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

Marco Gaido, Sara Papi, Luisa Bentivogli, Alessio Brutti, Mauro Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, Matteo Negri


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.
Anthology ID:
2024.emnlp-main.771
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13934–13947
Language:
URL:
https://aclanthology.org/2024.emnlp-main.771
DOI:
10.18653/v1/2024.emnlp-main.771
Bibkey:
Cite (ACL):
Marco Gaido, Sara Papi, Luisa Bentivogli, Alessio Brutti, Mauro Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, and Matteo Negri. 2024. MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13934–13947, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages (Gaido et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-main.771.pdf
Data:
 2024.emnlp-main.771.data.zip