dschlueter
a986cbe42a
- stt_core.py: Whisper-Engine (einmal laden, CPU-Fallback), Audio-Dekodierung jedes Formats, Halluzinationsfilter, Äußerungserkennung per Silero-VAD - stt_service.py: /health, /v1/audio/transcriptions (OpenAI), /transcribe (Rohdaten), WS /ws/stream; Inferenz im Worker-Thread, begrenzte Warteschlange (503) - stt_wyoming.py: Home-Assistant-Anbindung (TCP 10300) - stt_cli.py: Datei/Mikrofon über den Dienst oder lokal - Tests (Unit + Integration), README, requirements.txt, deploy/stt-service.service Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
26 lines
863 B
Text
26 lines
863 B
Text
# SpeechToText — Abhängigkeiten
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# Getestet mit Python 3.12, CUDA 12.x (ctranslate2 4.6), Ubuntu
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#
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# GPU: ctranslate2 braucht cuBLAS 12 + cuDNN 9 (z. B. über pip-Pakete
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# nvidia-cublas-cu12 / nvidia-cudnn-cu12 oder eine vorhandene CUDA-Installation).
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# --- STT-Kern (stt_core.py) ---
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faster-whisper>=1.2.0 # bringt ctranslate2, PyAV (Dekodierung) und Silero-VAD (onnxruntime) mit
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numpy>=1.26
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scipy>=1.11 # Resampling ganzer Signale
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soxr>=0.3 # Streaming-Resampling (optional, sonst Fallback auf scipy)
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# --- Dienst (stt_service.py) ---
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fastapi>=0.115,<1.0
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uvicorn[standard]>=0.32,<1.0
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python-multipart>=0.0.9 # /v1/audio/transcriptions (multipart)
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wyoming>=1.6,<2.0 # Home Assistant (stt_wyoming.py)
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# --- CLI (stt_cli.py) ---
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sounddevice>=0.4
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requests>=2.31
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websockets>=13
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# --- Tests ---
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pytest>=8
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httpx>=0.27
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