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Conference Papers Year : 2024

Audiocite.net: A Large Spoken Read Dataset in French

Abstract

The advent of self-supervised learning (SSL) in speech processing has allowed the use of large unlabeled datasets to learn pre-trained models, serving as powerful encoders for various downstream tasks. However, the application of these SSL methods to languages such as French has proved difficult due to the scarcity of large French speech datasets. To advance the emergence of pre-trained models for French speech, we present the Audiocite.net corpus composed of 6 682 hours of recordings from 130 readers. This corpus is built from audiobooks from the audiocite.net website. In addition to describing the creation process and final statistics, we also show how this dataset impacted the models of LeBenchmark project in its 14k version for speech processing downstream tasks.
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Dates and versions

hal-04533994 , version 1 (09-04-2024)

Identifiers

  • HAL Id : hal-04533994 , version 1

Cite

Soline Felice, Solène Evain, Solange Rossato, François Portet. Audiocite.net: A Large Spoken Read Dataset in French. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024, Turin, Italy. ⟨hal-04533994⟩
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