feat: VoiceCoach v2 - OpenClaw gateway + Speaches STT/TTS
- pipeline.py: completely rewritten to use Speaches STT/TTS and OpenClaw LLM - main.py: v2 with OPENCLAW_TOKEN support - requirements.txt: slimmed (removed anthropic, faster-whisper direct deps) - Dockerfile: slimmer build (no model pre-download) - whisper_stt.py, piper_tts.py: removed (replaced by Speaches API) - docker-compose.yml: v2 with speaches + openclaw services, piper-tts removed - openclaw-agent/system_prompt.md: new directory with agent system prompt
This commit is contained in:
@@ -1,24 +1,14 @@
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FROM python:3.11-slim
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RUN apt-get update && apt-get install -y \
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libsndfile1 \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install -y libsndfile1 ffmpeg && \
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rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Pre-download faster-whisper medium model at build time
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RUN python -c "\
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from faster_whisper import WhisperModel; \
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WhisperModel('medium', device='cpu', compute_type='int8'); \
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print('Whisper model downloaded.')"
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COPY . .
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HEALTHCHECK --interval=30s --timeout=10s --start-period=30s \
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CMD python -c "print('pipeline process alive')" || exit 1
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CMD python -c "import httpx; httpx.get('http://localhost:8080/health').raise_for_status()"
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CMD ["python", "main.py"]
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+26
-29
@@ -1,11 +1,13 @@
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"""
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Entry point for the Pipecat VoiceCoach agent.
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Runs both the token HTTP server (for web clients) and the voice pipeline.
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VoiceCoach pipeline entry point -- v2.
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Runs token server + pipeline with unique identity per session.
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"""
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import asyncio
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import logging
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import os
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import threading
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import uuid
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from pathlib import Path
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import structlog
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import uvicorn
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@@ -22,8 +24,9 @@ LIVEKIT_API_KEY = os.environ["LIVEKIT_API_KEY"]
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LIVEKIT_API_SECRET = os.environ["LIVEKIT_API_SECRET"]
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LIVEKIT_WS_URL = os.getenv("LIVEKIT_URL", "ws://livekit-server:7880")
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LIVEKIT_PUBLIC_URL = os.getenv("LIVEKIT_PUBLIC_URL", "wss://voice.hobbs.farm")
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OPENCLAW_TOKEN = os.getenv("OPENCLAW_TOKEN", "")
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# --- Token HTTP server (serves /token for web clients) ---
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# --- Token HTTP server ---
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token_app = FastAPI(title="VoiceCoach Token Service")
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@@ -39,17 +42,15 @@ class TokenResponse(BaseModel):
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@token_app.get("/health")
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async def health():
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return {"status": "ok"}
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return {"status": "ok", "version": "2.0"}
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@token_app.post("/token")
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async def get_token(req: TokenRequest):
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token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
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token.with_grants(livekit_api.VideoGrants(
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room_join=True,
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room="voicecoach",
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can_publish=True,
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can_subscribe=True,
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room_join=True, room="voicecoach",
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can_publish=True, can_subscribe=True,
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))
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token.identity = req.identity
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return TokenResponse(token=token.to_jwt(), url=LIVEKIT_PUBLIC_URL)
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@@ -59,35 +60,31 @@ def run_token_server():
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uvicorn.run(token_app, host="0.0.0.0", port=8080, log_level="info")
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# --- Main pipeline ---
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# --- Main ---
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async def main():
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anthropic_key = os.environ["ANTHROPIC_API_KEY"]
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# Start token server in a background thread
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server_thread = threading.Thread(target=run_token_server, daemon=True)
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server_thread.start()
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logger.info("Token server started on :8080")
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# Generate agent's own LiveKit token
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token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
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token.with_grants(livekit_api.VideoGrants(room_join=True, room="voicecoach"))
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token.identity = "voicecoach-agent"
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livekit_token = token.to_jwt()
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while True:
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agent_identity = f"voicecoach-agent-{uuid.uuid4().hex[:8]}"
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token = livekit_api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET)
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token.with_grants(livekit_api.VideoGrants(room_join=True, room="voicecoach"))
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token.identity = agent_identity
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livekit_token = token.to_jwt()
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session_context = {
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"baseline_context": "",
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"session_history": "",
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"current_mode": "coaching mode",
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}
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logger.info("Starting VoiceCoach v2 pipeline", identity=agent_identity)
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try:
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await run_voice_coach_pipeline(
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livekit_url=LIVEKIT_WS_URL,
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livekit_token=livekit_token,
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openclaw_token=OPENCLAW_TOKEN,
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)
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except Exception as e:
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logger.error("Pipeline error, restarting in 35s", error=str(e))
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logger.info("Starting VoiceCoach pipeline", livekit_url=LIVEKIT_WS_URL)
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await run_voice_coach_pipeline(
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livekit_url=LIVEKIT_WS_URL,
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livekit_token=livekit_token,
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anthropic_key=anthropic_key,
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session_context=session_context,
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)
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await asyncio.sleep(35)
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if __name__ == "__main__":
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+203
-129
@@ -1,157 +1,224 @@
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"""
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Main Pipecat pipeline assembly.
