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Taki zabieg byÅ‚ niezbÄ™dny ponieważ rozmiar sieci neuronowej jest staÅ‚y dla wszystkich prób ze zbioru uczacego. Nie wpÅ‚ywa to jednak na same sekwencjÄ™ ponieważ podczas predykcjÄ™ koÅ„czymy w momencie wygenerowania tokenu \pyth{}. -\section{Uczenie modelu} +\section{Ekperyment} -\section{Generowanie muzyki przy pomocy wytrenowanego modelu} +W tej części pokażę jak wykorzystać oprogramowanie, które stworzyÅ‚em aby wygenerować muzykÄ™ na przykÅ‚adzie. OmówiÄ™ caÅ‚y proces, nastÄ™pnie zademonstrujÄ™ wyniki. + +\subsection{Oprogramowanie} +Stworzone przeze mnie oprogramowanie, skÅ‚ada siÄ™ z 5 skryptów pythona. + +\begin{itemize} + \item \pyth{midi_procesing.py} - zawiera funkcjÄ™ potrzebne do pracy w plikami midi. + \item \pyth{model.py} - zawiera definicjÄ™ modelu sieci neuronowej + \item \pyth{extract.py} - sÅ‚uży do wydobycia w plików midi zbioru danych w postacji sekwencji. + \item \pyth{train.py} - wykonujÄ…c ten skrypt wykorzystujemy wygenerowane dane, aby wytrenować zestaw sieci neuronych. + \item \pyth{generate.py} - wykorzystuje wytrenowane modele aby wygenerować ostatecznie plik midi. +\end{itemize} + +\subsection{Zbiór danych} +W omawianym przykÅ‚adzie wykorzystaÅ‚em zbiór wybranych utworów midi zespoÅ‚u The Offstring. ZostaÅ‚ on skompletowany ze źródeÅ‚ dostÄ™pnych na stronie internetowej https://www.midiworld.com/. SkÅ‚ada siÄ™ z 7 utworów. + +\begin{itemize} + \item The Offspring - All I Want.mid + \item The Offspring - Change the World.mid + \item The Offspring - Nitro.mid + \item The Offspring - Original Prankster.mid + \item The Offspring - Self Esteem.mid + \item The Offspring - The Kids Arent Alright.mid + \item The Offspring - Why Dont You Get a Job.mid +\end{itemize} + +\subsection{Wydobycie danych} +Aby wydobyć dane z plików midi wykorzystamy skypt \pyth{extract.py}. Można uzyć flagi \pyth{-a}, aby najpierw zapoznać siÄ™ z zawartoÅ›ciÄ… muzycznÄ… zbioru plików midi. + +% 100\%|███████████████████████████████████| 7/7 [00:01<00:00, 3.67it/s] +\begin{python} + >>> python extract.py offspring -a + 1098 of Drums + 1037 of Guitar + 704 of Melody + 528 of Bass + 1 of Organ +\end{python} + + DziÄ™ki temu możemy zaobserować że w procesowanym zbiorze danych jest 1098 taktów perkusji, 1037 taktów gitar, 704 melodii, 528 basu oraz 1 takt organ. + + Na tym etapie musimy zdecydować, który instrment bÄ™dzie generatorem oraz jakie bÄ™dÄ… zaleznoÅ›ci miÄ™dzy partiami w zespole sieci neuronowych. + + W tym przykÅ‚adzie, zdecydowaÅ‚em że gitara bÄ™dzie generowana na podstawie losowego wektora, a melodia, bass oraz perkusja bÄ™de tworzone na podstawie gitary. + + Uruchamiajać skrypt \pyth{extract.py} bez flagi \pyth{-a} zdefiniujemy omawiany przepÅ‚yw za pomocÄ… prostego konfiguratora. + +\begin{python} +>>> python extract.py offspring +Please specify number of instruments +4 +Please specify a workflow step +>>> Guitar m +Please specify a workflow step +>>> Melody Guitar a +Please specify a workflow step +>>> Drums Guitar a +Please specify a workflow step +>>> Bass Guitar a + +Exporting: 'Guitar' +Exporting: ('Guitar', 'Melody') +Exporting: ('Guitar', 'Drums') +Exporting: ('Guitar', 'Bass') +Done. + +\end{python} +%Exporting: Guitar: 100\%||███████████████████████████████████| 7/7 [00:01<00:00, 3.82it/s] +%Exporting: ('Guitar', 'Melody'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00, 3.81it/s] +%Exporting: ('Guitar', 'Drums'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00, 3.79it/s] +%Exporting: ('Guitar', 'Bass'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00, 3.81it/s] + +Po tym etapie zostaÅ‚y utworzony pliki zawierajÄ…ce oczyszczone pary sekwencji dla każdej sieci neuronowej. ZostaÅ‚o wygenerowanych + +\begin{itemize} + \item 263 próby dla modelu gitary, + \item 622 próby par melodii i gitary, + \item 948 prób par perkusji i gitary, + \item 385 prób par basu i gitary. +\end{itemize} + +Na podstawie takiego zbioru danych, w nastepnym kroku zostanÄ… wytrenowane czrety sieci neuronowe, po jednej dla każdego intrumentu. + +\section{Trenowanie modelu} + +UzywajÄ…c skypt \pyth{train.py} możemy w prosty sposób wytrenować wszystkie modele, a wagi zapiszÄ… i bÄ™dzie można je wykorzystać w celu generowania, lub w celu dalszego uczenia. + +\begin{python} +>>> python train.py offspring --e 1 + +Using TensorFlow backend. + +Training: Guitar +Train on 210 samples, validate on 53 samples +Epoch 1/1 +210/210 [==============================] + - 2s 10ms/step - loss: 2.1553 - val_loss: 2.0384 +Training: Melody +Train on 497 samples, validate on 125 samples +Epoch 1/1 +497/497 [==============================] + - 6s 11ms/step - loss: 1.7045 - val_loss: 1.6693 +Training: Drums +Train on 758 samples, validate on 190 samples +Epoch 1/1 +758/758 [==============================] + - 9s 13ms/step - loss: 2.2218 - val_loss: 2.1255 +Training: Bass +Train on 308 samples, validate on 77 samples +Epoch 1/1 +308/308 [==============================] + - 4s 12ms/step - loss: 2.2721 - val_loss: 1.7813 +\end{python} + +Na potrzeby badaÅ„ trenowaÅ‚em i generowaÅ‚em klipy muzyczne dla epok 1, 10, 25, 50, 75, 100, 150. DziÄ™ki temu mogÄ™ porównać ze sobÄ… poszczególne etapy treningu sieci neuronowych. Wykresy funkcji kosztów dla zbioru uczÄ…cego oraz testowego zaprezentowane zostaÅ‚y na rysunku~\ref{fig:losses}. + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/training_losses.png} + \caption[]{WartoÅ›ci kosztu dla poszczególnych modeli.} + \label{fig:losses} +\end{figure} + +\section{Generowanie muzyki przy pomocy wytrenowanych modeli} + +Gdy zdefiniowane modele zostanÄ… wytrenowane możemy wykorzystać skrypt \pyth{generate.py}, wtedy generujÄ…ca sieć neuonowa zostanie zasilona losowym wekorem aby wygenerować partiÄ™. W tym przykÅ‚adzie gitary a wygenerowana partia posÅ‚uży jako dane wejÅ›ciowe na pozostaÅ‚ych modeli. Ostatecznie otrzymane sekwencje zostanÄ… skompilowane do pliku MIDI. W tym momencie zostajÄ… wykorzystane informacje o programach dla każdego z intrmentów, a tempo utworu domyÅ›lnie ustawione jest na 120 BPM. Możemy również zdecydować, czy zasilenie dekodera modelu generujÄ…cego odbÄ™dzie siÄ™ za pomocÄ… losowej sekwencji elementów ze sÅ‚ownika (\textit{from\textunderscore seq}), czy losowy wektor zasili bezpoÅ›rednio stany wewnÄ™trzne dekodera $h$ i $c$ (\textit{from\textunderscore