{"id":106128,"date":"2022-12-16T15:36:45","date_gmt":"2022-12-16T13:36:47","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/sistema-mashinnogo-obucheniya-stable-diffusion-adaptirovana-dlya-sinteza-muzyki"},"modified":"2022-12-16T15:36:45","modified_gmt":"2022-12-16T13:36:47","slug":"sistema-mashinnogo-obucheniya-stable-diffusion-adaptirovana-dlya-sinteza-muzyki","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/news\/sistema-mashinnogo-obucheniya-stable-diffusion-adaptirovana-dlya-sinteza-muzyki","title":{"rendered":"O sistema de aprendizado de m\u00e1quina Stable Diffusion \u00e9 adaptado para a s\u00edntese musical","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

O projeto Riffusion desenvolve uma variante do sistema de aprendizado de m\u00e1quina Stable Diffusion, adaptado para gerar m\u00fasica em vez de imagens. A m\u00fasica pode ser sintetizada a partir de uma descri\u00e7\u00e3o textual em linguagem natural ou com base em um modelo proposto. Os componentes para a s\u00edntese de m\u00fasica s\u00e3o escritos em Python usando o framework PyTorch e est\u00e3o dispon\u00edveis sob a licen\u00e7a MIT. A interface de liga\u00e7\u00e3o \u00e9 implementada em TypeScript e tamb\u00e9m \u00e9 distribu\u00edda sob a licen\u00e7a MIT. Os modelos treinados s\u00e3o abertos sob a licen\u00e7a permissiva Creative ML OpenRAIL-M, que permite o uso para fins comerciais. <\/p>\n

O projeto \u00e9 interessante porque continua a usar modelos de \"texto para imagem\" e \"imagem para imagem\" para gerar m\u00fasica, mas manipula espectrogramas como imagens. Em outras palavras, o cl\u00e1ssico Stable Diffusion foi treinado n\u00e3o em fotografias e imagens, mas em imagens de espectrogramas que refletem a altera\u00e7\u00e3o da frequ\u00eancia e amplitude da onda sonora ao longo do tempo. Assim, o resultado tamb\u00e9m \u00e9 um espectrograma, que \u00e9 ent\u00e3o transformado em uma representa\u00e7\u00e3o sonora.

\"O<\/center> <\/p>\n

O m\u00e9todo tamb\u00e9m pode ser utilizado para modificar composi\u00e7\u00f5es sonoras existentes e sintetizar m\u00fasica a partir de um modelo, de forma semelhante \u00e0 modifica\u00e7\u00e3o de imagens no Stable Diffusion. Por exemplo, durante a gera\u00e7\u00e3o, podem ser definidos modelos de espectrogramas com um estilo de refer\u00eancia, combinando diferentes estilos, realizando uma transi\u00e7\u00e3o suave de um estilo para outro ou fazendo altera\u00e7\u00f5es em um som existente para resolver tarefas como aumentar o volume de instrumentos individuais, mudar o ritmo e substituir instrumentos. Modelos tamb\u00e9m s\u00e3o usados para gerar composi\u00e7\u00f5es de longa dura\u00e7\u00e3o, compostas por uma s\u00e9rie de trechos pr\u00f3ximos uns dos outros, mudando um pouco ao longo do tempo. Trechos gerados separadamente s\u00e3o unidos em um fluxo cont\u00ednuo por meio da interpola\u00e7\u00e3o de par\u00e2metros internos do modelo.

\"O<\/center> <\/p>\n

Para criar um espectrograma a partir de som, utiliza-se a transforma\u00e7\u00e3o de Fourier por janela. Ao recriar o som a partir do espectrograma, surge o problema de determinar a fase (no espectrograma, apenas a frequ\u00eancia e a amplitude est\u00e3o presentes); para reconstruir isso, \u00e9 utilizado o algoritmo de aproxima\u00e7\u00e3o de Griffin-Lim.

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Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041f\u0440\u043e\u0435\u043a\u0442\u043e\u043c Riffusion \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f Stable Diffusion, \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0434\u043b\u044f \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043c\u0443\u0437\u044b\u043a\u0438 \u0432\u043c\u0435\u0441\u0442\u043e \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041c\u0443\u0437\u044b\u043a\u0430 \u043c\u043e\u0436\u0435\u0442 \u0441\u0438\u043d\u0442\u0435\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u043e\u0433\u043e \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043d\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u044f\u0437\u044b\u043a\u0435 \u0438\u043b\u0438 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u0440\u0435\u0434\u043b\u043e\u0436\u0435\u043d\u043d\u043e\u0433\u043e \u0448\u0430\u0431\u043b\u043e\u043d\u0430. \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u0441\u0438\u043d\u0442\u0435\u0437\u0430 \u043c\u0443\u0437\u044b\u043a\u0438 \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 PyTorch \u0438 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 MIT. \u041e\u0431\u0432\u044f\u0437\u043a\u0430 \u0441 \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u043e\u043c \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u0430 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 TypeScript \u0438 \u0442\u0430\u043a\u0436\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":106129,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-106128","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47O sistema de aprendizado de m\u00e1quina Stable Diffusion foi adaptado para a s\u00edntese de m\u00fasica | ProHoster","description":"O projeto Riffusion est\u00e1 desenvolvendo uma variante do sistema de aprendizado de 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\u0441\u0438\u043d\u0442\u0435\u0437\u0430 \u043c\u0443\u0437\u044b\u043a\u0438 | ProHoster","og:description":"\u041f\u0440\u043e\u0435\u043a\u0442\u043e\u043c Riffusion \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f Stable Diffusion, \u0430\u0434\u0430\u043f\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0434\u043b\u044f \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043c\u0443\u0437\u044b\u043a\u0438 \u0432\u043c\u0435\u0441\u0442\u043e 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