mlsanityData to 7 Oct 2026

MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages

arXiv 2410.01036 ↗First posted 1 Oct 2024cs.CL cs.AI cs.SDcited by 13

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Abstract (arXiv)

The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 42 of its 83 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

42 of 83 references.

References · 42

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30 more
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