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--- |
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license: cc-by-nc-sa-4.0 |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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splits: |
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- name: train |
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num_bytes: 6731807325 |
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num_examples: 820 |
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download_size: 6611613572 |
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dataset_size: 6731807325 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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language: |
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- multilingual |
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- en |
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task_categories: |
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- audio-to-audio |
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--- |
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The WikiTongues speech corpus is a collection of conversational audio across 700+ languages. |
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It can be used for spoken language modelling or speech representation learning. |
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This dataset includes the raw unsegmented audio in a 16kHz single channel format. |
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Each clip is usually 2-10 minutes long, and contains one or more speakers conversing in their language(s). |
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Sometimes, a speaker may switch languages within a single clip. |
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The total dataset size is around 70 hours. |
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**The current version of the dataset does not include labels for the language(s) being spoken in each clip. This information will be included in an update in the near future** |
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This dataset was crawled from the [WikiTongues project](https://wikitongues.org/), which collected the original recordings. |
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We use this corpus 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/). |
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License and Acknowledgement |
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WikiTongues is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license. |
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If you use this dataset, we ask that you cite our paper: |
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``` |
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@misc{chen2024robustspeechrepresentationlearning, |
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title={Towards Robust Speech Representation Learning for Thousands of Languages}, |
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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}, |
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year={2024}, |
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eprint={2407.00837}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2407.00837}, |
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} |
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``` |
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And credit the original creators of the audio. |
