Merge branch 'LSTM_music21_midi_encoding' of s444337/praca-magisterska into master
Simple LSTM Music Generator
This commit is contained in:
Gogs
commit
ec3296514a
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|
"c:/software/latex/texmf-dist/tex/latex/latexconfig/epstopdf-sys.cfg" 1550342073 678 4792914a8f45be57bb98413425e4c7af ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/mwcls/mwbk.cls" 1550342667 38805 274df96ed3aa756c4c77df610bde271a ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/mwcls/mwbk12.clo" 1550342667 5794 8b98de20f2007e1656b90be311a3ea14 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/epstopdf-base.sty" 1550342831 12095 5337833c991d80788a43d3ce26bd1c46 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/grfext.sty" 1550342831 7075 2fe3d848bba95f139de11ded085e74aa ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/kvoptions.sty" 1550342832 22417 1d9df1eb66848aa31b18a593099cf45c ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omlplm.fd" 1550343136 1553 7e55eec7e7ffbbc85187034f06b5977a ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omsplsy.fd" 1550343136 1472 cdbddbc0209f1dca4b38ccca913f65d7 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omxplex.fd" 1550343136 1283 a1205ac8611390c6de04f8d76756c3c4 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/ot4cmr.fd" 1550343136 2599 8c16345f9109faed2ca2ff52e16d8a6f ""
|
||||||
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"c:/software/latex/texmf-dist/tex/latex/polski/polski.sty" 1550343136 13310 401406f54546887504ce1857a307089b ""
|
||||||
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"c:/software/latex/texmf-dist/web2c/texmf.cnf" 1550341956 35176 ce7fee66be1fb01dfc6b702a7dbfa0ea ""
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||||||
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"c:/software/latex/texmf-var/fonts/map/pdftex/updmap/pdftex.map" 1550344777 3265577 7fb0ae9195d4a898deb882ebc26f3a7b ""
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||||||
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||||||
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"c:/software/latex/texmf.cnf" 1550344763 715 839ef4b14d931ce86420174d3a223d3e ""
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"chapter-style.sty" 1184432756 376 87871882de090607c810d89fd5ae18d9 ""
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|
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|
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|
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||||||
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|
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|
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|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
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|
||||||
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|
|||||||
This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.21) 28 MAY 2019 12:32
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This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.16) 13 JUN 2019 10:04
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||||||
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|
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|
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|
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|
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|
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|
||||||
**document.tex
|
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|
||||||
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|
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|
||||||
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|
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|
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|
||||||
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|
||||||
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|
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|
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|
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|
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|
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|
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\documentclass[utf8]{article}
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\documentclass[12pt,a4paper,reqno,twoside]{mwbk}
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\title{%
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\textheight 21.1 cm
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Generowanie muzyki \\
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przy pomocy głębokiego uczenia \\
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\large Music generation with deep learning}
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\author{%
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Cezary Pukownik \\
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\newline
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\small Opiekun pracy:\\
|
|
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dr hab. Tomasz Górecki}
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\date{2019-05-28}
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% Strona tytułowa
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|
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|
||||||
\newpage
|
\textsc{UNIWERSYTET IM. ADAMA MICKIEWICZA W POZNANIU}
|
||||||
|
|
||||||
|
\vglue 0.1 cm
|
||||||
|
|
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\textsc{WYDZIAŁ MATEMATYKI I INFORMATYKI}
|
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|
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|
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|
||||||
|
|
||||||
|
{\LARGE \bf Cezary Adam Pukownik}
|
||||||
|
|
||||||
|
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|
||||||
|
|
||||||
|
{\large Kierunek: Analiza i przetwarzanie danych}
|
||||||
|
|
||||||
|
\medskip
|
||||||
|
|
||||||
|
{\large Specjalność: Uczenie maszynowe}
|
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\medskip
|
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|
|
||||||
|
{\large Numer albumu: 444337}
|
||||||
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|
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|
\vspace{1.5cm}
|
||||||
|
|
||||||
|
{\Huge \bf Generowanie muzyki \\[4pt] przy pomocy głębokiego uczenia\\}
|
||||||
|
|
||||||
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\vspace{0.8cm}
|
||||||
|
|
||||||
|
{\large \bf Music generation with deep learning\\}
|
||||||
|
|
||||||
|
\end{center}
|
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\hspace{7.5cm}{napisana pod kierunkiem}\\[-12pt]
|
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|
\hspace{7.5cm}{dr hab. Tomasza Góreckiego}
|
||||||
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|
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|
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|
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|
\begin{center}
|
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|
\textsc{POZNAŃ 2020}
|
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|
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|
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|
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Poznań, dnia .....................
|
||||||
|
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|
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|
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||||||
|
\vglue 2.4 cm
|
||||||
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|
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|
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|
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\large \bf OŚWIADCZENIE
|
||||||
|
\end{center}
|
||||||
|
|
||||||
|
\vglue 1.2 cm
|
||||||
|
|
||||||
|
Ja, niżej podpisany Cezary Pukownik, student Wydziału Matematyki i Informatyki Uniwersytetu im. Adama Mickiewicza w Poznaniu oświadczam, że przedkładaną pracę dyplomową pt: "Generowanie muzyki przy pomocy głębokiego uczenia", napisałem samodzielnie. Oznacza to, że przy pisaniu pracy, poza niezbędnymi konsultacjami, nie korzystałem z pomocy innych osób, a w szczególności nie zlecałem opracowania rozprawy lub jej części innym osobom, ani nie odpisywałem tej rozprawy lub jej części od innych osób.
|
||||||
|
|
||||||
|
Oświadczam również, że egzemplarz pracy dyplomowej w wersji drukowanej jest całkowicie zgodny z egzemplarzem pracy dyplomowej w wersji elektronicznej.
|
||||||
|
|
||||||
|
Jednocześnie przyjmuję do wiadomości, że przypisanie sobie, w pracy dyplomowej, autorstwa istotnego fragmentu lub innych elementów cudzego utworu lub ustalenia naukowego stanowi podstawę stwierdzenia nieważności postępowania w sprawie nadania tytułu zawodowego.
|
||||||
|
|
||||||
|
\bigskip
|
||||||
|
|
||||||
|
\noindent $[TAK]^{\star}$ - wyrażam zgodę na udostępnianie mojej pracy w czytelni Archiwum UAM
|
||||||
|
|
||||||
|
\medskip
|
||||||
|
|
||||||
|
\noindent $[TAK]^{\star}$ - wyrażam zgodę na udostępnianie mojej pracy w zakresie koniecznym do ochrony mojego prawa do autorstwa lub praw osób trzecich
|
||||||
|
|
||||||
|
\vglue 1.2 cm
|
||||||
|
|
||||||
|
\noindent{\small $^{\star}$Należy wpisać TAK w przypadku wyrażenia zgody na udostępnianie pracy w czytelni Archiwum UAM, NIE w przypadku braku zgody. Niewypełnienie pola oznacza brak zgody na udostępnianie pracy.}
|
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|
|
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\hglue 6cm ............................................................
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% Koniec oświadczenia
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\tableofcontents
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|
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\chapter*{Streszczenie}
|
||||||
|
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||||||
\section{Wstęp}
|
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|
||||||
To jest wstep do pracy magisterskiej
|
\chapter*{Abstract}
|
||||||
|
|
||||||
\subsection{Muzyka}
|
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut fermentum lorem libero. Duis a magna arcu. Nam sit amet porta odio. Cras sit amet euismod elit. Etiam a turpis eget magna pharetra malesuada. Vivamus accumsan leo eget turpis efficitur, non interdum tortor pretium. Maecenas at massa nec elit imperdiet sagittis. Maecenas pellentesque libero et risus aliquam consectetur.