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Connects LiveKit -> Whisper STT -> SpeechBrain -> Claude -> Piper TTS -> LiveKit.
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Fully self-hosted STT/TTS (no external API dependencies except Anthropic).
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Pipecat pipeline -- v2 fully self-hosted.
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STT: Speaches (faster-whisper) via OpenAI-compatible /v1/audio/transcriptions
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LLM: OpenClaw VoiceCoach agent via OpenAI-compatible gateway
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TTS: Speaches (Kokoro 82M) via OpenAI-compatible /v1/audio/speech
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"""
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import asyncio
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import io
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import os
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import json
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import logging
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import os
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import uuid
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import wave
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import httpx
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from typing import Optional
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from datetime import datetime
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from pathlib import Path
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from typing import AsyncGenerator, Optional
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import httpx
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from pipecat.frames.frames import (
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AudioRawFrame, TranscriptionFrame, TextFrame, EndFrame, TTSAudioRawFrame
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AudioRawFrame, EndFrame, ErrorFrame, Frame, TextFrame, TranscriptionFrame,
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UserStartedSpeakingFrame, UserStoppedSpeakingFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.anthropic import AnthropicLLMService
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from pipecat.transports.services.livekit import LiveKitTransport, LiveKitParams
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from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
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from pipecat.vad.silero import SileroVADAnalyzer
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from pipecat.services.ai_services import STTService
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from pipecat.services.openai import OpenAILLMService, OpenAITTSService
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from pipecat.transports.services.livekit import LiveKitTransport, LiveKitParams
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from prompt_builder import build_system_prompt, build_user_message, VocalState
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from whisper_stt import LocalWhisperSTT
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from piper_tts import PiperTTSClient
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try:
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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except ImportError:
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from pipecat.vad.silero import SileroVADAnalyzer
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from pipecat.vad.vad_analyzer import VADParams
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from prompt_builder import build_user_message, VocalState
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logger = logging.getLogger(__name__)
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SPEECHBRAIN_URL = os.getenv("SPEECHBRAIN_SERVICE_URL", "http://speechbrain-service:8001")
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PIPER_URL = os.getenv("PIPER_SERVICE_URL", "http://piper-tts:8003")
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ANTHROPIC_MODEL = "claude-sonnet-4-6"
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WHISPER_MODEL = os.getenv("WHISPER_MODEL", "medium")
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SPEACHES_URL = os.getenv("SPEACHES_URL", "http://speaches:8000")
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OPENCLAW_URL = os.getenv("OPENCLAW_URL", "http://voicecoach-openclaw:3100")
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SPEECHBRAIN_URL = os.getenv("SPEECHBRAIN_URL", "http://speechbrain-service:8001")
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WHISPER_MODEL = os.getenv("WHISPER_MODEL", "Systran/faster-whisper-large-v3-turbo")
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KOKORO_VOICE = os.getenv("KOKORO_VOICE", "af_nova")
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OPENCLAW_AGENT_ID = os.getenv("OPENCLAW_AGENT_ID", "voicecoach")
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CLIPS_DIR = Path(os.getenv("CLIPS_DIR", "/data/clips/pending"))
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UNCERTAINTY_THRESHOLD = 0.60
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MIN_CLIP_DURATION = 1.0
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class WhisperSTTProcessor(FrameProcessor):
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"""Buffers audio from VAD and transcribes with faster-whisper on end-of-speech."""
