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YODAS: Youtube-Oriented Dataset for Audio and Speech

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arxiv 2406.00899 v1 pith:5THSX5GV submitted 2024-06-02 cs.CL cs.SDeess.AS

Xinjian Li , Shinnosuke Takamichi , Takaaki Saeki , William Chen , Sayaka Shiota , Shinji Watanabe This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords speechdatasetyodasaudiointroducelabeledlanguageslarge-scale
verification ladder T0 review T1 audit T2 compute T3 formal
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In this study, we introduce YODAS (YouTube-Oriented Dataset for Audio and Speech), a large-scale, multilingual dataset comprising currently over 500k hours of speech data in more than 100 languages, sourced from both labeled and unlabeled YouTube speech datasets. The labeled subsets, including manual or automatic subtitles, facilitate supervised model training. Conversely, the unlabeled subsets are apt for self-supervised learning applications. YODAS is distinctive as the first publicly available dataset of its scale, and it is distributed under a Creative Commons license. We introduce the collection methodology utilized for YODAS, which contributes to the large-scale speech dataset construction. Subsequently, we provide a comprehensive analysis of speech, text contained within the dataset. Finally, we describe the speech recognition baselines over the top-15 languages.

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