koko210Serve
eb03dfce4d
Major architectural overhaul of the speech-to-text pipeline for real-time voice chat: STT Server Rewrite: - Replaced RealtimeSTT dependency with direct Silero VAD + Faster-Whisper integration - Achieved sub-second latency by eliminating unnecessary abstractions - Uses small.en Whisper model for fast transcription (~850ms) Speculative Transcription (NEW): - Start transcribing at 150ms silence (speculative) while still listening - If speech continues, discard speculative result and keep buffering - If 400ms silence confirmed, use pre-computed speculative result immediately - Reduces latency by ~250-850ms for typical utterances with clear pauses VAD Implementation: - Silero VAD with ONNX (CPU-efficient) for 32ms chunk processing - Direct speech boundary detection without RealtimeSTT overhead - Configurable thresholds for silence detection (400ms final, 150ms speculative) Architecture: - Single Whisper model loaded once, shared across sessions - VAD runs on every 512-sample chunk for immediate speech detection - Background transcription worker thread for non-blocking processing - Greedy decoding (beam_size=1) for maximum speed Performance: - Previous: 400ms silence wait + ~850ms transcription = ~1.25s total latency - Current: 400ms silence wait + 0ms (speculative ready) = ~400ms (best case) - Single model reduces VRAM usage, prevents OOM on GTX 1660 Container Manager Updates: - Updated health check logic to work with new response format - Changed from checking 'warmed_up' flag to just 'status: ready' - Improved terminology from 'warmup' to 'models loading' Files Changed: - stt-realtime/stt_server.py: Complete rewrite with Silero VAD + speculative transcription - stt-realtime/requirements.txt: Removed RealtimeSTT, using torch.hub for Silero VAD - bot/utils/container_manager.py: Updated health check for new STT response format - bot/api.py: Updated docstring to reflect new architecture - backups/: Archived old RealtimeSTT-based implementation This addresses low latency requirements while maintaining accuracy with configurable speech detection thresholds.
511 lines
21 KiB
Python
511 lines
21 KiB
Python
#!/usr/bin/env python3
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"""
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RealtimeSTT WebSocket Server
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Provides real-time speech-to-text transcription using Faster-Whisper.
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Receives audio chunks via WebSocket and streams back partial/final transcripts.
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Protocol:
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- Client sends: binary audio data (16kHz, 16-bit mono PCM)
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- Client sends: JSON {"command": "reset"} to reset state
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- Server sends: JSON {"type": "partial", "text": "...", "timestamp": float}
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- Server sends: JSON {"type": "final", "text": "...", "timestamp": float}
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"""
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import asyncio
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import json
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import logging
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import time
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import threading
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import queue
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from typing import Optional, Dict, Any
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import numpy as np
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import websockets
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from websockets.server import serve
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from aiohttp import web
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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logger = logging.getLogger('stt-realtime')
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# Import RealtimeSTT
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from RealtimeSTT import AudioToTextRecorder
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# Global warmup state
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warmup_complete = False
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warmup_lock = threading.Lock()
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warmup_recorder = None
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class STTSession:
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"""
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Manages a single STT session for a WebSocket client.
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Key architectural point: We own the audio buffer and decoder.
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RealtimeSTT is used ONLY for VAD, not for transcription ownership.
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"""
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def __init__(self, websocket, session_id: str, config: Dict[str, Any]):
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self.websocket = websocket
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self.session_id = session_id
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self.config = config
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self.recorder: Optional[AudioToTextRecorder] = None
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self.running = False
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self.audio_queue = queue.Queue()
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self.feed_thread: Optional[threading.Thread] = None
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# OUR audio buffer - we own this, not RealtimeSTT
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self.float_buffer = [] # Rolling float32 buffer (0.0 to 1.0 range)
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self.max_buffer_duration = 30.0 # Keep max 30 seconds
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# Decode state
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self.last_decode_text = ""
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self.recording_active = False
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self.recording_stop_time = 0
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self.last_decode_time = 0
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self.final_sent = False # Track if we've sent final for this utterance
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self.last_audio_time = 0 # Track when we last received audio with speech
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self.speech_detected = False # Track if we've detected any speech
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logger.info(f"[{session_id}] Session created")
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def _on_recording_stop(self):
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"""Called when recording stops (silence detected)."""
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logger.info(f"[{self.session_id}] ⏹️ Recording stopped - will emit final in decode loop")
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self.recording_active = False
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self.recording_stop_time = time.time() # Track when recording stopped
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def _on_recording_start(self):
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"""Called when recording starts (speech detected)."""
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logger.info(f"[{self.session_id}] 🎙️ Recording started")
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self.recording_active = True
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self.float_buffer = [] # Reset buffer for new utterance
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self.last_decode_text = ""
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self.last_decode_time = 0
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self.final_sent = False # Reset final flag for new utterance
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async def _send_transcript(self, transcript_type: str, text: str):
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"""Send transcript to client via WebSocket."""
