task_categories:automatic-speech-recognition multilinguality:multilingual language:en language:de language:fr language:es language:pl language:it language:ro language:hu language:cs language:nl language:fi language:hr language:sk language:sl language:et language:lt license:cc0-1.0 license:other arxiv:2101.00390 region:us
---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopulo
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---

# Dataset Card for Voxpopuli

## Table of Contents
- [Table of Contents](#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/voxpopuli
- **Repository:** https://github.com/facebookresearch/voxpopuli
- **Paper:** https://arxiv.org/abs/2101.00390
- **Point of Contact:** [changhan@fb.com](mailto:changhan@fb.com), [mriviere@fb.com](mailto:mriviere@fb.com), [annl@fb.com](mailto:annl@fb.com)

### Dataset Summary

VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)

### Example usage

VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:

```python
from datasets import load_dataset

voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```

To load all the languages in a single dataset use "multilang" config name:

```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```

To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:

```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```

To load accented English data, use "en_accented" config name:

```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```

**Note that L2 English subset contains only `test` split.**


### Supported Tasks and Leaderboards

* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)

### Languages

VoxPopuli contains labelled (transcribed) data for 18 languages:

| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| Italian | It | 91 | 306 | 757K |


Accented speech transcribed data has 15 various L2 accents:

| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Italian | en_it | 1.11 | 15 |

## Dataset Structure

### Data Instances

```python
{
  'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
  'language': 11,  # "hr"
  'audio': {
    'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
    'array': array([-0.01434326, -0.01055908,  0.00106812, ...,  0.00646973], dtype=float32),
    'sampling_rate': 16000
  },
  'raw_text': '',
  'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
  'gender': 'female',
  'speaker_id': '119431',
  'is_gold_transcript': True,
  'accent': 'None'
}
```

### Data Fields

* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment 
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".

### Data Splits

All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

The raw data is collected from youtube

#### Who are the source language producers?

Speakers are participants of the European Parliament events, many of them are EU officials.