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac, co o tym sądze, oraz czy sztuczna inteligencja zastapi muzyków w przyszłości.
|
|
||||||
|
|
||||||
\section{MIDI, Muzyka jako Informacje}
|
|
||||||
Tutaj opiszę w jaki sposób muzyka jest zapisywana jako informacje komputerowe, protokuł midi, przedstawienie muzyki jako pianorolle.
|
|
||||||
|
|
||||||
\subsection{MIDI}
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
Tutaj opiszę protokuł MIDI
|
|
||||||
|
\chapter{Wstęp}
|
||||||
|
|
||||||
|
Uczenie maszynowe w ostatnich latach mocno zyskało na popularności. Zastosowania i możliwości różnych algorytmów Mashine Learning czasami przekraczają nasze wyobrażenie o tym, co komputer może zrobić. Niektóre aplikacje potrafią wręcz zaskoczyć użytkowników tym, co potrafią zrobić. Wśród takich aplikacji znajdują się takie, które potrafią przewidywać następne wartości akcji giełdowych, rozpoznawać na filmie obiekty w czasie rzeczywistym czy nawet prowadzić samochód. Algorytmy wyuczone proponują nam spersonalizowane reklamy, czy produkty na podstawie naszych upodobań. Najczęstsze zastosowania dotyczą przetwarzania obrazów lub tekstu, natomiast zastosowania w przetwarzaniu muzyki są niszowe i rzadko spotykane.
|
||||||
|
|
||||||
|
\section{Zastosowania uczenia maszynowego w muzyce}
|
||||||
|
Wśród najbardziej rozwiniętych zastosowań uczenia maszynowego w muzyce, można wymienić algorytmy polecania utworów w portalach streamingowych takich jak Spotify czy Tidal. Algorytmy potrafią znajdywać podobne do siebie utwory i polecać je nam na podstawie naszych preferencji.
|
||||||
|
|
||||||
|
\section{Modele generatywne}
|
||||||
|
Jednym z najnowszych modeli sieci neuronowych są sieci generatywne. Wychodzą one poza standardowe zastosowania klasyfikacji i regresji. Modele generatywne, uczą się ze zbioru uczącego najważniejszych ale i ogólnych cech i potrafią reprodukować podobne wyniki.
|
||||||
|
|
||||||
|
\section{Muzyka symboliczna, a muzyka}
|
||||||
|
Należy rozróżnić dwa pojęcia, które są od siebie różne w podstawowych założeniach. Muzyka symboliczna, i muzyka odegrana. Muzyka symboliczna, jest to utwór zapisany, skomponowany ale na papierze. Przedstawia to muzykę jako koncepcję, taki przepis na utwór. Taka muzyka zapisywana jest klasycznie na pięciolinii, czy komputerowo przy pomocy protokołu MIDI. Druga muzyka, jest to muzyka już odebrana, która nie przechowuje informacji o tym jak zagrać, czy odtworzyć utwór muzyczny, ale brzmienie tego utworu jako fala dźwiękowa. W tej pracy będę opisywał przede wszystkim generowanie muzyki symbolicznej.
|
||||||
|
|
||||||
|
\section{Cele tej pracy.}
|
||||||
|
Celem tej pracy, jest zastosowanie technik głębokiego uczenia, do generowania muzyki. Jest to bardzo ogólny cel, ponieważ muzyką może być prosta melodia oparta na kilku dźwiękach grana przez jeden instrument ale również aranżacja orkiestralna na wiele instrumentów, które razem współgrają i wybrzmiewają jako jeden pełny utwór.
|
||||||
|
|
||||||
|
|
||||||
|
\newpage
|
||||||
|
|
||||||
|
\chapter{Reprezentacja muzyki}
|
||||||
|
Muzyka jesy
|
||||||
|
|
||||||
|
\section{Podstawowe koncepcje}
|
||||||
|
\subsection{Nuta}
|
||||||
|
Każdy utwór muzyczny składa się nut. Nuta jest podstawowym obiektem w muzycznym słowniku. Każda nuta ma dwa parametry, wartości oraz wysokości. Wartość nuty określa jak długo będzie ona trwać w czasie, relatywnie do pozostałych nut. Wysokość noty oznacza z jaką częstotliwością fala dzwiękowa tej nuty ma wybrzmieć. Częstotliwości te są nazwane literami alfabetu ABCDEFG lub w zapisie polskim AHCDEFG.
|
||||||
|
|
||||||
|
\subsection{Skala}
|
||||||
|
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|
||||||
|
|
||||||
|
|
||||||
|
\subsection{Akord}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
|
\subsection{Utwór}
|
||||||
|
Aenean malesuada interdum hendrerit. Integer quis nisl et neque iaculis dapibus at in metus. Cras pretium bibendum magna at aliquet. Integer aliquet cursus augue, efficitur sollicitudin felis fringilla efficitur. Vivamus euismod bibendum justo, vitae suscipit nunc mattis a. Sed egestas porttitor velit, sit amet volutpat tortor suscipit vitae. Nulla nec dignissim mauris. Curabitur maximus viverra mollis. Suspendisse molestie turpis sit amet turpis interdum viverra ac eu lorem. Suspendisse iaculis ultricies ante, a condimentum odio congue nec. Integer varius lobortis diam, eget scelerisque nisl mattis at.
|
||||||
|
|
||||||
|
\section{Reprezentacja muzyki}
|
||||||
|
|
||||||
|
\subsection{Zapis klasyczny - pięciolinia}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
|
\subsection{Tabulatura}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{Pianoroll}
|
\subsection{Pianoroll}
|
||||||
Tutaj opisze co todsdsddsdss są pianorolle, jak je czytać i czemu służą.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{Muzyka jako trójwymiarowa tablica}
|
\subsection{Tekstowa}
|
||||||
Tutaj opisze dlaczego muzykę moża opisać jako trójwymiarowa tablicę.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\section{Generatwne sieci neuronowe - GANy, VAE, LSTMy}
|
\chapter{Sieci neuronowe}
|
||||||
Tutaj będzie opisane, dlaczego sieci neuronowe, radzą sobie lepiej w produkowaniu muzyki niż inne modele. Oraz jakie modele są odpowidnie do pewnych zastosowań, JAZZ - LSTM, bardziej ustrukturyzowana - VAE itp.
|
Tutaj będzie opisane, dlaczego sieci neuronowe, radzą sobie lepiej w produkowaniu muzyki niż inne modele. Oraz jakie modele są odpowidnie do pewnych zastosowań, JAZZ - LSTM, bardziej ustrukturyzowana - VAE itp.
|
||||||
|
|
||||||
\subsection{Autoencodery, VAE}
|
\subsection{Wstęp do sieci neuronowych, definicje wzory itp.}
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
|
\subsection{Autoencodery}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{LSTM}
|
\subsection{LSTM}
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\section{Modele generatywne stosowane w generowaniu muzyki}
|
\subsection{GAN}
|
||||||
Przykłady gotowych podeść do generowania muzyki, oraz jakie modele zostały zastosowane. dlaczego takie itp.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{Project Magenta}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
\subsection{MuseGAN}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
\subsection{VAE-MIDI}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
|
|
||||||
\section{Budowanie generatora muzyki}
|
\chapter*{Budowanie generatora muzyki}
|
||||||
W tym rozdzialę opiszę w jaki sposób zbudowałem swój własny geneator muzyki, jak przechodził procesz uczenia, jakie próbki udało mi się wygenrować. Opis kodu który napisałem.
|
W tym rozdzialę opiszę w jaki sposób zbudowałem swój własny geneator muzyki, jak przechodził procesz uczenia, jakie próbki udało mi się wygenrować. Opis kodu który napisałem.
|
||||||
|
|
||||||
\subsection{Wyodrębnienie danych z plików MIDI}
|
\subsection{Przygotowanie danych}
|
||||||
\subsection{Przygotowanie Modelu GAN}
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
\subsection{Proces uczenia, próbki co kilka epochów, costloss wykres}
|
\subsection{Architektura sieci neuronowej}
|
||||||
\subsection{Próbki końcowe, jaką muzykę da się z tego wygenerować}
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
\subsection{Proces treningowy}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
\subsection{Przykłady wygenerowanej muzyki}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\section{Podsumowanie}
|
\chapter*{Podsumowanie}
|
||||||
Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może złużyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obnizy koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity?
|
Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może złużyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obnizy koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity?
