
# Dataset Card for covost2

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## 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 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](https://github.com/facebookresearch/covost/blob/main/LICENSE)

### 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](https://github.com/patil-suraj) for adding this dataset.
task_categories:automatic-speech-recognition annotations_creators:expert-generated language_creators:crowdsourced language_creators:expert-generated multilinguality:multilingual size_categories:100K<n<1M source_datasets:extended|other-common-voice language:ar language:ca language:cy language:de language:es language:et language:fa language:fr language:id language:it language:ja language:lv language:mn language:nl language:pt language:ru language:sl language:sv language:ta language:tr language:zh license:cc-by-nc-4.0 arxiv:2007.10310 region:us
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TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|other-common-voice #language-Arabic #language-Catalan #language-Welsh #language-German #language-Spanish #language-Estonian #language-Persian #language-French #language-Indonesian #language-Italian #language-Japanese #language-Latvian #language-Mongolian #language-Dutch #language-Portuguese #language-Russian #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Chinese #license-cc-by-nc-4.0 #arxiv-2007.10310 #region-us 

Dataset Card for covost2
========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard:
* Point of Contact: Changhan Wang (changhan@URL), Juan Miguel Pino (juancarabina@URL), Jiatao Gu (jgu@URL)


### 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 URL .


### 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'.


### 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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### 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


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


CC BY-NC 4.0


### Contributions


Thanks to @patil-suraj for adding this dataset.



### 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 URL . ### 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'. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


CC BY-NC 4.0 ### Contributions


Thanks to @patil-suraj for adding this dataset.
TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|other-common-voice #language-Arabic #language-Catalan #language-Welsh #language-German #language-Spanish #language-Estonian #language-Persian #language-French #language-Indonesian #language-Italian #language-Japanese #language-Latvian #language-Mongolian #language-Dutch #language-Portuguese #language-Russian #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Chinese #license-cc-by-nc-4.0 #arxiv-2007.10310 #region-us 
 ### 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 URL . ### 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'. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


CC BY-NC 4.0 ### Contributions


Thanks to @patil-suraj for adding this dataset.
passage: TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|other-common-voice #language-Arabic #language-Catalan #language-Welsh #language-German #language-Spanish #language-Estonian #language-Persian #language-French #language-Indonesian #language-Italian #language-Japanese #language-Latvian #language-Mongolian #language-Dutch #language-Portuguese #language-Russian #language-Slovenian #language-Swedish #language-Tamil #language-Turkish #language-Chinese #license-cc-by-nc-4.0 #arxiv-2007.10310 #region-us 
### 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 URL .### 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
-----------------