feat(dross): voice Phase 2a — local whisper transcribe + mic (2.12.0)

faster-whisper (small.en, GPU+CPU fallback) on CT 102 → POST
/api/voice/transcribe (multer→whisper client) → mic in the bubble
records (MediaRecorder), uploads, drops the transcript into the input
to review-and-send. Infra scripts in deploy/whisper/. Retention (P2b)
next. NOTE: mic needs a secure context (the https domain), not the LAN IP.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

deploy/whisper/README.md

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+# faster-whisper service (Dross voice STT)
+
+Runs on **CT 102** (the Ollama box, `192.168.1.185`), bare-metal (no Docker), on the
+RTX A2000 with CPU fallback. OpenAI-style `/transcribe` consumed by void-app
+`lib/voice/whisper.js` (`WHISPER_URL=http://192.168.1.185:8001`).
+
+## Install (on CT 102)
+```
+scp deploy/whisper/{server.py,setup.sh} root@192.168.1.185:/opt/whisper_server.py /root/setup.sh
+ssh root@192.168.1.185 'bash /root/setup.sh && install -m644 /opt/whisper_server.py /opt/whisper/server.py && systemctl enable --now whisper'
+curl http://192.168.1.185:8001/health   # {"ok":true,"model":"small.en","device":"cuda"}
+```
+- venv at `/opt/whisper/venv`; model `small.en` (env `WHISPER_MODEL`); CUDA libs via
+  `nvidia-cublas-cu12`/`nvidia-cudnn-cu12` pip wheels (LD_LIBRARY_PATH in the unit).
+- GPU → CPU fallback is in `server.py` `load()`.
+- **CT 102 disk was expanded +20G** (was 89% full) before install.