- Updated caching methods in MLX, PyTorch, and backend to ensure models are fully downloaded before being marked as cached.
- Improved progress tracking to filter out non-download progress and provide accurate feedback during model downloads.
- Enhanced HFProgressTracker to skip non-byte progress bars and ensure meaningful progress reporting.
- Refactored progress initialization to provide immediate feedback while fetching metadata from HuggingFace.
- Added error handling and logging for better debugging during cache checks and download processes.
- Rearranged imports for consistency in useModelDownloadToast hook.
- Improved logging in useModelDownloadToast for better debugging during download events.
- Updated progress calculation to handle cases where progress exceeds 100%.
- Enhanced toast notifications to reflect download completion and error states.
- Introduced throttling in ProgressManager to optimize SSE updates and prevent overwhelming clients.
- Added new test scripts for monitoring SSE events during model downloads, ensuring accurate progress reporting.
- Introduced methods to check if models are cached locally in MLX and PyTorch backends.
- Enhanced progress tracking during model loading to filter out non-download progress when models are cached.
- Updated HFProgressTracker to conditionally report progress based on download status.
- Added test scripts for monitoring SSE events during model downloads and verifying progress tracking functionality.
- Improved overall error handling and logging for better debugging during model download processes.
- Updated HistoryTable to implement infinite scrolling for loading history items dynamically.
- Introduced state management for accumulated history and total item count.
- Added Intersection Observer for triggering additional data fetches when scrolling.
- Implemented cache clearing functionality in the backend to manage voice prompt caches effectively.
- Improved loading indicators and user feedback for data fetching states.
- Refactored code for better readability and maintainability.
- Updated README.md to highlight MLX backend performance improvements on Mac with Metal acceleration.
- Refined ProfileCard and ProfileForm components by optimizing imports and improving error handling for avatar uploads.
- Adjusted landing page content to better describe features, including a new multi-voice narrative editor and performance optimizations for different platforms.
- Bumped version to 0.1.11 in Cargo.lock to reflect recent changes.
- Updated hidden imports in build_binary.py to replace 'mlx_audio.asr' with 'mlx_audio.stt'.
- Enhanced model loading logic in MLX and PyTorch backends to ensure proper progress tracking during model downloads.
- Improved error handling and context management for progress tracking in both backends.
- Bumped version to 0.1.10 in Cargo.lock to reflect recent changes.
- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Implemented platform detection to dynamically select between MLX and PyTorch based on the runtime environment.
- Updated build process to include MLX-specific dependencies and configurations for macOS.
- Refactored backend code to improve model loading and inference logic, accommodating backend-specific requirements.
- Enhanced documentation to clarify backend selection and performance benefits for different platforms.
- Streamlined installation instructions and troubleshooting guidance for MLX-related issues.
- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Updated release workflow to include MLX-specific dependencies and configurations for macOS platforms.
- Refactored backend code to dynamically select between MLX and PyTorch based on the runtime environment.
- Enhanced model loading and inference logic to accommodate backend-specific requirements, including updated model IDs and hidden imports.
- Improved health check and model status reporting to reflect the active backend type.
- Streamlined caching mechanisms to support both backend types, ensuring compatibility and performance.
Jamie Pine