{"id":100885,"date":"2021-08-01T10:23:19","date_gmt":"2021-08-01T08:23:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-7-0"},"modified":"2021-08-01T10:23:19","modified_gmt":"2021-08-01T08:23:19","slug":"obnovlenie-golosovyh-dannyh-mozilla-common-voice-7-0","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/news\/obnovlenie-golosovyh-dannyh-mozilla-common-voice-7-0","title":{"rendered":"Aggiornamento dei dati vocali di Mozilla Common Voice 7.0","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Le aziende NVIDIA e Mozilla hanno presentato un aggiornamento dei set di dati vocali, raccolti nell'ambito dell'iniziativa Common Voice e includenti esempi di pronuncia di 182 mila persone, il che rappresenta un incremento del 25% rispetto a sei mesi fa. I dati sono stati pubblicati come dominio pubblico (CC0). I set proposti possono essere utilizzati in sistemi di apprendimento automatico per costruire modelli di riconoscimento e sintesi vocale. <\/p>\n

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Nella preparazione dei materiali in lingua inglese hanno partecipato pi\u00f9 di 75 mila persone, che hanno registrato 2637 ore di parlato confermato (rispetto a 66 mila partecipanti e 1686 ore). Interessante notare che al secondo posto per dimensione dei dati accumulati c'\u00e8 la lingua ruanda, per la quale sono state raccolte 2260 ore. Seguono il tedesco (1040), il catalano (920) e l'esperanto (840). Le lingue che stanno aumentando pi\u00f9 rapidamente la dimensione dei dati vocali includono il tailandese (crescita della base di 20 volte, da 12 a 250 ore), il luganda (da 8 a 80 ore), l'esperanto (da 100 a 840 ore) e il tamil (da 24 a 220 ore). <\/p>\n

Nel contesto della sua partecipazione al progetto Common Voice, NVIDIA ha preparato modelli gi\u00e0 addestrati basati su dati raccolti per sistemi di apprendimento automatico (supporto per PyTorch). I modelli sono distribuiti come parte dell'open toolset gratuito NVIDIA NeMo, che, ad esempio, \u00e8 gi\u00e0 utilizzato nei servizi vocali automatizzati di MTS e Sberbank. I modelli sono orientati all'uso in sistemi di riconoscimento vocale, sintesi vocale e trattamento delle informazioni in linguaggio naturale e possono risultare utili per i ricercatori impegnati nella creazione di sistemi di dialogo vocale, piattaforme di trascrizione e call center automatizzati. A differenza dei progetti disponibili in precedenza, i modelli pubblicati non sono limitati al riconoscimento della lingua inglese e coprono diverse lingue, accenti e forme di espressione. <\/p>\n

Ricordiamo che il progetto Common Voice \u00e8 mirato a organizzare un lavoro collaborativo per accumulare un database di esempi vocali che tenga conto di tutta la variet\u00e0 di voci e maniere di parlare. Gli utenti sono invitati a registrarne le frasi visualizzate sullo schermo o a valutare la qualit\u00e0 dei dati aggiunti da altri utenti. Il database accumulato con registrazioni di pronunce diverse di frasi standard del linguaggio umano pu\u00f2 essere utilizzato senza restrizioni in sistemi di apprendimento automatico e in progetti di ricerca. <\/p>\n

Secondo l'autore della libreria di riconoscimento vocale contiguo Vosk, i difetti del set Common Voice sono l'unilateralit\u00e0 del materiale vocale (prevalenza di uomini di et\u00e0 compresa tra i 20 e i 30 anni e mancanza di materiale con voci femminili, infantili e di anziani), l'assenza di variabilit\u00e0 del vocabolario (ripetizione delle stesse frasi) e la diffusione delle registrazioni in un formato MP3 che introduce distorsioni.
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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