  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.
