VoiceStudio/docs/install/linux.md

OmniVoice Studio — Install on Linux

This page is self-contained: follow it top to bottom and you'll end up with a working OmniVoice Studio install on a Debian / Ubuntu / Fedora / Arch host.

Prerequisites

Using the AppImage

• Linux x86_64 with a desktop session (X11 or Wayland) capable of running a Tauri / WebKitGTK app.
• ~10 GB free disk for the app, its Python environment, and model weights.
• Optional: an NVIDIA driver for CUDA GPU acceleration — the app runs CPU-only without one. For AMD GPUs see AMD GPU (ROCm). That's it — Python, FFmpeg/FFprobe, yt-dlp, and the model weights are bundled or bootstrapped by the app itself on first launch. No toolchain needed. (If no FFmpeg resolves anywhere, the app downloads its own checksummed static build in the background during setup; Settings → Audio tools shows exactly which binaries are in use and lets you override them or update yt-dlp.)

Building from source

Everything above, plus the toolchain:

• git — sudo apt install git (Debian/Ubuntu), sudo dnf install git (Fedora), or sudo pacman -S git (Arch).

• curl — usually preinstalled; used by the Bun and rustup install one-liners below.

• Python 3.11+ — typically sudo apt install python3.11 on Debian/Ubuntu, sudo dnf install python3.11 on Fedora, or already installed on Arch.

• Bun — curl -fsSL https://bun.sh/install | bash.

• Rust / Cargo — curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh or via your package manager (e.g., sudo apt install rustc cargo). If you use rustup, reopen the shell or source "$HOME/.cargo/env" before running bun run desktop-prod.

• GTK/WebKit deps for the Tauri shell:

  # Debian / Ubuntu
  sudo apt install libwebkit2gtk-4.1-dev libayatana-appindicator3-dev librsvg2-dev libssl-dev libxdo-dev build-essential
  
  # Fedora
  sudo dnf install webkit2gtk4.1-devel libappindicator-gtk3-devel librsvg2-devel openssl-devel
  
  # Arch
  sudo pacman -S --needed base-devel webkit2gtk-4.1 libayatana-appindicator librsvg openssl xdotool
  

• Optional: a Hugging Face token for diarization + the larger TTS engines (see docs/setup/huggingface-token.md).

Install (from source)

git clone https://github.com/debpalash/OmniVoice-Studio.git
cd OmniVoice-Studio
bun install
bun run desktop-prod

The first launch creates the Python venv via uv, syncs deps, and downloads model weights (~2.4 GB). Subsequent launches start in seconds.

Install (AppImage)

Download the latest AppImage from the Releases page, make it executable, and run:

chmod +x OmniVoice.Studio_*.AppImage
./OmniVoice.Studio_*.AppImage

No FUSE? Use --appimage-extract-and-run:

./OmniVoice.Studio_*.AppImage --appimage-extract-and-run

.deb package

Not currently published: .deb bundling is disabled in the release pipeline because of a tauri-cli bug (Failed to create control scripts) — see the comment in .github/workflows/release.yml for the tracking note. The AppImage above is the supported Linux install path until a tauri-cli version resolves it. apt install-able .debs shipped before v0.3 (see .deb ffprobe conflict below) if you're upgrading from one of those.

The desktop app uses these canonical paths (kept in sync with scripts/desktop-prod.sh by the docs-drift CI gate):

APP_ID="com.debpalash.omnivoice-studio"
APP_NAME="OmniVoice Studio"

AppImage white screen / EGL errors (Fedora 44, Ubuntu 24.04+, 26.04)

Two separate WebKitGTK rendering issues land the Tauri window as a fully-white frame with no UI. Which one you have depends on your WebKitGTK version (pkg-config --modversion webkit2gtk-4.1 prints it).

Modern WebKitGTK (2.48+ — Ubuntu 24.04 and newer, incl. 26.04): try this first. WebKit's DMA-BUF renderer fails against some GPU drivers; the terminal typically shows:

Could not create default EGL display: EGL_BAD_PARAMETER

Disable the DMA-BUF renderer before launching:

WEBKIT_DISABLE_DMABUF_RENDERER=1 ./OmniVoice.Studio_*.AppImage

WebKitGTK 2.44 / 2.46 (Fedora 44, Ubuntu 24.04 at release): a compositing-mode regression blanks the surface on first paint. Disable compositing mode instead:

WEBKIT_DISABLE_COMPOSITING_MODE=1 ./OmniVoice.Studio_*.AppImage

OmniVoice's AppRun launcher autodetects the broken 2.44/2.46 range and sets this second variable for you (shipped in v0.3+). The manual env-var path remains the documented fallback when running from a checked-out source tree.

Last resort — if neither variable alone helps, force software rendering (slower, but always paints):

WEBKIT_DISABLE_DMABUF_RENDERER=1 LIBGL_ALWAYS_SOFTWARE=1 ./OmniVoice.Studio_*.AppImage

Tracking issues: #62, #961.

