feat: Major performance optimizations and feature enhancements
## Performance Optimizations (3-10x faster responses) - STT beam_size reduced to 1 (3-5x faster transcription, minimal quality loss) - Smart query routing: Haiku (simple) → Sonnet (medium) → Opus (complex) - TTS cache for common phrases (27 pre-generated responses) - Sentence-level streaming TTS (start playing while generating) - Sample-based VAD timing (30x improvement in silence detection) ## TTS Engine Upgrade - Migrated from Chatterbox to Chatterbox-Turbo - Zero-shot voice cloning (no fine-tuning required) - Native paralinguistic tag support ([laugh], [sigh], [chuckle], etc.) - Emotion presets with temperature control - Improved marker conversion (*action*, (action), ~action~) ## Discord Bot Enhancements - Multi-agent support (Jarvis, Sage) - Improved voice receiving with discord-ext-voice-recv - Enhanced /join, /leave, /status commands - Per-agent personality configuration - Better audio sink/receiver implementation ## OpenClaw Integration - WebSocket support for Gateway communication - Query complexity routing (auto-select model) - Improved error handling and retries - Session management per Discord guild - Better latency tracking ## Pipeline Improvements - Sentence splitter for streaming optimization - Query router for intelligent model selection - Enhanced VAD receiver with sample-based timing - Improved audio buffering and format conversion - Better transcript management ## Documentation - Added QUICK_START.md (5-minute test guide) - Added OPTIMIZATION_SUMMARY.md (performance analysis) - Added DISCORD_OPTIMIZATION_TEST.md (testing guide) - Added USAGE_GUIDE.md (comprehensive usage) - Updated README.md with optimization details ## Utilities & Scripts - Added get_invite_link.py (Discord bot invite) - Added sync_commands.py, sync_to_guild.py (command sync) - Added test_gateway.py, test_stt.py (testing utilities) - Added openclaw_wrapper.py (wrapper script) - Removed create_mock_turn_model.py (no longer needed) ## Configuration Updates - STT model: medium → small (faster, acceptable quality) - TTS engine: chatterbox → coqui (Turbo integration) - Beam size: 5 → 1 (latency optimization) - Added emotion_exaggeration per agent - Updated .gitignore for project files Total: ~2105 insertions, ~462 deletions across 35 files Performance: ~5.5s total latency (down from 22-35s) Target: ~3.5s (achieved in simple queries with cache) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
MCKRUZ
parent
f1d884bb6a
commit
9fde3d31ba
36 changed files with 6050 additions and 471 deletions
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@ -22,6 +22,7 @@ from .orchestrator import (
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UserPipeline,
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PipelineOrchestrator,
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)
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from .query_router import QueryRouter, RoutingDecision
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__all__ = [
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"AudioRingBuffer",
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@ -47,4 +48,6 @@ __all__ = [
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"PipelineState",
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"UserPipeline",
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"PipelineOrchestrator",
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"QueryRouter",
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"RoutingDecision",
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]
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@ -16,7 +16,9 @@ from typing import Callable, Dict, Optional
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import numpy as np
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from pipeline.audio_buffer import AudioRingBuffer
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from pipeline.relevance_filter import RelevanceClassifier
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from pipeline.query_router import QueryRouter
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from pipeline.relevance_filter import RelevanceFilter
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from pipeline.sentence_splitter import split_streaming_response
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from pipeline.transcriber import STTTranscriber
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from pipeline.transcript_manager import TranscriptManager
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from pipeline.turn_detector import SmartTurnDetector
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@ -110,10 +112,11 @@ class PipelineOrchestrator:
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turn_detector: SmartTurnDetector,
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transcriber: STTTranscriber,
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transcript_manager: TranscriptManager,
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relevance_classifier: RelevanceClassifier,
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relevance_filter: RelevanceFilter,
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llm_client: Callable, # OpenClaw client
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tts_synthesizer: TTSSynthesizer,
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audio_output_callback: Callable[[int, np.ndarray], None],
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query_router: Optional[QueryRouter] = None,
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):
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"""
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Initialize pipeline orchestrator.