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class SpeachesSTTService(STTService):
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"""STT via Speaches OpenAI-compatible /v1/audio/transcriptions endpoint.
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def __init__(self, whisper: LocalWhisperSTT, **kwargs):
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pipecat-ai 0.0.48 does not include OpenAISTTService; this thin wrapper
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calls the Speaches HTTP API directly using the STTService base class.
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"""
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def __init__(self, base_url: str, model: str, language: str = "en", **kwargs):
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super().__init__(**kwargs)
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self._whisper = whisper
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self._audio_buffer: list[bytes] = []
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self._base_url = base_url.rstrip("/")
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self._model = model
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self._language = language
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self._http = httpx.AsyncClient(timeout=30.0)
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async def process_frame(self, frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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async def set_model(self, model: str):
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self._model = model
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if isinstance(frame, AudioRawFrame):
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self._audio_buffer.append(frame.audio)
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elif isinstance(frame, EndFrame):
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# VAD detected end of speech -- transcribe
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if self._audio_buffer:
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audio_bytes = b"".join(self._audio_buffer)
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self._audio_buffer = []
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text = self._whisper.transcribe(audio_bytes)
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if text.strip():
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await self.push_frame(
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TranscriptionFrame(text=text, user_id="jeremy", timestamp=""),
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direction,
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)
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await self.push_frame(frame, direction)
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else:
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await self.push_frame(frame, direction)
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async def set_language(self, language):
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self._language = str(language)
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async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
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if not audio:
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return
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# Wrap raw PCM in a WAV container for the API
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buf = io.BytesIO()
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with wave.open(buf, "wb") as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(16000)
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wf.writeframes(audio)
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wav_bytes = buf.getvalue()
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try:
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r = await self._http.post(
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f"{self._base_url}/v1/audio/transcriptions",
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files={"file": ("audio.wav", wav_bytes, "audio/wav")},
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data={"model": self._model, "language": self._language, "response_format": "json"},
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)
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r.raise_for_status()
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text = r.json().get("text", "").strip()
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if text:
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logger.info("STT transcription: %s", text[:100])
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yield TranscriptionFrame(text=text, user_id="user", timestamp=datetime.utcnow().isoformat())
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except Exception as e:
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logger.warning("Speaches STT error: %s", e)
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yield ErrorFrame(error=str(e))
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class VocalStateInjector(FrameProcessor):
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"""Intercepts transcriptions, fetches vocal analysis from SpeechBrain,
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and enriches the frame before passing to the LLM."""
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"""Buffers audio per utterance, sends to SpeechBrain for analysis,
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enriches TranscriptionFrame with [VOCAL_STATE] metadata,
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and saves clips for active labeling."""
