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# VALL-E
An unofficial (toy) implementation of VALL-E, based on the [encodec](https://github.com/facebookresearch/encodec) tokenizer.
An unofficial (toy) implementation of [VALL-E](https://valle-demo.github.io/), based on the [encodec](https://github.com/facebookresearch/encodec) tokenizer.
[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/enhuiz)
## Requirements
### 1. Clone the project
```
git clone --recurse-submodules https://github.com/enhuiz/vall-e.git
```
### 2. Install requirements
```
pip install -r requirements.txt
```
Note: You may need to install additional requirements as you run the script.
## Data Preparation
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.
5. Train the AR or NAR model using the following scripts:
```
python -m vall_e.train yaml=config/your_data/ar_or_nar.yml
```
## 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.
- [ ] Pre-trained checkpoint.
- [x] AR model for the first quantizer
- [x] Audio decoding from tokens
- [x] NAR model for the rest quantizers
- [x] Trainers for both models
- [ ] Pre-trained checkpoint and demos on LibriTTS

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requirements.txt Normal file
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coloredlogs==15.0.1
deepspeed==0.7.7
diskcache==5.4.0
einops==0.6.0
encodec==0.1.1
g2p_en==2.1.0
humanize==4.4.0
matplotlib==3.6.0
numpy==1.23.3
omegaconf==2.2.3
openTSNE==0.6.2
pandas==1.5.0
soundfile==0.11.0
torch==1.13.0+cu116
torchaudio==0.13.0+cu116
tqdm==4.64.1