Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Annotations Creators:
expert-generated
Source Datasets:
extended|other-common-voice
ArXiv:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ar
- ca
- cy
- de
- es
- et
- fa
- fr
- id
- it
- ja
- lv
- mn
- nl
- pt
- ru
- sl
- sv
- ta
- tr
- zh
language_bcp47:
- sv-SE
- zh-CN
license:
- 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_info:
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Dataset Card for covost2
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/facebookresearch/covost
- Repository: https://github.com/facebookresearch/covost
- Paper: https://arxiv.org/abs/2007.10310
- Leaderboard: [Needs More Information]
- Point of Contact: Changhan Wang (changhan@fb.com), Juan Miguel Pino (juancarabina@fb.com), Jiatao Gu (jgu@fb.com)
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 todataset.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 overdataset["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
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.