|
||||||
|
|
||||||
|
|
||||||
|
\begin{thebibliography}{99}
|
||||||
|
|
||||||
|
\bibitem{} Briot, J.P., Hadjeres, G., Pachet, F.D. (2019): {\em Deep Learning Techniques for Music Generation - A Survey. arXiv:1709.01620v3}
|
||||||
|
\bibitem{} Goodfellow, I., Bengio, Y., Courville, A. (2016): {\em Deep Learning. MIT Press.}
|
||||||
|
\bibitem{} Zocca, V., Spacagna, G., Slater, D., Roelants, P. (2018): {\em Deep Learning. Uczenie głębokie z językiem Python. Helion.}
|
||||||
|
\end{thebibliography}
|
||||||
|
|
||||||
|
|
||||||
|
\printindex
|
||||||
|
|
||||||
\end{document}
|
\end{document}
|
||||||
@ -1,19 +1,30 @@
|
|||||||
\contentsline {section}{\numberline {1}Wst\IeC {\k e}p}{2}%
|
\contentsline {chapter}{Streszczenie}{7}%
|
||||||
\contentsline {subsection}{\numberline {1.1}Muzyka}{2}%
|
\contentsline {chapter}{Abstract}{9}%
|
||||||
\contentsline {section}{\numberline {2}MIDI, Muzyka jako Informacje}{2}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 1\relax .\leavevmode@ifvmode \kern .5em Wst\IeC {\k e}p}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.1}MIDI}{2}%
|
\contentsline {section}{\numberline {1.1\relax .\leavevmode@ifvmode \kern .5em }Zastosowania uczenia maszynowego w muzyce}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.2}Pianoroll}{2}%
|
\contentsline {section}{\numberline {1.2\relax .\leavevmode@ifvmode \kern .5em }Modele generatywne}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.3}Muzyka jako tr\IeC {\'o}jwymiarowa tablica}{2}%
|
\contentsline {section}{\numberline {1.3\relax .\leavevmode@ifvmode \kern .5em }Muzyka symboliczna, a muzyka}{11}%
|
||||||
\contentsline {section}{\numberline {3}Generatwne sieci neuronowe - GANy, VAE, LSTMy}{2}%
|
\contentsline {section}{\numberline {1.4\relax .\leavevmode@ifvmode \kern .5em }Cele tej pracy.}{12}%
|
||||||
\contentsline {subsection}{\numberline {3.1}Autoencodery, VAE}{2}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 2\relax .\leavevmode@ifvmode \kern .5em Reprezentacja muzyki}{13}%
|
||||||
\contentsline {subsection}{\numberline {3.2}LSTM}{2}%
|
\contentsline {section}{\numberline {2.1\relax .\leavevmode@ifvmode \kern .5em }Podstawowe koncepcje}{13}%
|
||||||
\contentsline {section}{\numberline {4}Modele generatywne stosowane w generowaniu muzyki}{3}%
|
\contentsline {subsection}{\numberline {2.1.1\relax .\leavevmode@ifvmode \kern .5em }Nuta}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.1}Project Magenta}{3}%
|
\contentsline {subsection}{\numberline {2.1.2\relax .\leavevmode@ifvmode \kern .5em }Skala}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.2}MuseGAN}{3}%
|
\contentsline {subsection}{\numberline {2.1.3\relax .\leavevmode@ifvmode \kern .5em }Akord}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.3}VAE-MIDI}{3}%
|
\contentsline {subsection}{\numberline {2.1.4\relax .\leavevmode@ifvmode \kern .5em }Utw\IeC {\'o}r}{14}%
|
||||||
\contentsline {section}{\numberline {5}Budowanie generatora muzyki}{3}%
|
\contentsline {section}{\numberline {2.2\relax .\leavevmode@ifvmode \kern .5em }Reprezentacja muzyki}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.1}Wyodr\IeC {\k e}bnienie danych z plik\IeC {\'o}w MIDI}{3}%
|
\contentsline {subsection}{\numberline {2.2.1\relax .\leavevmode@ifvmode \kern .5em }Zapis klasyczny - pi\IeC {\k e}ciolinia}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.2}Przygotowanie Modelu GAN}{3}%
|
\contentsline {subsection}{\numberline {2.2.2\relax .\leavevmode@ifvmode \kern .5em }Tabulatura}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.3}Proces uczenia, pr\IeC {\'o}bki co kilka epoch\IeC {\'o}w, costloss wykres}{3}%
|
\contentsline {subsection}{\numberline {2.2.3\relax .\leavevmode@ifvmode \kern .5em }Pianoroll}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.4}Pr\IeC {\'o}bki ko\IeC {\'n}cowe, jak\IeC {\k a} muzyk\IeC {\k e} da si\IeC {\k e} z tego wygenerowa\IeC {\'c}}{3}%
|
\contentsline {subsection}{\numberline {2.2.4\relax .\leavevmode@ifvmode \kern .5em }Tekstowa}{15}%
|
||||||
\contentsline {section}{\numberline {6}Podsumowanie}{3}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 3\relax .\leavevmode@ifvmode \kern .5em Sieci neuronowe}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.1\relax .\leavevmode@ifvmode \kern .5em }Wst\IeC {\k e}p do sieci neuronowych, definicje wzory itp.}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.2\relax .\leavevmode@ifvmode \kern .5em }Autoencodery}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.3\relax .\leavevmode@ifvmode \kern .5em }LSTM}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.4\relax .\leavevmode@ifvmode \kern .5em }GAN}{18}%
|
||||||
|
\contentsline {chapter}{Budowanie generatora muzyki}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.5\relax .\leavevmode@ifvmode \kern .5em }Przygotowanie danych}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.6\relax .\leavevmode@ifvmode \kern .5em }Architektura sieci neuronowej}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.7\relax .\leavevmode@ifvmode \kern .5em }Proces treningowy}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.8\relax .\leavevmode@ifvmode \kern .5em }Przyk\IeC {\l }ady wygenerowanej muzyki}{20}%
|
||||||
|
\contentsline {chapter}{Podsumowanie}{21}%
|
||||||
|
\contentsline {chapter}{Bibliografia}{23}%
|
||||||
|
|||||||
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|
|||||||
|
#!python3
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
''' This module generates a sample, and create a midi file.
|
||||||
|
|
||||||
|
Usage:
|
||||||
|
>>> ./generate.py [trained_model_path] [output_path]
|
||||||
|
|
||||||
|
'''
|
||||||
|
import settings
|
||||||
|
import sys
|
||||||
|
import random
|
||||||
|
import pickle
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import tensorflow as tf
|
import tensorflow as tf
|
||||||
|
import pypianoroll as roll
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from tqdm import trange, tqdm
|
||||||
|
from music21 import converter, instrument, note, chord, stream
|
||||||
from keras.layers import Input, Dense, Conv2D
|
from keras.layers import Input, Dense, Conv2D
|
||||||
from keras.models import Model
|
from keras.models import Model
|
||||||
import settings
|
from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector
|
||||||
|
from keras.models import Model, Sequential
|
||||||
|
|
||||||
#model
|
|
||||||
input_shape = settings.midi_resolution*128
|
|
||||||
input_img = tf.keras.layers.Input(shape=(input_shape,))
|
|
||||||
encoded = tf.keras.layers.Dense(160, activation='relu')(input_img)
|
|
||||||
decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(encoded)
|
|
||||||
autoencoder = tf.keras.models.Model(input_img, decoded)
|
|
||||||
|
|
||||||
autoencoder.compile(optimizer='adadelta',
|
def choose_by_prob(list_of_probs):
|
||||||
loss='categorical_crossentropy',
|
''' This functions a list of values and assumed
|
||||||
metrics=['accuracy'])
|
that if the value is bigger it should by returned often
|
||||||
|
|
||||||
# load weights into new model
|
It was crated to give more options to choose than argmax function,
|
||||||
autoencoder.load_weights(settings.model_path)
|
thus is more than one way that you can develop a melody.
