libritts

• Description:

LibriTTS is a multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate, prepared by Heiga Zen with the assistance of Google Speech and Google Brain team members. The LibriTTS corpus is designed for TTS research. It is derived from the original materials (mp3 audio files from LibriVox and text files from Project Gutenberg) of the LibriSpeech corpus. The main differences from the LibriSpeech corpus are listed below:

1. The audio files are at 24kHz sampling rate.
2. The speech is split at sentence breaks.
3. Both original and normalized texts are included.
4. Contextual information (e.g., neighbouring sentences) can be extracted.
5. Utterances with significant background noise are excluded.

• Additional Documentation: Explore on Papers With Code

• Homepage: http://www.openslr.org/60

• Source code: tfds.datasets.libritts.Builder

• Versions:

  • 1.0.1 (default): No release notes.

• Download size: 78.42 GiB

• Dataset size: 271.41 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'dev_clean'	5,736
'dev_other'	4,613
'test_clean'	4,837
'test_other'	5,120
'train_clean100'	33,236
'train_clean360'	116,500
'train_other500'	205,044

• Feature structure:

FeaturesDict({
    'chapter_id': int64,
    'id': string,
    'speaker_id': int64,
    'speech': Audio(shape=(None,), dtype=int64),
    'text_normalized': Text(shape=(), dtype=string),
    'text_original': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
chapter_id	Tensor		int64
id	Tensor		string
speaker_id	Tensor		int64
speech	Audio	(None,)	int64
text_normalized	Text		string
text_original	Text		string

• Supervised keys (See as_supervised doc): ('text_normalized', 'speech')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{zen2019libritts,
  title = {LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
  author = {H. Zen and V. Dang and R. Clark and Y. Zhang and R. J. Weiss and Y. Jia and Z. Chen and Y. Wu},
  booktitle = {Proc. Interspeech},
  month = sep,
  year = {2019},
  doi = {10.21437/Interspeech.2019-2441},
}