Papers › Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

11 Jun 2021arXiv:2106.06103links table onlyarchive 2025-07-28

Jaehyeon Kim, Jungil Kong, Juhee Son

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Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.

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jaywalnut310/vits officialmentioned in papermentioned on GitHubpytorchMIT report
coqui-ai/TTS mentioned on GitHubpytorchMPL-2.0 report
daniilrobnikov/vits2 mentioned on GitHubpytorchMIT report
isletennos/mmvc_trainer mentioned on GitHubpytorch report
jmp84/vits mentioned on GitHubpytorch report
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lakahaga/dc-comix-tts mentioned on GitHubpytorch report
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open-creator/icefall mentioned on GitHubpytorchApache-2.0 report
p0p4k/vits2_pytorch mentioned on GitHubpytorchMIT report
wenet-e2e/wetts mentioned on GitHubpytorchApache-2.0 report
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2ran · honoured contract
6ran · our draft was wrong
4ran · fixture could not drive it
19ran
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