YODAS: Youtube-Oriented Dataset for Audio and Speech

Autor:	Li, Xinjian, Takamichi, Shinnosuke, Saeki, Takaaki, Chen, William, Shiota, Sayaka, Watanabe, Shinji
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: ASRU 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.00899 Zobrazit plný text záznamu View this record from Arxiv