- Updated `.gitignore` to include new build and generated content directories.
- Removed outdated Mintlify configuration files and documentation.
- Introduced new `MIGRATION.md` to outline the transition from Mintlify to Fumadocs.
- Added `mdx-components.tsx` for MDX component configuration and compatibility.
- Updated `package.json` and `next.config.mjs` for new dependencies and Next.js configuration.
- Created `source.config.ts` for content source configuration.
- Added OpenAPI specification in `openapi.json` for API documentation.
- Removed legacy files and adjusted project structure to align with Fumadocs conventions.
- Adds tooltips on hover for buttons of the generate box
- Replaces the message square icon with a sliders icon for the instruction mode toggle.
- Adds a tooltip to the instruction mode toggle button.
- Updates the placeholder text for the input field.
- Added server configurations for local and production environments in `main.py`.
- Removed outdated authentication and generation API documentation files.
- Updated documentation structure to reflect the removal of deprecated API endpoints.
- Adjusted links in the quick start and developer setup documentation to point to the new API reference.
- Enhanced global CSS styles for improved theming support.
- Created new directory structure for documentation under `/docs2`.
- Added `.gitignore` to exclude build artifacts and dependencies.
- Introduced `package.json`, `next.config.mjs`, and `postcss.config.mjs` for project configuration.
- Implemented MDX components in `mdx-components.tsx` for rendering documentation.
- Migrated existing documentation content and created new files for auto-updater and other features.
- Established compatibility layer for Mintlify components in `mintlify-compat.tsx`.
- Set up OpenAPI documentation in `openapi.json`.
- Updated README and migration guide to reflect new structure and usage instructions.
- Ensured all components and pages are ready for development and deployment with Fumadocs.
- 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.
- Rearranged imports for consistency across components.
- Enhanced the ModelManagement component to include detailed logging for download actions and errors.
- Updated the ModelProgress component to connect to SSE only when actively downloading, preventing connection exhaustion.
- Added a downloading state to the model status to indicate ongoing downloads.
- Improved toast notifications for model downloads with completion and error callbacks.
- Refactored the useModelDownloadToast hook to support new callbacks for download completion and error handling.
- Updated backend model status to reflect downloading state during active downloads.
- Added a step to install PyTorch with CUDA for Windows in the release workflow.
- Updated model references in backend/main.py to use openai/whisper models instead of mlx-community for the MLX backend.
- Bumped version numbers for @voicebox/app, @voicebox/landing, @voicebox/tauri, and @voicebox/web to 0.1.11.
- Added a new `useAutoUpdater` hook to check for app updates on startup and notify users with toast messages.
- Enhanced `UpdateStatus` component to handle version retrieval errors more gracefully.
- Updated dependencies in `package.json` for Tauri plugins to support new update functionalities.
- Added a default `type` prop set to 'button' in the CircleButton component to ensure proper button behavior.
- Enhanced the component's flexibility by allowing the type to be overridden through props.
- Implemented delete confirmation dialogs for both HistoryTable and SampleList components to enhance user experience and prevent accidental deletions.
- Added state management for handling the selected item to be deleted and the visibility of the delete dialog.
- Refactored delete handling functions to utilize the new dialog confirmation flow, improving code clarity and maintainability.
- Added `clear_profile_cache` function to manage cache files for specific profiles.
- Integrated cache clearing in `add_profile_sample`, `delete_profile`, and `delete_profile_sample` functions to ensure stale audio caches are invalidated after modifications.
- Enhanced `clear_voice_prompt_cache` to also delete combined audio files, improving overall cache management.
- Introduced a new directory for manual test scripts aimed at debugging and validating backend functionality.
- Added README.md detailing the purpose and usage of various test scripts, including tests for TTS generation, model downloads, and progress tracking.
- Included an __init__.py file to define the test suite structure and provide context for the tests.
- 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.
- Replaced direct Tauri API calls with a unified platform context across multiple components, enhancing code maintainability and readability.
- Removed the deprecated tauri.ts file, consolidating platform-related logic into the new PlatformContext.
- Updated components such as App, AudioPlayer, and ServerSettings to utilize the new platform context for lifecycle management and server interactions.
- Improved platform detection and handling for audio playback and system audio capture functionalities.
- Ensured consistent error handling and user feedback across the application when interacting with platform-specific features.
- Modified the file extension filters in useHistory.ts and useProfiles.ts to only allow 'zip' files, removing 'voicebox.zip' for a more streamlined export process.
- Added user-selected read-write permission in Entitlements.plist to enhance file handling capabilities.
- 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.
- Updated MLX_AUDIO.md to reflect validated status and included detailed validation results, model mapping, and API usage examples.
- Added a demo script (demo.py) for testing audio generation speed and functionality.
- Introduced a test script (test_tts.py) to validate MLX audio model loading and generation, ensuring robust testing for future developments.
- Created a .gitignore file in the mlx-test directory to exclude unnecessary files from version control.
- Introduced a new backend for MLX audio to enable GPU acceleration on macOS Apple Silicon, improving performance and user experience.
- Implemented platform detection to switch between MLX and PyTorch backends based on the runtime environment.
- Added new streaming capabilities for TTS and STT, enhancing real-time audio generation.
- Updated API endpoints and frontend components to support new features while maintaining backward compatibility.
- Created documentation for backend integration and performance comparisons.
Jamie Pine