transcription from ASR service
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user_text = ""
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if hasattr(self.asr_service, 'get_final_transcription'):
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# SiliconFlow ASR - get final transcription
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user_text = await self.asr_service.get_final_transcription()
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elif hasattr(self.asr_service, 'get_and_clear_text'):
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# Buffered ASR - get accumulated text
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user_text = self.asr_service.get_and_clear_text()
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# Skip if no meaningful text
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if not user_text or not user_text.strip():
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logger.debug("EOU detected but no transcription - skipping")
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# Reset for next utterance
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self._audio_buffer = b""
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self._last_sent_transcript = ""
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# Return to idle; don't force LISTENING which causes buffering on silence
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await self.conversation.set_state(ConversationState.IDLE)
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return
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logger.info(f"EOU detected - user said: {user_text[:100]}...")
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# Send final transcription to client
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await self.transport.send_event({
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"event": "transcript",
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"trackId": self.session_id,
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"text": user_text,
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"isFinal": True,
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"timestamp": self._get_timestamp_ms()
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})
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# Clear buffers
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self._audio_buffer = b""
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self._last_sent_transcript = ""
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# Process the turn - trigger LLM response
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# Cancel any existing turn to avoid overlapping assistant responses
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await self._stop_current_speech()
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await self.conversation.end_user_turn(user_text)
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self._current_turn_task = asyncio.create_task(self._handle_turn(user_text))
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async def _handle_turn(self, user_text: str) -> None:
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"""
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Handle a complete conversation turn.
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Uses sentence-by-sentence streaming TTS for lower latency.
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Args:
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user_text: User's transcribed text
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"""
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try:
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# Start latency tracking
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self._turn_start_time = time.time()
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self._first_audio_sent = False
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# Get AI response (streaming)
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messages = self.conversation.get_messages()
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full_response = ""
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await self.conversation.start_assistant_turn()
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self._is_bot_speaking = True
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self._interrupt_event.clear()
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# Sentence buffer for streaming TTS
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sentence_buffer = ""
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sentence_ends = {'，', '。', '！', '？', '\n'}
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first_audio_sent = False
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# Stream LLM response and TTS sentence by sentence
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async for text_chunk in self.llm_service.generate_stream(messages):
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if self._interrupt_event.is_set():
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break
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full_response += text_chunk
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sentence_buffer += text_chunk
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await self.conversation.update_assistant_text(text_chunk)
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# Send LLM response streaming event to client
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await self.transport.send_event({
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"event": "llmResponse",
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"trackId": self.session_id,
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"text": text_chunk,
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"isFinal": False,
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"timestamp": self._get_timestamp_ms()
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})
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# Check for sentence completion - synthesize immediately for low latency
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while any(end in sentence_buffer for end in sentence_ends):
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# Find first sentence end
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min_idx = len(sentence_buffer)
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for end in sentence_ends:
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idx = sentence_buffer.find(end)
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if idx != -1 and idx < min_idx:
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min_idx = idx
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if min_idx < len(sentence_buffer):
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sentence = sentence_buffer[:min_idx + 1].strip()
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sentence_buffer = sentence_buffer[min_idx + 1:]
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if sentence and not self._interrupt_event.is_set():
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# Send track start on first audio
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if not first_audio_sent:
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await self.transport.send_event({
