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 |
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Rok vydání: | 2023 |
Předmět: | |
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 |
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