---
license: other
tags:
- text-to-speech
- neural-vocoder
- diffusion probabilistic model
inference: false
datasets: 
- LJSpeech
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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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---
# FastDiff Model Card

## Model Details
- **Model type:** Diffusion-based text-to-speech generation model
- **Language(s):** English
- **Model Description:** A conditional diffusion probabilistic model capable of generating high fidelity speech efficiently.
- **Resources for more information:** [FastDiff GitHub Repository](https://github.com/Rongjiehuang/FastDiff), [FastDiff Paper](https://arxiv.org/abs/2204.09934). 
- **Cite as:**

      @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}
- 

*This model card was written based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*