torch is compiled against numpy 1.x. numpy 2.x changed the ABI version
returned by PyArray_GetNDArrayCVersion() (0x01000009 → 0x02000000), so
torch's is_numpy_available() always returns False and torch.from_numpy()
raises RuntimeError. This causes TTS generation to fail with:
ValueError: Unable to create tensor, you should probably activate
padding with 'padding=True'
Two fixes:
1. Pin numpy<2.0 in requirements.txt so new builds bundle a compatible
numpy version. (The existing comment already flagged this intention
but the upper bound was never added.)
2. Add a PyInstaller runtime hook (pyi_rth_numpy_compat.py) that installs
a ctypes memmove fallback for torch.from_numpy() at startup. Runtime
hooks run after FrozenImporter is registered so frozen torch is
importable. The fallback catches RuntimeError from the C-level ABI
check and copies the numpy array into a new tensor via raw memory copy,
bypassing the check entirely. This is a belt-and-suspenders fix that
works regardless of the bundled numpy version.
Co-authored-by: aimaaaimaa <[email protected]>
Co-authored-by: Claude Sonnet 4.6 <[email protected]>
Add Kokoro-82M as a new TTS engine — 82M params, CPU realtime, 8 languages,
Apache 2.0. Unlike cloning engines, Kokoro uses pre-built voice styles, which
required a new profile type system to support non-cloning engines cleanly.
Kokoro engine:
- New kokoro_backend.py implementing TTSBackend protocol
- 50 built-in voices across en/es/fr/hi/it/pt/ja/zh
- KPipeline API with language-aware G2P routing via misaki
- PyInstaller bundling for misaki, language_tags, espeakng_loader, en_core_web_sm
Voice profile type system:
- New voice_type column: 'cloned' | 'preset' | 'designed' (future)
- Preset profiles store engine + voice ID instead of audio samples
- default_engine field on profiles — auto-selects engine on profile pick
- Create Voice dialog: toggle between 'Clone from audio' and 'Built-in voice'
- Edit dialog shows preset voice info instead of sample list for preset profiles
- Engine selector locks to preset engine when preset profile is selected
- Profile grid filters by engine — shows Kokoro voices when Kokoro selected
- Custom empty state when no preset profiles exist for selected engine
Bug fixes:
- Fix relative audio paths in DB causing 404s in production builds
- config.set_data_dir() now resolves to absolute paths
- Startup migration converts existing relative paths to absolute
Also updates PROJECT_STATUS.md and tts-engines.mdx developer guide.
Upgrade CUDA toolkit from 12.6 (cu126) to 12.8 (cu128) for proper
RTX 50-series (Blackwell) GPU support. Users with RTX 5070/5080/5090
were reporting CUDA detection failures with cu126.
Also fix the GPU Acceleration settings panel where the 'Switch to CPU
Backend' button was unreachable — it was inside a conditional block
that required !isCurrentlyCuda, making it impossible to switch back
to CPU once running on CUDA.
Closes#315
Replace --collect-submodules + --collect-data with --collect-all for
qwen_tts. The qwen_tts runtime expects physical .py source files
(e.g. modeling_qwen3_tts.py) under _MEIPASS, which only --collect-all
provides. This is the same pattern used for inflect/typeguard.
Fixes#212
Switch CUDA builds from PyInstaller --onefile to --onedir and split the
output into two separately versioned archives:
1. Server core (~200-400MB) — versioned with the app, redownloaded on
every app update
2. CUDA libs (~2GB) — versioned independently (cu126-v1), only
redownloaded when the CUDA toolkit or torch version changes
This eliminates the ~2.4GB full redownload on every version bump.
After initial setup, most app updates only need ~200-400MB.
Closes#297
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.
Integrates HumeAI's TADA (Text-Acoustic Dual Alignment) speech-language
model as a new TTS engine. TADA uses a novel 1:1 token-audio alignment
that produces coherent speech over long sequences (700s+).
Two model variants:
- tada-1b: English-only, ~4GB, built on Llama 3.2 1B
- tada-3b-ml: 10 languages, ~8GB, built on Llama 3.2 3B
Backend uses the Encoder for voice prompt encoding with caching, and
TadaForCausalLM with flow-matching diffusion for generation. Supports
bf16 inference on CUDA, forces CPU on macOS (MPS compatibility).
Installed with --no-deps due to torch>=2.7 pin conflict; descript-audio-codec
and torchaudio added as explicit sub-dependencies.
