feat(speaker-id): Phase 1 — SpeechBrain ECAPA-TDNN Backend in whisper-bridge

Speaker-ID-Modul (Hermes-Style „echtes Gespraech ohne Wake-Word"-Vision,
Phase 1 von 5). Erkennt Stefans Stimme via 192-dim Embedding + Cosine-
Match gegen einen persistierten Fingerprint.

Module:
- speaker_id.py: lazy-loaded ECAPA-TDNN (HuggingFace), enroll/verify/
  status/delete. Fingerprint = L2-normalisierter Mittelwert aus N
  Enrollment-Samples in /voice-id/fingerprint.json.
  Fail-open: kein Fingerprint → verify() returnt (True, 0.0).
- bridge.py: 3 Message-Handler — voice_id_status_request,
  voice_id_enroll_request (samples[]: base64 16kHz int16 PCM),
  voice_id_delete_request. Enrollment laeuft im Executor (Torch
  blockt sonst die Event-Loop).
- Dockerfile: torch 2.3.1 + torchaudio mit CUDA-12.1-Wheels (sonst
  zieht speechbrain CPU-only Torch rein). Container ~1 GB groesser.
- docker-compose.yml: ./voice-id:/voice-id Bind-Mount fuer Fingerprint-
  Persistenz (ueberlebt Container-Restart).
- rvs/server.js: 6 neue Message-Types in ALLOWED_TYPES.

Phase 2 (next): App-Enrollment-Flow + Diagnostic-Voice-ID-Section.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

xtts/whisper/Dockerfile

@@ -1,14 +1,22 @@
 FROM nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04
 
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3 python3-pip ffmpeg \
+    python3 python3-pip ffmpeg git \
     && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
+# PyTorch CUDA-Wheels zuerst (sonst zieht speechbrain CPU-only Torch rein
+# falls f5tts den Cache noch nicht geseedet hat).
+RUN pip3 install --no-cache-dir torch==2.3.1 torchaudio==2.3.1 \
+    --index-url https://download.pytorch.org/whl/cu121
+
 COPY requirements.txt .
 RUN pip3 install --no-cache-dir -r requirements.txt
 
-COPY bridge.py .
+COPY bridge.py speaker_id.py ./
 
 CMD ["python3", "bridge.py"]