{"id":120993,"date":"2024-12-16T15:46:07","date_gmt":"2024-12-16T13:46:07","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-20"},"modified":"2024-12-16T15:46:07","modified_gmt":"2024-12-16T13:46:07","slug":"obnovlenie-golosovyh-dannyh-mozilla-common-voice-20","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-20","title":{"rendered":"Update of voice data in Mozilla Common Voice 20","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
Mozilla has updated the Common Voice voice data sets, which include pronunciation examples from over 200,000 individuals. 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. Compared to the previous update, the volume of speech material in the collection has increased from 32.6 to 33.1 thousand hours of speech, of which 22.1 thousand hours have undergone verification. The number of supported languages has increased from 129 to 133 \u2014 new languages added include Aragonese, isiNdebele, Southern Sotho, and Tupi. <\/p>\n
The preparation of materials in English involved 94.9 thousand people, who dictated 3,631 hours of speech (there were 93.9 thousand participants and 3,587 hours previously). The dataset for the Belarusian language encompasses 8,521 participants and 1,860 hours of speech material (previously there were 8,444 participants and 1,846 hours), for Russian\u20143,365 participants and 281 hours (previously 3,296 participants and 278 hours), for Uzbek\u20142,211 participants and 265 hours (previously 2,200 participants and 265 hours), and for Ukrainian\u20141,120 participants and 114 hours (previously 1,104 participants and 114 hours). <\/p>\n
The Common Voice project facilitates collaborative efforts to build a database of voice samples that account for the diversity of voices and speech styles. Users are invited to record displayed phrases or evaluate the quality of data added by other users. The accumulated database of 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":"