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README.md
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Automatic Speech Recognition
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#Languages
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Gujarati
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Automatic Speech Recognition
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#Languages
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Gujarati
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Identifier: SLR78
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Summary: Data set which contains recordings of native speakers of Gujarati.
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Category: Speech
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License: Attribution-ShareAlike 4.0 International
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Downloads (use a mirror closer to you):
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about.html [1.5K] (Information about the data set ) Mirrors: [China]
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LICENSE [20K] (License information for the data set ) Mirrors: [China]
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line_index_female.tsv [423K] (Lines recorded by the female speakers ) Mirrors: [China]
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line_index_male.tsv [393K] (Lines recorded by the male speakers ) Mirrors: [China]
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gu_in_female.zip [917M] (Archive containing recordings from female speakers ) Mirrors: [China]
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gu_in_male.zip [825M] (Archive file recordings from male speakers ) Mirrors: [China]
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About this resource:
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This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file.
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The data set has been manually quality checked, but there might still be errors.
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Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
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See LICENSE file for license information.
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Copyright 2018, 2019 Google, Inc.
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If you use this data in publications, please cite it as follows:
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@inproceedings{he-etal-2020-open,
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title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}},
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author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot},
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booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
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month = may,
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year = {2020},
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address = {Marseille, France},
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publisher = {European Language Resources Association (ELRA)},
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pages = {6494--6503},
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url = {https://www.aclweb.org/anthology/2020.lrec-1.800},
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ISBN = "{979-10-95546-34-4},
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}
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