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try:
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message = {
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"type": transcript_type,
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"text": text,
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"timestamp": time.time()
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}
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await self.websocket.send(json.dumps(message))
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except Exception as e:
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logger.error(f"[{self.session_id}] Failed to send transcript: {e}")
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def _feed_audio_thread(self):
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"""Thread that feeds audio to the recorder."""
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logger.info(f"[{self.session_id}] Audio feed thread started")
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while self.running:
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try:
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# Get audio chunk with timeout
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audio_chunk = self.audio_queue.get(timeout=0.1)
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if audio_chunk is not None and self.recorder:
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self.recorder.feed_audio(audio_chunk)
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except queue.Empty:
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continue
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except Exception as e:
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logger.error(f"[{self.session_id}] Error feeding audio: {e}")
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logger.info(f"[{self.session_id}] Audio feed thread stopped")
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async def start(self, loop: asyncio.AbstractEventLoop):
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"""Start the STT session."""
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self.loop = loop
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self.running = True
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logger.info(f"[{self.session_id}] Starting RealtimeSTT recorder...")
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logger.info(f"[{self.session_id}] Model: {self.config['model']}")
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logger.info(f"[{self.session_id}] Device: {self.config['device']}")
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try:
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# Create recorder in a thread to avoid blocking
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def init_recorder():
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# Build initialization kwargs
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recorder_kwargs = {
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# Model settings - ONLY turbo model, no dual-model setup
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'model': self.config['model'],
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'language': self.config['language'],
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'compute_type': self.config['compute_type'],
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'device': self.config['device'],
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# Disable microphone - we feed audio manually
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'use_microphone': False,
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# DISABLE realtime partials - we'll use incremental utterance decoding instead
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'enable_realtime_transcription': False, # ← KEY CHANGE: No streaming partials
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# VAD settings - optimized for longer utterances (per ChatGPT)
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'silero_sensitivity': self.config['silero_sensitivity'],
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'silero_use_onnx': True, # Faster
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'webrtc_sensitivity': self.config['webrtc_sensitivity'],
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'post_speech_silence_duration': self.config['silence_duration'],
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'min_length_of_recording': self.config['min_recording_length'],
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'min_gap_between_recordings': self.config['min_gap'],
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'pre_recording_buffer_duration': 1.2, # ChatGPT: ~1.2s before first decode
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# Callbacks
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'on_recording_start': self._on_recording_start,
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'on_recording_stop': self._on_recording_stop,
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'on_vad_detect_start': lambda: logger.debug(f"[{self.session_id}] VAD listening"),
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'on_vad_detect_stop': lambda: logger.debug(f"[{self.session_id}] VAD stopped"),
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# Other settings
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'spinner': False, # No spinner in container
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'level': logging.WARNING, # Reduce internal logging
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# Beam search settings - optimized for accuracy
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'beam_size': 5,
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# Batch sizes
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'batch_size': 16,
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'initial_prompt': "",
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}
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self.recorder = AudioToTextRecorder(**recorder_kwargs)
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logger.info(f"[{self.session_id}] ✅ Recorder initialized (incremental mode, transcript-stability silence detection)")
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# Run initialization in thread pool
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await asyncio.get_event_loop().run_in_executor(None, init_recorder)
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# Start audio feed thread
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self.feed_thread = threading.Thread(target=self._feed_audio_thread, daemon=True)
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self.feed_thread.start()
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# NOTE: We don't call recorder.start() - VAD callbacks don't work with use_microphone=False
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# Instead, we detect silence ourselves via transcript stability in the decode loop
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# Start CORRECT incremental decoding loop
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# Since RealtimeSTT VAD callbacks don't work with use_microphone=False,
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# we detect silence ourselves via transcript stability
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def run_decode_loop():
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"""
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Decode buffer periodically. Detect end-of-speech when:
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1. We have a transcript AND
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2. Transcript hasn't changed for silence_threshold seconds
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"""
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decode_interval = 0.7 # Re-decode every 700ms
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min_audio_before_first_decode = 1.2 # Wait 1.2s before first decode
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silence_threshold = 1.5 # If transcript stable for 1.5s, consider it final
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last_transcript_change_time = 0
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has_transcript = False
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logger.info(f"[{self.session_id}] Decode loop ready (silence detection: {silence_threshold}s)")
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while self.running:
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try:
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current_time = time.time()
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buffer_duration = len(self.float_buffer) / 16000.0 if self.float_buffer else 0
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# Only decode if we have enough audio
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if buffer_duration >= min_audio_before_first_decode:
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# Check if enough time since last decode
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if (current_time - self.last_decode_time) >= decode_interval:
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try:
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audio_array = np.array(self.float_buffer, dtype=np.float32)
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logger.debug(f"[{self.session_id}] 🔄 Decode (buffer: {buffer_duration:.1f}s)")
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result = self.recorder.perform_final_transcription(audio_array)