.deb ffprobe conflict

Pre-v0.3 .deb packages installed ffprobe into /usr/bin/ffprobe and clobbered the system copy on some distros. v0.3+ relocates the bundled binary into /usr/lib/omnivoice-studio/bin/ffprobe and the postrm script runs dpkg --search to undo the old conflict on upgrade. If you upgraded from a pre-v0.3 .deb and ffprobe -version now reports the wrong binary, re-install the system package:

sudo apt install --reinstall ffmpeg

Restricted networks (China / Russia)

If uv times out fetching the python-build-standalone tarball or PyPI:

# Use a faster Python source mirror (China only — verify a current mirror)
export UV_PYTHON_INSTALL_MIRROR=https://ghproxy.com/https://github.com/astral-sh/python-build-standalone/releases/download

# Use a PyPI mirror
export UV_DEFAULT_INDEX=https://pypi.tuna.tsinghua.edu.cn/simple

# Or skip the download entirely if you have a compatible system Python
export UV_PYTHON_PREFERENCE=only-system

# Be tolerant of slow links
export UV_HTTP_TIMEOUT=120
export UV_HTTP_RETRIES=5

The Phase 3 install milestone (INST-07..11) ships an OS-level mirror cascade that picks these defaults automatically; for v0.3 set them by hand.

AMD GPU (ROCm)

ROCm support is Linux-only and opt-in. The default install ships the CUDA build of PyTorch (the pytorch-cuda index in pyproject.toml), so on an AMD-only machine torch.cuda.is_available() is False and OmniVoice runs on CPU until you opt into the ROCm variant.

Running in Docker or Podman instead? There's a prebuilt ROCm image — ghcr.io/debpalash/omnivoice-studio:rocm — with GPU acceleration out of the box; see docker.md. The rest of this section is about source/desktop installs. (On Windows there is no ROCm path at all — PyTorch publishes no Windows ROCm wheels; see windows.md.)

Three ways to opt in, in order of preference:

1. First-run setup screen (recommended). On Linux the setup screen's Compute card offers "AMD GPU (ROCm, Linux)" next to the default Auto. When OmniVoice detects an AMD GPU and the ROCm userspace (/opt/rocm present, or rocminfo on PATH), the ROCm option is pre-selected; with an AMD GPU but no ROCm runtime it stays offered-but-unselected — install ROCm first (or continue on CPU). Choosing ROCm makes the bootstrap reinstall torch/torchaudio from the ROCm wheel index (https://download.pytorch.org/whl/rocm6.4 by default) right after the dependency sync — matched to the app's pinned torch==2.8.0 (the rocm6.2 index only ever published up to torch 2.5.1, so it silently failed the reinstall and left the CPU-only CUDA build in place).

2. Environment variable (existing installs / headless). Set OMNIVOICE_TORCH_VARIANT=rocm before launching — the next bootstrap performs the same ROCm reinstall. OMNIVOICE_TORCH_INDEX=<url> overrides the wheel index when you need a different ROCm version — e.g. AMD publishes newer driver-matched builds (7.2.x) at repo.radeon.com as a --find-links page rather than a PyPI-style index:

uv pip install --reinstall torch==2.8.0 torchaudio==2.8.0 \
  --find-links https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2.4/

run that manually if you want a specific ROCm point release; the OMNIVOICE_TORCH_INDEX env var only accepts a PEP 503 index URL, not a find-links page. If the reinstall fails (network, unsupported card), OmniVoice keeps the default torch build and warns instead of breaking the install.

3. Manual wheel swap (fallback). Replace torch with the ROCm wheel after the first-run install populates the venv:

# From the project directory (source install), into OmniVoice's uv venv.
# Matches the app's torch==2.8.0 pin — a different ROCm point release
# (e.g. rocm6.2, rocm7.x) may not carry that exact torch build.
uv pip install --reinstall torch torchaudio \
  --index-url https://download.pytorch.org/whl/rocm6.4

Once a ROCm build of PyTorch is in the venv, detection is automatic — get_best_device() returns the GPU (ROCm-built PyTorch reports through torch.cuda.is_available()), and OmniVoice auto-sets HSA_OVERRIDE_GFX_VERSION for consumer cards whose GFX ID isn't in the official ROCm support matrix. Relaunch and the Settings → System panel should report the GPU device instead of cpu. Verify the wheel sees your card:

uv run python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"

Notes:

• ROCm is exercised far less than the default CUDA/MPS/CPU paths — it works, but expect rough edges on consumer cards and report what you hit.
• Unsupported GFX (e.g. some consumer RDNA cards): if it still won't run, set HSA_OVERRIDE_GFX_VERSION yourself (e.g. export HSA_OVERRIDE_GFX_VERSION=11.0.0) to the nearest supported architecture before launching.
• ZLUDA (CUDA-on-ROCm translation) can work but is unsupported here — prefer a native ROCm wheel.

Tracking issue: #124.

Hugging Face token (optional but recommended)

See docs/setup/huggingface-token.md.

Troubleshooting

Hit a wall? See docs/install/troubleshooting.md.