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def __init__(self, **kwargs):
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def __init__(self, session_id: str, **kwargs):
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super().__init__(**kwargs)
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self._current_audio: list[bytes] = []
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self._http_client = httpx.AsyncClient(timeout=3.0)
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self._session_id = session_id
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self._audio_buffer: list[bytes] = []
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self._is_speaking = False
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self._http = httpx.AsyncClient(timeout=3.0)
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async def process_frame(self, frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, AudioRawFrame):
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self._current_audio.append(frame.audio)
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if isinstance(frame, UserStartedSpeakingFrame):
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self._is_speaking = True
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self._audio_buffer = []
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logger.info("VAD: user started speaking")
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await self.push_frame(frame, direction)
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elif isinstance(frame, AudioRawFrame) and self._is_speaking:
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self._audio_buffer.append(frame.audio)
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await self.push_frame(frame, direction)
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elif isinstance(frame, UserStoppedSpeakingFrame):
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self._is_speaking = False
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logger.info("VAD: user stopped speaking, buffer: %d chunks", len(self._audio_buffer))
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await self.push_frame(frame, direction)
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elif isinstance(frame, TranscriptionFrame):
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vocal_state = await self._analyze_buffered_audio()
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self._current_audio = []
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wav_bytes = self._flush_audio_to_wav()
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self._audio_buffer = []
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vocal_state = None
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if wav_bytes:
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vocal_state, analysis = await self._analyze(wav_bytes)
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if analysis and self._clip_worth_saving(analysis, wav_bytes):
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await self._save_clip(wav_bytes, analysis)
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enriched_text = build_user_message(frame.text, vocal_state)
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enriched_frame = TranscriptionFrame(
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text=enriched_text, user_id=frame.user_id, timestamp=frame.timestamp,
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logger.info("Enriched transcription: %s", enriched_text[:200])
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await self.push_frame(
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TranscriptionFrame(text=enriched_text, user_id=frame.user_id, timestamp=frame.timestamp),
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direction,
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)
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await self.push_frame(enriched_frame, direction)
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else:
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await self.push_frame(frame, direction)
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async def _analyze_buffered_audio(self) -> Optional[VocalState]:
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if not self._current_audio:
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def _flush_audio_to_wav(self) -> Optional[bytes]:
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if not self._audio_buffer:
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return None
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try:
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audio_bytes = b"".join(self._current_audio)
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buf = io.BytesIO()
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with wave.open(buf, "wb") as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(16000)
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wf.writeframes(audio_bytes)
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wav_bytes = buf.getvalue()
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raw = b"".join(self._audio_buffer)
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buf = io.BytesIO()
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with wave.open(buf, "wb") as wf:
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wf.setnchannels(1)
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wf.setsampwidth(2)
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wf.setframerate(16000)
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wf.writeframes(raw)
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return buf.getvalue()
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response = await self._http_client.post(
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async def _analyze(self, wav_bytes: bytes):
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try:
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r = await self._http.post(
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f"{SPEECHBRAIN_URL}/analyze",
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files={"audio": ("utterance.wav", wav_bytes, "audio/wav")},
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files={"audio": ("u.wav", wav_bytes, "audio/wav")},
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)
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response.raise_for_status()
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data = response.json()
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return VocalState(
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emotion=data.get("emotion", "neutral"),
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confidence=data.get("emotion_confidence", 0.0),
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pitch_mean_hz=data.get("pitch_mean_hz"),
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energy_rms=data.get("energy_rms", 0.0),
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speaking_rate_approx=data.get("speaking_rate_approx", 0.0),
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voiced_fraction=data.get("voiced_fraction", 0.0),
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r.raise_for_status()
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d = r.json()
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return (
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VocalState(
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emotion=d.get("emotion", "neutral"),
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confidence=d.get("emotion_confidence", 0.0),
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pitch_mean_hz=d.get("pitch_mean_hz"),
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energy_rms=d.get("energy_rms", 0.0),
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speaking_rate_approx=d.get("speaking_rate_approx", 0.0),
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voiced_fraction=d.get("voiced_fraction", 0.0),
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),
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d,
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)
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except Exception as e:
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logger.warning(f"SpeechBrain analysis failed (continuing without): {e}")
|
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return None
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logger.warning("SpeechBrain unavailable: %s -- continuing without vocal state", e)
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return None, None
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def _clip_worth_saving(self, analysis: dict, wav_bytes: bytes) -> bool:
|
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duration = len(wav_bytes) / (16000 * 2)
|
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return duration >= MIN_CLIP_DURATION
|
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|
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class PiperTTSProcessor(FrameProcessor):
|
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"""Converts text frames to audio via Piper TTS service."""
