router: deutsche Spracherkennung mit whisper.cpp (CPU-only)

- STT-Worker (stt_worker.py): langlebiger HTTP-Service auf Port 8084
  - whisper-cli als Subprozess (CPU-only, 8 Threads)
  - Audio-Vorbereitung via ffmpeg (WebM/Opus/M4A → 16 kHz WAV)
  - Nativ: WAV, MP3, OGG, FLAC
  - Health-Endpunkt: GET /status
  - Transkription: POST /transcribe (Multipart-Form-Data)

- Router-Integration:
  - POST /v1/audio/transcriptions (OpenAI-kompatibel)
  - GET /v1/audio/models (whisper-1, kokoro-german)
  - GET /v1/audio/voices (martin, victoria)
  - /status mit stt-Section
  - model=whisper-1 akzeptiert
  - response_format: json, verbose_json

- systemd-Service: mike-ai-whisper.service
  - Boot-Start, Restart on failure, journald
  - CPU-only, kein GPU-Lock

- Deploy-Dateien aktualisiert (deploy.sh, install.sh)
- Mock-STT-Worker für lokale Tests (dev/mock_stt_worker.py)
- Tests ergänzt: STT Status, WAV, language=de, unbekanntes Modell,
  Worker down, Recovery, Audio-Modelle, Audio-Voices,
  STT+Qwen parallel, STT+TTS parallel
- README.md: STT-Section mit Endpunkten, Benchmarks, Doku

Benchmarks (CPU-only, 8 Threads):
  7.3 s Audio → 8.9 s (RTF 1.22×)
  30 s Audio → 16.8 s (RTF 0.56×)
  50 s Audio → 18.5 s (RTF 0.37×)
  RAM: ~1.7 GB (Modell), Worker: ~20 MB

deploy/deploy.sh

@@ -10,8 +10,9 @@ STAGE="/tmp/ai-profile-router-$$"
 
 mkdir -p "$STAGE"
 cp router/ai_profile_router.py router/image_worker.py router/tts_worker.py \
+   router/stt_worker.py \
    deploy/install.sh deploy/mike-ai-profile-router.service \
-   deploy/mike-ai-kokoro.service "$STAGE/"
+   deploy/mike-ai-kokoro.service deploy/mike-ai-whisper.service "$STAGE/"
 
 echo "== Übertrage Dateien nach ${TARGET}:/tmp/ai-profile-router/"
 ssh -i "$SSH_KEY" "$TARGET" 'mkdir -p /tmp/ai-profile-router'