{"id":52569,"date":"2019-11-11T00:00:00","date_gmt":"2019-11-10T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/otkryt-kod-spleeter-sistemy-dlya-razdeleniya-muzyki-i-golosa"},"modified":"2020-02-18T14:00:20","modified_gmt":"2020-02-18T11:00:20","slug":"otkryt-kod-spleeter-sistemy-dlya-razdeleniya-muzyki-i-golosa","status":"publish","type":"post","link":"https:\/\/prohoster.info\/pt\/blog\/news\/otkryt-kod-spleeter-sistemy-dlya-razdeleniya-muzyki-i-golosa","title":{"rendered":"C\u00f3digo do Spleeter, sistema para separa\u00e7\u00e3o de m\u00fasica e voz, \u00e9 aberto","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

O provedor de streaming Deezer abriu<\/a><\/noindex> os c\u00f3digos-fonte do projeto experimental Spleeter, que desenvolve um sistema de aprendizado de m\u00e1quina para separar fontes sonoras de composi\u00e7\u00f5es musicais complexas. O programa permite remover a vocal de uma composi\u00e7\u00e3o e deixar apenas o acompanhamento musical, manipular o som de instrumentos individuais ou descartar a m\u00fasica e deixar a voz para sobreposi\u00e7\u00e3o em outra faixa, cria\u00e7\u00e3o de mixagens, karaok\u00ea ou transcri\u00e7\u00e3o. O c\u00f3digo do projeto \u00e9 escrito em Python usando o motor Tensorflow e \u00e9 distribu\u00eddo<\/a><\/noindex> sob a licen\u00e7a MIT.<\/p>\n

Para download s\u00e3o oferecidos<\/a><\/noindex> modelos j\u00e1 treinados para separa\u00e7\u00e3o de vocal (uma voz) do acompanhamento, bem como para separa\u00e7\u00e3o em 4 e 5 trilhas, incluindo vocal, bateria, baixo, piano e outros sons. O Spleeter pode ser utilizado como uma biblioteca Python, bem como na forma de uma ferramenta de linha de comando isolada. No caso mais simples, com base no arquivo original s\u00e3o criados<\/a><\/noindex> dois, quatro ou cinco arquivos com a voz e os componentes do acompanhamento (vocals.wav, drums.wav, bass.wav, piano.wav, other.wav).<\/p>\n

Na separa\u00e7\u00e3o em 2 e 4 trilhas, o Spleeter oferece um desempenho muito alto, por exemplo, ao utilizar GPU, a separa\u00e7\u00e3o de um arquivo de \u00e1udio em 4 trilhas leva 100 vezes menos tempo do que a dura\u00e7\u00e3o da composi\u00e7\u00e3o original. Em um sistema com uma GPU NVIDIA GeForce GTX 1080 e um CPU Intel Xeon Gold 6134 de 32 n\u00facleos, o processamento de uma cole\u00e7\u00e3o de teste musDB, com dura\u00e7\u00e3o de tr\u00eas horas e 27 minutos, foi realizado em 90 segundos.<\/p>\n

\"C\u00f3digo<\/a><\/noindex><\/center><\/p>\n


\n<\/p>\n


\n<\/center><\/p>\n

Entre as vantagens do Spleeter, em compara\u00e7\u00e3o com outras implementa\u00e7\u00f5es na \u00e1rea de separa\u00e7\u00e3o de \u00e1udio, como o projeto aberto Open-Unmix<\/a><\/noindex>, menciona-se a aplica\u00e7\u00e3o de modelos de melhor qualidade, constru\u00eddos com base em uma extensa cole\u00e7\u00e3o de arquivos de \u00e1udio. Devido a restri\u00e7\u00f5es de direitos autorais, os pesquisadores em aprendizado de m\u00e1quina est\u00e3o limitados ao acesso a cole\u00e7\u00f5es de arquivos musicais p\u00fablicas bastante escassas, enquanto os modelos do Spleeter foram constru\u00eddos com dados de um extenso cat\u00e1logo musical do Deezer. <\/p>\n

De acordo com compara\u00e7\u00e3o<\/a><\/noindex> com Open-Unmix, a ferramenta Spleeter realiza a separa\u00e7\u00e3o cerca de 35% mais r\u00e1pido em testes com CPU, suporta arquivos MP3 e gera resultados significativamente mais qualitativos (na separa\u00e7\u00e3o de voz em Open-Unmix, permanecem vest\u00edgios de alguns instrumentos, o que provavelmente se deve ao fato de que os modelos Open-Unmix foram treinados em uma cole\u00e7\u00e3o de apenas 150 composi\u00e7\u00f5es). <\/p>\n

<\/a><\/noindex><\/p>\n

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

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