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"event": "trackStart",
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"trackId": self.session_id,
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"timestamp": self._get_timestamp_ms()
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})
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first_audio_sent = True
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# Synthesize and send this sentence immediately
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await self._speak_sentence(sentence)
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else:
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break
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# Send final LLM response event
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if full_response and not self._interrupt_event.is_set():
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await self.transport.send_event({
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"event": "llmResponse",
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"trackId": self.session_id,
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"text": full_response,
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"isFinal": True,
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"timestamp": self._get_timestamp_ms()
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})
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# Speak any remaining text
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if sentence_buffer.strip() and not self._interrupt_event.is_set():
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if not first_audio_sent:
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await self.transport.send_event({
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"event": "trackStart",
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"trackId": self.session_id,
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"timestamp": self._get_timestamp_ms()
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})
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first_audio_sent = True
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await self._speak_sentence(sentence_buffer.strip())
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# Send track end
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if first_audio_sent:
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await self.transport.send_event({
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"event": "trackEnd",
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"trackId": self.session_id,
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"timestamp": self._get_timestamp_ms()
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})
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# End assistant turn
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await self.conversation.end_assistant_turn(
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was_interrupted=self._interrupt_event.is_set()
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)
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except asyncio.CancelledError:
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logger.info("Turn handling cancelled")
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await self.conversation.end_assistant_turn(was_interrupted=True)
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except Exception as e:
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logger.error(f"Turn handling error: {e}", exc_info=True)
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await self.conversation.end_assistant_turn(was_interrupted=True)
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finally:
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self._is_bot_speaking = False
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# Reset barge-in tracking when bot finishes speaking
|
||
self._barge_in_speech_start_time = None
|
||
self._barge_in_speech_frames = 0
|
||
self._barge_in_silence_frames = 0
|
||
|
||
async def _speak_sentence(self, text: str) -> None:
|
||
"""
|
||
Synthesize and send a single sentence.
|
||
|
||
Args:
|
||
text: Sentence to speak
|
||
"""
|
||
if not text.strip() or self._interrupt_event.is_set():
|
||
return
|
||
|
||
try:
|
||
async for chunk in self.tts_service.synthesize_stream(text):
|
||
# Check interrupt at the start of each iteration
|
||
if self._interrupt_event.is_set():
|
||
logger.debug("TTS sentence interrupted")
|
||
break
|
||
|
||
# Track and log first audio packet latency (TTFB)
|
||
if not self._first_audio_sent and self._turn_start_time:
|
||
ttfb_ms = (time.time() - self._turn_start_time) * 1000
|
||
self._first_audio_sent = True
|
||
logger.info(f"[TTFB] Server first audio packet latency: {ttfb_ms:.0f}ms (session {self.session_id})")
|
||
|
||
# Send TTFB event to client
|
||
await self.transport.send_event({
|
||
"event": "ttfb",
|
||
"trackId": self.session_id,
|
||
"timestamp": self._get_timestamp_ms(),
|
||
"latencyMs": round(ttfb_ms)
|
||
})
|
||
|
||
# Double-check interrupt right before sending audio
|
||
if self._interrupt_event.is_set():
|
||
break
|
||
|
||
await self.transport.send_audio(chunk.audio)
|
||
await asyncio.sleep(0.005) # Small delay to prevent flooding
|
||
except asyncio.CancelledError:
|
||
logger.debug("TTS sentence cancelled")
|
||
except Exception as e:
|
||
logger.error(f"TTS sentence error: {e}")
|
||
|
||
async def _speak(self, text: str) -> None:
|
||
"""
|
||
Synthesize and send speech.
|
||
|
||
Args:
|
||
text: Text to speak
|
||
"""
|
||
if not text.strip():
|
||
return
|
||
|
||
try:
|
||
# Start latency tracking for greeting
|
||
speak_start_time = time.time()
|
||
first_audio_sent = False
|
||
|
||
# Send track start event
|
||
await self.transport.send_event({
|
||
"event": "trackStart",
|
||
"trackId": self.session_id,
|
||
"timestamp": self._get_timestamp_ms()
|
||
})
|
||
|
||
self._is_bot_speaking = True
|
||
|
||
# Stream TTS audio
|
||
async for chunk in self.tts_service.synthesize_stream(text):
|
||
if self._interrupt_event.is_set():
|
||
logger.info("TTS interrupted by barge-in")
|
||
break
|
||
|
||
# Track and log first audio packet latency (TTFB)
|
||
if not first_audio_sent:
|
||
ttfb_ms = (time.time() - speak_start_time) * 1000
|
||
first_audio_sent = True
|
||
logger.info(f"[TTFB] Greeting first audio packet latency: {ttfb_ms:.0f}ms (session {self.session_id})")
|
||
|
||
# Send TTFB event to client
|
||
await self.transport.send_event({
|
||
"event": "ttfb",
|
||
"trackId": self.session_id,
|
||
"timestamp": self._get_timestamp_ms(),
|
||
"latencyMs": round(ttfb_ms)
|
||
})
|
||
|
||
# Send audio to client
|
||
await self.transport.send_audio(chunk.audio)
|
||
|
||
# Small delay to prevent flooding
|
||
await asyncio.sleep(0.01)
|
||
|
||
# Send track end event
|
||
await self.transport.send_event({
|
||
"event": "trackEnd",
|
||
"trackId": self.session_id,
|
||
"timestamp": self._get_timestamp_ms()
|
||
})
|
||
|
||
except asyncio.CancelledError:
|
||
logger.info("TTS cancelled")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"TTS error: {e}")
|
||
finally:
|
||
self._is_bot_speaking = False
|
||
|
||
async def _handle_barge_in(self) -> None:
|
||
"""Handle user barge-in (interruption)."""
