• Description:

LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.

It's recommended to use lazy audio decoding for faster reading and smaller dataset size: - install tensorflow_io library: pip install tensorflow-io - enable lazy decoding: tfds.load('librispeech', builder_kwargs={'config': 'lazy_decode'})

Split Examples
'dev_clean' 2,703
'dev_other' 2,864
'test_clean' 2,620
'test_other' 2,939
'train_clean100' 28,539
'train_clean360' 104,014
'train_other500' 148,688
  • Feature structure:
    'chapter_id': int64,
    'id': string,
    'speaker_id': int64,
    'speech': Audio(shape=(None,), dtype=int16),
    'text': Text(shape=(), dtype=string),
  • Feature documentation:
Feature Class Shape Dtype Description
chapter_id Tensor int64
id Tensor string
speaker_id Tensor int64
speech Audio (None,) int16
text Text string
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},

librispeech/default (default config)

  • Config description: Default dataset.

  • Versions:

    • 2.1.1 (default): Fix speech data type with dtype=tf.int16.
    • 2.1.2: Add 'lazy_decode' config.
  • Dataset size: 304.47 GiB

  • Examples (tfds.as_dataframe):


  • Config description: Raw audio dataset.

  • Versions:

    • 2.1.1: Fix speech data type with dtype=tf.int16.
    • 2.1.2 (default): Add 'lazy_decode' config.
  • Dataset size: 59.37 GiB

  • Examples (tfds.as_dataframe): Missing.