user_libri_audio

• Description:

UserLibri is a dataset containing paired audio-transcripts and additional text only data for each of 107 users. It is a reformatting of the LibriSpeech dataset found at http://www.openslr.org/12, reorganizing the data into users with an average of 52 LibriSpeech utterances and about 6,700 text example sentences per user. The UserLibriAudio class provides access to the audio-transcript pairs. See UserLibriText for the additional text data.

• Homepage: https://www.kaggle.com/datasets/google/userlibri

• Source code: tfds.audio.userlibri_audio_data.UserLibriAudio

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: Unknown size

• Dataset size: 3.37 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'audio': Audio(shape=(None,), dtype=int64, description=The audio clip containing a snippet from a book read aloud),
    'book_id': Text(shape=(), dtype=string),
    'id': Text(shape=(), dtype=string),
    'speaker_id': Text(shape=(), dtype=string),
    'transcript': Text(shape=(), dtype=string),
    'user_id': Text(shape=(), dtype=string),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): ('audio', 'transcript')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{breiner2022userlibri,
  title={UserLibri: A Dataset for ASR Personalization Using Only Text},
  author={Breiner, Theresa and Ramaswamy, Swaroop and Variani, Ehsan and Garg, Shefali and Mathews, Rajiv and Sim, Khe Chai and Gupta, Kilol and Chen, Mingqing and McConnaughey, Lara},
  booktitle={Proc. Interspeech 2022},
  year={2022}
}