69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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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 random
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import pickle
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from tqdm import trange, tqdm
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import sys
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from music21 import converter, instrument, note, chord, stream
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trained_model_path = sys.argv[1]
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output_path = sys.argv[2]
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# load and predict
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print('Loading... {}'.format(trained_model_path))
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model = pickle.load(open(trained_model_path, 'rb'))
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int_to_note = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
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seed = [random.randint(0,50) for x in range(8)]
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music = []
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print('Generating...')
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for i in trange(500):
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predicted_vector = model.predict(np.array(seed).reshape(1,8,1))
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predicted_index = np.argmax(predicted_vector)
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music.append(int_to_note[predicted_index])
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seed.append(predicted_index)
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seed = seed[1:9]
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print('Saving...')
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offset = 0
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output_notes = []
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for event in tqdm(music):
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if (' ' in event) or event.isdigit():
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notes_in_chord = event.split(' ')
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notes = []
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for current_note in notes_in_chord:
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new_note = note.Note(current_note)
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new_note.storedInstrument = instrument.Piano()
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notes.append(new_note)
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new_chord = chord.Chord(notes)
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new_chord.offset = offset
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output_notes.append(new_chord)
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else:
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new_note = note.Note(event)
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new_note.offset = offset
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new_note.storedInstrument = instrument.Piano()
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output_notes.append(new_note)
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offset += 0.5
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midi_stream = stream.Stream(output_notes)
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midi_stream.write('midi', fp='{}.mid'.format(output_path))
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print('Done!')
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