Speech dataset resources: Difference between revisions

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|[https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/TMEDTX LibriVoxDeEn]
|[https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/TMEDTX LibriVoxDeEn]
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|Audio books (.wav file format)
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|Creative Commons Attribution 4.0 Non-Commercial ShareAlike Internation License
|Creative Commons Attribution 4.0 Non-Commercial ShareAlike Internation License

Revision as of 11:46, 27 September 2023

This table summarizes dataset resources available

Note: You have editing rights to the table, so you can edit/adjust it to your needs*. Your instructors will clean it up afterwards.

Dataset name Type of data Annotation remarks License Metadata Name/s of people who are entering the data
LibriSpeech Audio books (clean, flac format)
  • Text is converted into upper-case, removed punctuation, expanding common abbreviations and non-standard words.
  • The transcriptions are aligned and segmented automatically.
Creative Commons Attribution 4.0 International License.
  • Language: English
  • The dataset is split into 3 sections with 100.6, 363.6, 496.7 hours of speech.
  • The gender ratio of speakers is about half and half.
  • Dataset is made by Vassil Panayotov.
  • Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.
Hugging Face
LibriVoxDeEn Audio books (.wav file format) Creative Commons Attribution 4.0 Non-Commercial ShareAlike Internation License
x
x

*) this means: you may also add additional pages and link to them in this table

Notes on LibriSpeech

  • We only used the development set to test our ASR code in Python;
  • The names of the speakers who recorded all the audiobooks contained in this corpus are also available in a separate text file;
  • Exhaustive information about this dataset can be found in this article;
  • This dataset was included in the Kaldi speech recognition toolkit.