Bundler still hangs on linuxdeploy download mid-rpm even with
createUpdaterArtifacts: false. Drop the ubuntu-22.04 matrix entry
until we figure out a reliable path; the ubuntu-specific setup steps
stay so we can re-enable by adding the matrix row back.
mlx-audio's STT path imports miniaudio, but we install mlx-audio
--no-deps to dodge its transformers>=5.x pin. Nothing else pulls
miniaudio transitively, so fresh Apple Silicon installs fail to
transcribe with ModuleNotFoundError: miniaudio. Listed explicitly
and updated the stale comments in requirements-mlx.txt and
release.yml that claimed it came from other engines.
Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
* feat(ci): re-enable Linux release builds (CPU, deb+rpm)
Linux shipped briefly in March 2026 (b580189..103e98b) then was removed
with the message "github runners suck." The post-mortem: standard
ubuntu-22.04 hit disk-pressure during pip+PyInstaller, and a namespace
custom-runner attempt proved flaky. Nothing in the app itself was the
problem — the NVIDIA-package exclusions in build_binary.py (~3 GB
shaved from CPU builds), the CPU-only torch install order, the
PulseAudio/PipeWire audio capture code, and the Tauri deb/rpm bundle
targets all still work.
This restores ubuntu-22.04 to the release matrix as a CPU-only Linux
build with a disk-space cleanup step (jlumbroso/free-disk-space) to
address the root cause of the March failures. Ships .deb + .rpm only
— AppImage was explicitly dropped in e18757b due to glibc portability
issues, keeping that decision.
CUDA-for-Linux is intentionally deferred to a follow-up PR: the
GpuAcceleration.tsx frontend hard-codes CUDA download as Tauri-only
without a Linux branch, and AMD users already get ROCm acceleration
for free on a stock CPU-torch install (backend/app.py:148-160). The
NVIDIA-on-Linux case is the only remaining gap and is non-blocking
for a v1 Linux ship.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* chore(release): bump version 0.4.1 → 0.4.2
tauri-action uses tauri.conf.json's version to name the release, so
pushing v0.4.2 as a tag alone was insufficient — the workflow was still
trying to publish to v0.4.1 (immutable) and failing. Bumps all workspace
package.json files, Cargo.toml, Cargo.lock, and tauri.conf.json.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* ci(release): add Linux bundle-step watchdog + verbose cargo logging
The first v0.4.2 attempt hung inside tauri-action at the bundling stage
on ubuntu-22.04 for ~28 min with no output. Same failure mode that drove
the March 2026 removal (commit 103e98b). Without visibility we're
guessing — probable causes include linuxdeploy/AppImage download stalls
(despite --bundles deb,rpm), cargo link on a cold cache, or disk
pressure during the final link step.
- timeout-minutes: 30 on tauri-action for Ubuntu (45 min elsewhere) —
fail fast with logs instead of waiting out the 6hr job timeout.
- --verbose added to the Linux build args so cargo streams progress.
- CARGO_TERM_VERBOSE + RUST_BACKTRACE=1 exported on tauri-action.
- New 'Disk / environment snapshot' step dumps df/free/tool versions
right before the tauri step so we can correlate with any later
OOM/ENOSPC failure.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* ci(release): disable Tauri updater artifacts on Linux (stops linuxdeploy hang)
Round-2 diagnosis from the v0.4.2 attempt: cargo finished in 3m 55s,
.deb and .rpm were bundled within 22s, then the step hung silently for
25 min until our 30-min watchdog killed it — no further log lines.
tauri.conf.json has `createUpdaterArtifacts: "v1Compatible"`. On Linux
the v1-compatible updater path wants a .AppImage.tar.gz, which means
downloading linuxdeploy-x86_64.AppImage from GitHub at build time.
That download is the silent blocker — same signature as the March 2026
"github runners suck" removal (commit 103e98b).
Fix: pass `--config {"bundle":{"createUpdaterArtifacts":false}}` on the
Linux build only. Mac/Windows continue to produce signed updater
artifacts as before. Linux users update via apt/dnf; Tauri in-app
auto-update for Linux can come later (and would require shipping
AppImage alongside deb/rpm).
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
* ci(release): pin free-disk-space action + stop removing LLVM
Review feedback on the Linux release workflow:
1. `jlumbroso/free-disk-space@main` was an unpinned ref in a job that
runs with `contents: write` and handles signing secrets. Pin to the
v1.3.1 commit SHA so a force-push or repo compromise can't inject
arbitrary code into our release flow.
2. `large-packages: true` runs `apt-get remove '^llvm-.*'`, wiping LLVM
just before the next step installs `llvm-dev`. That wastes CI time
and risks cascade-removal of reverse deps that won't be pulled back
in by `llvm-dev` alone. The remaining toggles (android, dotnet,
haskell, swap-storage) already clear ~20 GB, which is enough
headroom for the Python + torch + PyInstaller build.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
The previous fix (#481) capped transformers at 4.57.6 in requirements-mlx.txt,
but pip's clean resolver in CI can't satisfy that alongside mlx-audio>=0.3.1
(declares `transformers==5.0.0rc3` or `>=5.0.0`) — it backtracks through every
transformers and tokenizers version and exits with `ResolutionImpossible`.
The dev install worked only because mlx-audio 0.4.1 was already present, so
pip never tried to re-resolve.
mlx-audio 0.4.1 + mlx-lm 0.31.1 both declare transformers>=5.x but the API
surface we actually use works fine on 4.57.x in practice (verified across all
engines in dev). Install both --no-deps to bypass the resolver; transitive
runtime deps (huggingface_hub, librosa, numpy, numba, pyloudnorm, etc.) are
already pulled in by requirements.txt.
Co-authored-by: Claude Opus 4.7 (1M context) <[email protected]>
Set TORCH_CUDA_ARCH_LIST in the CUDA build step to include 12.0+PTX
for forward compatibility with Blackwell GPUs (RTX 5070 Ti, 5080, etc).
Pre-built PyTorch cu128 wheels only ship native kernels for sm_80/86/89/90.
Without this, Blackwell GPU users get "no kernel image is available for
execution on the device" at runtime.
Fixes#386
Related: #395, #396, #399, #400
Co-authored-by: Matt Van Horn <[email protected]>
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.
- Enhanced the formatDate function to handle date strings without timezone information by treating them as UTC.
- Updated binary assets including screenshots and application images for improved visual representation.
- 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.
- Updated the README to feature a new layout with centered headings and images for better visual appeal.
- Enhanced the introduction to clearly define Voicebox as an open-source voice synthesis studio.
- Added sections for API usage, tech stack, and roadmap to provide comprehensive information about the project.
- Removed outdated content and streamlined the structure for easier navigation and understanding.
- 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
Jamie Pine