Speech dataset resources: Difference between revisions
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|LibriSpeech | |LibriSpeech | ||
| | |Audio books (clean, flac format) | ||
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* Text is converted into upper-case, removed punctuation, expanding common abbreviations and non-standard words | * Text is converted into upper-case, removed punctuation, expanding common abbreviations and non-standard words. | ||
* The transcriptions are aligned and segmented automatically. | * The transcriptions are aligned and segmented automatically. | ||
|Creative Commons Attribution 4.0 International License. | |Creative Commons Attribution 4.0 International License. | ||
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* The dataset is split into 3 sections with 100.6, 363.6, 496.7 hours of speech | * Language: English | ||
* The gender ratio of speakers is about half and half. | |||
* 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. | |||
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* Dataset is made by Vassil Panayotov. | * Dataset is made by Vassil Panayotov. |
Revision as of 09:36, 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 |
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LibriSpeech | Audio books (clean, flac format) |
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Creative Commons Attribution 4.0 International License. |
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* 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.