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
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* 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
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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)
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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
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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>
Folds the image/parakeet_patches/apply.sh script into a one-click
dashboard action and adds drift detection so you can see at a glance
whether the parakeet-asr container has the latest Sortformer overlays
that spark-control ships.
Backend:
* image/app/speech_models.py - SpeechModelsManager: reads /health from
Parakeet, sha256s the local overlay files inside spark-control's
Docker image (/app/parakeet_patches), sha256s the same files inside
the parakeet-asr container via `docker exec ... sha256sum`, surfaces
in_sync / drift / missing status per file.
* GET /api/speech-models - status payload
* POST /api/speech-models/reapply - copies overlays into container,
verifies python syntax, restarts,
polls /health for ~120s, returns
step-by-step result
* POST /api/speech-models/restart - plain `docker restart parakeet-asr`
Dockerfile: now COPY parakeet_patches into the image at /app/parakeet_patches
so the runtime can read them. Future spark-control releases auto-carry
newer overlay versions; the panel surfaces drift after upgrade.
Frontend: new "Speech model patches" section on the dashboard with
* Status pill (in sync / drift / missing)
* Per-file SHA comparison (local vs container)
* Loaded-models pills (ASR + diarizer)
* Reapply + Restart buttons (both with confirmation modals)
* Live progress display during reapply with per-step ✓/✗
Verified post-install against the running cluster:
GET /api/speech-models shows both files in_sync (SHAs match) and both
models loaded ready on Spark 2.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Keysat