# VALL'E An unofficial PyTorch implementation of [VALL-E](https://vall-e-demo.ecker.tech/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder. A demo is available on HuggingFace [here](https://huggingface.co/spaces/ecker/vall-e). ## Requirements Besides a working PyTorch environment, the only hard requirement is [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/) for phonemizing text: - Linux users can consult their package managers on installing `espeak`/`espeak-ng`. - Windows users are required to install [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/releases/tag/1.51#Assets). + additionally, you may be required to set the `PHONEMIZER_ESPEAK_LIBRARY` environment variable to specify the path to `libespeak-ng.dll`. - In the future, an internal homebrew to replace this would be fantastic. ## Install Simply run `pip install git+https://git.ecker.tech/mrq/vall-e` or `pip install git+https://github.com/e-c-k-e-r/vall-e`. I've tested this repo under Python versions `3.10.9`, `3.11.3`, and `3.12.3`. ## Pre-Trained Model My pre-trained weights can be acquired from [here](https://huggingface.co/ecker/vall-e). A script to setup a proper environment and download the weights can be invoked with `./scripts/setup.sh`. This will automatically create a `venv`, and download the `ar+nar-llama-8` weights and config file to the right place. When inferencing, either through the web UI or CLI, if no model is passed, the default model will download automatically instead, and should automatically update. ## Documentation The provided documentation under [./docs/](./docs/) should provide thorough coverage over most, if not all, of this project. Markdown files should correspond directly to their respective file or folder under `./vall_e/`.