{"id":112482,"date":"2023-12-24T21:10:24","date_gmt":"2023-12-24T19:10:24","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-16-0"},"modified":"2023-12-24T21:10:24","modified_gmt":"2023-12-24T19:10:24","slug":"obnovlenie-golosovyh-dannyh-mozilla-common-voice-16-0","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-16-0","title":{"rendered":"Update of Mozilla Common Voice voice data 16.0","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
Mozilla has updated its Common Voice voice data sets, which include pronunciation samples from over 200,000 individuals. The data is published as public domain (CC0). The provided sets can be used in machine learning systems to build speech recognition and synthesis models. Compared to the last update, the volume of speech material in the collection has increased from 28.7 to 30.3 thousand hours of speech, of which 19.7 thousand hours have been validated. The number of supported languages has risen from 114 to 120 (Yiddish, Latgalian, Ligurian, Ossetian, Telugu, and Western Sierra Puebla Nahuatl have been added). <\/p>\n
A total of 90.67 thousand people participated in the preparation of materials in English, dictating 3438 hours of speech (there were 88.9 thousand participants and 3347 hours). The set for the Belarusian language includes 8249 participants and 1641 hours of speech material (previously there were 8205 participants and 1632 hours), for the Russian language there are 3133 participants and 265 hours (previously there were 3053 participants and 260 hours), for Uzbek there are 2151 participants and 264 hours (previously there were 2141 participants and 263 hours), and for Ukrainian there are 1058 participants and 108 hours (previously there were 1024 participants and 105 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. \n Source: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"