Create README.md

README.md

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+---
+license: other
+tags:
+- text-to-speech
+- neural-vocoder
+- diffusion probabilistic model
+inference: false
+datasets: 
+- LJSpeech
+extra_gated_prompt: |-
+  One more step before getting this model.
+  This model is open access and available to all, with a license further specifying rights and usage.
+  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.
+
+  
+  By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
+    
+extra_gated_fields:
+ I have read the License and agree with its terms: checkbox
+---
+# 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).*