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arXiv:2112.02418cs.SD

YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone

Edresson Casanova, Julian Weber, Christopher Shulby, Arnaldo Candido Junior, Eren Gölge, Moacir Antonelli Ponti

Abstract

YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.

arXiv comments
An Erratum was added on the last page of this paper
Journal reference
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:2709-2720, 2022
DOI
Subjects
cs.SDSound cs.CLComputation and Language eess.ASAudio and Speech Processing

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    • The Brazilian Portuguese model is a bit of an extreme showcase (and thus really cool!), as it was trained on a single speaker (entirely recorded by the main author of the paper, Edresson Casanova, who's Brazilian). The fact that it can do multi-lingual voice cloning at all in…

      reubenmoraisView on HNParent context

    • Demo: https://coqui.ai Code: https://github.com/coqui-ai/tts Blogpost: https://coqui.ai/blog/tts/yourtts-zero-shot-text-synthesis-l... Paper: https://arxiv.org/abs/2112.02418

      _josh_meyer_View on HNParent context

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