# VALL-E An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/), based on the [EnCodec](https://github.com/facebookresearch/encodec) tokenizer. [](https://www.buymeacoffee.com/enhuiz) ## Get Started > A toy Google Colab example: [](https://colab.research.google.com/drive/1wEze0kQ0gt9B3bQmmbtbSXCoCTpq5vg-?usp=sharing). > Please note that this example overfits a single utterance under the `data/test` and is not usable. > The pretrained model is yet to come. ### Requirements Since the trainer is based on [DeepSpeed](https://github.com/microsoft/DeepSpeed#requirements), you will need to have a GPU that DeepSpeed has developed and tested against, as well as a CUDA or ROCm compiler pre-installed to install this package. ### Install You can install vall-e using 3 methods: 1. Docker ``` git clone --recurse-submodules https://github.com/enhuiz/vall-e.git cd vall-e docker build -t valle/python3.10 . docker run -it --gpus=all --net=host --ipc=host -v $(pwd):/app/ valle/python3.10 ``` The last command will run the docker image using all the available GPUs, you can refer to the [documentation](https://docs.docker.com/config/containers/resource_constraints/#:~:text=an%20error%20occurs.-,GPU,-%F0%9F%94%97) to specify the GPUs you want to use. Also, it will use a volume that keeps all of the container's files shared between the container's `/app` and the current working directory of the server `$(pwd)`. 1. Pip ``` pip install git+https://github.com/enhuiz/vall-e ``` 1. Clone ``` git clone --recurse-submodules https://github.com/enhuiz/vall-e.git ``` Note that the code is only tested under `Python 3.10.7`. ### Train 1. Put your data into a folder, e.g. `data/your_data`. Audio files should be named with the suffix `.wav` and text files with `.normalized.txt`. 2. Quantize the data: ``` python -m vall_e.emb.qnt data/your_data ``` 3. Generate phonemes based on the text: ``` python -m vall_e.emb.g2p data/your_data ``` 4. Customize your configuration by creating `config/your_data/ar.yml` and `config/your_data/nar.yml`. Refer to the example configs in `config/test` and `vall_e/config.py` for details. You may choose different model presets, check `vall_e/vall_e/__init__.py`. 5. Train the AR or NAR model using the following scripts: ``` python -m vall_e.train yaml=config/your_data/ar_or_nar.yml ``` You may quit your training any time by just typing `quit` in your CLI. The latest checkpoint will be automatically saved. ### Export Both trained models need to be exported to a certain path. To export either of them, run: ``` python -m vall_e.export zoo/ar_or_nar.pt yaml=config/your_data/ar_or_nar.yml ``` This will export the latest checkpoint. ### Synthesis ``` python -m vall_e --ar-ckpt zoo/ar.pt --nar-ckpt zoo/nar.pt ``` ## TODO - [x] AR model for the first quantizer - [x] Audio decoding from tokens - [x] NAR model for the rest quantizers - [x] Trainers for both models - [x] Implement AdaLN for NAR model. - [x] Sample-wise quantization level sampling for NAR training. - [ ] Pre-trained checkpoint and demos on LibriTTS - [x] Synthesis CLI ## Notice - [EnCodec](https://github.com/facebookresearch/encodec) is licensed under CC-BY-NC 4.0. If you use the code to generate audio quantization or perform decoding, it is important to adhere to the terms of their license. ## Citations ```bibtex @article{wang2023neural, title={Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers}, author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others}, journal={arXiv preprint arXiv:2301.02111}, year={2023} } ``` ```bibtex @article{defossez2022highfi, title={High Fidelity Neural Audio Compression}, author={Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi}, journal={arXiv preprint arXiv:2210.13438}, year={2022} } ```