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

Frederico S. Oliveira, Edresson Casanova, Arnaldo Cândido Júnior, Anderson S. Soares, Arlindo R. Galvão Filho

6/16/2023

eess.AScs.AIcs.CL

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Abstract

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.

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Cite this paper

@article{oliveira2023cmltts,
  title  = {CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages},
  author = {Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho},
  year   = {2023},
  eprint = {2306.10097},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.10097v1}
}

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