The task was created but not imported in the Celery task registry,
causing "Received unregistered task" error when triggering re-render.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Break out TTS synthesis into a dedicated Celery worker (tts queue) with
concurrency=8 for parallel processing. Each AD cue is now synthesized as
a separate task, enabling up to 8 cues to be processed simultaneously.
Key changes:
- Add tts_synthesis.py with synthesize_cue_task for per-cue synthesis
- Refactor translate_and_synthesize.py to dispatch cue tasks in parallel
- Add tts-worker service to docker-compose.yml (concurrency=8)
- Add Cloud Run service config for production deployment
Benefits:
- Parallel synthesis even for single jobs (e.g., 50 cues → 8 concurrent)
- Natural rate limiting across multiple concurrent jobs
- Fault tolerance with per-cue retries and GCS persistence
🤖 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>
Add a dedicated Celery queue (ffmpeg) with concurrency=1 to serialize
all FFmpeg operations. This prevents CPU spikes when multiple render
tasks run in parallel with multiple languages.
Changes:
- Add ffmpeg_operations.py with run_ffmpeg_command and run_ffprobe_command tasks
- Update VideoRendererService to dispatch ffmpeg commands via the queue
- Add ffmpeg-worker service to docker-compose with --concurrency=1
- Configure main worker to exclude the ffmpeg queue
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add new deliverable type that renders video with audio descriptions embedded.
Supports two AI-determined methods:
- Direct Overlay: ducks original audio and overlays AD TTS (for minimal dialogue)
- Pause-Insert: freeze-frame video, insert AD, re-time subtitles (for significant dialogue)
Backend:
- Add Pydantic schemas for Gemini analysis response
- Add Gemini prompt and analyze_accessible_video_placement() method
- Add video_renderer.py service using FFmpeg for both rendering methods
- Add vtt_retimer.py service for pause-insert subtitle adjustment
- Add render_accessible_video.py Celery task
- Modify TTS service to return individual per-cue segments
- Update translate_and_synthesize.py to save segments and trigger rendering
- Update download endpoint to include accessible video outputs
Frontend:
- Add accessible_video_mp4 checkbox to NewJob form
- Update TypeScript types for new deliverable
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
