metadata

dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: timestamps_start
      sequence: float64
    - name: timestamps_end
      sequence: float64
    - name: speakers
      sequence: string
  splits:
    - name: dev
      num_bytes: 2338411143
      num_examples: 216
    - name: test
      num_bytes: 5015872396
      num_examples: 232
  download_size: 7296384603
  dataset_size: 7354283539
configs:
  - config_name: default
    data_files:
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
tags:
  - speaker diarization
  - voice activity detection
license: cc-by-4.0
language:
  - en

Dataset Card for the Voxconverse dataset

VoxConverse is an audio-visual diarisation dataset consisting of multispeaker clips of human speech, extracted from YouTube videos. Updates and additional information about the dataset can be found on the dataset website.

Note: This dataset has been preprocessed using diarizers. It makes the dataset compatible with diarizers to fine-tune pyannote segmentation models.

Example Usage

from datasets import load_dataset
ds = load_dataset("diarizers-community/voxconverse")

print(ds)

gives:

DatasetDict({
    train: Dataset({
        features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
        num_rows: 136
    })
    validation: Dataset({
        features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
        num_rows: 18
    })
    test: Dataset({
        features: ['audio', 'timestamps_start', 'timestamps_end', 'speakers'],
        num_rows: 16
    })
})

Dataset source

Homepage: https://www.robots.ox.ac.uk/~vgg/data/voxconverse/
Repository: https://github.com/joonson/voxconverse?tab=readme-ov-file
Preprocessed using diarizers

Citation

@article{chung2020spot,
  title={Spot the conversation: speaker diarisation in the wild},
  author={Chung, Joon Son and Huh, Jaesung and Nagrani, Arsha and Afouras, Triantafyllos and Zisserman, Andrew},
  booktitle={Interspeech},
  year={2020}
}

Contribution

Thanks to @kamilakesbi and @sanchit-gandhi for adding this dataset.