ffmpeg was missing from the base image, causing all pydub operations
(AudioSegment.from_file, export) to fail in worker and tts-worker containers.
Moved ffmpeg install from whisper-worker stage to the shared base stage so
all container variants (api, worker, tts-worker, whisper-worker) have it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Heavy pipeline tasks (ingest, translate, render, tts) now dispatch to
va-worker Cloud Run Job which has its own Dockerfile.cloudrun with ffmpeg.
API and lightweight Celery worker (notify/embed) don't need it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
poetry.lock was generated with 2.1.4 — using 1.8.2 caused
incompatible lock file error and failed Docker builds.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Allow configuring Celery worker concurrency via environment variables
to take advantage of Cloud Run autoscaling:
- Add WORKER_CONCURRENCY, WHISPER_WORKER_CONCURRENCY, FFMPEG_WORKER_CONCURRENCY
settings to config.py with recommended values documented
- Update Dockerfile to use ${WORKER_CONCURRENCY} and ${WHISPER_WORKER_CONCURRENCY}
environment variables instead of hardcoded values
- Update docker-compose.yml to pass concurrency env vars to worker commands
- Add WHISPER_SERVICE_URL and FFMPEG_SERVICE_URL to relevant workers
Recommended settings:
Local mode: WHISPER=1, FFMPEG=1 (CPU/RAM constrained)
Cloud Run mode: WHISPER=10, FFMPEG=20 (match autoscaling limits)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Medium model is faster and uses less memory (~1.5GB vs ~3GB)
while still providing good multilingual transcription quality.
Updated in:
- config.py
- docker-compose.yml
- whisper-worker-service.yaml
- cloudbuild.yaml
- Dockerfile (pre-download)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Downloads the model (~3GB) at build time to avoid cold start delays.
Also updated comment to reflect large-v3 memory usage (~4-6GB).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implements word-level speech analysis using faster-whisper to refine
AD pause points. Gemini's timestamps are snapped to natural speech gaps
(sentence/phrase boundaries) to prevent pauses mid-word.
Key changes:
- Add WhisperService for transcription and gap detection
- Add dedicated Celery task routed to 'whisper' queue
- Integrate refinement into render_accessible_video task
- Cache Whisper transcripts in MongoDB for reuse across languages
- Add dedicated whisper-worker with concurrency=1 to prevent OOM
Configuration:
- Uses faster-whisper 'base' model (multilingual, ~145MB)
- 5-second search window after Gemini's recommended point
- Falls back to original timestamp if no gap found
Infrastructure:
- New Docker stage: whisper-worker
- New Cloud Run service: accessible-video-whisper-worker
- Updated docker-compose.yml with whisper-worker service
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The accessible video render task was being dispatched to the 'render' queue
but no worker was listening to it. Added 'render' to:
- Dockerfile CMD args for worker queue list
- celery_worker.py import and log message
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The Gemini TTS service uses pydub which requires ffmpeg to convert
audio formats. Previously only the Worker container had ffmpeg.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
