{"id":103904,"date":"2022-04-28T21:36:49","date_gmt":"2022-04-28T19:36:49","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-9-0"},"modified":"2022-04-28T21:36:49","modified_gmt":"2022-04-28T19:36:49","slug":"obnovlenie-golosovyh-dannyh-mozilla-common-voice-9-0","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-9-0","title":{"rendered":"Update of voice data in Mozilla Common Voice 9.0.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
Mozilla has released an update to the Common Voice voice data sets, including pronunciation examples from around 200,000 people. The data is published as public domain (CC0). The proposed sets can be used in machine learning systems to build speech recognition and synthesis models. <\/p>\n
Compared to the previous update, the volume of speech material in the collection has increased by 10% \u2014 from 18.2 to 20.2 thousand hours of speech. The number of supported languages has risen from 87 to 93. For 27 languages, over 100 hours of speech data has been accumulated, and for 9 languages, over 500 hours of speech data. For 9 languages, a share of female speech of at least 45% has also been achieved. <\/p>\n
Over 81 thousand people participated in the preparation of materials in English, dictating 2953 hours of speech (there were 79 thousand participants and 2886 hours). The dataset for Belarusian covers 6326 participants and 1054 hours of speech material (there were 6160 participants and 987 hours), for Russian \u2014 2585 participants and 201 hours (there were 2452 participants and 193 hours), for Uzbek \u2014 1503 participants and 231 hours (there were 1355 participants and 227 hours), and for Ukrainian \u2014 696 participants and 79 hours (there were 684 participants and 76 hours). <\/p>\n
The Common Voice project aims to organize collaborative efforts to accumulate a database of voice templates that reflects the full diversity of voices and speech styles. Users are invited to voice the phrases displayed on the screen or assess the quality of data contributed by other users. The accumulated database, containing recordings of various pronunciations of standard phrases in human speech, can be used without restrictions in machine learning systems and research projects. <\/p>\n