|
||||||
print("Loaded model from {}".format(settings.model_path))
|
|
||||||
|
|
||||||
# generate_seed = np.random.rand(12288).reshape(1,12288)
|
Returns a index of choosen value from given list.
|
||||||
generate_seed = np.load(settings.samples_path)['arr_0'][15].reshape(1,12288)
|
'''
|
||||||
|
sum_prob = np.array(list_of_probs).sum()
|
||||||
|
prob_normalized = [x/sum_prob for x in list_of_probs]
|
||||||
|
cumsum = np.array(prob_normalized).cumsum()
|
||||||
|
prob_cum = cumsum.tolist()
|
||||||
|
random_x = random.random()
|
||||||
|
for i, x in enumerate(prob_cum):
|
||||||
|
if random_x < x:
|
||||||
|
return i
|
||||||
|
|
||||||
generated_sample = autoencoder.predict(generate_seed)
|
trained_model_path = sys.argv[1]
|
||||||
np.savez_compressed(settings.generated_sample_path, generated_sample)
|
output_path = sys.argv[2]
|
||||||
|
|
||||||
|
# load model and dictionary that can translate back index_numbers to notes
|
||||||
|
# this dictionary is generated with model
|
||||||
|
print('Loading... {}'.format(trained_model_path))
|
||||||
|
model = pickle.load(open(trained_model_path, 'rb'))
|
||||||
|
int_to_note, n_vocab, seq_len = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
|
||||||
|
|
||||||
|
seed = [random.randint(0,n_vocab) for x in range(seq_len)]
|
||||||
|
music = []
|
||||||
|
|
||||||
|
print('Generating...')
|
||||||
|
for i in trange(124):
|
||||||
|
predicted_vector = model.predict(np.array(seed).reshape(1,seq_len,1))
|
||||||
|
# using best fitted note
|
||||||
|
# predicted_index = np.argmax(predicted_vector)
|
||||||
|
# using propability distribution for choosing note
|
||||||
|
# to prevent looping
|
||||||
|
predicted_index = choose_by_prob(predicted_vector)
|
||||||
|
music.append(int_to_note[predicted_index])
|
||||||
|
seed.append(predicted_index)
|
||||||
|
seed = seed[1:1+seq_len]
|
||||||
|
|
||||||
|
|
||||||
|
print('Saving...')
|
||||||
|
offset = 0
|
||||||
|
output_notes = []
|
||||||
|
for _event in tqdm(music):
|
||||||
|
event, note_len = _event.split(';')
|
||||||
|
if (' ' in event) or event.isdigit():
|
||||||
|
notes_in_chord = event.split(' ')
|
||||||
|
notes = []
|
||||||
|
for current_note in notes_in_chord:
|
||||||
|
new_note = note.Note(current_note)
|
||||||
|
new_note.storedInstrument = instrument.Piano()
|
||||||
|
notes.append(new_note)
|
||||||
|
new_chord = chord.Chord(notes)
|
||||||
|
new_chord.offset = offset
|
||||||
|
output_notes.append(new_chord)
|
||||||
|
else:
|
||||||
|
new_note = note.Note(event)
|
||||||
|
new_note.offset = offset
|
||||||
|
new_note.storedInstrument = instrument.Piano()
|
||||||
|
output_notes.append(new_note)
|
||||||
|
|
||||||
|
offset += float(note_len)
|
||||||
|
|
||||||
|
midi_stream = stream.Stream(output_notes)
|
||||||
|
|
||||||
|
midi_stream.write('midi', fp='{}.mid'.format(output_path))
|
||||||
|
|
||||||
|
print('Done!')
|
||||||
|
|||||||
179
project/midi.py
179
project/midi.py
@ -1,82 +1,133 @@
|
|||||||
|
#!python3
|
||||||
#!/usr/bin/env python3
|
#!/usr/bin/env python3
|
||||||
|
|
||||||
|
''' This module contains functions to endocing midi files into data samples
|
||||||
|
that is prepared for model training.
|
||||||
|
|
||||||
|
midi_folder_path - the path to directiory containing midi files
|
||||||
|
output_path - the output path where will be created samples of data
|
||||||
|
|
||||||
|
Usage:
|
||||||
|
>>> ./midi.py <midi_folder_path> <output_path> <sequence_lenth>
|
||||||
|
'''
|
||||||
|
|
||||||
import settings
|
import settings
|
||||||
import pypianoroll as roll
|
import pypianoroll as roll
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import os
|
import os
|
||||||
from tqdm import tqdm
|
from tqdm import tqdm
|
||||||
from math import floor
|
from math import floor
|
||||||
import sys
|
import sys
|
||||||
|
from collections import defaultdict
|
||||||
|
import pickle
|
||||||
|
from music21 import converter, instrument, note, chord, stream
|
||||||
|
import music21
|
||||||
|
|
||||||
def to_samples(midi_file_path, midi_res=settings.midi_resolution):
|
class MidiParseError(Exception):
|
||||||
|
"""Error that is raised then midi file cannot be parsed"""
|
||||||
# this function export a samples from midi file:
|
|
||||||
# and for every track in midi file chopped pianoroll
|
|
||||||
# for a samples of given beat_lenth (midi_res)
|
|
||||||
# every track is single line
|
|
||||||
|
|
||||||
print('Exporting samples from: {}'.format(midi_file_path))
|
|
||||||
|
|
||||||
all_beats = np.empty((0, settings.midi_resolution, 128))
|
|
||||||
|
|
||||||
for track in roll.Multitrack(midi_file_path).tracks:
|
|
||||||
print('Track: {}'.format(track.name))
|
|
||||||
if not track.is_drum:
|
|
||||||
number_of_beats = floor(track.pianoroll.shape[0] / midi_res)
|
|
||||||
track_pianoroll = track.pianoroll[: number_of_beats * midi_res]
|
|
||||||
track_beats = track_pianoroll.reshape(number_of_beats, midi_res, 128)
|
|
||||||
all_beats = np.concatenate([track_beats, all_beats], axis=0)
|
|
||||||
|
|
||||||
print('Exported {} samples of {}'.format(number_of_beats, settings.midi_program[track.program]))
|
|
||||||
else:
|
|
||||||
# add code for drums samples
|
|
||||||
pass
|
pass
|
||||||
return all_beats
|
|
||||||
|
|
||||||
def to_midi(samples, output_path=settings.generated_midi_path, program=0, tempo=120, beat_resolution=settings.beat_resolution):
|
|
||||||
tracks = [roll.Track(samples, program=program)]
|
|
||||||
return_midi = roll.Multitrack(tracks=tracks, tempo=tempo, downbeat=[0, 96, 192, 288], beat_resolution=beat_resolution)
|
|
||||||
roll.write(return_midi, settings.generated_midi_path)
|
|
||||||
|
|
||||||
# todo: this function is running too slow.
|
def parse_argv(argv):
|
||||||
def delete_empty_samples(sample_pack):
|
'''This function is parsing given arguments when running a midi script.
|
||||||
print('Deleting empty samples...')
|
Returns a tuple consinting of midi_folder_path, output_path, seq_len'''
|
||||||
temp_sample_pack = sample_pack
|
try:
|
||||||
index_manipulator = 1
|
midi_folder_path = argv[1]
|
||||||
for index, sample in enumerate(sample_pack):
|
output_path = argv[2]
|
||||||
if sample.sum() == 0:
|
seq_len = int(argv[3])
|
||||||
temp_sample_pack = np.delete(temp_sample_pack, index-index_manipulator, axis=0)
|
return midi_folder_path, output_path, seq_len
|
||||||
index_manipulator = index_manipulator + 1
|
except IndexError:
|
||||||
print('Deleted {} empty samples'.format(index_manipulator-1))
|
raise AttributeError('You propably didnt pass parameters to run midi.py script.\
|
||||||
return temp_sample_pack
|
>>> ./midi.py <midi_folder_path> <output_path> <sequence_lenth>')
|
||||||
|
|
||||||
|
def to_sequence(midi_path, seq_len):
|
||||||
|
''' This function is supposed to be used on one midi file in directory loop.