state}). + +\begin{python} +>>> python generate.py offstring --i 10 --m from_state + +Using TensorFlow backend. +Loading models... +Generating music... +Done. +\end{python} + +Parametr \pyth{--i} sÅ‚uży do okreslenia liczby wygenerowanych utworów a parametr \pyth{--m} pozwala zdefiniować metodÄ™ generowania, omówionÄ… wyżej. W tym momencie pomyÅ›lnie zostaÅ‚y wygenerowane fragmenty muzyczne przy pomocy gÅ‚Ä™bokiego uczenia. \section{Wyniki} + + + \section{Wnioski} \chapter{Podsumowanie} diff --git a/docs/document.toc b/docs/document.toc index 5c3c0fa..810347a 100644 --- a/docs/document.toc +++ b/docs/document.toc @@ -38,9 +38,13 @@ \contentsline {subsection}{\numberline {3.4.2\relax .\enspace }SÅ‚ownik}{49}% \contentsline {subsection}{\numberline {3.4.3\relax .\enspace }Elementy specjalne}{50}% \contentsline {subsection}{\numberline {3.4.4\relax .\enspace }Zakodowanie sekwencji}{50}% -\contentsline {section}{\numberline {3.5\relax .\enspace }Uczenie modelu}{51}% -\contentsline {section}{\numberline {3.6\relax .\enspace }Generowanie muzyki przy pomocy wytrenowanego modelu}{51}% -\contentsline {section}{\numberline {3.7\relax .\enspace }Wyniki}{51}% -\contentsline {section}{\numberline {3.8\relax .\enspace }Wnioski}{51}% -\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\enspace Podsumowanie}{53}% 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Binary files /dev/null and b/docs/images/offspring_midis/offspring/offspring_75epoch/offspring_9_from_state.mid differ diff --git a/docs/images/plots.py b/docs/images/plots.py index b7b5aa3..e2ec0bc 100644 --- a/docs/images/plots.py +++ b/docs/images/plots.py @@ -122,9 +122,47 @@ plot.savefig('waveform_axis.png') # print(samplingFrequency) +# %% +import pandas as pd +import matplotlib.pyplot as plt +import os +# %% +cols = ['epoch', 'val_loss', 'loss'] + +guitar_df = pd.read_csv('offspring_history/guitar_history.csv', header=None) +bass_df = pd.read_csv('offspring_history/bass_history.csv', header=None) +drums_df = pd.read_csv('offspring_history/drums_history.csv', header=None) +melody_df = pd.read_csv('offspring_history/melody_history.csv', header=None) + +guitar_df.columns = cols +bass_df.columns = cols +drums_df.columns = cols +melody_df.columns = cols +fig, axs = plt.subplots(2, 2, figsize=(10,10)) + +l11, l21 = axs[0][0].plot(guitar_df[['loss', 'val_loss']]) +l21, l22 = axs[0][1].plot(bass_df[['loss', 'val_loss']]) +l31, l32 = axs[1][0].plot(drums_df[['loss', 'val_loss']]) +l41, l42 = axs[1][1].plot(melody_df[['loss', 'val_loss']]) + +axs[0][0].set_title('Guitar') +axs[0][1].set_title('Bass') +axs[1][0].set_title('Drums') +axs[1][1].set_title('Melody') + +axs[0][0].set_xlabel('epochs') +axs[0][1].set_xlabel('epochs') +axs[1][0].set_xlabel('epochs') +axs[1][1].set_xlabel('epochs') +axs[0][0].set_ylabel('loss') +axs[0][1].set_ylabel('loss') +axs[1][0].set_ylabel('loss') +axs[1][1].set_ylabel('loss') +axs[0][0].legend(labels=('loss', 'val_loss')) +plt.savefig('training_losses.png') #%% diff --git a/docs/images/training_losses.png b/docs/images/training_losses.png new file mode 100644 index 0000000..74142b5 Binary files /dev/null and b/docs/images/training_losses.png differ