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

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arxiv 2306.10097 v1 pith:WTDWVPRV submitted 2023-06-16 eess.AS cs.AIcs.CL

Frederico S. Oliveira , Edresson Casanova , Arnaldo Cândido Júnior , Anderson S. Soares , Arlindo R. Galvão Filho This is my paper · ORCID
classification eess.AScs.AIcs.CL
keywords cml-ttsdatasethourslanguagesmodelmodelsmulti-lingualmultilingual
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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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  1. From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Fine-tuning TTS models on tens of hours of real audio enables generation of 500,000 hours of synthetic speech that reduces ASR error rates by over 30% on Whisper-large-v3.

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