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arXiv:2406.04904eess.AS

XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model

Edresson Casanova, Kelly Davis, Eren Gölge, Görkem Göknar, Iulian Gulea, Logan Hart, Aya Aljafari, Joshua Meyer, Reuben Morais, Samuel Olayemi, Julian Weber

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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.

arXiv comments
Accepted at INTERSPEECH 2024
Journal reference
DOI
Subjects
eess.ASAudio and Speech Processing cs.CLComputation and Language cs.SDSound

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    • They just shared the paper for XTTS, which got accepted to Interspeech and might be the reason for this being posted now: https://arxiv.org/abs/2406.04904

      eginhardView on HNParent context

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