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README.md
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MMS ulab v2 is a a massively multilingual speech dataset that contains **8900 hours** of unlabeled speech across **4023 languages**.
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This dataset includes the raw unsegmented audio in a 16kHz single channel format. It can be segmented into utterances with a voice activity detection (VAD) model such as [this one](https://github.com/wiseman/py-webrtcvad).
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We use 6700 hours of MMS ulab v2 (post-segmentation) to train [XEUS](), a multilingual speech encoder for 4000+ languages.
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## License and Acknowledgement
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MMS ulab v2 is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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MMS ulab v2 is a a massively multilingual speech dataset that contains **8900 hours** of unlabeled speech across **4023 languages**. In total, it contains 189 language families.
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It can be used for language identification, spoken language modelling, or speech representation learning.
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MMS ulab v2 is a reproduced and extended version of the MMS ulab dataset originally proposed in [Scaling Speech Technology to 1000+ Languages](https://arxiv.org/abs/2305.13516), covering more languages and containing more data.
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This dataset includes the raw unsegmented audio in a 16kHz single channel format. It can be segmented into utterances with a voice activity detection (VAD) model such as [this one](https://github.com/wiseman/py-webrtcvad).
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We use 6700 hours of MMS ulab v2 (post-segmentation) to train [XEUS](), a multilingual speech encoder for 4000+ languages.
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For more details about the dataset and its usage, please refer to our [paper]().
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## License and Acknowledgement
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MMS ulab v2 is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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