CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages

Autor:	Oliveira, Frederico S., Casanova, Edresson, Júnior, Arnaldo Cândido, Soares, Anderson S., Filho, Arlindo R. Galvão
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this paper, we present CML-TTS, a recursive acronym for CML-Multi-Lingual-TTS, a new Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is based on Multilingual LibriSpeech (MLS) and adapted for training TTS models, consisting of audiobooks in seven languages: Dutch, French, German, Italian, Portuguese, Polish, and Spanish. Additionally, we provide the YourTTS model, a multi-lingual TTS model, trained using 3,176.13 hours from CML-TTS and also with 245.07 hours from LibriTTS, in English. Our purpose in creating this dataset is to open up new research possibilities in the TTS area for multi-lingual models. The dataset is publicly available under the CC-BY 4.0 license1. Comment: 12 pages, 5 figures, Accepted at the 25th International Conference on Text, Speech and Dialogue (TSD 2022)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.10097 Zobrazit plný text záznamu View this record from Arxiv