Uploaded/recorded voice samples were rejected outright whenever the peak
exceeded 0.99 ("Audio is clipping (reduce input gain)"). That wasn't
actionable: a recording in the app has no pre-gain control, and an
already-captured file can't be re-taken by the user. The Settings
"Normalize audio" toggle only affects generated TTS output, so users who
enabled it expecting it to help with sample uploads were still blocked.
Replace the hard reject with a small, always-on preprocess step that
runs right after load:
- DC-offset removal
- Conservative edge-silence trim (top_db=30) with 100 ms padding kept
- Peak cap at 0.95 if the input peak exceeds that
Duration and RMS checks now run on the preprocessed waveform, so
samples that were previously rejected for being "hot" are accepted and
stored with safe headroom. True in-waveform clipping artifacts still
can't be repaired — peak scaling only prevents downstream re-clipping
during multi-sample combination and TTS inference.
Adds a unit-test file (previously none existed for audio.py).
Fixes#456.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Two small safety improvements:
1. Voice prompt cache (cache.py): add weights_only=True to torch.load()
so cached .prompt files are loaded using the safe unpickler instead of
the unrestricted pickle deserializer. This follows the PyTorch 2.6+
best practice of opting in to safe loading for all torch.load() calls.
2. SPA catch-all (app.py): replace str.startswith() path guard with
Path.is_relative_to(). The string prefix check passes for sibling
paths like /app/frontend_evil/ that share the /app/frontend prefix.
is_relative_to() correctly tests directory containment.
Qwen TTS and Whisper Base make network calls to HuggingFace even when
model weights are fully cached locally, because from_pretrained()
defaults to local_files_only=False. This causes failures for offline
users.
Add a reusable force_offline_if_cached() context manager that sets
HF_HUB_OFFLINE=1 during model loading when is_model_cached() is True.
Applied to all four affected load paths:
- PyTorchTTSBackend (Qwen TTS)
- PyTorchSTTBackend (Whisper)
- MLXTTSBackend (refactored from inline implementation)
- MLXSTTBackend (previously unprotected)
Closes#82
Remove @torch.jit.script from the DAC shim's snake() function —
TorchScript calls inspect.getsource() which fails in PyInstaller
binaries (no .py source files).
Update all user-facing docs: 4 → 5 TTS engines, add TADA row to
every engine comparison table, mark TADA as Shipped in the upcoming
engines list, update architecture diagrams and tech stack tables.
The real descript-audio-codec package pulls in descript-audiotools,
which transitively requires onnx, tensorboard, protobuf, matplotlib,
pystoi, and other heavy dependencies. onnx fails to build from source
on macOS due to CMake version incompatibility.
TADA only uses Snake1d (a 7-line PyTorch module) from DAC. This commit
adds a shim in backend/utils/dac_shim.py that registers fake dac.*
modules in sys.modules with just the Snake1d class, completely
eliminating the DAC/audiotools dependency chain.
Move audio validation and saving to thread pool so librosa/ffmpeg decoding
doesn't block the async event loop. Combine validate + load into a single
pass to avoid decoding the file twice. Add 50 MB upload limit and chunked
reads to prevent unbounded memory allocation.
Closes#278
Wrap SSE generators with BrokenPipeError/ConnectionResetError handling
so client disconnects during generation status polling, download progress,
or audio streaming don't produce unhandled Errno 32 errors.
Closes#248
Replace verbose startup messages with a clean summary:
- App version, Python version, OS/arch
- Database path (fix None display), data directory
- Profile and generation counts
- Backend, GPU, model cache path
- Clean up stale loading_model status on startup
- Remove noisy progress manager log line
Backend:
- Generation service reports 'loading_model' status only when model
is not yet in memory, then 'generating' once inference starts
- Migrate hf_offline_patch.py from print() to logging module
- Update ADDING_TTS_ENGINES.md for post-refactor file paths
Frontend:
- HistoryTable shows 'Loading model...' vs 'Generating...' based on step
- FloatingGenerateBox: replace instruct toggle + inline effects editor
with an effects preset dropdown (third dropdown after language and engine)
- Instruct UI removed for now (form field preserved for future models)
- Remove focus ring from Select component globally
- Force tqdm disable=False in TrackedTqdm so byte progress works in prod
(huggingface_hub disables tqdm based on logger level, which prevents
self.n from updating — our progress tracking needs the counter even
though we don't render to terminal)
- Harden devnull redirect to test writability, not just None check
- Add full traceback logging to all backend error handlers
- Add chatterbox/luxtts/zipvoice hidden imports and metadata to spec
Adds a full effects pipeline powered by Spotify's pedalboard library,
enabling users to apply professional DSP effects (flanger, reverb, delay,
compressor, pitch shift, filters, gain) to generated audio.
