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import tensorflow as tfimport numpy as npimport librosaimport scipy.signal as signalfrom operator import itemgetterfrom pyknon.genmidi import Midifrom pyknon.music import NoteSeq, Note, Restimport music21
filename = 'title.wav'
def print_pdf(filename) : chords = ['C', 'D', 'E', 'F', 'G', 'A', 'B', 'HC'] path1 = '/Users/seungwoomun/Documents/MYC/train/Grand Piano/' path2 = '/Users/seungwoomun/Documents/MYC/train/Classical Grand/' path3 = '/Users/seungwoomun/Documents/MYC/train/test_wav/' path4 = '/Users/seungwoomun/Documents/MYC/train/Electric Piano/' path5 = '/Users/seungwoomun/Documents/MYC/code/wav/' lable = [[1, 0, 0, 0, 0, 0, 0, 0], # 도 C [0, 1, 0, 0, 0, 0, 0, 0], # 레 D [0, 0, 1, 0, 0, 0, 0, 0], # 미 E [0, 0, 0, 1, 0, 0, 0, 0], # 파 F [0, 0, 0, 0, 1, 0, 0, 0], # 솔 G [0, 0, 0, 0, 0, 1, 0, 0], # 라 A [0, 0, 0, 0, 0, 0, 1, 0], # 시 B [0, 0, 0, 0, 0, 0, 0, 1]] # 도 HC test_pred = [] test_result = [] music = '' # filename = 'star_1.wav'
# X, Y 데이터 추출 audio_sample, sampling_rate = librosa.load(path5 + filename, sr = None) input_data = np.abs(librosa.stft(audio_sample, n_fft = 1024, hop_length = 512, win_length = 1024, window = signal.hann))
filename = filename.replace('.wav',"")
# input_data의 배열 형태를 알아본다 shape = np.shape(input_data) nb_samples = shape[0] nb_windows = shape[1]
# [nb_samples][nb_windows] 형태이므로, 딥러닝을 수행하기 위한 배열 형태 [nb_windows][nb_samples]로 맞춰준다 input_data = input_data.T
# 학습용 레이블 배열을 생성한다 lable_tmp = [lable[0] for row in range(nb_windows)]
# 하나의 배열로 합친다
x_test = np.array(input_data) y_test = np.array(lable_tmp)
model = tf.keras.models.load_model("./DNN_Model_500.h5")
print("####### Model 평가 #######") loss, acc = model.evaluate(x_test, y_test) print("모델의 정확도: {:5.2f}%".format(100 * acc))
prediction = model.predict(x_test)
# 예측값에서 음계 구분 for i in prediction : if i.argmax() == 0: test_pred.append("도") # test_pred = np.r_["도"] elif i.argmax() == 1 : test_pred.append("레") # test_pred = np.r_["레"] elif i.argmax() == 2 : test_pred.append("미") # test_pred = np.r_["미"] elif i.argmax() == 3 : test_pred.append("파") # test_pred = np.r_["파"] elif i.argmax() == 4 : test_pred.append("솔") # test_pred = np.r_["솔"] elif i.argmax() == 5 : test_pred.append("라") # test_pred = np.r_["라"] elif i.argmax() == 6 : test_pred.append("시") # test_pred = np.r_["시"] elif i.argmax() == 7 : test_pred.append("h_도") # test_pred = np.r_["h_도"]
# 예측값에서 음계 개수 추출 # print(test_pred) # print(len(test_pred)) # print(test_pred[448]) print(np.shape(test_pred)) print("도 : " + str(test_pred.count("도"))) print("레 : " + str(test_pred.count("레"))) print("미 : " + str(test_pred.count("미"))) print("파 : " + str(test_pred.count("파"))) print("솔 : " + str(test_pred.count("솔"))) print("라 : " + str(test_pred.count("라"))) print("시 : " + str(test_pred.count("시"))) print("h_도 : " + str(test_pred.count("h_도")))
# 이전 음계 추출 # for i in range(0, len(test_pred) - 1) : # if test_pred[i] != test_pred[i + 1] : # test_result.append(test_pred[i]) # if i + 1 == len(test_pred) - 1 : # test_result.append(test_pred[i + 1])
toc = 0 # coc = 0 # 최종 음계 추출 for i in range(0, len(test_pred) - 1) : if test_pred[i] == test_pred[i + 1] : toc += 1 # coc += 1 else : toc = 0 # coc = 0
if toc >= 40 : toc = 0 test_result.append(test_pred[i]) # if toc >= 40 and toc < 50 : # toc = 0 # if coc > 80 : # coc = 0 # else : # test_result.append(test_pred[i])
# if toc >= 30 : # toc = 0 # # test_result.append(test_pred[i]) # elif toc >= 20 and toc < 25 : # toc = 0 # test_result.append(test_pred[i]) print(test_result) print(np.shape(test_result)) print("음 개수 : " + str(len(test_result)))
# midi file 생성 notes = NoteSeq(music)
# midi file 생성을 위한 구분 for index in test_result : if index == '도' : # music = music + 'C4' notes.append(Note(12, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '레' : # music = music + 'D4' notes.append(Note(14, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '미' : # music = music + 'E4' notes.append(Note(16, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '파' : # music = music + 'F4' notes.append(Note(17, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '솔' : # music = music + 'G4' notes.append(Note(19, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '라' : # music = music + 'A4' notes.append(Note(21, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == '시' : # music = music + 'B4' notes.append(Note(23, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C elif index == 'h_도' : # music = music + 'C4' notes.append(Note(24, octave = 4, dur = 1/4)) # 21 A 22 A# 23 B 24 C # music = music + ' '
# notes.append(Note(24, octave = 4, dur = 1/8)) # 21 A 22 A# 23 B 24 C
midi = Midi(number_tracks = 1, tempo = 90) midi.seq_notes(notes, track = 0) midi.write('Midi/' + filename + '.mid')
# Midi to pdf us = music21.environment.UserSettings() # music21 환경 설정
midi_file = music21.converter.parse('Midi/' + filename + '.mid') midi_file.insert(0, music21.metadata.Metadata()) midi_file.metadata.title = 'Make Your Chord' midi_file.metadata.composer = 'MYC' midi_file.write('musicxml.pdf', fp = 'pdf/'+ filename + '.pdf') print(midi_file) # index, value = max(enumerate(prediction[0]), key = itemgetter(1)) # print(index, value)
# # for i in x_test :
# 검증 # print("####### Model 검증 #######") # for i in range(0, len(x_test)) : # test = np.array([x_test[i]]) # # print(test) # print(model.predict(test))
print_pdf(filename)
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