STT-Dienst: Kern, HTTP/WebSocket/Wyoming-Service, CLI, Tests, systemd-Unit

- 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>

deploy/stt-service.service

@@ -0,0 +1,21 @@
+[Unit]
+Description=SpeechToText Service (faster-whisper, HTTP :9998, Wyoming :10300)
+After=network.target
+
+[Service]
+Type=simple
+WorkingDirectory=%h/Python_Programs/SpeechToText
+ExecStart=%h/Python_Programs/SpeechToText/.venv/bin/uvicorn stt_service:app --host 0.0.0.0 --port 9998
+Restart=on-failure
+RestartSec=5
+StandardOutput=journal
+StandardError=journal
+# GPU eindeutig per UUID (nvidia-smi -L), NICHT per Index: CUDA zählt "fastest first",
+# nvidia-smi in PCI-Reihenfolge. GPU 2 = RTX 3090, teilt sich mit chatterbox-tts (~2,4 GB VRAM).
+Environment="CUDA_VISIBLE_DEVICES=GPU-83ba6d1f-ebbe-f8c8-1433-153e0ebc3866"
+Environment="STT_MODEL=large-v3-turbo"
+Environment="STT_LANGUAGE=de"
+Environment="STT_WYOMING_PORT=10300"
+
+[Install]
+WantedBy=default.target