title: MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation
    Model Training on EU Languages
summary: 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.
paper_authors._id: 66fe8ce315148a910ce929e0
paper_authors.name: Marco Gaido
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T13:34:39.061Z
paper_authors._id: 662e58f32b1b529a43c8b2f4
paper_authors.avatarUrl: /avatars/bd26a7b26596882c6bc8a7375ff42cac.svg
paper_authors.fullname: Marco Gaido
paper_authors.type: user
paper_authors.user: mgaido91
paper_authors._id: 66fe8ce315148a910ce929e1
paper_authors.name: Sara Papi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:38:39.975Z
paper_authors._id: 66309b3833ccd9e68c5d5171
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66309b3833ccd9e68c5d5171/UGt7pZazJHhtwjg0iiyBu.jpeg
paper_authors.fullname: Sara Papi
paper_authors.type: user
paper_authors.user: spapi
paper_authors._id: 66fe8ce315148a910ce929e2
paper_authors.name: Luisa Bentivogli
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T16:41:47.711Z
paper_authors._id: 662eb48f5ade870b60db4380
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/ozHyP-GBIAEhR-l-TkBFb.jpeg
paper_authors.fullname: Luisa Bentivogli
paper_authors.type: user
paper_authors.user: lubentivogli
paper_authors._id: 66fe8ce315148a910ce929e3
paper_authors.name: Alessio Brutti
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-03T15:01:43.727Z
paper_authors._id: 66fe8ee6bb54785c355d4549
paper_authors.avatarUrl: /avatars/6e2a49b20e1caa6e4005411420845b72.svg
paper_authors.fullname: Alessio Brutti
paper_authors.type: user
paper_authors.user: AlessioBrutti
paper_authors._id: 66fe8ce315148a910ce929e4
paper_authors.name: Mauro Cettolo
paper_authors._id: 66fe8ce315148a910ce929e5
paper_authors.name: Roberto Gretter
paper_authors._id: 66fe8ce315148a910ce929e6
paper_authors.name: Marco Matassoni
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T08:35:01.157Z
paper_authors._id: 61f244bb88b9b5abbe184a7f
paper_authors.avatarUrl: /avatars/ffde1bac886cc5a74c2eedecb3ce33d3.svg
paper_authors.fullname: Marco Matassoni
paper_authors.type: user
paper_authors.user: matasso
paper_authors._id: 66fe8ce315148a910ce929e7
paper_authors.name: Mohamed Nabih
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T07:31:50.233Z
paper_authors._id: 659eab38d55b5c4fa49172d7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/659eab38d55b5c4fa49172d7/b6FHt_DIw2rHfcLumjv4f.png
paper_authors.fullname: Mohamed Nabih Ali
paper_authors.type: user
paper_authors.user: mnabihali
paper_authors._id: 66fe8ce315148a910ce929e8
paper_authors.name: Matteo Negri
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T09:06:12.146Z
paper_authors._id: 66350219843f549fdac86347
paper_authors.avatarUrl: /avatars/1ad7aa4e8e6d80c5d50bf4de502f11a4.svg
paper_authors.fullname: Matteo Negri
paper_authors.type: user
paper_authors.user: MNegri
paper_title: MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation
    Model Training on EU Languages
paper_summary: 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.
paper_ai_summary: Researchers address the lack of compliance with open-source principles in speech foundation models by releasing a multilingual dataset and transcripts to facilitate the creation of EU language-specific open-source models.
paper_ai_keywords: foundation models speech foundation models open-source principles training data automatic speech recognition unlabeled speech corpora CC-BY license