{
 "id": "mythicinfinity/libritts",
 "owner": "mythicinfinity",
 "name": "libritts",
 "downloads": 6615,
 "likes": 34,
 "last_modified": "2024-02-09T21:19:32.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-to-speech"
 ],
 "citation": "@ARTICLE{Zen2019-kz,\n  title         = \"{LibriTTS}: A corpus derived from {LibriSpeech} for\n                   text-to-speech\",\n  author        = \"Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and\n                   Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui\",\n  abstract      = \"This paper introduces a new speech corpus called\n                   ``LibriTTS'' designed for text-to-speech use. It is derived\n                   from the original audio and text materials of the\n                   LibriSpeech corpus, which has been used for training and\n                   evaluating automatic speech recognition systems. The new\n                   corpus inherits desired properties of the LibriSpeech corpus\n                   while addressing a number of issues which make LibriSpeech\n                   less than ideal for text-to-speech work. The released corpus\n                   consists of 585 hours of speech data at 24kHz sampling rate\n                   from 2,456 speakers and the corresponding texts.\n                   Experimental results show that neural end-to-end TTS models\n                   trained from the LibriTTS corpus achieved above 4.0 in mean\n                   opinion scores in naturalness in five out of six evaluation\n                   speakers. The corpus is freely available for download from\n                   http://www.openslr.org/60/.\",\n  month         =  apr,\n  year          =  2019,\n  copyright     = \"http://arxiv.org/licenses/non",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive."
}