05bde8326d
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> |
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| .. | ||
| __pycache__ | ||
| audit_logger.py | ||
| emailer.py | ||
| gcs.py | ||
| gemini.py | ||
| gemini_tts.py | ||
| microsoft_auth.py | ||
| secrets_manager.py | ||
| translate.py | ||
| tts.py | ||
| validation.py | ||
| video_renderer.py | ||
| vtt_retimer.py | ||
| websocket.py | ||
| websocket_publisher.py | ||
| whisper_service.py | ||