- Filter out empty audio segments before processing to prevent dimension errors
- Normalize tensor dimensions before crossfade operations
- Handle mixed 1D/2D tensors properly in both simple and crossfade concatenation
- Fixes 'too many indices for tensor of dimension 1' error
- Prevents empty segments from causing 0.00s audio generation issues
- Handle mixed 1D/2D tensor dimensions properly in concatenation mode
- Fix IndexError when accessing dimension 1 on 1D tensors
- Add proper tensor normalization for consistent concatenation
- Support both mono (1D) and stereo (2D) audio segments in same sequence
Fix SRT overlapping functionality system-wide
Technical implementation:
- Add unified timing mode routing in AudioAssemblyEngine with assemble_by_timing_mode()
- Fix ChatterBox Official 23-Lang processor to use correct assembly method for pad_with_silence mode
- Now properly uses assemble_with_overlaps() instead of assemble_smart_natural() for overlaps
- All engines (ChatterBox, F5-TTS, Higgs Audio, VibeVoice) now handle overlaps consistently
- Resolves audio being cut off when SRT segments overlap in timing
- Create utils/timing/overlap_detection.py with SRTOverlapHandler class
- Centralize overlap detection and smart_natural fallback logic
- Update ChatterBox, F5-TTS, and Higgs Audio to use modular utility
- Remove duplicate _detect_overlaps methods from all engines
- Ensure consistent overlap handling behavior across engines
Now all engines use the same logic for detecting overlapping SRT segments
and automatically falling back from smart_natural to pad_with_silence mode.
- Enhanced geometric feature extraction for comprehensive mouth shape analysis
- Implemented vowel classification (A, E, I, O, U) based on lip geometry
- Added new SRT format option to display detected viseme sequences
- New toggle for enabling/disabling viseme detection
- Caching system supports viseme detection state
- Viseme sequences grouped for readability (A3-E2-I-O4)
Technical implementation:
- Extract 8 geometric features: lip width/height, mouth area, roundedness, curvature
- Rule-based vowel classification with confidence scoring
- Frame-by-frame viseme detection integrated with existing movement analysis
- ~20% performance impact when enabled, fully cached for repeated analysis
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