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# ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech |
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#### Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, Yi Ren |
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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. |
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[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2207.06389) |
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[![GitHub Stars](https://img.shields.io/github/stars/Rongjiehuang/ProDiff?style=social)](https://github.com/Rongjiehuang/ProDiff) |
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![visitors](https://visitor-badge.glitch.me/badge?page_id=Rongjiehuang/ProDiff) |
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[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/Rongjiehuang/ProDiff) |
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We provide our implementation and pretrained models as open source in this repository. |
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Visit our [demo page](https://prodiff.github.io/) for audio samples. |
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## News |
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- April, 2022: Our previous work **[FastDiff](https://arxiv.org/abs/2204.09934) (IJCAI 2022)** released in [Github](https://github.com/Rongjiehuang/FastDiff). |
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- September, 2022: **[ProDiff](https://arxiv.org/abs/2207.06389) (ACM Multimedia 2022)** released in Github. |
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## Key Features |
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- **Extremely-Fast** diffusion text-to-speech synthesis pipeline for potential **industrial deployment**. |
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- **Tutorial and code base** for speech diffusion models. |
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- More **supported diffusion mechanism** (e.g., guided diffusion) will be available. |
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## Quick Started |
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We provide an example of how you can generate high-fidelity samples using ProDiff. |
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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. |
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### Support Datasets and Pretrained Models |
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Simply run following command to download the weights |
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```python |
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from huggingface_hub import snapshot_download |
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downloaded_path = snapshot_download(repo_id="Rongjiehuang/ProDiff") |
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``` |
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and move the downloaded checkpoints to `checkpoints/$Model/model_ckpt_steps_*.ckpt` |
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```bash |
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mv ${downloaded_path}/checkpoints/ checkpoints/ |
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``` |
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Details of each folder are as in follows: |
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| Model | Dataset | Config | |
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|-------------------|-------------|-------------------------------------------------| |
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| ProDiff Teacher | LJSpeech | `modules/ProDiff/config/prodiff_teacher.yaml` | |
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| ProDiff | LJSpeech | `modules/ProDiff/config/prodiff.yaml` | |
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More supported datasets are coming soon. |
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### Dependencies |
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See requirements in `requirement.txt`: |
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- [pytorch](https://github.com/pytorch/pytorch) |
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- [librosa](https://github.com/librosa/librosa) |
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- [NATSpeech](https://github.com/NATSpeech/NATSpeech) |
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### Multi-GPU |
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By default, this implementation uses as many GPUs in parallel as returned by `torch.cuda.device_count()`. |
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You can specify which GPUs to use by setting the `CUDA_DEVICES_AVAILABLE` environment variable before running the training module. |
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## Extremely-Fast Text-to-Speech with diffusion probabilistic models |
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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) |
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1. Prepare acoustic model (ProDiff or ProDiff Teacher): Download LJSpeech checkpoint and put it in `checkpoints/ProDiff` or `checkpoints/ProDiff_Teacher` |
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2. Prepare neural vocoder (FastDiff): Download LJSpeech checkpoint and put it in `checkpoints/FastDiff` |
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3. Specify the input `$text`, and set `N` for reverse sampling in neural vocoder, which is a trade off between quality and speed. |
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4. Run the following command for extreme fast speed `(2-iter ProDiff + 4-iter FastDiff)`: |
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```bash |
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CUDA_VISIBLE_DEVICES=$GPU python inference/ProDiff.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --hparams="N=4,text='$txt'" --reset |
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``` |
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Generated wav files are saved in `infer_out` by default.<br> |
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Note: For better quality, it's recommended to finetune the FastDiff neural vocoder [here](https://github.com/Rongjiehuang/FastDiff). |
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5. Enjoy speed-quality trade-off: `(4-iter ProDiff Teacher + 6-iter FastDiff)`: |
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```bash |
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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 |
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``` |
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# Train your own model |
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### Data Preparation and Configuraion ## |
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1. Set `raw_data_dir`, `processed_data_dir`, `binary_data_dir` in the config file |
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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. |
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3. Preprocess Dataset |
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```bash |
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# Preprocess step: unify the file structure. |
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python data_gen/tts/bin/pre_align.py --config $path/to/config |
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# Align step: MFA alignment. |
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python data_gen/tts/runs/train_mfa_align.py --config $CONFIG_NAME |
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# Binarization step: Binarize data for fast IO. |
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CUDA_VISIBLE_DEVICES=$GPU python data_gen/tts/bin/binarize.py --config $path/to/config |
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``` |
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You could also build a dataset via [NATSpeech](https://github.com/NATSpeech/NATSpeech), which shares a common MFA data-processing procedure. |
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We also provide our processed LJSpeech dataset [here](https://zjueducn-my.sharepoint.com/:f:/g/personal/rongjiehuang_zju_edu_cn/Eo7r83WZPK1GmlwvFhhIKeQBABZpYW3ec9c8WZoUV5HhbA?e=9QoWnf). |
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### Training Teacher of ProDiff |
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```bash |
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CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml --exp_name ProDiff_Teacher --reset |
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``` |
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### Training ProDiff |
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```bash |
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CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --reset |
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``` |
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### Inference using ProDiff Teacher |
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```bash |
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CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml --exp_name ProDiff_Teacher --infer |
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``` |
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### Inference using ProDiff |
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```bash |
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CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --infer |
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``` |
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## Acknowledgements |
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This implementation uses parts of the code from the following Github repos: |
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[FastDiff](https://github.com/Rongjiehuang/FastDiff), |
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[DiffSinger](https://github.com/MoonInTheRiver/DiffSinger), |
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[NATSpeech](https://github.com/NATSpeech/NATSpeech), |
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as described in our code. |
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## Citations ## |
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If you find this code useful in your research, please cite our work: |
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```bib |
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@inproceedings{huang2022prodiff, |
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title={ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech}, |
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author={Huang, Rongjie and Zhao, Zhou and Liu, Huadai and Liu, Jinglin and Cui, Chenye and Ren, Yi}, |
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booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, |
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year={2022} |
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} |
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@article{huang2022fastdiff, |
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title={FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis}, |
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author={Huang, Rongjie and Lam, Max WY and Wang, Jun and Su, Dan and Yu, Dong and Ren, Yi and Zhao, Zhou}, |
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booktitle = {Proceedings of the Thirty-First International Joint Conference on |
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Artificial Intelligence, {IJCAI-22}}, |
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publisher = {International Joint Conferences on Artificial Intelligence Organization}, |
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year={2022} |
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} |
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``` |
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## Disclaimer ## |
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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. |
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"# ProDiff" |
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