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license: other |
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tags: |
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- text-to-speech |
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- neural-vocoder |
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- diffusion probabilistic model |
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inference: false |
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datasets: |
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- LJSpeech |
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extra_gated_prompt: |- |
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One more step before getting this model. |
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This model is open access and available to all, with a license further specifying rights and usage. |
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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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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. |
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extra_gated_fields: |
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I have read the License and agree with its terms: checkbox |
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--- |
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# ProDiff and FastDiff Model Card |
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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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## Model Details |
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- **Developed by:** Robin Rombach, Patrick Esser |
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- **Model type:** Diffusion-based text-to-speech generation model |
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- **Language(s):** English |
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- **License:** |
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- **Model Description:** A conditional diffusion probabilistic model capable of generating high fidelity speech efficiently. |
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- **Resources for more information:** [FastDiff GitHub Repository](https://github.com/Rongjiehuang/FastDiff), [FastDiff Paper](https://arxiv.org/abs/2204.09934). [ProDiff GitHub Repository](https://github.com/Rongjiehuang/ProDiff), [ProDiff Paper](https://arxiv.org/abs/2207.06389). |
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- **Cite as:** |
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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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@inproceedings{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 Artificial Intelligence, {IJCAI-22}}, |
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year={2022} |
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*This model card was written based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
