metadata

task_categories:
  - translation
  - automatic-speech-recognition
language:
  - gl
  - en
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text_gl
      dtype: string
    - name: text_en
      dtype: string
  splits:
    - name: train
      num_bytes: 1867365673.628
      num_examples: 2742
    - name: validation
      num_bytes: 336601848
      num_examples: 496
    - name: test
      num_bytes: 143321367
      num_examples: 212
  download_size: 2338654742
  dataset_size: 2347288888.6280003

Dataset Details

FLEURS-SpeechT-GL-EN is Galician-to-English dataset for Speech Translation task.

This dataset has been compiled from Google's FLEURS data set. It contains ~10h11m of galician audios along with its text transcriptions and the correspondant English translations.

Preprocessing

This dataset is based on Google's FLEURS speech dataset, by aligning English and Galician data. The alignment process has been performed following ymoslem's FLEURS dataset processing script

English translations quality

To get a sense of the quality of the english text with respect to the galician transcriptions, a Quality Estimation model has been applied.

QE model: Unbabel/wmt23-cometkiwi-da-xl
Average QE score: 0.76

Dataset Structure

DatasetDict({
    train: Dataset({
        features: ['id', 'audio', 'text_gl', 'text_en'],
        num_rows: 3450
    })
})

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},

Yasmin Moslem preprocessing script: https://github.com/ymoslem/Speech/blob/main/FLEURS-GA-EN.ipynb

Dataset Card Contact

Juan Julián Cea Morán ([email protected])