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

Author:

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

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Artificial Intelligence (cs.AI), Computation and Language (cs.CL)

journal:

date:

2023-06-15 16:00:00

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.

PDF: CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages.pdf