mythicinfinity/libritts_opus

Dataset Card for LibriTTS

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.

Overview

This is the LibriTTS dataset, adapted for the datasets library.

Transcoding notes

• Source dataset: mythicinfinity/libritts
• Target codec: opus 48kbps

Usage

Splits

There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):

• dev.clean
• dev.other
• test.clean
• test.other
• train.clean.100
• train.clean.360
• train.other.500

Configurations

There are 3 configurations, each which limits the splits the load_dataset() function will download.

The default configuration is "all".

• "dev": only the "dev.clean" split (good for testing the dataset quickly)
• "clean": contains only "clean" splits
• "other": contains only "other" splits
• "all": contains only "all" splits

Example

Loading the clean config with only the train.clean.360 split.

load_dataset("blabble-io/libritts", "clean", split="train.clean.100")

Streaming is also supported.

load_dataset("blabble-io/libritts", streaming=True)

Columns

{
    "audio": datasets.Audio(sampling_rate=24_000),
    "text_normalized": datasets.Value("string"),
    "text_original": datasets.Value("string"),
    "speaker_id": datasets.Value("string"),
    "path": datasets.Value("string"),
    "chapter_id": datasets.Value("string"),
    "id": datasets.Value("string"),
}

Example Row

{
  'audio': {
    'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav', 
    'array': ..., 
    'sampling_rate': 24000
  }, 
  'text_normalized': 'How quickly he disappeared!"',
  'text_original': 'How quickly he disappeared!"',
  'speaker_id': '3081', 
  'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS/dev-clean/3081/166546/3081_166546_000028_000002.wav', 
  'chapter_id': '166546', 
  'id': '3081_166546_000028_000002'
}

Dataset Details

Dataset Sources [optional]

• Homepage: https://www.openslr.org/60/
• Paper: https://arxiv.org/abs/1904.02882

Citation

@ARTICLE{Zen2019-kz,
  title         = "{LibriTTS}: A corpus derived from {LibriSpeech} for
                   text-to-speech",
  author        = "Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and
                   Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui",
  abstract      = "This paper introduces a new speech corpus called
                   ``LibriTTS'' designed for text-to-speech use. It is derived
                   from the original audio and text materials of the
                   LibriSpeech corpus, which has been used for training and
                   evaluating automatic speech recognition systems. The new
                   corpus inherits desired properties of the LibriSpeech corpus
                   while addressing a number of issues which make LibriSpeech
                   less than ideal for text-to-speech work. The released corpus
                   consists of 585 hours of speech data at 24kHz sampling rate
                   from 2,456 speakers and the corresponding texts.
                   Experimental results show that neural end-to-end TTS models
                   trained from the LibriTTS corpus achieved above 4.0 in mean
                   opinion scores in naturalness in five out of six evaluation
                   speakers. The corpus is freely available for download from
                   http://www.openslr.org/60/.",
  month         =  apr,
  year          =  2019,
  copyright     = "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  archivePrefix = "arXiv",
  primaryClass  = "cs.SD",
  eprint        = "1904.02882"
}