mikael-lovqvists-claude-agent
20873be786
- README explaining experimental/transparency purpose - faster-whisper STT backend (fw-stt.mjs, faster-whisper-server.py, install-faster-whisper.sh) - Bug fixes: Buffer alignment in on_audio, --debug-waveform URL parsing, silent fetch errors, instant dispatch timer leak - Global uncaughtException/unhandledRejection handlers in query-demo.mjs - Design docs: CHANGELOG, COMMAND-DISPATCH, INTERFACE-THEORY, VOICE-POLICY - Systemd service unit templates Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
82 lines
2.7 KiB
Python
Executable File
82 lines
2.7 KiB
Python
Executable File
"""
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faster-whisper transcription server.
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Protocol (stdin/stdout, one JSON line per exchange):
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Request: {"audio_b64": "<base64 float32 LE>", "sample_rate": 16000}
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Response: {"text": "...", "words": [{"word": "...", "start": 0.0, "end": 0.1, "probability": 0.99}]}
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Startup: writes "ready\n" to stdout when the model is loaded.
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"""
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import sys
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import json
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import base64
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import argparse
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import traceback
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import numpy as np
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='base.en')
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parser.add_argument('--device', default='cuda')
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parser.add_argument('--compute-type', default='int8')
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args = parser.parse_args()
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sys.stderr.write(f'[fw-server] loading {args.model} ({args.device}, {args.compute_type})...\n')
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sys.stderr.flush()
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try:
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from faster_whisper import WhisperModel
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except ImportError:
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sys.stderr.write('[fw-server] faster-whisper not installed — run: pip install faster-whisper\n')
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sys.exit(1)
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try:
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model = WhisperModel(args.model, device=args.device, compute_type=args.compute_type)
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sys.stderr.write(f'[fw-server] ready on {args.device}\n')
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except Exception as e:
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sys.stderr.write(f'[fw-server] {args.device} failed ({e}), falling back to cpu\n')
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model = WhisperModel(args.model, device='cpu', compute_type='int8')
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sys.stderr.write('[fw-server] ready on cpu\n')
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sys.stdout.write('ready\n')
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sys.stdout.flush()
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for raw_line in sys.stdin:
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line = raw_line.strip()
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if not line:
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continue
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try:
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req = json.loads(line)
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audio = np.frombuffer(base64.b64decode(req['audio_b64']), dtype=np.float32)
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sr = req.get('sample_rate', 16000)
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segments, _ = model.transcribe(
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audio,
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language='en',
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word_timestamps=True,
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vad_filter=False, # VAD already done upstream
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)
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text = ''
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words = []
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for seg in segments:
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text += seg.text
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for w in (seg.words or []):
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words.append({
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'word': w.word,
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'start': round(float(w.start), 4),
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'end': round(float(w.end), 4),
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'probability': round(float(w.probability), 4),
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})
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sys.stderr.write(f'[fw-server] {json.dumps(text)} ({len(words)} words)\n')
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sys.stderr.flush()
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sys.stdout.write(json.dumps({'text': text, 'words': words}) + '\n')
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sys.stdout.flush()
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except Exception:
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sys.stderr.write(f'[fw-server] error:\n{traceback.format_exc()}\n')
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sys.stderr.flush()
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sys.stdout.write(json.dumps({'text': '', 'words': [], 'error': traceback.format_exc()}) + '\n')
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sys.stdout.flush()
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