title: 'Extremely-Fast diffusion text-to-speech synthesis pipeline with ProDiff and FastDiff' description: | Gradio demo for **2-iter** ProDiff and **4-iter** FastDiff. To use it, simply add your audio, or click one of the examples to load them. **This space is running on CPU, inference will be slower.** ## 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. article: | ## Reference Link to ProDiff Github REPO If you find this code useful in your research, please cite our work: ``` @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} @inproceedings{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}}, 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. example_inputs: - |- the invention of movable metal letters in the middle of the fifteenth century may justly be considered as the invention of the art of printing. - |- Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition. inference_cls: inference.ProDiff.ProDiffInfer exp_name: ProDiff config: modules/ProDiff/config/prodiff.yaml