dschlueter
53b0dd4055
5-state machine (LAUSCH/AUFNAHME/GENERATOR/VORLESE/DIALOG) with: - Wake-word detection via openwakeword (hey_jarvis) - Speech-to-text via faster-whisper large-v3 (GPU) - LLM streaming via OpenAI-compatible backends (llama.cpp/ollama/openai) - TTS via piper with automatic language detection and voice selection - Selectable mic input (ReSpeaker, MOTU M2, camera, bluetooth, index) - Selectable audio output sink - Control words during playback (Stopp/Pause/Weiter/Noch einmal/…) - "Stopp" abort in all active states → LAUSCH Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
115 lines
4 KiB
Python
Executable file
115 lines
4 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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Live-Transkription via faster-whisper (large-v3, GPU).
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Aufnahme-Logik:
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- Puffert Audio in CHUNK_SEC-Sekunden-Blöcken
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- Faster-whisper VAD filtert Stille heraus
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- Ausgabe: fortlaufender Text auf stdout
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Verwendung:
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python3 transcribe.py # ReSpeaker, GPU 2
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python3 transcribe.py --mic motu # MOTU M2
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python3 transcribe.py --mic camera # Kamera-Mikrofon
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python3 transcribe.py --mic 17 # sounddevice-Index direkt
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python3 transcribe.py --list-mics # alle Eingabegeräte anzeigen
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python3 transcribe.py [--lang de] [--model large-v3] [--gpu 1]
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GPU-Belegung:
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GPU 0 NVIDIA T600 (4 GB) — reserviert
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GPU 1 RTX 3090 (24 GB) — llama.cpp / Port 8001
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GPU 2 RTX 3090 (24 GB) — Whisper (default)
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"""
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import argparse
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import queue
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import sys
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import numpy as np
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import sounddevice as sd
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from faster_whisper import WhisperModel
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from scipy.signal import resample_poly
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WHISPER_RATE = 16000
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CHUNK_SEC = 5 # Sekunden pro Transkriptions-Durchlauf
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CHANNELS = 1
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sys.path.insert(0, __import__("os").path.dirname(__file__))
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from mic import resolve_mic, print_mic_list, DEFAULT_MIC # noqa: E402
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def main():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument("--mic", default=DEFAULT_MIC,
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help="Eingabegerät: respeaker | motu | camera | default | "
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"bluetooth | <index> | <substring>")
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parser.add_argument("--list-mics", action="store_true",
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help="Verfügbare Mikrofone anzeigen und beenden")
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parser.add_argument("--lang", default="de", help="Sprache (de/en/…)")
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parser.add_argument("--model", default="large-v3", help="Whisper-Modell")
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parser.add_argument("--gpu", type=int, default=1,
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help="CUDA-Index innerhalb CUDA_VISIBLE_DEVICES (default: 1 = phys. GPU 2)")
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args = parser.parse_args()
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if args.list_mics:
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print_mic_list()
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return
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device_idx, capture_rate, device_label = resolve_mic(args.mic)
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chunk_samples = CHUNK_SEC * capture_rate
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print(f"Mikrofon : {device_label} (Index {device_idx}, {capture_rate} Hz → resample → {WHISPER_RATE} Hz)")
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print(f"Modell : {args.model} | Sprache: {args.lang} | GPU: {args.gpu}")
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print("Lade Modell …", flush=True)
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model = WhisperModel(
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args.model,
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device="cuda",
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device_index=args.gpu,
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compute_type="float16",
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)
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print("Modell geladen. Spreche …\n", flush=True)
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audio_q: "queue.Queue[np.ndarray]" = queue.Queue()
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def callback(indata, frames, time_info, status):
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if status:
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print(f"[audio status] {status}", file=sys.stderr)
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audio_q.put(indata[:, 0].copy())
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try:
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with sd.InputStream(
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device=device_idx,
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channels=CHANNELS,
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samplerate=capture_rate,
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dtype="float32",
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blocksize=capture_rate // 4, # 250 ms Blöcke
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callback=callback,
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):
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buffer = np.empty(0, dtype="float32")
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while True:
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while buffer.shape[0] < chunk_samples:
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buffer = np.concatenate([buffer, audio_q.get()])
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chunk = buffer[:chunk_samples].copy()
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buffer = buffer[chunk_samples:]
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chunk_16k = resample_poly(chunk, WHISPER_RATE, capture_rate)
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segments, _ = model.transcribe(
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chunk_16k,
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language=args.lang,
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vad_filter=True,
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vad_parameters={"min_silence_duration_ms": 500},
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beam_size=5,
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)
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text = " ".join(s.text.strip() for s in segments).strip()
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if text:
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print(text, flush=True)
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except KeyboardInterrupt:
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print("\nBeendet.")
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if __name__ == "__main__":
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main()
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