{"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\/da\/blog\/news\/otkryt-kod-spleeter-sistemy-dlya-razdeleniya-muzyki-i-golosa","title":{"rendered":"Koden til Spleeter, systemet til separation af musik og stemme, er blevet \u00e5bnet","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Streamingudbyderen Deezer har \u00e5bnet<\/a><\/noindex> de originale koder til det eksperimentelle projekt Spleeter, der udvikler et system til maskinl\u00e6ring for at separere lydkilder fra komplekse musikkompositioner. Programmet g\u00f8r det muligt at fjerne vokalen fra en komposition og kun beholde det musikalske akkompagnement, manipulere lyden af enkelte instrumenter eller kassere musikken og kun beholde stemmen til at l\u00e6gge ovenp\u00e5 et andet lydspor, skabe mixes, karaoke eller transskription. Projektkoden er skrevet i Python ved brug af Tensorflow-motoren og distribueres<\/a><\/noindex> under MIT-licensen.<\/p>\n

For at downloade tilbydes<\/a><\/noindex> allerede tr\u00e6nede modeller til at adskille vokalen (\u00e9n stemme) fra akkompagnementet, samt til at opdele i 4 og 5 spor, der inkluderer vokal, trommer, bas, klaver og \u00f8vrig lyd. Spleeter kan anvendes b\u00e5de som en Python-bibliotek og som et separat kommandolinjev\u00e6rkt\u00f8j. I den enkleste form oprettes der baseret p\u00e5 indgangsfilen to, fire eller fem filer med stemmen og komponenter fra akkompagnementet (vocals.wav, drums.wav, bass.wav, piano.wav, other.wav).<\/a><\/noindex> Ved opdeling i 2 og 4 spor leverer Spleeter meget h\u00f8j ydeevne; for eksempel tager det at opdele en lydfil i 4 spor p\u00e5 en GPU 100 gange mindre tid end varigheden af den oprindelige komposition. P\u00e5 et system med GPU NVIDIA GeForce GTX 1080 og 32-kernet CPU Intel Xeon Gold 6134 blev behandling af testkolleksjonen musDB, der varer tre timer og 27 minutter, udf\u00f8rt p\u00e5 90 sekunder.<\/p>\n

Blandt fordelene ved Spleeter, sammenlignet med andre udviklinger inden for lydadskillelse, s\u00e5som det \u00e5bne projekt<\/p>\n

\"Koden<\/a><\/noindex><\/center><\/p>\n


\n<\/p>\n


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

Open-Unmix , n\u00e6vnes anvendelsen af mere kvalitetsfulde modeller, der er bygget p\u00e5 basis af en omfattende samling lydfiler. P\u00e5 grund af ophavsretsbegr\u00e6nsninger er forskere inden for maskinl\u00e6ring begr\u00e6nset til adgangen til ret sparsomme offentligt tilg\u00e6ngelige samlinger af musikfiler, mens modellerne til Spleeter er bygget med data fra et omfattende musikbibliotek fra Deezer.<\/a><\/noindex>sammenligning <\/p>\n

If\u00f8lge sammenligning<\/a><\/noindex> Med Open-Unmix er Spleeter i stand til at udf\u00f8re adskillelse cirka 35% hurtigere ved testning p\u00e5 CPU, underst\u00f8tter MP3-filer og genererer betydeligt bedre resultater (ved udtr\u00e6kning af stemmen i Open-Unmix forbliver der spor af nogle instrumenter, hvilket sandsynligvis skyldes, at Open-Unmix-modellerne er tr\u00e6net p\u00e5 en samling af blot 150 kompositioner). <\/p>\n

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

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

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