|
||
if not self._is_bot_speaking:
|
||
return
|
||
|
||
logger.info("Barge-in detected - interrupting bot speech")
|
||
|
||
# Reset barge-in tracking
|
||
self._barge_in_speech_start_time = None
|
||
self._barge_in_speech_frames = 0
|
||
self._barge_in_silence_frames = 0
|
||
|
||
# IMPORTANT: Signal interruption FIRST to stop audio sending
|
||
self._interrupt_event.set()
|
||
self._is_bot_speaking = False
|
||
|
||
# Send interrupt event to client IMMEDIATELY
|
||
# This must happen BEFORE canceling services, so client knows to discard in-flight audio
|
||
await self.transport.send_event({
|
||
"event": "interrupt",
|
||
"trackId": self.session_id,
|
||
"timestamp": self._get_timestamp_ms()
|
||
})
|
||
|
||
# Cancel TTS
|
||
if self.tts_service:
|
||
await self.tts_service.cancel()
|
||
|
||
# Cancel LLM
|
||
if self.llm_service and hasattr(self.llm_service, 'cancel'):
|
||
self.llm_service.cancel()
|
||
|
||
# Interrupt conversation
|
||
await self.conversation.interrupt()
|
||
|
||
# Reset for new user turn
|
||
await self.conversation.start_user_turn()
|
||
self._audio_buffer = b""
|
||
self.eou_detector.reset()
|
||
|
||
async def _stop_current_speech(self) -> None:
|
||
"""Stop any current speech task."""
|
||
if self._current_turn_task and not self._current_turn_task.done():
|
||
self._interrupt_event.set()
|
||
self._current_turn_task.cancel()
|
||
try:
|
||
await self._current_turn_task
|
||
except asyncio.CancelledError:
|
||
pass
|
||
|
||
self._is_bot_speaking = False
|
||
self._interrupt_event.clear()
|
||
|
||
async def cleanup(self) -> None:
|
||
"""Cleanup pipeline resources."""
|
||
logger.info(f"Cleaning up DuplexPipeline for session {self.session_id}")
|
||
|
||
self._running = False
|
||
await self._stop_current_speech()
|
||
|
||
# Disconnect services
|
||
if self.llm_service:
|
||
await self.llm_service.disconnect()
|
||
if self.tts_service:
|
||
await self.tts_service.disconnect()
|
||
if self.asr_service:
|
||
await self.asr_service.disconnect()
|
||
|
||
def _get_timestamp_ms(self) -> int:
|
||
"""Get current timestamp in milliseconds."""
|
||
import time
|
||
return int(time.time() * 1000)
|
||
|
||
@property
|
||
def is_speaking(self) -> bool:
|
||
"""Check if bot is currently speaking."""
|
||
return self._is_bot_speaking
|
||
|
||
@property
|
||
def state(self) -> ConversationState:
|
||
"""Get current conversation state."""
|
||
return self.conversation.state
|