Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8599-8608, 2021.

Abstract

Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-popov21a, title = {Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech}, author = {Popov, Vadim and Vovk, Ivan and Gogoryan, Vladimir and Sadekova, Tasnima and Kudinov, Mikhail}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8599--8608}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/popov21a/popov21a.pdf}, url = {https://proceedings.mlr.press/v139/popov21a.html}, abstract = {Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score.} }
Endnote
%0 Conference Paper %T Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech %A Vadim Popov %A Ivan Vovk %A Vladimir Gogoryan %A Tasnima Sadekova %A Mikhail Kudinov %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-popov21a %I PMLR %P 8599--8608 %U https://proceedings.mlr.press/v139/popov21a.html %V 139 %X Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score.
APA
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T. & Kudinov, M.. (2021). Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:8599-8608 Available from https://proceedings.mlr.press/v139/popov21a.html.

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