| # 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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| [](https://arxiv.org/abs/2207.06389) |
| [](https://github.com/Rongjiehuang/ProDiff) |
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| [](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 |
| - 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. |
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| ## 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. |
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| ## Quick Started |
| 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 |
| ```python |
| from huggingface_hub import snapshot_download |
| downloaded_path = snapshot_download(repo_id="Rongjiehuang/ProDiff") |
| ``` |
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| and move the downloaded checkpoints to `checkpoints/$Model/model_ckpt_steps_*.ckpt` |
| ```bash |
| mv ${downloaded_path}/checkpoints/ checkpoints/ |
| ``` |
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| Details of each folder are as in follows: |
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| | Model | Dataset | Config | |
| |-------------------|-------------|-------------------------------------------------| |
| | ProDiff Teacher | LJSpeech | `modules/ProDiff/config/prodiff_teacher.yaml` | |
| | ProDiff | LJSpeech | `modules/ProDiff/config/prodiff.yaml` | |
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| More supported datasets are coming soon. |
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| ### Dependencies |
| See requirements in `requirement.txt`: |
| - [pytorch](https://github.com/pytorch/pytorch) |
| - [librosa](https://github.com/librosa/librosa) |
| - [NATSpeech](https://github.com/NATSpeech/NATSpeech) |
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| ### Multi-GPU |
| By default, this implementation uses as many GPUs in parallel as returned by `torch.cuda.device_count()`. |
| You can specify which GPUs to use by setting the `CUDA_DEVICES_AVAILABLE` environment variable before running the training module. |
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| ## Extremely-Fast Text-to-Speech with diffusion probabilistic models |
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| Here we provide a speech synthesis pipeline using diffusion probabilistic models: ProDiff (acoustic model) + FastDiff (neural vocoder). [](https://huggingface.co/spaces/Rongjiehuang/ProDiff) |
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| 1. Prepare acoustic model (ProDiff or ProDiff Teacher): Download LJSpeech checkpoint and put it in `checkpoints/ProDiff` or `checkpoints/ProDiff_Teacher` |
| 2. Prepare neural vocoder (FastDiff): Download LJSpeech checkpoint and put it in `checkpoints/FastDiff` |
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| 3. Specify the input `$text`, and set `N` for reverse sampling in neural vocoder, which is a trade off between quality and speed. |
| 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.<br> |
| 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)`: |
| ```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 |
| ``` |
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| # Train your own model |
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| ### 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 $path/to/config |
| ``` |
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| You could also build a dataset via [NATSpeech](https://github.com/NATSpeech/NATSpeech), which shares a common MFA data-processing procedure. |
| We also provide our processed LJSpeech dataset [here](https://zjueducn-my.sharepoint.com/:f:/g/personal/rongjiehuang_zju_edu_cn/Eo7r83WZPK1GmlwvFhhIKeQBABZpYW3ec9c8WZoUV5HhbA?e=9QoWnf). |
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| ### Training Teacher of ProDiff |
| ```bash |
| CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml --exp_name ProDiff_Teacher --reset |
| ``` |
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| ### Training ProDiff |
| ```bash |
| CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --reset |
| ``` |
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| ### Inference using ProDiff Teacher |
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| ```bash |
| CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff_teacher.yaml --exp_name ProDiff_Teacher --infer |
| ``` |
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| ### Inference using ProDiff |
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| ```bash |
| CUDA_VISIBLE_DEVICES=$GPU python tasks/run.py --config modules/ProDiff/config/prodiff.yaml --exp_name ProDiff --infer |
| ``` |
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| ## 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. |
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| ## 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} |
| } |
| ``` |
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| ## 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. |
| "# ProDiff" |
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