A new 143-indicator rubric applied to 114 human-voice datasets shows that documentation of consent, privacy, and harmful content is rare, and that scraping yields scale at the cost of documented ethical practices.
YODAS: Youtube-Oriented Dataset for Audio and Speech
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abstract
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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TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation
A new 143-indicator rubric applied to 114 human-voice datasets shows that documentation of consent, privacy, and harmful content is rare, and that scraping yields scale at the cost of documented ethical practices.