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feat: implement cross-mode robustness fixes (phases 1-8)
Complete implementation of all core robustness improvements: Phase 1-2: Infrastructure - Add ErrorReport TypedDict for structured error handling - Add ProgressCallback type alias and stage constants - Configure SSL globally using certifi - Update HuggingFace API to use 'token' parameter Phase 3: Reliable Progress Reporting (US1) - Standardize float-based (0.0-1.0) progress callbacks - Update all services to emit consistent progress - Fix dictionary key access issues across web/CLI - Support both modern and legacy progress formats Phase 4: Consistent Error Handling (US2) - Implement ErrorReport with error_type categorization - Add structured error handling to all services - Generate JSON error reports in web interface - Add rich color-coded error messages in CLI Phase 5: Environment Compatibility (US3) - Verify SSL context for torch.hub and HuggingFace models - Audit all model loading for correct API parameters - Remove invalid ProcessingJob parameters Phase 6: UI State Management …95e1515 -
refactor: rename Voice Profiler to Voice Tools throughout codebase
Update all references from 'Voice Profiler' to 'Voice Tools': - CLI command headers and descriptions - Web interface title and description - Benchmark script output - Package docstrings - remove .space and .spacesrc (not needed for HF Spaces deployment) - update benchmark.py docstring to Voice Tools This aligns with the project rename completed in previous commits.03cad88 -
feat: complete audio speaker separation feature with 3 workflows
Implement comprehensive audio processing system with speaker separation, extraction, and denoising capabilities. All 85 tasks completed across 6 phases with full test coverage and documentation. Features: - Speaker Separation: Auto-detect and separate multi-speaker audio - Speaker Extraction: Extract specific speaker using reference clip - Voice Denoising: Remove silence and background noise with VAD Implementation: - 3 core services with pyannote.audio and Silero VAD integration - AudioConcatenationUtility with equal power crossfade - CLI commands: separate, extract-speaker, denoise - Gradio web UI tabs for all workflows - Comprehensive error handling and validation - Memory optimization for 2+ hour files - Processing metadata logging with resource tracking Technical: - Models: pyannote/speaker-diarization-3.1, wespeaker embeddings, Silero VAD - Audio I/O: FFmpeg integration for M4A/WAV conversion - Quality: Equal power crossfade, zero-crossing detection - Performance: Processing ≤…cb39c05