40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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import midi
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import tensorflow as tf
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import pypianoroll as roll
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from keras.layers import Input, Dense, Conv2D
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from keras.models import Model
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from tensorflow.keras import layers
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from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector
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from keras.models import Model, Sequential
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import matplotlib.pyplot as plt
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import settings
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import pickle
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import sys
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trained_model_path = sys.argv[1]
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output_path = sys.argv[2]
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treshold = float(sys.argv[3])
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# load and predict
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model = pickle.load(open(trained_model_path, 'rb'))
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music = np.empty((4,96,128))
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for x in range(4):
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generate_seed = np.random.randint(0, 127, 12288).reshape(1,96,128)
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music[x] = model.predict(generate_seed).reshape(96,128)
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generated_sample = music.reshape(4*96,128)
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# binarize generated music
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generated_sample = generated_sample > treshold * generated_sample.max()
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# generated_sample = np.clip(generated_sample,0,1) * 128
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# save to midi
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generated_midi = midi.to_midi(generated_sample, output_path='{}.mid'.format(output_path), is_drum=True, program=0, )
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#save plot for preview
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roll.plot(generated_midi, filename='{}.png'.format(output_path))
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