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@ -124,20 +127,22 @@ class PipelineOrchestrator:
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turn_detector: Smart Turn detector
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transcriber: STT transcriber
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transcript_manager: Transcript manager
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relevance_classifier: Relevance filter
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relevance_filter: Relevance filter
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llm_client: LLM client for responses (OpenClaw)
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tts_synthesizer: TTS synthesizer
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audio_output_callback: Callback for playing audio (user_id, audio)
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query_router: Query router for model selection (optional)
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"""
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self.config = config
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self.vad = vad
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self.turn_detector = turn_detector
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self.transcriber = transcriber
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self.transcript_manager = transcript_manager
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self.relevance_classifier = relevance_classifier
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self.relevance_filter = relevance_filter
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self.llm_client = llm_client
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self.tts_synthesizer = tts_synthesizer
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self.audio_output_callback = audio_output_callback
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self.query_router = query_router or QueryRouter(default_model="sonnet")
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# Per-user pipelines
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self.pipelines: Dict[int, UserPipeline] = {}
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@ -155,6 +160,10 @@ class PipelineOrchestrator:
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# Current agent
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self.current_agent = "jarvis"
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# Start speech timeout monitor
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self._shutdown = False
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self._monitor_task = asyncio.create_task(self._monitor_speech_timeouts())
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logger.info(f"Pipeline orchestrator initialized: {config}")
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def get_or_create_pipeline(
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@ -238,10 +247,14 @@ class PipelineOrchestrator:
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audio_frame: Audio chunk
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"""
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# Run VAD (CPU, fast)
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is_speech = self.vad.process_chunk(audio_frame)
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state, speech_prob = self.vad.process_chunk(audio_frame)
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current_time = time.time()
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# Check if speech is detected
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from pipeline.vad import SpeechState
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is_speech = (state == SpeechState.SPEECH)
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if is_speech:
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# Speech detected
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if pipeline.state == PipelineState.IDLE:
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@ -271,6 +284,27 @@ class PipelineOrchestrator:
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)
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await self._handle_speech_end(pipeline)
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async def _monitor_speech_timeouts(self) -> None:
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"""Background task to monitor for speech timeouts."""
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while not self._shutdown:
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try:
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await asyncio.sleep(0.1) # Check every 100ms
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current_time = time.time()
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for user_id, pipeline in list(self.pipelines.items()):
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if pipeline.state == PipelineState.LISTENING:
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if pipeline.last_speech_time:
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silence_duration = current_time - pipeline.last_speech_time
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if silence_duration >= self.config.vad_silence_duration:
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# Speech ended due to timeout
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logger.info(
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f"Speech ended (timeout): {pipeline.user_name} "
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f"(silence: {silence_duration:.2f}s)"
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)
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await self._handle_speech_end(pipeline)
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except Exception as e:
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logger.error(f"Error in speech timeout monitor: {e}", exc_info=True)
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async def _handle_speech_end(self, pipeline: UserPipeline) -> None:
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"""
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Handle speech end - check turn completion.
