v0.12.0:0 - WhisperX as a one-click dashboard install + managed service

Replaces the manual rsync+build+run with a proper spark-control feature.
First in the audio path that doesn't require shell access on Spark 2.

What's in the box
─────────────────
* image/whisperx_container/   - the build context (Dockerfile, requirements,
  app/main.py FastAPI wrapper). Mainline pipeline: faster-whisper for STT +
  pyannote 3.1 for diarization + wav2vec2 forced alignment. Single endpoint
  /v1/audio/transcribe-with-speakers returns the exact same shape spark-
  control's existing endpoint does, so the recap-relay PR spec needs no
  changes when we cut over.

* image/app/whisperx_install.py - install manager. ships build context to
  Spark 2 over SSH, runs `docker build`, runs `docker run` with 40 GB
  memory cap (vs Sortformer's unbounded which thrashed Spark 2 on a 90-min
  file), polls /health until both Whisper + pyannote report loaded.

* Audio proxy: /api/audio/transcribe-with-speakers now prefers WhisperX
  when its /health reports diarizer_loaded=true, falls back to the legacy
  Parakeet + Sortformer path otherwise. Same response shape either way.
  Clean cutover, easy rollback (`docker rm whisperx-asr`).

* Dashboard (Audio / Speech tab):
  - "Add WhisperX" banner appears when not installed, with a primary
    "Install WhisperX" button. One click triggers the install.
  - Build progress dialog with phase + elapsed timer + live build log via
    SSE (`/api/whisperx/install/{job_id}/stream`).
  - After install, WhisperX auto-registers as a managed service alongside
    Parakeet and Magpie (Start/Restart/Stop, deep-check, auto-restart).
  - Banner self-hides once /api/whisperx/status reports healthy.

New endpoints
─────────────
  GET  /api/whisperx/status
  POST /api/whisperx/install
  GET  /api/whisperx/install/{job_id}
  GET  /api/whisperx/install/{job_id}/stream  (SSE phase + log)

Config additions (env)
──────────────────────
  WHISPERX_HOST       (defaults to spark2_host)
  WHISPERX_USER       (defaults to spark2_user)
  WHISPERX_CONTAINER  (default: whisperx-asr)
  WHISPERX_PORT       (default: 8002)
  WHISPERX_MODEL      (default: medium; tiny/base/small/medium/large-v3)

Dockerfile
──────────
Added COPY whisperx_container /app/whisperx_container so the runtime
install manager can read the build context from inside the spark-control
image and ship it over SSH.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

image/whisperx_container/Dockerfile

@@ -0,0 +1,51 @@
+# WhisperX ASR + diarization container for Spark 2 (Blackwell GB10, sm_120).
+#
+# Replaces the custom Parakeet wrapper + Sortformer overlay with a single
+# mainline pipeline: faster-whisper for transcription + pyannote.audio 3.1
+# for diarization + wav2vec2 forced alignment for word-level timestamps.
+#
+# Build (on Spark 2, where Blackwell + nvcr.io credentials are available):
+#   docker build -t whisperx-asr:latest .
+#
+# Run:
+#   docker run -d --restart unless-stopped --name whisperx-asr \
+#     --gpus all --memory=40g \
+#     -p 8002:8002 \
+#     -v whisperx-models:/root/.cache/huggingface \
+#     -e HF_TOKEN="$(cat ~/.cache/huggingface/token)" \
+#     -e WHISPER_MODEL=medium \
+#     whisperx-asr:latest
+#
+# The memory cap is intentional: even if WhisperX hits a pathological input,
+# it gets OOM-killed cleanly instead of swap-thrashing the whole Spark.
+
+FROM nvcr.io/nvidia/pytorch:25.11-py3
+
+# WhisperX runs ffmpeg under the hood for audio decoding
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends ffmpeg \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install whisperx + the FastAPI wrapper deps. --break-system-packages because
+# the NGC PyTorch image has its own managed Python that's flagged "system".
+COPY requirements.txt /tmp/requirements.txt
+RUN pip install --break-system-packages --no-cache-dir -r /tmp/requirements.txt
+
+# Pre-warm the default Whisper + alignment models at build time so first-call
+# latency on a fresh container is small. (~3 GB cached into the image; if you
+# want a smaller image, comment this out and accept the first-call download.)
+ARG WHISPER_MODEL=medium
+ENV WHISPER_MODEL=${WHISPER_MODEL}
+RUN python3 -c "import whisperx; whisperx.load_model('${WHISPER_MODEL}', 'cpu', compute_type='int8')" \
+ && python3 -c "import whisperx; whisperx.load_align_model(language_code='en', device='cpu')"
+
+WORKDIR /opt/whisperx
+COPY app /opt/whisperx/app
+
+# Expose for spark-control's proxy on Spark 2
+EXPOSE 8002
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=180s \
+  CMD python3 -c "import urllib.request; urllib.request.urlopen('http://localhost:8002/health')" || exit 1
+
+CMD ["python3", "-m", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8002", "--workers", "1"]