README.md

---
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-*
language:
- aaa
- aab
- aac
- aad
- aaf
- aai
- aal
- aao
- aap
- aar
- aau
- aaw
- aaz
- aba
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- ace
- acf
- ach
- acm
- acn
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- add
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- adj
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- aix
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- aqg
- aqm
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- are
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- btg
- btm
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- btu
- btx
- bub
- bud
- buf
- bug
- buh
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- buj
- buk
- bul
- bum
- bun
- buo
- bus
- buu
- buw
- bux
- buz
- bva
- bvc
- bvd
- bvh
- bvi
- bvm
- bvr
- bvu
- bvw
- bvz
- bwd
- bwe
- bwf
- bwi
- bwm
- bwo
- bwq
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- bwt
- bwu
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- bwx
- bxa
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- bxg
- bxh
- bxk
- bxl
- bxq
- bxr
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- bya
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- byd
- bye
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- bys
- byv
- byx
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- bza
- bzd
- bze
- bzf
- bzh
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- bzv
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- bzx
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- cao
- cap
- caq
- car
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- cat
- cav
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- cbd
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- cbj
- cbk
- cbn
- cbo
- cbr
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- cbt
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- cbv
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- ccg
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- ccj
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- cde
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- cdo
- cdr
- cdz
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- cfg
- cfm
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- cmr
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- cnq
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- cnw
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- cod
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- coh
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- com
- con
- cos
- cou
- cov
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- coz
- cpa
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- ctd
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- ctg
- ctl
- cto
- ctp
- ctt
- ctu
- ctz
- cua
- cub
- cuc
- cui
- cuk
- cul
- cut
- cuv
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- cvg
- cvn
- cya
- cyb
- cym
- cyo
- czh
- czn
- czt
- daa
- dad
- dag
- dai
- dak
- dan
- dao
- daq
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- dav
- daw
- dax
- dbb
- dbd
- dbi
- dbj
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- dbn
- dbq
- dbv
- dby
- dcc
- dde
- ddg
- ddn
- dee
- def
- deg
- deh
- dei
- dem
- der
- deu
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- dga
- dgc
- dgd
- dge
- dgg
- dgh
- dgi
- dgo
- dgr
- dgx
- dgz
- dhd
- dhg
- dhi
- dhm
- dhn
- dho
- dhv
- dhw
- dia
- dib
- did
- dig
- dih
- dij
- dik
- dil
- dim
- dio
- dip
- dir
- dis
- diu
- div
- diw
- diz
- djc
- dje
- djk
- djm
- djn
- djo
- djr
- dka
- dks
- dkx
- dln
- dma
- dme
- dmg
- dmo
- dmr
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- dmw
- dna
- dnd
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- dnj
- dnn
- dnw
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- doa
- dob
- dof
- doo
- dop
- dor
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- dot
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- dox
- doy
- doz
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- dre
- drg
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- dsh
- dsn
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- due
- dug
- duh
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- duq
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- dus
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- duv
- duw
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- dwa
- dwr
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- dwz
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- dyi
- dyo
- dyu
- dza
- dzg
- dzl
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- eit
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- elm
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- ems
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- enb
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- enl
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- enq
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- eot
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- erh
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- ert
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- esh
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- etr
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- eyo
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- foi
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- khc
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- khg
- khj
- khk
- khl
- khm
- khn
- khq
- khr
- khs
- kht
- khu
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- sya
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- syk
- syl
- sym
- sys
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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:

```
@misc{chen2024robustspeechrepresentationlearning,
      title={Towards Robust Speech Representation Learning for Thousands of Languages}, 
      author={William Chen and Wangyou Zhang and Yifan Peng and Xinjian Li and Jinchuan Tian and Jiatong Shi and Xuankai Chang and Soumi Maiti and Karen Livescu and Shinji Watanabe},
      year={2024},
      eprint={2407.00837},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.00837}, 
}

@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.