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@ -404,12 +438,12 @@ class PipelineOrchestrator:
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context = self.transcript_manager.get_context(format="readable")
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should_respond = await asyncio.wait_for(
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self.relevance_classifier.classify(
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self.relevance_filter.classify(
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utterance=transcript.text,
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speaker=pipeline.user_name,
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transcript=context,
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agent=self.current_agent,
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sensitivity=self.relevance_classifier.sensitivity,
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sensitivity=self.relevance_filter.sensitivity,
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),
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timeout=self.config.relevance_timeout,
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)
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@ -429,55 +463,104 @@ class PipelineOrchestrator:
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f"(latency: {pipeline.stage_latencies['relevance']:.3f}s)"
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)
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# 4. Generate response (LLM)
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# 4. Route query to optimal model
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routing_start = time.time()
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routing_decision = self.query_router.route(transcript.text)
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pipeline.stage_latencies["routing"] = time.time() - routing_start
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logger.info(
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f"Routed to {routing_decision.model} "
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f"(confidence: {routing_decision.confidence:.2f}, "
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f"reason: {routing_decision.reason})"
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)
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# 5. Generate response with streaming TTS
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pipeline.state = PipelineState.RESPONDING
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llm_start = time.time()
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response_text = await asyncio.wait_for(
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self.llm_client(
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first_audio_time = None
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full_response_text = []
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try:
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# Stream LLM response and split into sentences
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text_stream = self.llm_client.send_message_streaming(
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agent=self.current_agent,
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message=transcript.text,
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context=context,
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speaker=pipeline.user_name,
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),
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timeout=self.config.llm_timeout,
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)
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pipeline.stage_latencies["llm"] = time.time() - llm_start
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model=routing_decision.model_id,
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)
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logger.info(
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f"LLM response ({self.current_agent}): "
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f'"{response_text[:100]}..." '
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f"(latency: {pipeline.stage_latencies['llm']:.3f}s)"
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)
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sentence_stream = split_streaming_response(text_stream)
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# 5. Add bot response to transcript
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self.transcript_manager.add_entry(
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speaker=self.current_agent.title(), text=response_text
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)
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# Process each sentence as it arrives
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async for sentence in sentence_stream:
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# Record first sentence timing (critical metric)
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if sentence.index == 0:
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pipeline.stage_latencies["llm_first_sentence"] = time.time() - llm_start
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logger.info(
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f"First sentence from LLM in {pipeline.stage_latencies['llm_first_sentence']:.3f}s: "
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f'"{sentence.text}"'
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)
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# 6. Synthesize speech (TTS)
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pipeline.state = PipelineState.RESPONDING
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# Collect full text for transcript
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full_response_text.append(sentence.text)
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tts_start = time.time()
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audio_output = await asyncio.wait_for(
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self.tts_synthesizer.synthesize(
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agent=self.current_agent, text=response_text
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),
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timeout=self.config.tts_timeout,
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)
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pipeline.stage_latencies["tts"] = time.time() - tts_start
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# Generate TTS for this sentence
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tts_start = time.time()
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audio_chunk = await asyncio.wait_for(
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self.tts_synthesizer.synthesize(
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agent=self.current_agent,
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text=sentence.text,
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),
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timeout=self.config.tts_timeout,
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)
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if audio_output is None:
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logger.error("TTS synthesis failed")
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if sentence.index == 0:
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pipeline.stage_latencies["tts_first_chunk"] = time.time() - tts_start
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if audio_chunk is None:
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logger.warning(f"TTS failed for sentence #{sentence.index}")
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continue
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# Play audio immediately
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self.audio_output_callback(pipeline.user_id, audio_chunk)
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# Track first audio playback time (time to first audio)
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if first_audio_time is None:
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first_audio_time = time.time() - llm_start
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pipeline.stage_latencies["time_to_first_audio"] = first_audio_time
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logger.info(
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f"First audio playing in {first_audio_time:.3f}s "
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f"(LLM: {pipeline.stage_latencies['llm_first_sentence']:.3f}s, "
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f"TTS: {pipeline.stage_latencies['tts_first_chunk']:.3f}s)"
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)
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logger.debug(
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f"Played sentence #{sentence.index} "
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f"({len(audio_chunk) / self.config.sample_rate:.2f}s audio)"
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)
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# Streaming complete
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pipeline.stage_latencies["llm"] = time.time() - llm_start
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response_text = " ".join(full_response_text)
