| Texto completo | |
| Autor(es): Mostrar menos - |
Casanova, Edresson
;
Weber, Julian
;
Shulby, Christopher
;
Candido Junior, Arnaldo
;
Goelge, Eren
;
Ponti, Moacir Antonelli
;
Chaudhuri, K
;
Jegelka, S
;
Song, L
;
Szepesvari, C
;
Niu, G
;
Sabato, S
Número total de Autores: 12
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162; v. N/A, p. 12-pg., 2022-01-01. |
| Resumo | |
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 multispeaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multispeaker 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 multispeaker 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-theart 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. (AU) | |
| Processo FAPESP: | 19/07316-0 - Teoria de singularidades e aplicações a geometria diferencial, equações diferenciais e visão computacional |
| Beneficiário: | Farid Tari |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |