37 lines
1.9 KiB
Markdown
Executable File
37 lines
1.9 KiB
Markdown
Executable File
<p align="center">
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<img src="./vall-e.png" width="500px"></img>
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</p>
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# VALL'E
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An unofficial PyTorch implementation of [VALL-E](https://vall-e-demo.ecker.tech/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder.
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A demo is available on HuggingFace [here](https://huggingface.co/spaces/ecker/vall-e).
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## Requirements
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Besides a working PyTorch environment, the only hard requirement is [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/) for phonemizing text:
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- Linux users can consult their package managers on installing `espeak`/`espeak-ng`.
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- Windows users are required to install [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/releases/tag/1.51#Assets).
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+ additionally, you may be required to set the `PHONEMIZER_ESPEAK_LIBRARY` environment variable to specify the path to `libespeak-ng.dll`.
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- In the future, an internal homebrew to replace this would be fantastic.
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## Install
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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`.
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I've tested this repo under Python versions `3.10.9`, `3.11.3`, and `3.12.3`.
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## Pre-Trained Model
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My pre-trained weights can be acquired from [here](https://huggingface.co/ecker/vall-e).
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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.
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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.
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## Documentation
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The provided documentation under [./docs/](./docs/) should provide thorough coverage over most, if not all, of this project.
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Markdown files should correspond directly to their respective file or folder under `./vall_e/`. |