|
||||
|
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def __init__(self, piper_client: PiperTTSClient, **kwargs):
|
||||
super().__init__(**kwargs)
|
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self._piper = piper_client
|
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|
||||
async def process_frame(self, frame, direction: FrameDirection):
|
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await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TextFrame):
|
||||
wav_bytes = await self._piper.synthesize(frame.text)
|
||||
if wav_bytes:
|
||||
await self.push_frame(
|
||||
TTSAudioRawFrame(audio=wav_bytes, sample_rate=22050, num_channels=1),
|
||||
direction,
|
||||
)
|
||||
else:
|
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await self.push_frame(frame, direction)
|
||||
async def _save_clip(self, wav_bytes: bytes, analysis: dict):
|
||||
try:
|
||||
CLIPS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
clip_id = f"{self._session_id}_{uuid.uuid4().hex[:8]}"
|
||||
(CLIPS_DIR / f"{clip_id}.wav").write_bytes(wav_bytes)
|
||||
meta = {
|
||||
"clip_id": clip_id,
|
||||
"session_id": self._session_id,
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"duration_seconds": len(wav_bytes) / (16000 * 2),
|
||||
"speechbrain_emotion": analysis.get("emotion"),
|
||||
"speechbrain_confidence": analysis.get("emotion_confidence"),
|
||||
"emotion_scores": analysis.get("emotion_scores", {}),
|
||||
"pitch_mean_hz": analysis.get("pitch_mean_hz"),
|
||||
"energy_rms": analysis.get("energy_rms"),
|
||||
"speaking_rate_approx": analysis.get("speaking_rate_approx"),
|
||||
"labeled": False,
|
||||
"confirmed_label": None,
|
||||
"uncertain": analysis.get("emotion_confidence", 1.0) < UNCERTAINTY_THRESHOLD,
|
||||
}
|
||||
(CLIPS_DIR / f"{clip_id}.json").write_text(json.dumps(meta, indent=2))
|
||||
logger.info("Saved clip: %s", clip_id)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to save clip: %s", e)
|
||||
|
||||
|
||||
async def run_voice_coach_pipeline(
|
||||
livekit_url: str,
|
||||
livekit_token: str,
|
||||
anthropic_key: str,
|
||||
session_context: dict,
|
||||
openclaw_token: str,
|
||||
):
|
||||
"""Assemble and run the full voice coaching pipeline."""
|
||||
|
||||
whisper = LocalWhisperSTT(model_size=WHISPER_MODEL, device="cpu", compute_type="int8")
|
||||
piper_client = PiperTTSClient(base_url=PIPER_URL)
|
||||
session_id = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
|
||||
|
||||
transport = LiveKitTransport(
|
||||
url=livekit_url,
|
||||
@@ -160,39 +227,52 @@ async def run_voice_coach_pipeline(
|
||||
params=LiveKitParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(
|
||||
confidence=0.5,
|
||||
start_secs=0.2,
|
||||
stop_secs=0.8,
|
||||
min_volume=0.1,
|
||||
)),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
whisper_stt = WhisperSTTProcessor(whisper)
|
||||
vocal_injector = VocalStateInjector()
|
||||
# STT: Speaches / faster-whisper via OpenAI-compatible API
|
||||
stt = SpeachesSTTService(
|
||||
base_url=SPEACHES_URL,
|
||||
model=WHISPER_MODEL,
|
||||
language="en",
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
api_key=anthropic_key,
|
||||
model=ANTHROPIC_MODEL,
|
||||
vocal_injector = VocalStateInjector(session_id=session_id)
|
||||
|
||||
# LLM: OpenClaw VoiceCoach agent via OpenAI-compatible gateway
|
||||
llm = OpenAILLMService(
|
||||
api_key=openclaw_token,
|
||||
base_url=f"{OPENCLAW_URL}/v1",
|
||||
model=f"openclaw/{OPENCLAW_AGENT_ID}",
|
||||
max_tokens=512,
|
||||
)
|
||||
|
||||
system_prompt = build_system_prompt(
|
||||
baseline_context=session_context.get("baseline_context", ""),
|
||||
session_history=session_context.get("session_history", ""),
|
||||
current_mode=session_context.get("current_mode", "coaching mode"),
|
||||
)
|
||||
|
||||
context = OpenAILLMContext(
|
||||
messages=[{"role": "system", "content": system_prompt}]
|
||||
)
|
||||
context = OpenAILLMContext(messages=[])
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
piper_tts = PiperTTSProcessor(piper_client)
|
||||
# TTS: Speaches / Kokoro via OpenAI-compatible API
|
||||
tts = OpenAITTSService(
|
||||
api_key="speaches",
|
||||
base_url=f"{SPEACHES_URL}/v1",
|
||||
model="kokoro",
|
||||
voice=KOKORO_VOICE,
|
||||
)
|
||||
|
||||
pipeline = Pipeline([
|
||||
transport.input(),
|
||||
whisper_stt,
|
||||
stt,
|
||||
vocal_injector,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
piper_tts,
|
||||
tts,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
])
|
||||
@@ -201,22 +281,16 @@ async def run_voice_coach_pipeline(
|
||||
pipeline,
|
||||
params=PipelineParams(allow_interruptions=True),
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
logger.info(f"Participant joined: {participant['identity']}")
|
||||
await task.queue_frames([
|
||||
TextFrame("VoiceCoach connected. How are you doing today?")
