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https://github.com/Rongjiehuang/ProDiff

PyTorch Implementation of ProDiff (ACM-MM'22) with a Extremely-Fast diffusion speech synthesis pipeline
https://github.com/Rongjiehuang/ProDiff

diffusion-models speech-synthesis text-to-speech

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PyTorch Implementation of ProDiff (ACM-MM'22) with a Extremely-Fast diffusion speech synthesis pipeline

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# ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech



#### Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, Yi Ren



PyTorch Implementation of [ProDiff (ACM Multimedia'22)](https://arxiv.org/abs/2207.06389): a conditional diffusion probabilistic model capable of generating high fidelity speech efficiently.



[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2207.06389)

[![GitHub Stars](https://img.shields.io/github/stars/Rongjiehuang/ProDiff?style=social)](https://github.com/Rongjiehuang/ProDiff)

![visitors](https://visitor-badge.glitch.me/badge?page_id=Rongjiehuang/ProDiff)

[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/Rongjiehuang/ProDiff)



We provide our implementation and pretrained models as open source in this repository.



Visit our [demo page](https://prodiff.github.io/) for audio samples.



## News

- April, 2022: Our previous work **[FastDiff](https://arxiv.org/abs/2204.09934) (IJCAI 2022)** released in [Github](https://github.com/Rongjiehuang/FastDiff). 

- September, 2022: **[ProDiff](https://arxiv.org/abs/2207.06389) (ACM Multimedia 2022)** released in Github.



## Key Features

- **Extremely-Fast** diffusion text-to-speech synthesis pipeline for potential **industrial deployment**.

- **Tutorial and code base** for speech diffusion models.

- More **supported diffusion mechanism** (e.g., guided diffusion) will be available.



## Quick Started

We provide an example of how you can generate high-fidelity samples using ProDiff.



To try on your own dataset, simply clone this repo in your local machine provided with NVIDIA GPU + CUDA cuDNN and follow the below instructions.



### Support Datasets and Pretrained Models



Simply run following command to download the weights

```python

  from huggingface_hub import snapshot_download 

  downloaded_path = snapshot_download(repo_id="Rongjiehuang/ProDiff")

```



and move the downloaded checkpoints to `checkpoints/$Model/model_ckpt_steps_*.ckpt`

```bash

   mv ${downloaded_path}/checkpoints/  checkpoints/

```



Details of each folder are as in follows:



| Model             | Dataset     | Config                                          | 

|-------------------|-------------|-------------------------------------------------|

| ProDiff Teacher   | LJSpeech    | `modules/ProDiff/config/prodiff_teacher.yaml`   | 

| ProDiff           | LJSpeech    | `modules/ProDiff/config/prodiff.yaml`           | 



More supported datasets are coming soon.



### Dependencies

See requirements in `requirement.txt`:

- [pytorch](https://github.com/pytorch/pytorch)

- [librosa](https://github.com/librosa/librosa)

- [NATSpeech](https://github.com/NATSpeech/NATSpeech)



### Multi-GPU

By default, this implementation uses as many GPUs in parallel as returned by `torch.cuda.device_count()`. 

You can specify which GPUs to use by setting the `CUDA_DEVICES_AVAILABLE` environment variable before running the training module.



## Extremely-Fast Text-to-Speech with diffusion probabilistic models 



Here we provide a speech synthesis pipeline using diffusion probabilistic models: ProDiff (acoustic model) + FastDiff (neural vocoder). [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/Rongjiehuang/ProDiff)



1. Prepare acoustic model (ProDiff or ProDiff Teacher): Download LJSpeech checkpoint and put it in `checkpoints/ProDiff` or `checkpoints/ProDiff_Teacher`

2. Prepare neural vocoder (FastDiff): Download LJSpeech checkpoint and put it in `checkpoints/FastDiff`



3. Specify the input `$text`, and set `N` for reverse sampling in neural vocoder, which is a trade off between quality and speed. 

4. Run the following command for extreme fast speed `(2-iter ProDiff + 4-iter FastDiff)`:

```bash

CUDA_VISIBLE_DEVICES=$GPU python inference/ProDiff.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --hparams="N=4,text='$txt'" --reset

```

Generated wav files are saved in `infer_out` by default.


Note: For better quality, it's recommended to finetune the FastDiff neural vocoder [here](https://github.com/Rongjiehuang/FastDiff).



5. Enjoy speed-quality trade-off:  `(4-iter ProDiff Teacher + 6-iter FastDiff)`:

```bash

CUDA_VISIBLE_DEVICES=$GPU python inference/ProDiff_teacher.py --config modules/ProDiff/config/prodiff_teacher.yaml --exp_name ProDiff_Teacher --hparams="N=6,text='$txt'" --reset

```



# Train your own model



### Data Preparation and Configuraion ##

1. Set `raw_data_dir`, `processed_data_dir`, `binary_data_dir` in the config file

2. Download dataset to `raw_data_dir`. Note: the dataset structure needs to follow `egs/datasets/audio/*/pre_align.py`, or you could rewrite `pre_align.py` according to your dataset.

3. Preprocess Dataset 

```bash

# Preprocess step: unify the file structure.

python data_gen/tts/bin/pre_align.py --config $path/to/config

# Align step: MFA alignment.

python data_gen/tts/runs/train_mfa_align.py --config $CONFIG_NAME

# Binarization step: Binarize data for fast IO.

CUDA_VISIBLE_DEVICES=$GPU python data_gen/tts/bin/binarize.py --config $path/to/config

```



You could also build a dataset via [NATSpeech](https://github.com/NATSpeech/NATSpeech), which shares a common MFA data-processing procedure.

We also provide our processed LJSpeech dataset [here](https://zjueducn-my.sharepoint.com/:f:/g/personal/rongjiehuang_zju_edu_cn/Eo7r83WZPK1GmlwvFhhIKeQBABZpYW3ec9c8WZoUV5HhbA?e=9QoWnf).



### Training Teacher of ProDiff 

```bash

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml  --exp_name ProDiff_Teacher --reset

```



### Training ProDiff

```bash

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml  --exp_name ProDiff --reset

```



### Inference using ProDiff Teacher



```bash

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml  --exp_name ProDiff_Teacher --infer

```



### Inference using ProDiff



```bash

CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml  --exp_name ProDiff --infer

```



## Acknowledgements

This implementation uses parts of the code from the following Github repos:

[FastDiff](https://github.com/Rongjiehuang/FastDiff),

[DiffSinger](https://github.com/MoonInTheRiver/DiffSinger),

[NATSpeech](https://github.com/NATSpeech/NATSpeech),

as described in our code.



## Citations ##

If you find this code useful in your research, please cite our work:

```bib

@inproceedings{huang2022prodiff,

  title={ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech},

  author={Huang, Rongjie and Zhao, Zhou and Liu, Huadai and Liu, Jinglin and Cui, Chenye and Ren, Yi},

  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},

  year={2022}

}



@article{huang2022fastdiff,

  title={FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis},

  author={Huang, Rongjie and Lam, Max WY and Wang, Jun and Su, Dan and Yu, Dong and Ren, Yi and Zhao, Zhou},

  booktitle = {Proceedings of the Thirty-First International Joint Conference on

               Artificial Intelligence, {IJCAI-22}},

  publisher = {International Joint Conferences on Artificial Intelligence Organization},

  year={2022}

}

```



## Disclaimer ##

Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.