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text = result.strip() if result else ""
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if text:
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if text != self.last_decode_text:
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# Transcript changed - update and reset stability timer
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self.last_decode_text = text
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last_transcript_change_time = current_time
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has_transcript = True
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logger.info(f"[{self.session_id}] 📝 Partial: {text}")
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asyncio.run_coroutine_threadsafe(
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self._send_transcript("partial", text),
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self.loop
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)
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# else: transcript same, stability timer continues
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self.last_decode_time = current_time
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except Exception as e:
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logger.error(f"[{self.session_id}] Decode error: {e}", exc_info=True)
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# Check for silence (transcript stable for threshold)
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if has_transcript and last_transcript_change_time > 0:
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time_since_change = current_time - last_transcript_change_time
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if time_since_change >= silence_threshold:
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# Transcript has been stable - emit final
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logger.info(f"[{self.session_id}] ✅ Final (stable {time_since_change:.1f}s): {self.last_decode_text}")
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asyncio.run_coroutine_threadsafe(
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self._send_transcript("final", self.last_decode_text),
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self.loop
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)
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# Reset for next utterance
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self.float_buffer = []
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self.last_decode_text = ""
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self.last_decode_time = 0
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last_transcript_change_time = 0
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has_transcript = False
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time.sleep(0.1) # Check every 100ms
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except Exception as e:
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if self.running:
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logger.error(f"[{self.session_id}] Decode loop error: {e}", exc_info=True)
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break
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self.text_thread = threading.Thread(target=run_decode_loop, daemon=True)
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self.text_thread.start()
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logger.info(f"[{self.session_id}] ✅ Session started successfully")
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except Exception as e:
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logger.error(f"[{self.session_id}] Failed to start session: {e}", exc_info=True)
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raise
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def feed_audio(self, audio_data: bytes):
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"""Feed audio data to the recorder AND our buffer."""
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if self.running:
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# Convert bytes to numpy array (16-bit PCM)
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audio_np = np.frombuffer(audio_data, dtype=np.int16)
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# Feed to RealtimeSTT for VAD only
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self.audio_queue.put(audio_np)
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# Also add to OUR float32 buffer (normalized to -1.0 to 1.0)
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float_audio = audio_np.astype(np.float32) / 32768.0
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self.float_buffer.extend(float_audio)
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# Keep buffer size bounded (max 30 seconds at 16kHz = 480k samples)
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max_samples = int(self.max_buffer_duration * 16000)
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if len(self.float_buffer) > max_samples:
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self.float_buffer = self.float_buffer[-max_samples:]
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def reset(self):
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"""Reset the session state."""
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logger.info(f"[{self.session_id}] Resetting session")
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self.float_buffer = []
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self.last_decode_text = ""
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# Clear audio queue
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while not self.audio_queue.empty():
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try:
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self.audio_queue.get_nowait()
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except queue.Empty:
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break
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async def stop(self):
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"""Stop the session and cleanup."""
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logger.info(f"[{self.session_id}] Stopping session...")
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self.running = False
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# Wait for threads to finish
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if self.feed_thread and self.feed_thread.is_alive():
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self.feed_thread.join(timeout=2)
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# Shutdown recorder
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if self.recorder:
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try:
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self.recorder.shutdown()
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except Exception as e:
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logger.error(f"[{self.session_id}] Error shutting down recorder: {e}")
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logger.info(f"[{self.session_id}] Session stopped")
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class STTServer:
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"""
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WebSocket server for RealtimeSTT.
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Handles multiple concurrent clients (one per Discord user).
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"""
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def __init__(self, host: str = "0.0.0.0", port: int = 8766, config: Dict[str, Any] = None):
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self.host = host
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self.port = port
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self.sessions: Dict[str, STTSession] = {}
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self.session_counter = 0
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# Config must be provided
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if not config:
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raise ValueError("Configuration dict must be provided to STTServer")
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self.config = config
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logger.info("=" * 60)
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logger.info("RealtimeSTT Server Configuration:")
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logger.info(f" Host: {host}:{port}")
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logger.info(f" Model: {self.config['model']}")
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logger.info(f" Language: {self.config.get('language', 'auto-detect')}")
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logger.info(f" Device: {self.config['device']}")
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logger.info(f" Compute Type: {self.config['compute_type']}")
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logger.info(f" Silence Duration: {self.config['silence_duration']}s")
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logger.info(f" Realtime Pause: {self.config.get('realtime_processing_pause', 'N/A')}s")
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logger.info("=" * 60)
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async def handle_client(self, websocket):
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"""Handle a WebSocket client connection."""
