---
license: cc-by-nc-sa-4.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: iso3
    dtype: string
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  splits:
  - name: train
    num_bytes: 572226455183.244
    num_examples: 221689
  download_size: 569642045980
  dataset_size: 572226455183.244
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
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- sya
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- syk
- syl
- sym
- sys
- syw
- szb
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task_categories:
- audio-to-audio
- audio-classification
---

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.
It can be used for language identification, spoken language modelling, or speech representation learning.

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.
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).
We use 6700 hours of MMS ulab v2 (post-segmentation) to train [XEUS](https://huggingface.co/espnet/xeus), a multilingual speech encoder for 4000+ languages. 

For more details about the dataset and its usage, please refer to our [paper](https://wanchichen.github.io/pdf/xeus.pdf) or [project page](https://www.wavlab.org/activities/2024/xeus/).

## License and Acknowledgement

MMS ulab v2 is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.

If you use this dataset, we ask that you cite the following papers:

```
@article{pratap2024scaling,
  title={Scaling speech technology to 1,000+ languages},
  author={Pratap, Vineel and Tjandra, Andros and Shi, Bowen and Tomasello, Paden and Babu, Arun and Kundu, Sayani and Elkahky, Ali and Ni, Zhaoheng and Vyas, Apoorv and Fazel-Zarandi, Maryam and others},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={97},
  pages={1--52},
  year={2024}
}

```

And also reference [The Global Recordings Network](https://globalrecordings.net/en/copyright), the original source of the data.