Key features:
- Effects chain editor with drag-and-drop reordering (dnd-kit)
- Generation versions: clean copy always saved, processed versions created on top
- Built-in presets (Robotic, Radio, Echo Chamber, Deep Voice) + custom user presets
- Per-profile default effects chain (auto-applied to new generations)
- Per-generation effects override from the generation form
- Apply effects to existing generations from history (creates new version)
- Ephemeral preview endpoint for auditioning effects without persisting
- Dedicated Effects sidebar tab with preset management and live preview
- Version switcher in history cards with expandable panel
- Backward-compatible: existing generations backfilled as clean versions
Text exceeding max_chunk_chars (default 800) is automatically split at
sentence boundaries, generated per-chunk, and concatenated with a 50ms
crossfade. Works with all engines (Qwen, LuxTTS, Chatterbox, Turbo).
- Abbreviation-aware sentence splitter (Dr., Mr., e.g., decimals)
- CJK sentence-ending punctuation support
- Paralinguistic tag preservation ([laugh], [cough], etc.)
- Per-chunk seed variation to avoid correlated RNG artefacts
- Per-chunk Chatterbox trim (catches hallucination at each boundary)
- max_chunk_chars exposed as per-request param on GenerationRequest
- Text max_length raised to 50,000 characters
Closes#99
soundfile cannot infer format from .tmp extension, causing all
generations to fail with 'No format specified and unable to get
format from file extension'
- save_audio() now writes to .tmp then os.replace() for atomic writes
- /generate endpoint catches OSError with specific messages for ENOENT, EACCES, ENOSPC, and BrokenPipeError
- New /health/filesystem endpoint checks directory existence, write permissions, and disk space
- New DirectoryCheck and FilesystemHealthResponse models
Cherry-picked and expanded from #178 (@Vaibhavee89)
- New ChatterboxTTSBackend wrapping ChatterboxMultilingualTTS (ResembleAI/chatterbox)
- Supports 23 languages including Hebrew, forces CPU on macOS (MPS issue)
- Monkey-patches torch.load for CPU loading, forces eager attention for compatibility
- trim_tts_output utility cuts trailing silence/hallucination from Chatterbox output
- Full engine integration: /generate, /generate/stream, model status/download/delete
- Hebrew (he) added to supported languages in frontend and backend validation
- Single flat model dropdown extended with Chatterbox option in both generation UIs
- ModelManagement UI groups LuxTTS and Chatterbox under 'Other Voice Models' section
- Fix transcribe_audio to use whisper-large-v3 mapping (not openai/whisper-large)
- Propagate error field in progress-only fallback path for get_active_tasks
- Use removed return value in cancel endpoint to vary response message
- Add error rollback to handleCancel with toast on failure
- Make isCancelling per-model instead of global
- Fix inverted chevron icons in Problems panel
- Move all clears under lock in clear_all_tasks
- Simplify cancel_download to use dict.pop()
- Add cancel (X) button on downloading and errored model items
- Add collapsible Problems panel (VS Code-style) showing error details
- Add "Clear All" button to reset all stale download/error state
- Add POST /models/download/cancel endpoint to dismiss individual downloads
- Add POST /tasks/clear endpoint to reset all task and progress state
- Include error messages in /tasks/active response for visibility
- Capture SSE error messages client-side for immediate display
- Fix whisper-large using wrong HF repo (openai/whisper-large → openai/whisper-large-v3)
- Fix Whisper HF repo mapping in both PyTorch and MLX backends
- Shorten error toast to point users to Problems panel instead of wall of text
Implements offline mode patch for API stability issues:
- Add hf_offline_patch.py to monkey-patch huggingface_hub
- Force cache-only lookups before mlx_audio imports
- Create symlink from original Qwen repo to MLX community version
when only MLX version is cached
This fixes:
- Issue #150: Internet required even with cached models
- Issue #151: API crashes when HF network fails
The patch ensures that if models are locally cached, no network
requests are made to HuggingFace during speech generation.
- 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.
- 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 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 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.
- 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.
- Added functionality to upload, delete, and retrieve avatar images for voice profiles.
- Introduced new API endpoints for avatar management, including upload and delete operations.
- Enhanced profile forms and components to support avatar image handling, including previews and error handling.
- Updated database schema to include avatar_path for profiles and added necessary migrations.
- Implemented image validation and processing utilities to ensure proper avatar uploads.
- Added thread-safe mechanisms to the ProgressManager for handling model download progress updates.
- Introduced a main event loop setter to ensure safe operations from background threads.
- Improved listener notification to handle updates in a thread-safe manner.
- Updated methods to ensure thread safety when accessing progress data.
- Introduced a flag to track download status in useGenerationForm, ensuring proper UI updates during model downloads.
- Updated ProgressManager to only send initial progress updates if the model is actively downloading or extracting, preventing outdated status messages from being sent.
- Improved error handling and logging for better visibility into model download processes.
- Updated CONTRIBUTING.md to include instructions for building with a local Qwen3-TTS development version, facilitating easier testing and development.
- Refactored FloatingGenerateBox component to streamline the rendering of text and instruct fields, improving code readability and maintainability.
- Added functionality to handle auto-resizing of text areas based on content changes, enhancing user experience.
- Improved event handling for keyboard interactions in StoryTrackEditor, allowing for play/pause functionality with the spacebar.
- Introduced a MiniSamplePlayer component in SampleList for better audio playback control, including play, pause, and seek features.
- Implemented sample update functionality in the backend, allowing users to edit reference text for audio samples, with appropriate error handling and user feedback.
