Keysat
5a0bfba6a3
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>
WhisperX container for Spark 2
Replaces the custom Parakeet wrapper + Sortformer overlay (v0.10/v0.11) with a single mainline pipeline:
- faster-whisper (CTranslate2-optimized) for STT
- pyannote.audio 3.1 for speaker diarization (sliding-window — handles long files in bounded memory, fixes the Sortformer OOM on 90-min audio)
- wav2vec2 forced alignment for word-level timestamps
Exposes the same API surface spark-control already proxies to, so the cutover is a one-URL change in the audio proxy:
GET /health— readiness probeGET /v1/models— model listPOST /v1/audio/transcriptions— OpenAI-shaped STTPOST /v1/audio/transcribe-with-speakers— merged diarized transcript (matches spark-control's response shape exactly)
Deploy to Spark 2
# 1. Copy this directory to Spark 2
rsync -av --delete image/whisperx_container/ <spark-user>@<spark-2-ip>:~/whisperx-build/
# 2. SSH in and build
ssh <spark-user>@<spark-2-ip>
cd ~/whisperx-build
docker build -t whisperx-asr:latest .
# 3. Run alongside the existing parakeet-asr (which stays on 8000 for now)
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
# 4. Watch first-start logs (model load + first health check)
docker logs -f whisperx-asr
Model size knobs
WHISPER_MODEL env var. Defaults to medium. Options:
| Model | Size | Speed (GB10) | Quality |
|---|---|---|---|
tiny |
~75M | ~120x rt | low |
base |
~74M | ~80x rt | ok |
small |
~244M | ~50x rt | good |
medium |
~769M | ~30x rt | excellent (default) |
large-v3 |
~1.5B | ~15x rt | best |
For a 90-min file, medium takes ~3 min STT + ~9 min diarize ≈ ~12 min total.
Memory budget
The --memory=40g cap is intentional. Spark 2 has 122 GB unified, of which
~35 GB is consumed by parakeet-asr + magpie-tts. The 40 GB cap leaves
comfortable headroom for both the model weights (~5 GB) and pyannote's
in-memory features (~5–15 GB for a 90-min audio). If WhisperX hits a
pathological input it gets OOM-killed cleanly instead of swap-thrashing the
whole Spark — the symptom we hit with the unbounded Sortformer container.
Rollback to Parakeet+Sortformer
docker stop whisperx-asr && docker rm whisperx-asr
The parakeet-asr container stays running throughout — spark-control's proxy URL switch is reversible via config or version downgrade.