arXiv:2112.02418cs.SD
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
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.
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