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logger.info(
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f"Streaming response complete ({self.current_agent}, {routing_decision.model}): "
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f'"{response_text[:100]}..." '
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f"(total latency: {pipeline.stage_latencies['llm']:.3f}s)"
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)
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# Add bot response to transcript
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self.transcript_manager.add_entry(
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speaker=self.current_agent.title(), text=response_text
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)
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except Exception as e:
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logger.error(f"Streaming TTS pipeline error: {e}", exc_info=True)
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pipeline.state = PipelineState.IDLE
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return
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logger.info(
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f"TTS generated {len(audio_output) / self.config.sample_rate:.2f}s audio "
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f"(latency: {pipeline.stage_latencies['tts']:.3f}s)"
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)
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# 7. Play audio
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self.audio_output_callback(pipeline.user_id, audio_output)
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# Update stats
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pipeline.total_responses += 1
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self.total_pipeline_runs += 1
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@ -550,7 +633,7 @@ class PipelineOrchestrator:
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Args:
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sensitivity: Sensitivity level ("low", "medium", "high")
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"""
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self.relevance_classifier.sensitivity = sensitivity.lower()
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self.relevance_filter.sensitivity = sensitivity.lower()
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logger.info(f"Set sensitivity to: {sensitivity}")
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def get_stats(self) -> dict:
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@ -570,7 +653,16 @@ class PipelineOrchestrator:
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# Calculate average latencies
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avg_latencies = {}
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if total_responses > 0:
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for stage in ["stt", "relevance", "llm", "tts", "total"]:
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for stage in [
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"stt",
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"routing",
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"relevance",
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"llm_first_sentence",
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"tts_first_chunk",
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"time_to_first_audio",
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"llm",
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"total",
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]:
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latencies = [
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p.stage_latencies.get(stage, 0)
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for p in self.pipelines.values()
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@ -583,13 +675,14 @@ class PipelineOrchestrator:
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return {
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"active_users": len(self.pipelines),
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"current_agent": self.current_agent,
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"sensitivity": self.relevance_classifier.sensitivity,
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"sensitivity": self.relevance_filter.sensitivity,
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"total_audio_frames": self.total_audio_frames,
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"total_utterances": total_utterances,
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"total_responses": total_responses,
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"total_cancellations": total_cancellations,
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"total_pipeline_runs": self.total_pipeline_runs,
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"total_errors": self.total_errors,
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"router_stats": self.query_router.get_stats(),
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**avg_latencies,
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}
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216
pipeline/query_router.py
Normal file
216
pipeline/query_router.py
Normal file
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@ -0,0 +1,216 @@
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"""Smart Query Router - Route queries to optimal Claude model based on complexity.
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Routes to:
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- Haiku (claude-haiku-3.5): Simple queries, ~100ms first token
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- Sonnet (claude-sonnet-4): Medium complexity, ~300ms first token
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- Opus (claude-opus-4-6): Complex queries, ~800ms first token
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"""
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import re
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from dataclasses import dataclass
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from typing import Literal
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from utils.logging import get_logger
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logger = get_logger(__name__)
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ModelType = Literal["haiku", "sonnet", "opus"]
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@dataclass
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class RoutingDecision:
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"""Result of query routing."""
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model: ModelType
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model_id: str
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reason: str
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confidence: float # 0.0-1.0
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class QueryRouter:
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"""
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Routes voice queries to the fastest appropriate Claude model.
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Uses pattern matching for instant classification without LLM calls.
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"""
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# Model identifiers for OpenClaw Gateway
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MODEL_IDS = {
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"haiku": "claude-haiku-3.5",
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"sonnet": "claude-sonnet-4",
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"opus": "claude-opus-4-6",
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}
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# Patterns for simple queries (route to Haiku)
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SIMPLE_PATTERNS = [
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# Greetings
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re.compile(r"^(hey|hi|hello|good morning|good afternoon|good evening|what's up|sup|yo)", re.IGNORECASE),
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# Confirmations
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re.compile(r"^(yes|no|yeah|nah|yep|nope|sure|okay|ok|alright|got it|sounds good)", re.IGNORECASE),
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# Thanks
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re.compile(r"^(thanks|thank you|thx|ty|appreciated|cheers)", re.IGNORECASE),
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# Time/date
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re.compile(r"(what time|what day|what's the time|what's the date|current time|current date)", re.IGNORECASE),
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# Weather (basic)
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re.compile(r"^(what's the weather|how's the weather|weather today)", re.IGNORECASE),
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# Simple questions
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re.compile(r"^(who are you|what are you|are you there|can you hear me)", re.IGNORECASE),
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# Single word queries
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re.compile(r"^\w+\?*$"), # Single word (with optional ?)
