jeremy
d901208032
- pipeline.py: switched LLM from OpenClaw/Claude to local Ollama, added settings loading, sentence aggregator, async SpeechBrain, tuned VAD - main.py: simplified (no more token server thread, just pipeline) - token_server.py: NEW - standalone token + settings API server - docker-compose.yml: all services on GPU machine, added token-server service, CUDA speaches, Ollama LLM - web-client/index.html: added settings panel with sliders - web-client/app.js: settings management, localStorage persistence, push to server - web-client/style.css: settings panel styles - web-client/nginx.conf: proxy to token-server instead of pipecat-agent - patch_livekit.py: updated with proper indentation-safe patching
36 lines
1.2 KiB
Python
Executable File
36 lines
1.2 KiB
Python
Executable File
"""
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Local STT service using faster-whisper.
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Replaces Deepgram with fully self-hosted speech-to-text.
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"""
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import io
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import logging
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import wave
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import numpy as np
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from faster_whisper import WhisperModel
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logger = logging.getLogger(__name__)
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class LocalWhisperSTT:
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"""Wraps faster-whisper for use in the Pipecat pipeline."""
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def __init__(self, model_size: str = "medium", device: str = "cpu", compute_type: str = "int8"):
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logger.info(f"Loading faster-whisper model: {model_size} on {device}")
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self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
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logger.info("faster-whisper model loaded.")
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def transcribe(self, audio_bytes: bytes, sample_rate: int = 16000) -> str:
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"""Transcribe raw PCM audio bytes to text."""
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audio_array = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
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segments, info = self.model.transcribe(
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audio_array,
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beam_size=5,
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language="en",
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=500),
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)
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text = " ".join(seg.text.strip() for seg in segments)
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return text
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