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self.session_counter += 1
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session_id = f"session_{self.session_counter}"
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session = None
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try:
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logger.info(f"[{session_id}] Client connected from {websocket.remote_address}")
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# Create session
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session = STTSession(websocket, session_id, self.config)
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self.sessions[session_id] = session
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# Start session
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await session.start(asyncio.get_event_loop())
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# Process messages
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async for message in websocket:
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try:
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if isinstance(message, bytes):
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# Binary audio data
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session.feed_audio(message)
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else:
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# JSON command
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data = json.loads(message)
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command = data.get('command', '')
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if command == 'reset':
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session.reset()
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elif command == 'ping':
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await websocket.send(json.dumps({
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'type': 'pong',
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'timestamp': time.time()
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}))
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else:
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logger.warning(f"[{session_id}] Unknown command: {command}")
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except json.JSONDecodeError:
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logger.warning(f"[{session_id}] Invalid JSON message")
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except Exception as e:
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logger.error(f"[{session_id}] Error processing message: {e}")
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except websockets.exceptions.ConnectionClosed:
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logger.info(f"[{session_id}] Client disconnected")
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except Exception as e:
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logger.error(f"[{session_id}] Error: {e}", exc_info=True)
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finally:
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# Cleanup
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if session:
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await session.stop()
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del self.sessions[session_id]
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async def run(self):
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"""Run the WebSocket server."""
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logger.info(f"Starting RealtimeSTT server on ws://{self.host}:{self.port}")
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async with serve(
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self.handle_client,
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self.host,
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self.port,
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ping_interval=30,
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ping_timeout=10,
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max_size=10 * 1024 * 1024, # 10MB max message size
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):
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logger.info("✅ Server ready and listening for connections")
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await asyncio.Future() # Run forever
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async def warmup_model(config: Dict[str, Any]):
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"""
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Warmup is DISABLED - it wastes memory by loading a model that's never reused.
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The first session will load the model when needed.
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"""
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global warmup_complete
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logger.info("⚠️ Warmup disabled to save VRAM - model will load on first connection")
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warmup_complete = True # Mark as complete so health check passes
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async def health_handler(request):
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"""HTTP health check endpoint"""
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if warmup_complete:
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return web.json_response({
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"status": "ready",
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"warmed_up": True,
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"model": "small.en",
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"device": "cuda"
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})
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else:
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return web.json_response({
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"status": "warming_up",
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"warmed_up": False,
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"model": "small.en",
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"device": "cuda"
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}, status=503)
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async def start_http_server(host: str, http_port: int):
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"""Start HTTP server for health checks"""
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app = web.Application()
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app.router.add_get('/health', health_handler)
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runner = web.AppRunner(app)
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await runner.setup()
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site = web.TCPSite(runner, host, http_port)
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await site.start()
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logger.info(f"✅ HTTP health server listening on http://{host}:{http_port}")
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def main():
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"""Main entry point."""
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import os
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# Get configuration from environment
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host = os.environ.get('STT_HOST', '0.0.0.0')
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port = int(os.environ.get('STT_PORT', '8766'))
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http_port = int(os.environ.get('STT_HTTP_PORT', '8767')) # HTTP health check port
|
|
|
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# Configuration - ChatGPT's incremental utterance decoding approach
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|
config = {
|
|
'model': 'turbo', # Fast multilingual model
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|
'language': 'en', # SET LANGUAGE! Auto-detect adds 4+ seconds latency (change to 'ja', 'bg' as needed)
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'compute_type': 'float16',
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|
'device': 'cuda',
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|
|
|
# VAD settings - ChatGPT: "minimum speech ~600ms, end-of-speech silence ~400-600ms"
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|
'silero_sensitivity': 0.6,
|
|
'webrtc_sensitivity': 3,
|
|
'silence_duration': 0.5, # 500ms end-of-speech silence
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|
'min_recording_length': 0.6, # 600ms minimum speech
|
|
'min_gap': 0.3,
|
|
}
|
|
|
|
# Create and run server
|
|
server = STTServer(host=host, port=port, config=config)
|
|
|
|
async def run_all():
|
|
# Start warmup in background
|
|
asyncio.create_task(warmup_model(config))
|
|
|
|
# Start HTTP health server
|
|
asyncio.create_task(start_http_server(host, http_port))
|
|
|
|
# Start WebSocket server
|
|
await server.run()
|
|
|
|
try:
|
|
asyncio.run(run_all())
|
|
except KeyboardInterrupt:
|
|
logger.info("Server shutdown requested")
|
|
except Exception as e:
|
|
logger.error(f"Server error: {e}", exc_info=True)
|
|
raise
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|