vall-e/README.md

VALL'E

An unofficial PyTorch implementation of VALL-E, based on the EnCodec tokenizer.

Requirements

• DeepSpeed:

  • DeepSpeed training is Linux only. Installation under Windows should ignore trying to install DeepSpeed.
  • If your config YAML has the training backend set to [deepspeed], 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.

• espeak-ng:

  • For phonemizing text, this repo requires espeak/espeak-ng installed.
  • Linux users can consult their package managers on installing espeak/espeak-ng.
  • Windows users are required to install espeak-ng.
    • additionally, you may be require dto set the PHONEMIZER_ESPEAK_LIBRARY environment variable to specify the path to libespeak-ng.dll.

Install

Simply run pip install git+https://git.ecker.tech/mrq/vall-e.

I've tested this repo under Python versions 3.10.9 and 3.11.3.

Try Me

To quickly try it out, you can choose between the following modes:

• AR only: python -m vall_e.models.ar yaml="./data/config.yaml"
• NAR only: python -m vall_e.models.nar yaml="./data/config.yaml"
• AR+NAR: python -m vall_e.models.base yaml="./data/config.yaml"

Each model file has a barebones trainer and inference routine.

Pre-Trained Model

My pre-trained weights can be acquired from here.

For example:

git lfs clone --exclude "*.h5" https://huggingface.co/ecker/vall-e ./data/
python -m vall_e "The birch canoe slid on the smooth planks." "./path/to/an/utterance.wav" --out-path="./output.wav" yaml="./data/config.yaml"

Train

Training is very dependent on:

• the quality of your dataset.
• how much data you have.
• the bandwidth you quantized your audio to.

Notices

Modifying prom_levels, resp_levels, Or tasks For A Model

If you're wanting to increase the prom_levels for a given model, or increase the tasks levels a model accepts, you will need to export your weights and set train.load_state_dict to True in your configuration YAML.

Pre-Processed Dataset

Note A pre-processed "libre" is being prepared. This contains only data from the LibriTTS and LibriLight datasets (and MUSAN for noise), and culled out any non-libre datasets.

Leverage Your Own Dataset

Note It is highly recommended to utilize mrq/ai-voice-cloning with --tts-backend="vall-e" to handle transcription and dataset preparations.

1. Put your data into a folder, e.g. ./data/custom. Audio files should be named with the suffix .wav and text files with .txt.

2. Quantize the data: python -m vall_e.emb.qnt ./data/custom

3. Generate phonemes based on the text: python -m vall_e.emb.g2p ./data/custom

4. Customize your configuration and define the dataset by modifying ./data/config.yaml. Refer to ./vall_e/config.py for details. If you want to choose between different model presets, check ./vall_e/models/__init__.py.

If you're interested in creating an HDF5 copy of your dataset, simply invoke: python -m vall_e.data --action='hdf5' yaml='./data/config.yaml'

5. Train the AR and NAR models using the following scripts: python -m vall_e.train yaml=./data/config.yaml

You may quit your training any time by just typing quit in your CLI. The latest checkpoint will be automatically saved.

Dataset Formats

Two dataset formats are supported:

• the standard way:
  • data is stored under ${speaker}/${id}.phn.txt and ${speaker}/${id}.qnt.pt
• using an HDF5 dataset:
  • you can convert from the standard way with the following command: python3 -m vall_e.data yaml="./path/to/your/config.yaml"
  • this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths)
  • be sure to also define use_hdf5 in your config YAML.

Export

Both trained models can be exported, but is only required if loading them on systems without DeepSpeed for inferencing (Windows systems). To export the models, run: python -m vall_e.export yaml=./data/config.yaml.

This will export the latest checkpoints under ./data/ckpt/ar-retnet-2/fp32.pth and ./data/ckpt/nar-retnet-2/fp32.pth to be loaded on any system with PyTorch.

Synthesis

To synthesize speech, invoke either (if exported the models): python -m vall_e <text> <ref_path> <out_path> --ar-ckpt ./models/ar.pt --nar-ckpt ./models/nar.pt or python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>

Some additional flags you can pass are:

• --max-ar-steps: maximum steps for inferencing through the AR model. Each second is 75 steps.
• --ar-temp: sampling temperature to use for the AR pass. During experimentation, 0.95 provides the most consistent output.
• --nar-temp: sampling temperature to use for the NAR pass. During experimentation, 0.2 provides the most clean output.
• --device: device to use (default: cuda, examples: cuda:0, cuda:1, cpu)

To-Do

• reduce load time for creating / preparing dataloaders.
• train and release a model.
• extend to multiple languages (VALL-E X) and extend to train SpeechX features.

Notice

• 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.

Unless otherwise credited/noted, this repository is licensed under AGPLv3.

Citations

@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}
}

@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}
}