The /generate endpoint created the voice prompt before loading the
user's requested model size. Since create_voice_prompt() internally
calls load_model_async(None), it fell back to the hardcoded default
of "1.7B", causing the 1.7B model to be downloaded even when the
user explicitly selected 0.6B.
This reorders the operations so the requested model is loaded first,
ensuring create_voice_prompt() and generate() use the correct model.
Co-Authored-By: Claude Opus 4.6 <[email protected]>
- 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.
- 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.
- Removed unnecessary isDownloading variable and related logic.
- Consolidated model download state reset to the finally block for improved clarity and reliability.
- Enhanced error handling by ensuring model download state is reset in case of failure.
