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yua|EB9^77SS=o3^T+}ax`$yd*0GF2v*K3gWn{(_DecJt2A%|CxCft)B=l=niK}eDS diff --git a/docs/document.tex b/docs/document.tex index 8287cc1..334cc33 100644 --- a/docs/document.tex +++ b/docs/document.tex @@ -729,8 +729,10 @@ note_on channel=0 note=60 velocity=0 time=0 Kluczowe było zauważenie podobieństwa między językiem naturalnym oraz muzyką. Zarówno zdanie jak i partia muzyczna składa się z sekwencji elementów rozmieszczonych w czasie. Elementy te są zależne od długoterminowego kontekstu, oraz od tego jaki element był ustawiony wcześniej. Dla języka naturalnego są to słowa, dla muzyki są to nuty i akordy. Dodatkowo pomyślałem, że różne instrumenty można porównać do różnych języków świata. Wtedy aby stworzyć melodię, np. basu, tak aby pasowała pod partię gitary, należy ,,przetłumaczy'' język gitary na język basu. Do tłumaczeń języka naturalnego wykorzystuje się modele sequence-to-sequence, dlatego postanowiłem w modelu generowania muzyki wykorzystać właśnie tą architekturę. Dodatkowo modele sequence-to-sequence mają tę cechę, że liczba elementów sekwencji wejściowej może być inna niż liczba elementów sekwencji wyjściowej. Idealnie sprawdzi się w przypadku muzyki, ponieważ o długości trwania ścieżki muzycznej nie świadczy liczba nut tylko suma ich wartości. \section{Wstępne przygotowanie danych do treningu} + \label{section:midi} \subsection{Muzyczne ,,słowo''} + \label{section:midi_words} Na potrzeby dostosowania danych muzycznych do koncepcji słów w zdaniu zakodowałem pojedyncze słowo muzyczne jako @@ -1031,6 +1033,7 @@ else: Funkcja sprawdza liczbę pojedynczych nut i akordów w ścieżce oraz zagęszczenie nut w takcie. Jeśli jest więcej niż 90\% pojedynczych nut w ścieżce oraz jest średnio więcej nut w takcie niż dwie wtedy uznaję, że partia instrumentalna utworu jest melodią. \section{Definicja modelu} +\label{section:model} Wszystkie modele sieci neuonowych zastosowane w tej pracy, zostały napisane z wykorzystaniem środowiska Keras. Keras jest to środowisko wyższego poziomu, służące do tworzenia modelu głębokiego uczenia. @@ -1330,7 +1333,7 @@ Warto zwrócić uwagę, że przed opisaną transformacją sekwencje były różn W tej części pokażę jak wykorzystać oprogramowanie, które stworzyłem aby wygenerować muzykę na przykładzie. Omówię cały proces, następnie zademonstruję wyniki. \subsection{Oprogramowanie} -Stworzone przeze mnie oprogramowanie, składa się z 5 skryptów pythona. +Stworzone przeze mnie oprogramowanie, napisane w języku Python, składa się z 2 bibiotek oraz 3 skryptów. \begin{itemize} \item \pyth{midi_procesing.py} - zawiera funkcję potrzebne do pracy w plikami midi. @@ -1340,6 +1343,8 @@ Stworzone przeze mnie oprogramowanie, składa się z 5 skryptów pythona. \item \pyth{generate.py} - wykorzystuje wytrenowane modele aby wygenerować ostatecznie plik midi. \end{itemize} +Fragmenty bilbiotek \pyth{midi_procesing.py} i \pyth{model.py} zostały szczegółowo omówione w rozdziałach ~\ref{section:midi} i ~\ref{section:model}. Natomiast skrypty \pyth{extract.py}, \pyth{train.py} i \pyth{generate.py} zostały napisane aby ułatwić proces generowania oraz zapewniają powtarzalność i skalowalność prowadzonych ekperymentów. + \subsection{Zbiór danych} W omawianym przykładzie wykorzystałem zbiór wybranych utworów midi zespołu The Offstring. Został on skompletowany ze źródeł dostępnych na stronie internetowej https://www.midiworld.com/. Składa się z 7 utworów. @@ -1455,7 +1460,7 @@ Na potrzeby badań trenowałem i generowałem klipy muzyczne dla epok 1, 10, 25, Gdy zdefiniowane modele zostaną wytrenowane możemy wykorzystać skrypt \pyth{generate.py}, wtedy generująca sieć neuonowa zostanie zasilona losowym wekorem aby wygenerować partię. W tym przykładzie gitary a wygenerowana partia posłuży jako dane wejściowe na pozostałych modeli. Ostatecznie otrzymane sekwencje zostaną skompilowane do pliku MIDI. W tym momencie zostają wykorzystane informacje o programach dla każdego z intrmentów, a tempo utworu domyślnie ustawione jest na 120 BPM. Możemy również zdecydować, czy zasilenie dekodera modelu generującego odbędzie się za pomocą losowej sekwencji elementów ze słownika (\textit{from\textunderscore seq}), czy losowy wektor zasili bezpośrednio stany wewnętrzne dekodera $h$ i $c$ (\textit{from\textunderscore state}). \begin{python} ->>> python generate.py offstring --i 10 --m from_state +>>> python generate.py offspring --i 10 --m from_state Using TensorFlow backend. Loading models... @@ -1463,7 +1468,49 @@ Generating music... Done. \end{python} -Parametr \pyth{--i} służy do okreslenia liczby wygenerowanych utworów a parametr \pyth{--m} pozwala zdefiniować metodę generowania, omówioną wyżej. W tym momencie pomyślnie zostały wygenerowane fragmenty muzyczne przy pomocy głębokiego uczenia. +Parametr \pyth{--i} służy do okreslenia liczby wygenerowanych utworów a parametr \pyth{--m} pozwala zdefiniować metodę generowania, omówioną wyżej. + +Przekazywanie wygenerowanych patrii to odpowiednich modeli wykonywane jest przy pomocy poniższego fragmenu kodu. + +\begin{python} + notes = dict() + + for instrument, (model, program, generator) in band.items(): + if generator == None: + notes[instrument] = model.develop(mode=MODE) + else: + input_data = seq_to_numpy(notes[generator], + model.transformer.x_max_seq_length, + model.transformer.x_vocab_size, + model.transformer.x_transform_dict) + notes[instrument] = model.predict(input_data)[:-1] +\end{python} + +Słownik \pyth{band} przechowuje dane dotyczące całego zespołu modeli. Zmienna \pyth{intrument} jest nazwą intrumentu, \pyth{model} przechowuje obiekt modelu \\ +sequance-to-sequance, \pyth{program} jest liczbą naturalną odpowiającą programowi MIDI z kolekcji GM a \pyth{generator} to nazwa intrumentu na podstawie którego powinna zostać wygenerowana kolejna partia intrumentalna. + +Kompilacja do MIDI zachodzi z wykorzystaniem klas biblioteki \\ +\pyth{midi_processing.py}, które wspierają format sekwencji słów muzycznych omówionych w rozdziale ~\ref{section:midi_words}. + +\begin{python} +generated_midi = MultiTrack() +for instrument, (model, program, generator) in band.items(): + if instrument == 'Drums': + is_drums = True + else: + is_drums = False + + stream = Stream(first_tick=0, notes=notes[instrument]) + track = SingleTrack(name=instrument, + program=program, + is_drum=is_drums, + stream=stream) + generated_midi.tracks.append(track) + +generated_midi.save(save_path) +\end{python} + +W ten sposób pomyślnie zostały wygenerowane fragmenty muzyczne przy pomocy głębokiego uczenia. \section{Wyniki} diff --git a/docs/document.toc b/docs/document.toc index 810347a..a3036fe 100644 --- a/docs/document.toc +++ b/docs/document.toc @@ -33,18 +33,18 @@ \contentsline {section}{\numberline {3.3\relax .\enspace }Definicja modelu}{44}% \contentsline {subsection}{\numberline {3.3.1\relax .\enspace }Model w trybie uczenia}{44}% \contentsline {subsection}{\numberline {3.3.2\relax .\enspace }Model w trybie wnioskowania}{46}% -\contentsline {section}{\numberline {3.4\relax .\enspace }Transformacja danych dla modelu}{48}% -\contentsline {subsection}{\numberline {3.4.1\relax .\enspace }Enkodowanie one-hot}{48}% +\contentsline {section}{\numberline {3.4\relax .\enspace }Transformacja danych dla modelu}{49}% +\contentsline {subsection}{\numberline {3.4.1\relax .\enspace }Enkodowanie one-hot}{49}% \contentsline {subsection}{\numberline {3.4.2\relax .\enspace }Słownik}{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 }Ekperyment}{51}% -\contentsline {subsection}{\numberline {3.5.1\relax .\enspace }Oprogramowanie}{51}% -\contentsline {subsection}{\numberline {3.5.2\relax .\enspace }Zbiór danych}{51}% +\contentsline {subsection}{\numberline {3.5.1\relax .\enspace }Oprogramowanie}{52}% +\contentsline {subsection}{\numberline {3.5.2\relax .\enspace }Zbiór danych}{52}% \contentsline {subsection}{\numberline {3.5.3\relax .\enspace }Wydobycie danych}{52}% \contentsline {section}{\numberline {3.6\relax .\enspace }Trenowanie modelu}{53}% \contentsline {section}{\numberline {3.7\relax .\enspace }Generowanie muzyki przy pomocy wytrenowanych modeli}{54}% -\contentsline {section}{\numberline {3.8\relax .\enspace }Wyniki}{55}% -\contentsline {section}{\numberline {3.9\relax .\enspace }Wnioski}{55}% +\contentsline {section}{\numberline {3.8\relax .\enspace }Wyniki}{56}% +\contentsline {section}{\numberline {3.9\relax .\enspace }Wnioski}{56}% \contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\enspace Podsumowanie}{57}% \contentsline {chapter}{Bibliografia}{59}%