jeremy
f62772663d
- 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
297 lines
11 KiB
Python
Executable File
297 lines
11 KiB
Python
Executable File
"""
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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 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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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, 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.processors.frame_processor import FrameProcessor, FrameDirection
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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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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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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 SpeachesSTTService(STTService):
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"""STT via Speaches OpenAI-compatible /v1/audio/transcriptions endpoint.
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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._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 set_model(self, model: str):
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self._model = model
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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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"""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, session_id: str, **kwargs):
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super().__init__(**kwargs)
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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, 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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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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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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else:
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await self.push_frame(frame, direction)
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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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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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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": ("u.wav", wav_bytes, "audio/wav")},
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)
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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("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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async def _save_clip(self, wav_bytes: bytes, analysis: dict):
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try:
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CLIPS_DIR.mkdir(parents=True, exist_ok=True)
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clip_id = f"{self._session_id}_{uuid.uuid4().hex[:8]}"
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(CLIPS_DIR / f"{clip_id}.wav").write_bytes(wav_bytes)
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meta = {
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"clip_id": clip_id,
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"session_id": self._session_id,
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"timestamp": datetime.utcnow().isoformat(),
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"duration_seconds": len(wav_bytes) / (16000 * 2),
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"speechbrain_emotion": analysis.get("emotion"),
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"speechbrain_confidence": analysis.get("emotion_confidence"),
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"emotion_scores": analysis.get("emotion_scores", {}),
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"pitch_mean_hz": analysis.get("pitch_mean_hz"),
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"energy_rms": analysis.get("energy_rms"),
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"speaking_rate_approx": analysis.get("speaking_rate_approx"),
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"labeled": False,
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"confirmed_label": None,
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"uncertain": analysis.get("emotion_confidence", 1.0) < UNCERTAINTY_THRESHOLD,
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}
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(CLIPS_DIR / f"{clip_id}.json").write_text(json.dumps(meta, indent=2))
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logger.info("Saved clip: %s", clip_id)
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except Exception as e:
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logger.warning("Failed to save clip: %s", e)
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async def run_voice_coach_pipeline(
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livekit_url: str,
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livekit_token: str,
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openclaw_token: str,
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):
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session_id = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
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transport = LiveKitTransport(
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url=livekit_url,
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token=livekit_token,
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room_name="voicecoach",
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params=LiveKitParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(
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confidence=0.5,
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start_secs=0.2,
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stop_secs=0.8,
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min_volume=0.1,
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)),
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vad_audio_passthrough=True,
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),
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)
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# STT: Speaches / faster-whisper via OpenAI-compatible API
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stt = SpeachesSTTService(
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base_url=SPEACHES_URL,
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model=WHISPER_MODEL,
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language="en",
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)
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vocal_injector = VocalStateInjector(session_id=session_id)
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# LLM: OpenClaw VoiceCoach agent via OpenAI-compatible gateway
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llm = OpenAILLMService(
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api_key=openclaw_token,
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base_url=f"{OPENCLAW_URL}/v1",
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model=f"openclaw/{OPENCLAW_AGENT_ID}",
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max_tokens=512,
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)
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context = OpenAILLMContext(messages=[])
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context_aggregator = llm.create_context_aggregator(context)
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# TTS: Speaches / Kokoro via OpenAI-compatible API
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tts = OpenAITTSService(
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api_key="speaches",
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base_url=f"{SPEACHES_URL}/v1",
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model="kokoro",
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voice=KOKORO_VOICE,
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)
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pipeline = Pipeline([
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transport.input(),
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stt,
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vocal_injector,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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])
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task = PipelineTask(
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pipeline,
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params=PipelineParams(allow_interruptions=True),
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)
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runner = PipelineRunner()
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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logger.info("Participant joined: %s", participant)
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await task.queue_frames([TextFrame("session_start")])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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logger.info("Session ending: %s", session_id)
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await task.queue_frames([EndFrame()])
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await runner.run(task)
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