meetween/mumospee_mosel

Dataset Summary

This dataset is a modified version of the MOSEL and VoxPopuli corpus, converted into parquet format to facilitate optimized I/O operations in high-performance and distributed computing environments. 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. 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.

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Source Data

• Original Dataset: MOSEL, VoxPopuli
• License: This derived dataset is shared under the same license, with modifications only to format for efficiency.

Modifications

• Data Format: Converted to parquet format to enhance I/O performance for distributed training, reducing latency during data loading and retrieval.
• Efficiency Optimization: Restructured for reduced storage footprint and faster I/O on high-performance clusters by leveraging parquet’s efficient compression and columnar storage.

Dataset Structure

• File Format: Parquet files.
• Languages: 24 languages retained from the original MOSEL and VoxPopuli dataset.
• Audio Sampling Rate: Matches original dataset specifications for high-fidelity speech data.
• Multilingual Speaker Representation: Over 441K hours across multiple languages, preserving MOSEL’s speaker diversity and multilingual alignment.

Usage

This dataset is ideal for use in large-scale multilingual speech-to-text translation tasks, especially in distributed and high-performance computing environments. The parquet format enhances usability by minimizing I/O overhead, making it well-suited for high-throughput training.

Attribution

This dataset is based on the original MOSEL and VoxPopuli dataset, with modifications for I/O optimization by converting to parquet format. Please cite the original CoVoST dataset in any publications or projects using this dataset.

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",
}