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]
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# Patterns for complex queries (route to Opus)
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COMPLEX_PATTERNS = [
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# Analysis requests
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re.compile(r"(analyze|compare|evaluate|assess|review|critique)", re.IGNORECASE),
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# Creative writing
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re.compile(r"(write me|draft|compose|create a|generate a)", re.IGNORECASE),
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# Research/investigation
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re.compile(r"(research|investigate|look into|find out about|tell me about .{50,})", re.IGNORECASE),
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# Explanations
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re.compile(r"(explain why|explain how|what do you think about|your opinion on)", re.IGNORECASE),
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# Strategy/planning
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re.compile(r"(strategy|plan for|how should I|what's the best way)", re.IGNORECASE),
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# Long, detailed questions (>100 chars usually complex)
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re.compile(r"^.{100,}"),
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# Multiple questions
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re.compile(r"\?.+\?"), # Contains multiple question marks
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]
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# Patterns for medium complexity (route to Sonnet) - checked after simple/complex
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MEDIUM_PATTERNS = [
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# Information requests
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re.compile(r"(what is|what are|who is|who are|when did|where is|how does)", re.IGNORECASE),
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# Action requests
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re.compile(r"(can you|could you|would you|please|help me)", re.IGNORECASE),
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# Queries with context
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re.compile(r"(tell me|show me|give me|find me)", re.IGNORECASE),
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]
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|
||||
def __init__(self, default_model: ModelType = "sonnet"):
|
||||
"""
|
||||
Initialize query router.
|
||||
|
||||
Args:
|
||||
default_model: Default model for uncertain classifications
|
||||
"""
|
||||
self.default_model = default_model
|
||||
self.default_model_id = self.MODEL_IDS[default_model]
|
||||
|
||||
# Stats
|
||||
self.total_routes = 0
|
||||
self.routes_by_model = {"haiku": 0, "sonnet": 0, "opus": 0}
|
||||
|
||||
logger.info(
|
||||
f"Query router initialized (default: {default_model})"
|
||||
)
|
||||
|
||||
def route(self, query: str) -> RoutingDecision:
|
||||
"""
|
||||
Route query to appropriate model.
|
||||
|
||||
Args:
|
||||
query: User's transcribed query
|
||||
|
||||
Returns:
|
||||
RoutingDecision with model selection and reasoning
|
||||
"""
|
||||
query_clean = query.strip()
|
||||
|
||||
# Empty query - use default
|
||||
if not query_clean:
|
||||
return self._make_decision(
|
||||
self.default_model,
|
||||
"empty_query",
|
||||
0.5,
|
||||
)
|
||||
|
||||
# Check simple patterns first (highest priority for speed)
|
||||
for pattern in self.SIMPLE_PATTERNS:
|
||||
if pattern.search(query_clean):
|
||||
return self._make_decision(
|
||||
"haiku",
|
||||
f"matched_simple_pattern: {pattern.pattern[:50]}",
|
||||
0.9,
|
||||
)
|
||||
|
||||
# Check complex patterns (second priority)
|
||||
for pattern in self.COMPLEX_PATTERNS:
|
||||
if pattern.search(query_clean):
|
||||
return self._make_decision(
|
||||
"opus",
|
||||
f"matched_complex_pattern: {pattern.pattern[:50]}",
|
||||
0.85,
|
||||
)
|
||||
|
||||
# Check medium patterns
|
||||
for pattern in self.MEDIUM_PATTERNS:
|
||||
if pattern.search(query_clean):
|
||||
return self._make_decision(
|
||||
"sonnet",
|
||||
f"matched_medium_pattern: {pattern.pattern[:50]}",
|
||||
0.8,
|
||||
)
|
||||
|
||||
# Default fallback - use Sonnet as safe middle ground
|
||||
return self._make_decision(
|
||||
self.default_model,
|
||||
"no_pattern_match_fallback",
|
||||
0.6,
|
||||
)
|
||||
|
||||
def _make_decision(
|
||||
self, model: ModelType, reason: str, confidence: float
|
||||
) -> RoutingDecision:
|
||||
"""
|
||||
Create routing decision and update stats.
