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
- Fix is_nvidia_file() to match NVIDIA DLLs in _internal/torch/lib/
(PyInstaller 6.18 + torch 2.10 no longer uses nvidia/ subdirectories)
- Remove deprecated split_binary.py (both archives are under 2GB)
- Update torch_compat range to >=2.6.0,<2.11.0
- Update build docs for new dual-archive packaging flow
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
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.
cu121 only ships kernels up to SM 9.0 (Ada Lovelace). RTX 50-series
(Blackwell, SM 12.0) and RTX 6000 Pro need cu126 which includes SM 12.0
support while remaining backward compatible with older GPUs.
Closes#289
- Backfill CHANGELOG.md from all 17 GitHub releases (was stale at v0.1.0)
- Add draft-release-notes and release-bump agent skills
- Extract release notes from CHANGELOG.md in release CI instead of hardcoded placeholder
- Remove stale PATCH_NOTES.md, mlx-test/, move PROJECT_STATUS to docs/notes
- Reorganize API reference docs from unknown/ to named groups
- Update openapi.json
The previous ordering let requirements.txt pull CUDA-enabled torch
with all nvidia deps first, then the CPU override only swapped the
torch wheel while leaving ~3GB of nvidia packages installed.
- Add openPath and pickDirectory to PlatformFilesystem interface
- Remove direct @tauri-apps/plugin-shell and plugin-dialog imports from app
- Merge build-cuda.yml into release.yml as a parallel job
Cherry-picked and adapted from PR #89 and #214:
- Linux audio capture via PulseAudio/PipeWire monitor sources (cpal)
- AMD ROCm GPU support: HSA_OVERRIDE_GFX_VERSION env var, ROCm detection
- Whisper Turbo model (openai/whisper-large-v3-turbo) in all endpoints
- Cleaner Whisper language handling via generate_kwargs
- tauri::async_runtime::spawn fix to prevent panic on app shutdown
- Enable Linux (ubuntu-22.04) in release CI matrix
- Use YAML block scalar for inline run with colons (build-cuda.yml)
- Explicitly set VOICEBOX_BACKEND_VARIANT=cpu instead of setdefault (server.py)
- Use Path.replace() for atomic move on all platforms (cuda_download.py)
- Log actual exception in checksum fetch warning (cuda_download.py)
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
- 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.
- 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.
- Introduced steps to install the Apple API key and codesigning certificate for macOS platforms.
- Enhanced the release workflow to support secure handling of Apple credentials for code signing.
- Updated environment variables to include necessary Apple signing information for Tauri builds.
- Added a new UpdateNotification component to inform users about available updates and facilitate installation.
- Integrated useAutoUpdater hook for managing update checks and installations.
- Updated package.json with new build and generate scripts for release preparation and key generation.
- Enhanced tauri configuration to support autoupdater with signing keys and endpoints.
- Created scripts for preparing signed releases and updating icons, ensuring a streamlined workflow for asset management.
- Added detailed documentation for autoupdater setup and icon update workflow.
llvmlite only supports LLVM up to version 20, but brew install llvm
installs version 21. Update macOS runners to install llvm@20 specifically.
Co-Authored-By: Claude Sonnet 4.5 (1M context) <[email protected]>
- 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
James Pine