license:apache-2.0 size_categories:1K<n<10K format:parquet modality:audio modality:text library:datasets library:dask library:mlcroissant library:polars arxiv:2205.12446 region:us
---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: text_yo
    dtype: string
  - name: text_en
    dtype: string
  splits:
  - name: train
    num_bytes: 3174127504.6271706
    num_examples: 3150
  - name: test
    num_bytes: 176340437.12373167
    num_examples: 175
  - name: validation
    num_bytes: 176340413.12373167
    num_examples: 175
  download_size: 3526876016
  dataset_size: 3526808354.874634
---

## Dataset Summary

This is the Yoruba-to-English translation dataset culled from the Google FLEURS dataset. The Yoruba portion consists of utterances corresponding to 13:48:32 hours of audio data in the train set, 44:32 minutes of audio data in validation set, and 45:27 minutes of audio data in the test set. The audio recordings are sampled at 16kHz. The dataset consists of the audio recording with Yoruba transcriptions and corresponding English translations.

## Dataset Structure

DatasetDict({
    train: Dataset({
        features: ['id', 'audio', 'text_yo', 'text_en'],
        num_rows: 3150
    })
    test: Dataset({
        features: ['id', 'audio', 'text_yo', 'text_en'],
        num_rows: 175
    })
    validation: Dataset({
        features: ['id', 'audio', 'text_yo', 'text_en'],
        num_rows: 175
    })
})


## Dataset Features

The dataset contains the following features:
* audio: Audio recordings sampled at 16 kHz.
* yo: Yoruba transcription of the audio.
* en: English translation of the Yoruba transcription.

## Dataset Processing

The dataset was extracted directly from the google/fleurs dataset. https://huggingface.co/datasets/google/fleurs.
The columns were reduced to only id, audio, and transcription for the Yoruba language portion, and id and transcription for the English language portion.
The audio sample was resampled to 16kHz.
The Yoruba transcriptions and English translations were aligned with each other.


## Citation
"""
>@article{fleurs2022arxiv,
> title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
> author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
> journal={arXiv preprint arXiv:2205.12446},
> url = {https://arxiv.org/abs/2205.12446},
> year = {2022},
"""