- Rename Server tab to Settings with horizontal sub-tab navigation (General, Generation, GPU, Logs, Changelog)
- All sub-tabs are proper routes under /settings/* with /server redirect for backwards compat
- General: connection settings, link cards (docs + discord), API reference card, app updates
- Generation: auto-chunking, crossfade, normalize, autoplay as SettingRow components
- GPU: info card with platform-aware icons (Apple logo for MPS), CUDA management, explainer text
- Logs: real-time server log viewer piped from Tauri sidecar via event system (Tauri-only)
- Changelog: parsed from CHANGELOG.md at build time via Vite virtual module plugin
- New reusable SettingRow/SettingSection components for consistent settings layout
- New Toggle (switch) UI component replacing checkboxes in settings
- Toast viewport now offsets when audio player is open
- Sidebar stays active on settings sub-routes (fuzzy matching)
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
- Use DB COUNT query instead of list length for take-N label to avoid
TOCTOU race between list_versions and create_version
- Add focus:bg-muted to SelectTrigger for keyboard focus visibility
Critical:
- Remove dead backend.utils.validation PyInstaller hidden import
- Fix story_items table rebuild to preserve track/trim/version columns
- Guard cache migration against same source/destination path
- Fix regeneration audio overwrite (use random uuid suffix per take)
Major:
- Engine selector: validate language on Qwen switch, clear stale modelSize
- Sync language validation regex between profile create and generate (22 langs)
- Guard CUDA download against duplicate concurrent requests
- Only set model_size for engines that support multiple sizes
- Fix 404 swallowed by generic except in history export
- Validate audio_path before FileResponse in export-audio
- Transcription: stream uploads in 1MB chunks, use robust cache check,
call complete_download() on Whisper download success
- Set clean version as default when effects chain validation fails
- Return explicit error when Windows port occupied by non-voicebox process
Split the 2,578-line main.py (90 routes) into 12 domain-specific router
modules under routes/. main.py is now a 45-line entry point.
New structure:
- app.py: FastAPI instance, CORS, startup/shutdown, safe_content_disposition
- routes/: health, profiles, channels, generations, history, transcription,
stories, effects, audio, models, tasks, cuda
- services/cuda.py: moved from cuda_download.py
Also includes Phase 5 database/ package (from parallel agent):
- database/__init__.py re-exports all symbols for backward compat
- database/models.py, session.py, migrations.py, seed.py
All 90 routes verified registered and app imports cleanly.
- collect-all piper_phonemize to bundle espeak-ng-data for LuxTTS phonemization
- Set ESPEAK_DATA_PATH in frozen builds so the C library finds bundled data
- collect-all perth to bundle pretrained watermark model for Chatterbox
- Add multiprocessing.freeze_support() to fix resource_tracker subprocess crash
Add the ability to download a CUDA-enabled backend binary (~2.4 GB) and
swap it in via a backend-only restart, solving the #1 user pain point
(19 open 'GPU not detected' issues caused by GitHub's 2 GB asset limit).
Backend:
- cuda_download.py: download from R2 (primary) or GitHub split-parts
(fallback), SHA-256 verification, atomic writes, progress via SSE
- 4 new endpoints: GET/POST/DELETE /backend/cuda-*, GET cuda-progress
- server.py: --version flag, auto-detect variant from binary name
- build_binary.py: --cuda flag for CUDA PyInstaller builds
- split_binary.py: split large binaries into <2GB GitHub Release assets
- CI workflow for building CUDA binary
Tauri:
- restart_server command (stop -> wait -> start)
- start_server prefers CUDA binary from {data_dir}/backends/ if present
- Version mismatch check: runs --version before launching CUDA binary
Frontend:
- GpuAcceleration component: download, progress, restart, switch, delete
- API client + types for CUDA status and management
- Platform lifecycle: restartServer() on Tauri/Web
- Aggressive 1s health polling during restart for fast reconnection
The distributed macOS aarch64 binary shipped without MLX acceleration despite
the model and backend code supporting it. Two root causes:
1. **OSError not caught in platform_detect.py**
PyInstaller bundles isolate the filesystem, so when MLX tries to load its
Metal shader libraries (.metallib) it raises OSError, not ImportError.
platform_detect.get_backend_type() only caught ImportError, causing a
silent fallback to PyTorch even on Apple Silicon hardware.
Fix: broaden the except clause to (ImportError, OSError, RuntimeError)
and import mlx.core instead of mlx (forces native lib loading eagerly).
2. **collect_data_files used instead of collect_all for MLX**
build_binary.py and voicebox-server.spec used --collect-data /
collect_data_files for mlx and mlx_audio. This copies Python source and
pure-Python data, but NOT native shared libraries (.dylib, .metallib).
Fix: switch to --collect-all / collect_all which captures binaries too,
then pass them to Analysis(binaries=...) in the spec.
Result: macOS Apple Silicon users now get MLX inference (~4-5x faster than
PyTorch CPU), matching the performance documented in the README.
- 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 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.
- Added exclusions for 'torch.utils.tensorboard', 'scipy', 'PIL', 'tkinter', 'unittest', and 'test' to reduce bundle size and improve build efficiency.
- Install llvm-dev on Ubuntu for llvmlite compilation
- Install LLVM via Homebrew on macOS and configure PATH
- Exclude matplotlib, IPython, notebook, pytest, tensorboard from bundle to reduce size
- Keep --onefile mode with module exclusions to avoid 4GB limit
