YourTTS

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.

System architecture:

Zero-shot Multi-Speaker TTS

Audio Samples for VCTK test speakers

Audio Samples for LibriTTS test speakers

Audio Samples for MLS Portuguese test speakers

Speaker Adaptation

Zero-Shot Voice Conversion

Each row of the table shows the voice of the speaker of the current row generated through a reference of the speaker present in the column. Therefore, all samples of a row should sound similar.

English speakers to English Speakers

Female to Female

Male to Male

Female to Male

Male to Female

Portuguese Speakers to Portuguese Speakers

Female to Female

Male to Male

Female to Male

Male to Female

English Speakers to Portuguese Speakers

Female to Female

Male to Male

Female to Male

Male to Female

Portuguese Speakers to English Speakers

Female to Female

Male to Male

Female to Male

Male to Female

Citation

@ARTICLE{2021arXiv211202418C,
  author = {{Casanova}, Edresson and {Weber}, Julian and {Shulby}, Christopher and {Junior}, Arnaldo Candido and {G{\"o}lge}, Eren and {Antonelli Ponti}, Moacir},
  title = "{YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone}",
  journal = {arXiv e-prints},
  keywords = {Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing},
  year = 2021,
  month = dec,
  eid = {arXiv:2112.02418},
  pages = {arXiv:2112.02418},
  archivePrefix = {arXiv},
  eprint = {2112.02418},
  primaryClass = {cs.SD},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv211202418C},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}