XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model
Audio and Speech Processing
2024-06-10 v1 Computation and Language
Sound
Abstract
Most Zero-shot Multi-speaker TTS (ZS-TTS) systems support only a single language. Although models like YourTTS, VALL-E X, Mega-TTS 2, and Voicebox explored Multilingual ZS-TTS they are limited to just a few high/medium resource languages, limiting the applications of these models in most of the low/medium resource languages. In this paper, we aim to alleviate this issue by proposing and making publicly available the XTTS system. Our method builds upon the Tortoise model and adds several novel modifications to enable multilingual training, improve voice cloning, and enable faster training and inference. XTTS was trained in 16 languages and achieved state-of-the-art (SOTA) results in most of them.
Keywords
Cite
@article{arxiv.2406.04904,
title = {XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model},
author = {Edresson Casanova and Kelly Davis and Eren Gölge and Görkem Göknar and Iulian Gulea and Logan Hart and Aya Aljafari and Joshua Meyer and Reuben Morais and Samuel Olayemi and Julian Weber},
journal= {arXiv preprint arXiv:2406.04904},
year = {2024}
}
Comments
Accepted at INTERSPEECH 2024