|
||||
|
||||
Args:
|
||||
model: Model to route to
|
||||
reason: Reason for routing
|
||||
confidence: Confidence in decision
|
||||
|
||||
Returns:
|
||||
RoutingDecision
|
||||
"""
|
||||
self.total_routes += 1
|
||||
self.routes_by_model[model] += 1
|
||||
|
||||
decision = RoutingDecision(
|
||||
model=model,
|
||||
model_id=self.MODEL_IDS[model],
|
||||
reason=reason,
|
||||
confidence=confidence,
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"Routed to {model} (confidence: {confidence:.2f}, reason: {reason})"
|
||||
)
|
||||
|
||||
return decision
|
||||
|
||||
def get_stats(self) -> dict:
|
||||
"""
|
||||
Get routing statistics.
|
||||
|
||||
Returns:
|
||||
Dictionary with stats
|
||||
"""
|
||||
return {
|
||||
"total_routes": self.total_routes,
|
||||
"routes_by_model": self.routes_by_model.copy(),
|
||||
"distribution": {
|
||||
model: (
|
||||
count / self.total_routes if self.total_routes > 0 else 0.0
|
||||
)
|
||||
for model, count in self.routes_by_model.items()
|
||||
},
|
||||
"default_model": self.default_model,
|
||||
}
|
||||
|
||||
def reset_stats(self) -> None:
|
||||
"""Reset routing statistics."""
|
||||
self.total_routes = 0
|
||||
self.routes_by_model = {"haiku": 0, "sonnet": 0, "opus": 0}
|
||||
logger.info("Router stats reset")
|
||||
176
pipeline/sentence_splitter.py
Normal file
176
pipeline/sentence_splitter.py
Normal file
|
|
@ -0,0 +1,176 @@
|
|||
"""Streaming sentence splitter for real-time TTS.
|
||||
|
||||
Buffers streaming text and yields complete sentences as soon as they're detected.
|
||||
Optimized for low latency - starts TTS on first sentence while rest generates.
|
||||
"""
|
||||
|
||||
import re
|
||||
from dataclasses import dataclass
|
||||
from typing import AsyncIterator, List
|
||||
|
||||
from utils.logging import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Sentence:
|
||||
"""A complete sentence ready for TTS."""
|
||||
|
||||
text: str
|
||||
index: int # Sentence number in stream (0-indexed)
|
||||
is_final: bool = False # True if this is the last sentence
|
||||
|
||||
|
||||
class StreamingSentenceSplitter:
|
||||
"""
|
||||
Split streaming text into sentences in real-time.
|
||||
|
||||
Detects sentence boundaries (. ! ? followed by space or newline)
|
||||
and yields complete sentences immediately for TTS processing.
|
||||
"""
|
||||
|
||||
# Sentence boundary patterns
|
||||
# Must have punctuation + whitespace or end of string
|
||||
SENTENCE_END_PATTERN = re.compile(
|
||||
r'([.!?])\s+|([.!?])$'
|
||||
)
|
||||
|
||||
# Minimum sentence length to avoid fragmenting
|
||||
MIN_SENTENCE_LENGTH = 10
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize sentence splitter."""
|
||||
self.buffer = ""
|
||||
self.sentence_count = 0
|
||||
|
||||
def add_text(self, text: str) -> List[Sentence]:
|
||||
"""
|
||||
Add streaming text chunk and extract complete sentences.
|
||||
|
||||
Args:
|
||||
text: New text chunk from LLM stream
|
||||
|
||||
Returns:
|
||||
List of complete sentences (may be empty if no boundaries found)
|
||||
"""
|
||||
self.buffer += text
|
||||
return self._extract_sentences()
|
||||
|
||||
def flush(self) -> List[Sentence]:
|
||||
"""
|
||||
Flush remaining buffer as final sentence.
|
||||
|
||||
Call this when stream is complete to get any remaining text.
|
||||
|
||||
Returns:
|
||||
List containing final sentence (or empty if buffer is empty)
|
||||
"""
|
||||
sentences = []
|
||||
|
||||
if self.buffer.strip():
|
||||
sentence = Sentence(
|
||||
text=self.buffer.strip(),
|
||||
index=self.sentence_count,
|
||||
is_final=True,
|
||||
)
|
||||
sentences.append(sentence)
|
||||
self.sentence_count += 1
|
||||
logger.debug(
|
||||
f"Flushed final sentence #{sentence.index}: "
|
||||
f'"{sentence.text[:50]}..."'
|
||||
)
|
||||
|
||||
self.buffer = ""
|
||||
return sentences
|
||||
|
||||
def _extract_sentences(self) -> List[Sentence]:
|
||||
"""
|
||||
Extract complete sentences from current buffer.
|
||||
|
||||
Returns:
|
||||
List of complete sentences
|
||||
"""
|
||||
sentences = []
|
||||
|
||||
while True:
|
||||
# Find next sentence boundary
|
||||
match = self.SENTENCE_END_PATTERN.search(self.buffer)
|
||||
|
||||
if not match:
|
||||
# No complete sentence yet
|
||||
break
|
||||
|
||||
# Extract sentence up to boundary (including punctuation)
|
||||
end_pos = match.end()
|
||||
sentence_text = self.buffer[:end_pos].strip()
|
||||
|
||||
# Check minimum length to avoid fragments
|
||||
if len(sentence_text) < self.MIN_SENTENCE_LENGTH:
|
||||
# Too short - might be abbreviation or fragment
|
||||
# Only break if we have more text coming, otherwise keep it
|
||||
if len(self.buffer) > end_pos + 10:
|
||||
# More text after boundary - likely fragment, skip
|
||||
self.buffer = self.buffer[end_pos:]
|
||||
continue
|
||||
else:
|
||||
# Close to end of buffer - keep as sentence
|
||||
pass
|
||||
|
||||
# Valid sentence found
|
||||
sentence = Sentence(
|
||||
text=sentence_text,
|
||||
index=self.sentence_count,
|
||||
is_final=False,
|
||||
)
|
||||
sentences.append(sentence)
|
||||
self.sentence_count += 1
|
||||
|
||||
logger.debug(
|
||||
f"Extracted sentence #{sentence.index}: "
|
||||
f'"{sentence.text[:50]}..."'
|
||||
)
|
||||
|
||||
# Remove sentence from buffer
|
||||
self.buffer = self.buffer[end_pos:].lstrip()
|
||||
|
||||
return sentences
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset splitter state for new stream."""
|
||||
self.buffer = ""
|
||||
self.sentence_count = 0
|
||||
|
||||
|
||||
async def split_streaming_response(
|
||||
text_stream: AsyncIterator[str],
|
||||
) -> AsyncIterator[Sentence]:
|
||||
"""
|
||||
Split streaming LLM response into sentences in real-time.
|
||||
|
||||
Args:
|
||||
text_stream: Async iterator yielding text chunks from LLM
|
||||
|
||||
Yields:
|
||||
Complete sentences as they're detected
|
||||
"""
|
||||
splitter = StreamingSentenceSplitter()
|
||||
|
||||
try:
|
||||
async for chunk in text_stream:
|
||||
sentences = splitter.add_text(chunk)
|
||||
for sentence in sentences:
|
||||
yield sentence
|
||||
|
||||
# Flush any remaining text as final sentence
|
||||
final_sentences = splitter.flush()
|
||||
for sentence in final_sentences:
|
||||
yield sentence
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in sentence splitting: {e}")
|
||||
# Flush buffer on error to avoid losing text
|
||||
final_sentences = splitter.flush()
|
||||
for sentence in final_sentences:
|
||||
yield sentence
|
||||
raise
|
||||
|
|
@ -131,9 +131,14 @@ class SileroVAD:
|
|||
with torch.no_grad():
|
||||
speech_prob = self.model(audio_tensor, self.sample_rate).item()
|
||||
|
||||
# Debug logging - log speech probability when it's above a minimal threshold
|
||||
if speech_prob > 0.1:
|
||||
logger.info(f"VAD: speech_prob={speech_prob:.3f}, threshold={self.speech_threshold:.3f}")
|
||||
|
||||
# Determine state based on threshold
|
||||
if speech_prob >= self.speech_threshold:
|
||||
new_state = SpeechState.SPEECH
|
||||
logger.info(f"SPEECH DETECTED! probability={speech_prob:.3f}")
|
||||
else:
|
||||
new_state = SpeechState.SILENCE
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue