aiqbench analysis
The release of YODAS v3 marks a significant milestone in open-source speech data, offering over 1.1 million hours of high-fidelity stereo audio across 147 languages to advance multilingual ASR and neural codec research. By introducing balanced collection methodologies for underrepresented languages, this dataset directly fuels the development of more robust and inclusive machine learning models. Its availability under a permissive license lowers barriers for training diverse speech recognition systems without relying on proprietary corpora.
YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speech
arXiv:2609.29448v1 Announce Type: new Abstract: We present YODAS v3, a weakly-labeled speech corpus containing over 1.1 million hours of 48kHz multi-channel audio in 147 languages, released under a CC BY 3.0 license. YODAS v3 is not only the largest open speech dataset to date, but also the first truly large-scale speech corpus with high-fidelity stereo audio. We first provide the collection methodology for the corpus, where we introduce new techniques for gathering language-balanced speech data. The effectiveness of our approach is shown by the language distribution of the crawled data: 22 languages in YODAS v3 have over 10K hours and 73 languages have over 5K hours of data. We then conduct extensive analyses on the composition of the data, such as the distribution of languages, audio quality, and transcription quality. Finally, we train baseline speech recognition and neural codec models to show the effectiveness of the dataset. Download at https://huggingface.co/datasets/espnet/yodas3.
Abstract:We present YODAS v3, a weakly-labeled speech corpus containing over 1.1 million hours of 48kHz multi-channel audio in 147 languages, released under a CC BY 3.0 license. YODAS v3 is not only the largest open speech dataset to date, but also the first truly large-scale speech corpus with high-fidelity stereo audio. We first provide the collection methodology for the corpus, where we introduce new techniques for gathering language-balanced speech data. The effectiveness of our approach is shown by the language distribution of the crawled data: 22 languages in YODAS v3 have over 10K hours and 73 languages have over 5K hours of data. We then conduct extensive analyses on the composition of the data, such as the distribution of languages, audio quality, and transcription quality. Finally, we train baseline speech recognition and neural codec models to show the effectiveness of the dataset. Download at this https URL.
Computation and Language (cs.CL)
arXiv:2609.29448 [cs.CL]
(or arXiv:2609.29448v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.29448 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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