* fix(setup): install mlx-lm and mlx-audio in setup-python on Apple Silicon
The dev setup installed requirements-mlx.txt but not mlx-audio/mlx-lm
themselves, so POST /transcribe failed on a fresh Apple Silicon setup
with "No module named 'mlx_audio'" (then "No module named 'mlx_lm'").
The release workflow already installs both with --no-deps (they declare
transformers>=5.x, conflicting with our <=4.57.x cap); mirror that in
the setup-python recipe with the same pins.
Co-Authored-By: Claude Fable 5 <[email protected]>
* test: add MLX smoke test for the --no-deps mlx-audio/mlx-lm install
mlx-audio and mlx-lm are installed --no-deps, so a missing transitive
dependency only surfaces at import time. Add a pytest-discoverable
smoke test (skipped off Apple Silicon) covering the exact entry points
the backend uses: mlx_audio.tts.load, mlx_audio.stt.load (which also
exercises the miniaudio dep from issue #505), mlx_lm.load/generate,
and a basic mlx.core op.
Co-Authored-By: Claude Fable 5 <[email protected]>
---------
Co-authored-by: Claude Fable 5 <[email protected]>
The Windows `build-server` just recipe only built and copied the
voicebox-server sidecar, omitting the voicebox-mcp stdio shim that the
Unix scripts/build-server.sh builds via `build_binary.py --shim`.
As a result `just build` on Windows produced only one sidecar and the
Tauri bundle step failed with:
resource path `binaries\voicebox-mcp-<triple>.exe` doesn't exist
Build and copy the shim sidecar after the server, mirroring
build-server.sh. Hoist the triple/binaries-dir setup ahead of both
builds so the shim step reuses them.
Co-authored-by: namu.shin <[email protected]>
Co-authored-by: Claude Opus 4.8 (1M context) <[email protected]>
* Fix ROCm setup for Linux AMD GPUs
- Ensure Docker ROCm builds resolve PyTorch packages from the ROCm wheel index so later dependency installs do not replace them with CUDA wheels.
- Move ROCm device group handling to a runtime entrypoint that joins the groups owning /dev/kfd and /dev/dri, avoiding distro-specific render/video GID defaults.
- Leave HSA_OVERRIDE_GFX_VERSION unset by default in the ROCm compose overlay so newer RDNA GPUs can use native ROCm detection.
- Add Linux GPU detection to the Unix setup recipe so AMD systems install ROCm torch wheels and NVIDIA systems install CUDA wheels before backend dependencies.
* docs(changelog): add Linux ROCm setup entry
* fix(setup): pin ROCm torch wheels and prefer NVIDIA over amdgpu
- Install torch/torchaudio from the ROCm index only, before the pooled
requirements install, so a plain PyPI (CUDA) wheel can't outrank +rocm
- Detect NVIDIA before AMD and gate ROCm on /dev/kfd, so hybrid
AMD+NVIDIA hosts get CUDA instead of ROCm
Address CodeRabbit review feedback and user-reported GPU acceleration failure:
- Use shared manual_seed() in chatterbox, chatterbox_turbo, and luxtts
backends so XPU (and future accelerators) get proper device seeding
- Add XPU branch to _get_gpu_status() so startup log reports Intel Arc
GPUs instead of 'None (CPU only)'
- Add XPU VRAM reporting and correct backend_variant fallback in the
/health endpoint
- Switch justfile GPU detection from Get-WmiObject to Get-CimInstance,
simplify the Arc regex to match 'Arc' (not 'Intel.*Arc'), log
detected GPUs, and print manual install instructions on miss
Resolves the root cause where IPEX was silently not installed due to
WMI detection failure, causing CPU-only fallback on Intel Arc systems.
Auto-detect Intel Arc GPUs during Windows setup and install PyTorch
with XPU support + intel-extension-for-pytorch. Enable allow_xpu=True
on all TTS backends (Chatterbox, Chatterbox Turbo, Hume TADA, LuxTTS)
that previously only supported CUDA. Add shared empty_device_cache()
and manual_seed() helpers in base.py to handle XPU memory management
and reproducible seeding alongside CUDA.
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
- Wrap download/verify/extract in try/finally so .download-*.tmp is
always deleted, even on mid-download or extraction failures
- Fix justfile build-server-cuda to use sh.voicebox.app (production path)
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.
chatterbox-tts 0.1.6 pins numpy<1.26 and torch==2.6 which are
incompatible with Python 3.12+. Install with --no-deps and list
its sub-dependencies explicitly in requirements.txt.
Also removes HFProgressTracker from chatterbox backend to avoid
'generator didn't stop after throw()' errors from tqdm patching.
Adds 'just' as the recommended dev tool: 'just setup' for one-time
install, 'just dev' to run backend + frontend in one terminal.
Updates CONTRIBUTING.md to document just as the primary setup method.
