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metadata
annotations_creators:
  - machine-generated
task_categories:
  - automatic-speech-recognition
  - text-to-speech
language:
  - en
  - bg
  - hr
  - cs
  - da
  - nl
  - et
  - fi
  - fr
  - de
  - el
  - hu
  - ga
  - it
  - lv
  - lt
  - mt
  - pl
  - pt
  - ro
  - sk
  - sl
  - es
  - sv
language_creators:
  - found
modality:
  - text
  - audio
multilinguality:
  - multilingual
pretty_name: MOSEL
license: cc-by-4.0
tags:
  - speech
  - speech-to-text
  - open-source
  - whisper
configs:
  - config_name: bg
    data_files:
      - split: train_voxpopuli
        path: bg/voxpopuli*
  - config_name: cs
    data_files:
      - split: train_voxpopuli
        path: cs/voxpopuli*
  - config_name: da
    data_files:
      - split: train_voxpopuli
        path: da/voxpopuli*
  - config_name: de
    data_files:
      - split: train_voxpopuli
        path: de/voxpopuli*
  - config_name: el
    data_files:
      - split: train_voxpopuli
        path: el/voxpopuli*
  - config_name: en
    data_files:
      - split: train_voxpopuli
        path: en/voxpopuli*
      - split: train_librilight
        path: en/librilight*
  - config_name: es
    data_files:
      - split: train_voxpopuli
        path: es/voxpopuli*
  - config_name: et
    data_files:
      - split: train_voxpopuli
        path: et/voxpopuli*
  - config_name: fi
    data_files:
      - split: train_voxpopuli
        path: fi/voxpopuli*
  - config_name: fr
    data_files:
      - split: train_voxpopuli
        path: fr/voxpopuli*
  - config_name: hr
    data_files:
      - split: train_voxpopuli
        path: hr/voxpopuli*
  - config_name: hu
    data_files:
      - split: train_voxpopuli
        path: hu/voxpopuli*
  - config_name: it
    data_files:
      - split: train_voxpopuli
        path: it/voxpopuli*
  - config_name: lt
    data_files:
      - split: train_voxpopuli
        path: lt/voxpopuli*
  - config_name: lv
    data_files:
      - split: train_voxpopuli
        path: lv/voxpopuli*
  - config_name: mt
    data_files:
      - split: train_voxpopuli
        path: mt/voxpopuli*
  - config_name: nl
    data_files:
      - split: train_voxpopuli
        path: nl/voxpopuli*
  - config_name: pl
    data_files:
      - split: train_voxpopuli
        path: pl/voxpopuli*
  - config_name: pt
    data_files:
      - split: train_voxpopuli
        path: pt/voxpopuli*
  - config_name: ro
    data_files:
      - split: train_voxpopuli
        path: ro/voxpopuli*
  - config_name: sk
    data_files:
      - split: train_voxpopuli
        path: sk/voxpopuli*
  - config_name: sl
    data_files:
      - split: train_voxpopuli
        path: sl/voxpopuli*
  - config_name: sv
    data_files:
      - split: train_voxpopuli
        path: sv/voxpopuli*

Dataset Description, Collection, and Source

The MOSEL corpus is a multilingual dataset collection including up to 950K hours of open-source speech recordings covering the 24 official languages of the European Union. We collect data by surveying labeled and unlabeled speech corpora under open-source compliant licenses. In particular, MOSEL includes the automatic transcripts of 441k hours of unlabeled speech from VoxPopuli and LibriLight. The data is transcribed using Whisper large v3. Whisper is released under the OS Apache 2.0 License which allows releasing the generated content under any license. Since LibriLight, differently from VoxPopuli, contains segments longer than Whisper's maximum duration limit of 30sec, we split them into chunks of up to 30sec.

  • Curated by: Marco Gaido, Sara Papi, Luisa Bentivogli, Alessio Brutti, Mauro Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, and Matteo Negri
  • Funded by: FAIR, Meetween, and CINECA
  • Shared by: Fondazione Bruno Kessler

License

  • CC-BY-4.0

Dataset Sources

Dataset Structure

Data Config

The dataset is split into folders corresponding to the languages using the 2-letters ISO codes, one for each language. Within each folder, a split for each psuedo-labeled dataset is provided.

Data Field

id: alphanumeric identifier for the segment

language: extended language (e.g., "english")

text: the content of the psuedo label

hall_repeated_ngrams: True/False - indicates the repetition of an n-gram in text for a minimum number of times; for n in 1 to 2, the threshold is 4, for n in 3 to 5, it is 3

hall_long_word: True/False - indicates the presence of a word of at least 40 characters in text

hall_frequent_single_word: True/False - indicates that text consists of only one word which is the most frequent inside the whole text

Dataset Statistics (in hours)

Language (LangID) Labeled Unlabeled Total
Bulgarian (bg) 111 17609 17720
Croatian (hr) 55 8106 8161
Czech (cs) 591 18705 19296
Danish (da) 20 13600 13620
Dutch (nl) 3395 19014 22409
English (en) 437239 84704 521943
Estonian (et) 60 10604 10664
Finnish (fi) 64 14200 14264
French (fr) 26984 22896 49880
German (de) 9236 23228 32464
Greek (el) 35 17703 17738
Hungarian (hu) 189 17701 17890
Irish (ga) 17 0 17
Italian (it) 3756 21933 25689
Latvian (lv) 173 13100 13273
Lithuanian (lt) 36 14400 14436
Maltese (mt) 19 9100 9119
Polish (pl) 510 21207 21717
Portuguese (pt) 5492 17526 23018
Romanian (ro) 121 17906 18021
Slovak (sk) 61 12100 12161
Slovenian (sl) 32 11300 11332
Spanish (es) 17471 21526 38997
Swedish (sv) 58 16300 16358
Total 505725 444467 950192

Dataset Creation

To reproduce the dataset creation, please refer to the MOSEL README in the fbk-llm repository.

Citation

Release 1.0:

@inproceedings{mosel,
  title = {{MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages}},
  author = {Marco Gaido and Sara Papi and Luisa Bentivogli and Alessio Brutti and Mauro Cettolo and Roberto Gretter and Marco Matassoni and Mohamed Nabihand Matteo Negri},
  booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
  month = nov,
  year = "2024",
  address = "Miami, United States",
  publisher = "Association for Computational Linguistics",
}

Dataset Card Contact

@spapi