|
||||||
|
Its encoding midi files, into sequances of given lenth as a train_X,
|
||||||
|
and the next note as a train_y. Also splitting midi samples into
|
||||||
|
instrument group.
|
||||||
|
|
||||||
|
Use for LSTM neural network.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
- midi_path: path to midi file
|
||||||
|
- seq_len: lenght of sequance before prediction
|
||||||
|
|
||||||
|
Returns: Tuple of train_X, train_y dictionaries consisinting of samples of song grouped by instruments
|
||||||
|
'''
|
||||||
|
|
||||||
|
seq_by_instrument = defaultdict( lambda : [] )
|
||||||
|
|
||||||
|
try:
|
||||||
|
midi_file = music21.converter.parse(midi_path)
|
||||||
|
except music21.midi.MidiException:
|
||||||
|
raise MidiParseError
|
||||||
|
stream = music21.instrument.partitionByInstrument(midi_file)
|
||||||
|
for part in stream:
|
||||||
|
for event in part:
|
||||||
|
if part.partName != None:
|
||||||
|
if isinstance(event, music21.note.Note):
|
||||||
|
to_export_event = '{};{}'.format(str(event.pitch), float(event.quarterLength))
|
||||||
|
seq_by_instrument[part.partName].append(to_export_event)
|
||||||
|
elif isinstance(event, music21.chord.Chord):
|
||||||
|
to_export_event = '{};{}'.format(' '.join(str(note) for note in event.pitches), float(event.quarterLength))
|
||||||
|
seq_by_instrument[part.partName].append(to_export_event)
|
||||||
|
|
||||||
|
X_train_by_instrument = defaultdict( lambda : [] )
|
||||||
|
y_train_by_instrument = defaultdict( lambda : [] )
|
||||||
|
|
||||||
|
for instrument, sequence in seq_by_instrument.items():
|
||||||
|
for i in range(len(sequence)-(seq_len)) :
|
||||||
|
X_train_by_instrument[instrument].append(np.array(sequence[i:i+seq_len])) # <seq lenth
|
||||||
|
y_train_by_instrument[instrument].append(np.array(sequence[i+seq_len]))
|
||||||
|
|
||||||
|
return X_train_by_instrument, y_train_by_instrument
|
||||||
|
|
||||||
|
def colect_samples(midi_folder_path, seq_len):
|
||||||
|
'''This function is looping throuth given directories and
|
||||||
|
collecting samples from midi files.
|
||||||
|
|
||||||
|
Parameters: midi_folder_path - a path to directory with midi files
|
||||||
|
seq_len - a lenth of train_X sample that tells
|
||||||
|
how many notes is given do LSTM to predict the next note.
|
||||||
|
|
||||||
|
Returns: Tuple of train_X, train_y dictionaries consisinting
|
||||||
|
of samples of all songs in directory grouped by instruments.
|
||||||
|
'''
|
||||||
|
|
||||||
|
print('Collecting samples...')
|
||||||
|
train_X = defaultdict( lambda : [] )
|
||||||
|
train_y = defaultdict( lambda : [] )
|
||||||
|
|
||||||
|
for directory, subdirectories, files in os.walk(midi_folder_path):
|
||||||
|
for midi_file in tqdm(files):
|
||||||
|
midi_file_path = os.path.join(directory, midi_file)
|
||||||
|
try:
|
||||||
|
_X_train, _y_train = to_sequence(midi_file_path, seq_len)
|
||||||
|
except MidiParseError:
|
||||||
|
continue
|
||||||
|
for (X_key, X_value), (y_key, y_value) in zip(_X_train.items(), _y_train.items()):
|
||||||
|
train_X[X_key].extend(np.array(X_value))
|
||||||
|
train_y[y_key].extend(np.array(y_value))
|
||||||
|
|
||||||
|
return train_X, train_y
|
||||||
|
|
||||||
|
def save_samples(output_path, samples):
|
||||||
|
'''This function save samples to npz packages, splitted by instrument.'''
|
||||||
|
|
||||||
|
print('Saving...')
|
||||||
|
|
||||||
|
if not os.path.exists(output_path):
|
||||||
|
os.makedirs(output_path)
|
||||||
|
|
||||||
|
train_X, train_y = samples
|
||||||
|
for (X_key, X_value), (y_key, y_value) in tqdm(zip(train_X.items(), train_y.items())):
|
||||||
|
if X_key == y_key:
|
||||||
|
np.savez_compressed('{}/{}.npz'.format(output_path, X_key), np.array(X_value), np.array(y_value))
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
|
midi_folder_path, output_path, seq_len = parse_argv(sys.argv)
|
||||||
if sys.argv[1]=='export':
|
save_samples(output_path, colect_samples(midi_folder_path, seq_len))
|
||||||
print('Exporting started...')
|
print('Done!')
|
||||||
|
|
||||||
sample_pack = np.empty((0,settings.midi_resolution,128))
|
|
||||||
|
|
||||||
for midi_file in os.listdir(settings.midi_dir):
|
|
||||||
midi_file_path = '{}/{}'.format(settings.midi_dir, midi_file)
|
|
||||||
midi_samples = to_samples(midi_file_path)
|
|
||||||
if midi_samples is None:
|
|
||||||
continue
|
|
||||||
sample_pack = np.concatenate((midi_samples, sample_pack), axis=0)
|
|
||||||
|
|
||||||
# I commented out this line, because it was too slow
|
|
||||||
# sample_pack = delete_empty_samples(sample_pack)
|
|
||||||
|
|
||||||
np.savez_compressed(settings.samples_dir, sample_pack)
|
|
||||||
print('Exported {} samples'.format(sample_pack.shape[0]))
|
|
||||||
|
|
||||||
fig, axes = plt.subplots(nrows=10, ncols=10, figsize=(20, 20))
|
|
||||||
for idx, ax in enumerate(axes.ravel()):
|
|
||||||
n = np.random.randint(0, sample_pack.shape[0])
|
|
||||||
sample = sample_pack[n]
|
|
||||||
ax.imshow(sample, cmap = plt.get_cmap('gray'))
|
|
||||||
plt.savefig(settings.sample_preview_path)
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
main()
|
main()
|
||||||
|
|||||||
@ -1,16 +0,0 @@
|
|||||||
## MUSIC GENERATION USING DEEP LEARNING ##
|
|
||||||
## AUTHOR: CEZARY PUKOWNIK
|
|
||||||
|
|
||||||
How to use:
|
|
||||||
|
|
||||||
1. Use midi.py to export data from midi files
|
|
||||||
|
|
||||||
./midi.py export <midi_folder_path> <output_path>
|
|
||||||
|
|
||||||
2. Use train.py to train a model (this can take a while)
|
|
||||||
|
|
||||||
./train.py <input_training_data> <model_save_path>
|
|
||||||
|
|
||||||