|
||||
])
|
||||
logger.info("Participant joined: %s", participant)
|
||||
await task.queue_frames([TextFrame("session_start")])
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
logger.info(f"Participant left: {participant['identity']}")
|
||||
logger.info("Session ending: %s", session_id)
|
||||
await task.queue_frames([EndFrame()])
|
||||
|
||||
try:
|
||||
await runner.run(task)
|
||||
finally:
|
||||
await piper_client.close()
|
||||
await runner.run(task)
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
"""
|
||||
Piper TTS client for Pipecat pipeline.
|
||||
Calls the self-hosted Piper HTTP service.
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import wave
|
||||
import httpx
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PiperTTSClient:
|
||||
"""HTTP client for the Piper TTS service."""
|
||||
|
||||
def __init__(self, base_url: str = "http://piper-tts:8003"):
|
||||
self.base_url = base_url.rstrip("/")
|
||||
self._client = httpx.AsyncClient(timeout=10.0)
|
||||
|
||||
async def synthesize(self, text: str) -> bytes:
|
||||
"""Convert text to WAV audio bytes via Piper service."""
|
||||
try:
|
||||
response = await self._client.post(
|
||||
f"{self.base_url}/synthesize",
|
||||
json={"text": text, "length_scale": 0.95, "sentence_silence": 0.15},
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.content
|
||||
except Exception as e:
|
||||
logger.error(f"Piper TTS failed: {e}")
|
||||
return b""
|
||||
|
||||
async def close(self):
|
||||
await self._client.aclose()
|
||||
@@ -1,12 +1,7 @@
|
||||
pipecat-ai[livekit,silero,openai]==0.0.48
|
||||
fastapi==0.115.0
|
||||
uvicorn[standard]==0.30.0
|
||||
anthropic==0.34.0
|
||||
faster-whisper==1.0.3
|
||||
requests==2.32.3
|
||||
httpx==0.27.0
|
||||
python-dotenv==1.0.1
|
||||
structlog==24.4.0
|
||||
numpy==1.26.4
|
||||
soundfile==0.12.1
|
||||
livekit-api==0.7.1
|
||||
fastapi==0.115.0
|
||||
uvicorn[standard]==0.30.0
|
||||
|
||||
@@ -1,35 +0,0 @@
|
||||
"""
|
||||
Local STT service using faster-whisper.
|
||||
Replaces Deepgram with fully self-hosted speech-to-text.
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import wave
|
||||
import numpy as np
|
||||
from faster_whisper import WhisperModel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LocalWhisperSTT:
|
||||
"""Wraps faster-whisper for use in the Pipecat pipeline."""
|
||||
|
||||
def __init__(self, model_size: str = "medium", device: str = "cpu", compute_type: str = "int8"):
|
||||
logger.info(f"Loading faster-whisper model: {model_size} on {device}")
|
||||
self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
|
||||
logger.info("faster-whisper model loaded.")
|
||||
|
||||
def transcribe(self, audio_bytes: bytes, sample_rate: int = 16000) -> str:
|
||||
"""Transcribe raw PCM audio bytes to text."""
|
||||
audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
|
||||
|
||||
segments, info = self.model.transcribe(
|
||||
audio_array,
|
||||
beam_size=5,
|
||||
language="en",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=500),
|
||||
)
|
||||
|
||||
text = " ".join(seg.text.strip() for seg in segments)
|
||||
return text
|
||||
Reference in New Issue
Block a user