Installation¶

In order to use EveryVoice on GPUs, you must install PyTorch and Cuda, Python 3.10 or more recent, a number of other dependencies, and EveryVoice itself. The following sections describe three ways to accomplish this:

Scripted installation -- recommended¶

While the EveryVoice installation process has gotten simpler as the project matures, we maintain a script to automate the process and keep it reliable.

Install miniconda or conda.

Clone the EveryVoice repo and its submodules:

git clone https://github.com/EveryVoiceTTS/EveryVoice.git
cd EveryVoice
git submodule update --init

Run our automated environment creation script
```
./make-everyvoice-env --name EveryVoice
conda activate EveryVoice
```
Add the option --cuda CUDA_VERSION if you need to override the default CUDA version, or --cpu to use Torch compiled for CPU use only.

Install from PyPI¶

Direct installation from PyPI has become fairly reliable:

Follow the instructions in README.md to install sox, ffmpeg, torch and torchaudio.
Install EveryVoice:

pip install everyvoice==0.1.0a

Manual installation using Conda¶

If you prefer to do the complete installation process manually, or if the automated process does not work for you, follow these steps.

TL;DR¶

conda create --name EveryVoice python=3.10
conda activate EveryVoice
conda install sox -c conda-forge
conda install ffmpeg
CUDA_TAG=cu118 pip install -r requirements.torch.txt --find-links https://download.pytorch.org/whl/torch_stable.html
pip install -e .

Install Conda¶

Install miniconda or conda.

Create the environment¶

Use conda to create a new environment based on Python 3.10:

conda create --name EveryVoice python=3.10
conda activate EveryVoice

Pytorch dependencies¶

Install our pytorch requirements from requirements.torch.txt, replacing cu118 below (for CUDA 11.8) by your actual CUDA version tag (118 or higher), or by cpu for a CPU-only installation:

CUDA_TAG=cu118 pip install -r requirements.torch.txt --find-links https://download.pytorch.org/whl/torch_stable.html

Alternatively, you can follow the PyTorch installation instructions relevant to your hardware. Make sure you specify the version declared in requirements.torch.txt, which is 2.3.1 at the moment if you install EveryVoice from GitHub, but 2.1.0 if you install it from PyPI.

Non-Python dependencies¶

Install sox and ffmpeg if you didn't install them using OS packages:

conda install sox -c conda-forge
conda install ffmpeg

Handling running out of temp disk space¶

Installation will require a fair bit of space on ~/.cache and your $TMPDIR (/tmp by default, if $TMPDIR is not set). If you get the error OSError: [Errno 28] No space left on device during installation, you may need to do one or both of these operations: - export TMPDIR=/path/to/a/large/tmp/space (or maybe export TMPDIR=.) - mkdir /path/to/a/large/filesystem/.cache; ln -s /path/to/a/large/filesystem/.cache ~/.cache

Install EveryVoice itself¶

Install EveryVoice locally from your cloned sandbox:

pip install -e .

Dev dependencies¶

Before you can run the test suites, you'll also need to install the dev dependencies:

pip install -e .[dev]

Git hooks¶

If you plan to contribute to the project, please install our Git hooks:

pre-commit install
gitlint install-hook
git submodule foreach 'pre-commit install'
git submodule foreach 'gitlint install-hook'

Installation using `uv`¶

If you can install sox, libsox-dev and ffmpeg using your OS packages or by other means (see README.md), you can now install EveryVoice in a uv venv, which is much faster to create and activate.

uv venv -p 3.10 .venv-EveryVoice
source .venv-EveryVoice/bin/activate
uv pip install torch==2.3.1+cu118 torchaudio==2.3.1+cu118 --find-links https://download.pytorch.org/whl/torch_stable.html
uv pip install -e .[dev]

(If needed, change the +cu118 qualifier on torch* to your actual version of CUDA, or to +cpu. See Pytorch dependencies above.)