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facebook
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covost2

Tasks:
Automatic Speech Recognition
Languages:
Arabic
Catalan
Welsh
Size:
100K<n<1M
ArXiv:
License:
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metadata
annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
  - expert-generated
languages:
  - fr
  - de
  - es
  - ca
  - it
  - ru
  - zh-CN
  - pt
  - fa
  - et
  - mn
  - nl
  - tr
  - ar
  - sv-SE
  - lv
  - sl
  - ta
  - ja
  - id
  - cy
licenses:
  - cc-by-nc-4-0
multilinguality:
  - multilingual
size_categories:
  - 100K<n<1M
source_datasets:
  - extended|other-common-voice
task_categories:
  - automatic-speech-recognition
task_ids: []
paperswithcode_id: null
pretty_name: CoVoST 2

Dataset Card for covost2

Table of Contents

Dataset Description

Dataset Summary

CoVoST 2 is a large-scale multilingual speech translation corpus covering translations from 21 languages into English
and from English into 15 languages. The dataset is created using Mozillas open-source Common Voice database of
crowdsourced voice recordings. There are 2,900 hours of speech represented in the corpus.

Supported Tasks and Leaderboards

speech-translation: The dataset can be used for Speech-to-text translation (ST). The model is presented with an audio file in one language and asked to transcribe the audio file to written text in another language. The most common evaluation metric is the BLEU score. Examples can be found at https://github.com/pytorch/fairseq/blob/master/examples/speech_to_text/docs/covost_example.md .

Languages

The dataset contains the audio, transcriptions, and translations in the following languages, French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian, Chinese, Welsh, Catalan, Slovenian, Estonian, Indonesian, Arabic, Tamil, Portuguese, Latvian, and Japanese.

Dataset Structure

Data Instances

A typical data point comprises the path to the audio file, usually called file, its transcription, called sentence, and the translation in target language called translation.

{'client_id': 'd277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658',
 'file': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3',
 'audio': {'path': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3',
           'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
           'sampling_rate': 48000},
 'id': 'common_voice_en_18540003',
 'sentence': 'When water is scarce, avoid wasting it.',
 'translation': 'Wenn Wasser knapp ist, verschwenden Sie es nicht.'}

Data Fields

  • file: A path to the downloaded audio file in .mp3 format.

  • audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].

  • sentence: The transcription of the audio file in source language.

  • translation: The transcription of the audio file in the target language.

  • id: unique id of the data sample.

Data Splits

config train validation test
en_de 289430 15531 15531
en_tr 289430 15531 15531
en_fa 289430 15531 15531
en_sv-SE 289430 15531 15531
en_mn 289430 15531 15531
en_zh-CN 289430 15531 15531
en_cy 289430 15531 15531
en_ca 289430 15531 15531
en_sl 289430 15531 15531
en_et 289430 15531 15531
en_id 289430 15531 15531
en_ar 289430 15531 15531
en_ta 289430 15531 15531
en_lv 289430 15531 15531
en_ja 289430 15531 15531
fr_en 207374 14760 14760
de_en 127834 13511 13511
es_en 79015 13221 13221
ca_en 95854 12730 12730
it_en 31698 8940 8951
ru_en 12112 6110 6300
zh-CN_en 7085 4843 4898
pt_en 9158 3318 4023
fa_en 53949 3445 3445
et_en 1782 1576 1571
mn_en 2067 1761 1759
nl_en 7108 1699 1699
tr_en 3966 1624 1629
ar_en 2283 1758 1695
sv-SE_en 2160 1349 1595
lv_en 2337 1125 1629
sl_en 1843 509 360
ta_en 1358 384 786
ja_en 1119 635 684
id_en 1243 792 844
cy_en 1241 690 690

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

CC BY-NC 4.0

Citation Information 

@misc{wang2020covost,
    title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
    author={Changhan Wang and Anne Wu and Juan Pino},
    year={2020},
    eprint={2007.10310},
    archivePrefix={arXiv},
    primaryClass={cs.CL}

Contributions

Thanks to @patil-suraj for adding this dataset.