VoiceStudio/docs/install/windows.md

OmniVoice Studio — Install on Windows

This page is self-contained: follow it top to bottom and you'll end up with a working OmniVoice Studio install on Windows 10 / 11 (x64).

Prerequisites

Using the MSI installer

• Windows 10 (21H2 or newer) or Windows 11, x64.
• ~10 GB free disk for the app, its Python environment, and model weights.
• Optional: an NVIDIA GPU + driver for CUDA acceleration — see GPU support on Windows. AMD GPUs run CPU-only on Windows.

That's it — Python, FFmpeg, and the model weights are bundled or bootstrapped by the app itself on first launch. No toolchain needed.

Building from source

Everything above, plus the toolchain:

• Git for Windows — winget install --id Git.Git -e. Needed for git clone, and it includes Git Bash, which bun run desktop-prod uses to run its build-and-launch script. Without it, desktop-prod stops with an error telling you to install it.
• Python 3.11+ — winget install Python.Python.3.11 (or download from python.org).
• Microsoft C++ Build Tools — required by some PyPI source distributions (pyannote.audio, occasional torch wheel rebuild). Install via the Visual Studio 2022 Build Tools with the "Desktop development with C++" workload checked.
• Bun — powershell -c "irm bun.sh/install.ps1 | iex".
• FFmpeg — winget install Gyan.FFmpeg.
• Rust / Cargo — winget install Rust.Rustup or download rustup-init.exe from rustup.rs. After installing Rustup, close and reopen PowerShell before running bun run desktop-prod.

GPU support on Windows

GPU acceleration on Windows is NVIDIA/CUDA-only. The Windows install ships the CUDA build of PyTorch; with an NVIDIA GPU and a regular NVIDIA driver it's picked up automatically (no CUDA Toolkit install needed).

AMD GPUs — including Ryzen / Ryzen AI integrated Radeon graphics — run CPU-only on Windows. ROCm is not supported on Windows: PyTorch publishes no Windows ROCm wheels, and OmniVoice's ROCm option is Linux-only. (The Ryzen AI NPU is likewise not used.) Everything still works on CPU, just slower. If you have an AMD GPU and want GPU acceleration, run OmniVoice on Linux instead — see linux.md — AMD GPU (ROCm).

Install (from source)

Run from a regular (non-admin) PowerShell:

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. The splash screen shows progress.

Note: bun run desktop-prod runs a bash script under the hood. You can launch it from PowerShell or cmd as shown — it finds Git Bash automatically (installed with Git for Windows, see Prerequisites). If no Git Bash is found, it prints instructions instead of failing silently. Alternatives that don't need bash: bun run desktop (dev mode) or the pre-built MSI below.

Install (pre-built MSI)

Download the latest MSI from the Releases page, run it, follow the wizard. The shortcut lands in the Start menu as OmniVoice Studio.

Portable install (Windows)

OmniVoice Studio has a Portable mode: instead of scattering data across %APPDATA% and %LOCALAPPDATA%, the whole install — Python env, model weights, voices, projects, settings — lives in a single OmniVoiceStudio-Data folder created next to the executable. Moving or copying the app folder (exe + that data folder together) relocates the entire install, USB-stick style.

The first-run setup screen offers Portable whenever the folder next to OmniVoice Studio.exe is writable. A default MSI install goes to C:\Program Files, which is not user-writable — that's why Portable shows as greyed out after a default install (#766). To enable it, install to a user-writable folder instead:

• Re-run the MSI and choose a custom destination folder in the setup wizard (e.g. D:\Apps\OmniVoice), or
• From a terminal: msiexec /i OmniVoice.Studio_<version>_x64_en-US.msi INSTALLDIR="D:\Apps\OmniVoice"

On the next launch, pick Portable on the first-run setup screen. What lives next to the exe afterwards:

D:\Apps\OmniVoice\
├── OmniVoice Studio.exe        ← the app
└── OmniVoiceStudio-Data\       ← the whole install, self-contained
    ├── config.json             ← install-mode + app settings
    ├── env\                    ← Python venv + backend code
    └── data\                   ← voices, projects, settings DB
        └── models\             ← model weights (HF cache)

Prefer the default Program Files install? Installed mode is the same app — data just lives in %APPDATA%\OmniVoice and the model cache in %LOCALAPPDATA%\OmniVoice\hf_cache.

HF_TOKEN persistence

The recommended path is the in-app Settings → API Keys panel: it writes the token to OmniVoice's encrypted SQLite store and to the canonical huggingface_hub location, so every subprocess the app spawns picks it up.

If you prefer setting an environment variable directly (power-user / CLI runs from source), use PowerShell with [Environment]::SetEnvironmentVariable:

[Environment]::SetEnvironmentVariable("HF_TOKEN","hf_yourtokenhere","User")

That writes to the user-scope environment and is picked up by every new shell — close and reopen PowerShell or your terminal to see it.

Don't use setx. setx HF_TOKEN "hf_..." works in theory but has three real gotchas that produce "I set it but it's empty" bug reports: it doesn't propagate to the current shell, it silently truncates values longer than 1024 chars, and it doesn't escape % characters. Use the in-app panel or the PowerShell one-liner above.

Full HF token guide: docs/setup/huggingface-token.md.

Triton / torch.compile OOM

On Windows, certain TTS engines (notably IndexTTS-2 and some CosyVoice paths) trigger torch.compile / Triton kernel compilation during the first synthesise call. On machines with <16 GB VRAM, that compile step can OOM before the audio render even begins — the error usually surfaces as OutOfMemoryError: CUDA out of memory or RuntimeError: Triton compilation failed.

The one-click fix: open Settings → Performance in the app and toggle "Disable torch.compile (Windows)" on. That sets the TORCH_COMPILE_DISABLE=1 env var on every engine subprocess OmniVoice spawns, which falls back to the eager-mode kernel path. You'll lose a few percent of peak throughput in exchange for the engine actually loading.

From the CLI / from source: set the env var manually before launching:

$env:TORCH_COMPILE_DISABLE = "1"
bun run desktop-prod

This setting is a no-op on macOS and Linux (the OOM is Windows-specific — the torch.compile kernel cache behaves differently on the other platforms). Tracking issue: #65.

Hugging Face token (optional but recommended)

See docs/setup/huggingface-token.md.

Troubleshooting

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