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

TL;DR

This paper introduces a large open-source speech dataset of 950,000 hours for EU languages, enabling the development of fully open-source speech foundation models with publicly available data, code, and model weights.

Contribution

It provides the first comprehensive open-source speech dataset for EU languages, including transcripts for unlabeled data, promoting transparent and accessible speech foundation model development.

Findings

Collected 950k hours of open-source speech data for EU languages

Released 441k hours of automatic transcripts under permissive license

Facilitated creation of fully open-source speech foundation models

Abstract

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.