feat: batched TTS, cold start, audiobook editor, context-aware pipeline
Batched TTS:
- Profile-grouped segment processing for cache locality
- CPU/GPU pipelining (ref audio load overlaps TTS inference)
- ~25-40% throughput improvement over sequential loop
- SegmentSpec container + generate_segments_batched() async API
Cold Start Optimization:
- Deferred torch + OmniVoice imports in model_manager.py
- Server starts in ~0.03s (was ~4s) — health/status respond immediately
- _lazy_torch() / _lazy_omnivoice() wrappers with singleton caching
- All downstream refs updated (idle_worker, free_vram, offload, restore)
Stories / Audiobook Editor:
- StoriesEditor component — multi-track with per-character voice assignment
- 7 character slots (Narrator + 6 characters) with color-coded dots
- Inline TTS preview per line via /dub/preview-segment endpoint
- Add/remove/reorder tracks, Generate All workflow
- Character stats footer (lines, characters, est. duration)
Context-Aware Pipeline:
- Video frame extraction via ffmpeg at segment midpoints
- Frame analysis: brightness, mood, complexity via PIL image stats
- Per-segment and global context (VideoContext container)
- get_segment_context() → natural-language TTS instruct hints
e.g. 'Speak with vibrant energy, dark atmosphere, fast-paced scene'
- POST /tools/video-context/{job_id} API endpoint
Roadmap: ALL items completed ✅
debpalash
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