3. Use generate.py to generate music from trained models
|
|
||||||
|
|
||||||
./generate.py <model_weights_path> <output_path>
|
|
||||||
26
project/readme.md
Normal file
26
project/readme.md
Normal file
@ -0,0 +1,26 @@
|
|||||||
|
###MUSIC GENERATION USING DEEP LEARNING
|
||||||
|
###AUTHOR: CEZARY PUKOWNIK
|
||||||
|
|
||||||
|
#Files:
|
||||||
|
- midi.py - code for data extraction, and midi convertion
|
||||||
|
- train.py - code for model definition, and training session
|
||||||
|
- generate.py - code for model loading, predicting ang saving to midi_dir
|
||||||
|
- settings.py - file where deafult settings are stored
|
||||||
|
- readme.md - this file
|
||||||
|
|
||||||
|
#Directories:
|
||||||
|
- data/midi - directory where input midi are stored
|
||||||
|
- data/models - directory where trained models are stored
|
||||||
|
- data/output - directory where generated music is stored
|
||||||
|
- data/samples - directory where extracted data from midi is stored
|
||||||
|
- data/samples.npz - deprecated
|
||||||
|
|
||||||
|
#How to use:
|
||||||
|
1. Use midi.py to export data from midi files
|
||||||
|
>>> ./midi.py [midi_folder_path] [output_path]
|
||||||
|
|
||||||
|
2. Use train.py to train a model (this can take a while)
|
||||||
|
>>> ./train.py [input_training_data] [model_save_path] [epochs]
|
||||||
|
|
||||||
|
3. Use generate.py to generate music from trained models
|
||||||
|
>>> ./generate.py [trained_model_path] [output_path] [treshold]
|
||||||
@ -1,18 +0,0 @@
|
|||||||
import pypianoroll as roll
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import numpy as np
|
|
||||||
import os
|
|
||||||
import settings
|
|
||||||
|
|
||||||
instruments = np.load(settings.generated_sample_path)['arr_0'][0]
|
|
||||||
|
|
||||||
instruments = instruments.reshape(96,128)
|
|
||||||
# instruments = instruments>0.5
|
|
||||||
instruments = instruments*255
|
|
||||||
|
|
||||||
i = roll.Track(instruments, program=0)
|
|
||||||
generated_midi = roll.Multitrack(tracks=[i], tempo=120.0, downbeat=[0, 96, 192, 288], beat_resolution=24)
|
|
||||||
roll.write(generated_midi, settings.generated_midi_path)
|
|
||||||
|
|
||||||
plt.imshow(instruments.T, cmap='gray')
|
|
||||||
plt.savefig(settings.generated_pianoroll_path)
|
|
||||||
@ -15,11 +15,11 @@ beats_per_sample = 1
|
|||||||
ignore_note_lenght = False
|
ignore_note_lenght = False
|
||||||
|
|
||||||
#train_settings
|
#train_settings
|
||||||
epochs = 1000
|
epochs = 1
|
||||||
|
|
||||||
#extras
|
#extras
|
||||||
midi_program = {
|
midi_program = {
|
||||||
0 : 'Perc',
|
# Piano
|
||||||
1 : 'Acoustic Grand Piano',
|
1 : 'Acoustic Grand Piano',
|
||||||
2 : 'Bright Acoustic Piano',
|
2 : 'Bright Acoustic Piano',
|
||||||
3 : 'Electric Grand Piano',
|
3 : 'Electric Grand Piano',
|
||||||
@ -28,6 +28,7 @@ midi_program = {
|
|||||||
6 : 'Electric Piano 2',
|
6 : 'Electric Piano 2',
|
||||||
7 : 'Harpsichord',
|
7 : 'Harpsichord',
|
||||||
8 : 'Clavi',
|
8 : 'Clavi',
|
||||||
|
# Chromatic Percussion
|
||||||
9 : 'Celesta',
|
9 : 'Celesta',
|
||||||
10 : 'Glockenspiel',
|
10 : 'Glockenspiel',
|
||||||
11 : 'Music Box',
|
11 : 'Music Box',
|
||||||
@ -36,6 +37,7 @@ midi_program = {
|
|||||||
14 : 'Xylophone',
|
14 : 'Xylophone',
|
||||||
15 : 'Tubular Bells',
|
15 : 'Tubular Bells',
|
||||||
16 : 'Dulcimer',
|
16 : 'Dulcimer',
|
||||||
|
# Organ
|
||||||
17 : 'Drawbar Organ',
|
17 : 'Drawbar Organ',
|
||||||
18 : 'Percussive Organ',
|
18 : 'Percussive Organ',
|
||||||
19 : 'Rock Organ',
|
19 : 'Rock Organ',
|
||||||
@ -44,6 +46,7 @@ midi_program = {
|
|||||||
22 : 'Accordion',
|
22 : 'Accordion',
|
||||||
23 : 'Harmonica',
|
23 : 'Harmonica',
|
||||||
24 : 'Tango Accordion',
|
24 : 'Tango Accordion',
|
||||||
|
# Guitar
|
||||||
25 : 'Acoustic Guitar (nylon)',
|
25 : 'Acoustic Guitar (nylon)',
|
||||||
26 : 'Acoustic Guitar (steel)',
|
26 : 'Acoustic Guitar (steel)',
|
||||||
27 : 'Electric Guitar (jazz)',
|
27 : 'Electric Guitar (jazz)',
|
||||||
@ -52,6 +55,7 @@ midi_program = {
|
|||||||
30 : 'Overdriven Guitar',
|
30 : 'Overdriven Guitar',
|
||||||
31 : 'Distortion Guitar',
|
31 : 'Distortion Guitar',
|
||||||
32 : 'Guitar harmonics',
|
32 : 'Guitar harmonics',
|
||||||
|
# Bass
|
||||||
33 : 'Acoustic Bass',
|
33 : 'Acoustic Bass',
|
||||||
34 : 'Electric Bass (finger)',
|
34 : 'Electric Bass (finger)',
|
||||||
35 : 'Electric Bass (pick)',
|
35 : 'Electric Bass (pick)',
|
||||||
@ -60,6 +64,7 @@ midi_program = {
|
|||||||
38 : 'Slap Bass 2',
|
38 : 'Slap Bass 2',
|
||||||
39 : 'Synth Bass 1',
|
39 : 'Synth Bass 1',
|
||||||
40 : 'Synth Bass 2',
|
40 : 'Synth Bass 2',
|
||||||
|
# Strings
|
||||||
41 : 'Violin',
|
41 : 'Violin',
|
||||||
42 : 'Viola',
|
42 : 'Viola',
|
||||||
43 : 'Cello',
|
43 : 'Cello',
|
||||||
@ -68,6 +73,7 @@ midi_program = {
|
|||||||
46 : 'Pizzicato Strings',
|
46 : 'Pizzicato Strings',
|
||||||
47 : 'Orchestral Harp',
|
47 : 'Orchestral Harp',
|
||||||
48 : 'Timpani',
|
48 : 'Timpani',
|
||||||
|
# Ensemble
|
||||||
49 : 'String Ensemble 1',
|
49 : 'String Ensemble 1',
|
||||||
50 : 'String Ensemble 2',
|
50 : 'String Ensemble 2',
|
||||||
51 : 'SynthStrings 1',
|
51 : 'SynthStrings 1',
|
||||||
@ -76,6 +82,7 @@ midi_program = {
|
|||||||
54 : 'Voice Oohs',
|
54 : 'Voice Oohs',
|
||||||
55 : 'Synth Voice',
|
55 : 'Synth Voice',
|
||||||
56 : 'Orchestra Hit',
|
56 : 'Orchestra Hit',
|
||||||
|
# Brass
|
||||||
57 : 'Trumpet',
|
57 : 'Trumpet',
|
||||||
58 : 'Trombone',
|
58 : 'Trombone',
|
||||||
59 : 'Tuba',
|
59 : 'Tuba',
|
||||||
@ -84,6 +91,7 @@ midi_program = {
|
|||||||
62 : 'Brass Section',
|
62 : 'Brass Section',
|
||||||
63 : 'SynthBrass 1',
|
63 : 'SynthBrass 1',
