MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916420567171072 |
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| author | Gaido, Marco Papi, Sara Bentivogli, Luisa Brutti, Alessio Cettolo, Mauro Gretter, Roberto Matassoni, Marco Nabih, Mohamed Negri, Matteo |
| author_facet | Gaido, Marco Papi, Sara Bentivogli, Luisa Brutti, Alessio Cettolo, Mauro Gretter, Roberto Matassoni, Marco Nabih, Mohamed Negri, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages Gaido, Marco Papi, Sara Bentivogli, Luisa Brutti, Alessio Cettolo, Mauro Gretter, Roberto Matassoni, Marco Nabih, Mohamed Negri, Matteo Computation and Language Artificial Intelligence Sound Audio and Speech Processing 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. |
| title | MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages |
| topic | Computation and Language Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.01036 |