|
||||||
64 : 'SynthBrass 2',
|
64 : 'SynthBrass 2',
|
||||||
|
# Reed
|
||||||
65 : 'Soprano Sax',
|
65 : 'Soprano Sax',
|
||||||
66 : 'Alto Sax',
|
66 : 'Alto Sax',
|
||||||
67 : 'Tenor Sax',
|
67 : 'Tenor Sax',
|
||||||
@ -92,6 +100,7 @@ midi_program = {
|
|||||||
70 : 'English Horn',
|
70 : 'English Horn',
|
||||||
71 : 'Bassoon',
|
71 : 'Bassoon',
|
||||||
72 : 'Clarinet',
|
72 : 'Clarinet',
|
||||||
|
# Pipe
|
||||||
73 : 'Piccolo',
|
73 : 'Piccolo',
|
||||||
74 : 'Flute',
|
74 : 'Flute',
|
||||||
75 : 'Recorder',
|
75 : 'Recorder',
|
||||||
@ -100,6 +109,7 @@ midi_program = {
|
|||||||
78 : 'Shakuhachi',
|
78 : 'Shakuhachi',
|
||||||
79 : 'Whistle',
|
79 : 'Whistle',
|
||||||
80 : 'Ocarina',
|
80 : 'Ocarina',
|
||||||
|
# Synth Lead
|
||||||
81 : 'Lead 1 (square)',
|
81 : 'Lead 1 (square)',
|
||||||
82 : 'Lead 2 (sawtooth)',
|
82 : 'Lead 2 (sawtooth)',
|
||||||
83 : 'Lead 3 (calliope)',
|
83 : 'Lead 3 (calliope)',
|
||||||
@ -108,6 +118,7 @@ midi_program = {
|
|||||||
86 : 'Lead 6 (voice)',
|
86 : 'Lead 6 (voice)',
|
||||||
87 : 'Lead 7 (fifths)',
|
87 : 'Lead 7 (fifths)',
|
||||||
88 : 'Lead 8 (bass + lead)',
|
88 : 'Lead 8 (bass + lead)',
|
||||||
|
# Synth Pad
|
||||||
89 : 'Pad 1 (new age)',
|
89 : 'Pad 1 (new age)',
|
||||||
90 : 'Pad 2 (warm)',
|
90 : 'Pad 2 (warm)',
|
||||||
91 : 'Pad 3 (polysynth)',
|
91 : 'Pad 3 (polysynth)',
|
||||||
@ -116,6 +127,7 @@ midi_program = {
|
|||||||
94 : 'Pad 6 (metallic)',
|
94 : 'Pad 6 (metallic)',
|
||||||
95 : 'Pad 7 (halo)',
|
95 : 'Pad 7 (halo)',
|
||||||
96 : 'Pad 8 (sweep)',
|
96 : 'Pad 8 (sweep)',
|
||||||
|
# Synth Effects
|
||||||
97 : 'FX 1 (rain)',
|
97 : 'FX 1 (rain)',
|
||||||
98 : 'FX 2 (soundtrack)',
|
98 : 'FX 2 (soundtrack)',
|
||||||
99 : 'FX 3 (crystal)',
|
99 : 'FX 3 (crystal)',
|
||||||
@ -124,6 +136,7 @@ midi_program = {
|
|||||||
102 : 'FX 6 (goblins)',
|
102 : 'FX 6 (goblins)',
|
||||||
103 : 'FX 7 (echoes)',
|
103 : 'FX 7 (echoes)',
|
||||||
104 : 'FX 8 (sci-fi)',
|
104 : 'FX 8 (sci-fi)',
|
||||||
|
# Ethnic
|
||||||
105 : 'Sitar',
|
105 : 'Sitar',
|
||||||
106 : 'Banjo',
|
106 : 'Banjo',
|
||||||
107 : 'Shamisen',
|
107 : 'Shamisen',
|
||||||
@ -132,6 +145,7 @@ midi_program = {
|
|||||||
110 : 'Bag pipe',
|
110 : 'Bag pipe',
|
||||||
111 : 'Fiddle',
|
111 : 'Fiddle',
|
||||||
112 : 'Shanai',
|
112 : 'Shanai',
|
||||||
|
# Percussive
|
||||||
113 : 'Tinkle Bell',
|
113 : 'Tinkle Bell',
|
||||||
114 : 'Agogo',
|
114 : 'Agogo',
|
||||||
115 : 'Steel Drums',
|
115 : 'Steel Drums',
|
||||||
@ -140,6 +154,7 @@ midi_program = {
|
|||||||
118 : 'Melodic Tom',
|
118 : 'Melodic Tom',
|
||||||
119 : 'Synth Drum',
|
119 : 'Synth Drum',
|
||||||
120 : 'Reverse Cymbal',
|
120 : 'Reverse Cymbal',
|
||||||
|
# Sound Effects
|
||||||
121 : 'Guitar Fret Noise',
|
121 : 'Guitar Fret Noise',
|
||||||
122 : 'Breath Noise',
|
122 : 'Breath Noise',
|
||||||
123 : 'Seashore',
|
123 : 'Seashore',
|
||||||
@ -149,3 +164,150 @@ midi_program = {
|
|||||||
127 : 'Applause',
|
127 : 'Applause',
|
||||||
128 : 'Gunshot'
|
128 : 'Gunshot'
|
||||||
}
|
}
|
||||||
|
|
||||||
|
midi_group = {
|
||||||
|
# Piano
|
||||||
|
1 : 'Piano',
|
||||||
|
2 : 'Piano',
|
||||||
|
3 : 'Piano',
|
||||||
|
4 : 'Piano',
|
||||||
|
5 : 'Piano',
|
||||||
|
6 : 'Piano',
|
||||||
|
7 : 'Piano',
|
||||||
|
8 : 'Piano',
|
||||||
|
# Chromatic Percussion
|
||||||
|
9 : 'Chromatic_Percussion',
|
||||||
|
10 : 'Chromatic_Percussion',
|
||||||
|
11 : 'Chromatic_Percussion',
|
||||||
|
12 : 'Chromatic_Percussion',
|
||||||
|
13 : 'Chromatic_Percussion',
|
||||||
|
14 : 'Chromatic_Percussion',
|
||||||
|
15 : 'Chromatic_Percussion',
|
||||||
|
16 : 'Chromatic_Percussion',
|
||||||
|
# Organ
|
||||||
|
17 : 'Organ',
|
||||||
|
18 : 'Organ',
|
||||||
|
19 : 'Organ',
|
||||||
|
20 : 'Organ',
|
||||||
|
21 : 'Organ',
|
||||||
|
22 : 'Organ',
|
||||||
|
23 : 'Organ',
|
||||||
|
24 : 'Organ',
|
||||||
|
# Guitar
|
||||||
|
25 : 'Guitar',
|
||||||
|
26 : 'Guitar',
|
||||||
|
27 : 'Guitar',
|
||||||
|
28 : 'Guitar',
|
||||||
|
29 : 'Guitar',
|
||||||
|
30 : 'Guitar',
|
||||||
|
31 : 'Guitar',
|
||||||
|
32 : 'Guitar',
|
||||||
|
# Bass
|
||||||
|
33 : 'Bass',
|
||||||
|
34 : 'Bass',
|
||||||
|
35 : 'Bass',
|
||||||
|
36 : 'Bass',
|
||||||
|
37 : 'Bass',
|
||||||
|
38 : 'Bass',
|
||||||
|
39 : 'Bass',
|
||||||
|
40 : 'Bass',
|
||||||
|
# Strings
|
||||||
|
41 : 'Strings',
|
||||||
|
42 : 'Strings',
|
||||||
|
43 : 'Strings',
|
||||||
|
44 : 'Strings',
|
||||||
|
45 : 'Strings',
|
||||||
|
46 : 'Strings',
|
||||||
|
47 : 'Strings',
|
||||||
|
48 : 'Strings',
|
||||||
|
# Ensemble
|
||||||
|
49 : 'Ensemble',
|
||||||
|
50 : 'Ensemble',
|
||||||
|
51 : 'Ensemble',
|
||||||
|
52 : 'Ensemble',
|
||||||
|
53 : 'Ensemble',
|
||||||
|
54 : 'Ensemble',
|
||||||
|
55 : 'Ensemblee',
|
||||||
|
56 : 'Ensemble',
|
||||||
|
# Brass
|
||||||
|
57 : 'Brass',
|
||||||
|
58 : 'Brass',
|
||||||
|
59 : 'Brass',
|
||||||
|
60 : 'Brass',
|
||||||
|
61 : 'Brass',
|
||||||
|
62 : 'Brass',
|
||||||
|
63 : 'Brass',
|
||||||
|
64 : 'Brass',
|
||||||
|
# Reed
|
||||||
|
65 : 'Reed',
|
||||||
|
66 : 'Reed',
|
||||||
|
67 : 'Reed',
|
||||||
|
68 : 'Reed',
|
||||||
|
69 : 'Reed',
|
||||||
|
70 : 'Reed',
|
||||||
|
71 : 'Reed',
|
||||||
|
72 : 'Reed',
|
||||||
|
# Pipe
|
||||||
|
73 : 'Pipe',
|
||||||
|
74 : 'Pipe',
|
||||||
|
75 : 'Pipe',
|
||||||
|
76 : 'Pipe',
|
||||||
|
77 : 'Pipe',
|
||||||
|
78 : 'Pipe',
|
||||||
|
79 : 'Pipe',
|
||||||
|
80 : 'Pipe',
|
||||||
|
# Synth Lead
|
||||||
|
81 : 'Synth_Lead',
|
||||||
|
82 : 'Synth_Lead',
|
||||||
|
83 : 'Synth_Lead',
|
||||||
|
84 : 'Synth_Lead',
|
||||||
|
85 : 'Synth_Lead',
|
||||||
|
86 : 'Synth_Lead',
|
||||||
|
87 : 'Synth_Lead',
|
||||||
|
88 : 'Synth_Lead',
|
||||||
|
# Synth Pad
|
||||||
|
89 : 'Synth_Pad',
|
||||||
|
90 : 'Synth_Pad',
|
||||||
|
91 : 'Synth_Pad',
|
||||||
|
92 : 'Synth_Pad',
|
||||||
|
93 : 'Synth_Pad',
|
||||||
|
94 : 'Synth_Pad',
|
||||||
|
95 : 'Synth_Pad',
|
||||||
|
96 : 'Synth_Pad',
|
||||||
|
# Synth Effects
|
||||||
|
97 : 'Synth_Effects',
|
||||||
|
98 : 'Synth_Effects',
|
||||||
|
99 : 'Synth_Effects',
|
||||||
|
100 : 'Synth_Effects',
|
||||||
|
101 : 'Synth_Effects',
|
||||||
|
102 : 'Synth_Effects',
|
||||||
|
103 : 'Synth_Effects',
|
||||||
|
104 : 'Synth_Effects',
|
||||||
|
# Ethnic
|
||||||
|
105 : 'Ethnic',
|
||||||
|
106 : 'Ethnic',
|
||||||
|
107 : 'Ethnic',
|
||||||
|
108 : 'Ethnic',
|
||||||
|
109 : 'Ethnic',
|
||||||
|
110 : 'Ethnic',
|
||||||
|
111 : 'Ethnic',
|
||||||
|
112 : 'Ethnic',
|
||||||
|
# Percussive
|
||||||
|
113 : 'Percussive',
|
||||||
|
114 : 'Percussive',
|
||||||
|
115 : 'Percussive',
|
||||||
|
116 : 'Percussive',
|
||||||
|
117 : 'Percussive',
|
||||||
|
118 : 'Percussive',
|
||||||
|
119 : 'Percussive',
|
||||||
|
120 : 'Percussive',
|
||||||
|
# Sound Effects
|
||||||
|
121 : 'Sound_Effects',
|
||||||
|
122 : 'Sound_Effects',
|
||||||
|
123 : 'Sound_Effects',
|
||||||
|
124 : 'Sound_Effects',
|
||||||
|
125 : 'Sound_Effects',
|
||||||
|
126 : 'Sound_Effects',
|
||||||
|
127 : 'Sound_Effects',
|
||||||
|
128 : 'Sound_Effects'
|
||||||
|
}
|
||||||
|
|||||||
@ -1,33 +1,68 @@
|
|||||||
|
#!python3
|
||||||
#!/usr/bin/env python3
|
#!/usr/bin/env python3
|
||||||
|
import sys
|
||||||
import tensorflow as tf
|
|
||||||
import settings
|
|
||||||
from tensorflow.keras import layers
|
|
||||||
from keras.layers import Input, Dense, Conv2D, Flatten
|
|
||||||
from keras.models import Model, Sequential
|
|
||||||
import numpy as np
|
|
||||||
from sys import exit
|
|
||||||
import pickle
|
import pickle
|
||||||
|
import settings
|
||||||
|
|
||||||
print('Reading samples from: {}'.format(settings.samples_path))
|
import numpy as np
|
||||||
|
from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
|
||||||
|
from keras.models import Model, Sequential
|
||||||
|
from keras.utils.np_utils import to_categorical
|
||||||
|
|
||||||
train_X = np.load(settings.samples_path)['arr_0']
|
|
||||||
|
|
||||||
n_samples = train_X.shape[0]
|
def load_data(samples_path):
|
||||||
input_shape = settings.midi_resolution*128
|
print('Loading... {}'.format(train_data_path))
|
||||||
train_X = train_X.reshape(n_samples, input_shape)
|
train_X = np.load(train_data_path, allow_pickle=True)['arr_0']
|
||||||
|
train_y = np.load(train_data_path, allow_pickle=True)['arr_1']
|
||||||
|
return train_X, train_y
|
||||||
|
|
||||||
# encoder model
|
# TODO: make transformer class with fit, transform and reverse definitions
|
||||||
input_img = tf.keras.layers.Input(shape=(input_shape,))
|
def preprocess_samples(train_X, train_y):
|
||||||
encoded = tf.keras.layers.Dense(160, activation='relu')(input_img)
|
vocab_X = np.unique(train_X)
|
||||||
decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(encoded)
|
vocab_y = np.unique(train_y)
|
||||||
autoencoder = tf.keras.models.Model(input_img, decoded)
|
vocab = np.concatenate([vocab_X, vocab_y])
|
||||||
|
n_vocab = vocab.shape[0]
|
||||||
|
note_to_int = dict((note, number) for number, note in enumerate(vocab))
|
||||||
|
int_to_note = dict((number, note) for number, note in enumerate(vocab))
|
||||||
|
_train_X = []
|
||||||
|
_train_y = []
|
||||||
|
for sample in train_X:
|
||||||
|
# TODO: add normalizasion
|
||||||
|
_train_X.append([note_to_int[note] for note in sample])
|
||||||
|
|
||||||
autoencoder.compile(optimizer='adam',
|
train_X = np.array(_train_X).reshape(train_X.shape[0], train_X.shape[1], 1)
|
||||||
loss='binary_crossentropy',
|
train_y = np.array([note_to_int[note] for note in train_y]).reshape(-1,1)
|
||||||
metrics=['accuracy'])
|
train_y = to_categorical(train_y)
|
||||||
|
|
||||||
autoencoder.fit(train_X, train_X, epochs=settings.epochs, batch_size=32)
|
return train_X, train_y, n_vocab, int_to_note
|
||||||
|
|
||||||
autoencoder.save_weights(settings.model_path)
|
train_data_path = sys.argv[1]
|
||||||
print("Model save to {}".format(settings.model_path))
|
|
||||||
|
train_X, train_y = load_data(train_data_path)
|
||||||
|
train_X, train_y, n_vocab, int_to_note = preprocess_samples(train_X, train_y)
|
||||||
|
|
||||||
|
save_model_path = sys.argv[2]
|
||||||
|
epochs = int(sys.argv[3])
|
||||||
|
|
||||||
|
model = Sequential()
|
||||||
|
model.add(LSTM(512, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))
|
||||||
|
model.add(Dropout(0.3))
|
||||||
|
model.add(LSTM(512, return_sequences=True))
|
||||||
|
model.add(Dropout(0.3))
|
||||||
|
model.add(LSTM(512))
|
||||||
|
model.add(Dense(256))
|
||||||
|
model.add(Dropout(0.3))
|
||||||
|
model.add(Dense(n_vocab))
|
||||||
|
model.add(Activation('softmax'))
|
||||||
|
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
|
||||||
|
|
||||||
|
# This code will train our model, with given by parameter number of epochs
|
||||||
|
print('Training...')
|
||||||
|
model.fit(train_X, train_y, epochs=epochs, batch_size=64)
|
||||||
|
|
||||||
|
# it saves model, and additional informations of model
|
||||||
|
# that is needed to generate music from it
|
||||||
|
pickle.dump(model, open(save_model_path,'wb'))
|
||||||
|
pickle.dump((int_to_note, n_vocab, train_X.shape[1]), open('{}_dict'.format(save_model_path),'wb'))
|
||||||
|
print('Done!')
|
||||||
|
print("Model saved to: {}".format(save_model_path))
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user