77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
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import settings
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import pypianoroll as roll
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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from math import floor
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MIDI_DIRECTORY = settings.midi_path
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SAMPLES_DIRECTORY = settings.samples_path
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MIDI_RESOLUTION = settings.midi_resolution
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BEAT_PER_BATCH = settings.beats_per_sample
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samples = np.empty((0,BEAT_PER_BATCH,96,128))
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def erase_note_lenth(pianoroll):
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if pianoroll.ndim != 2:
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raise ValueError('pianoroll should be two dimentional')
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now_block = []
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for x in pianoroll:
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this = None
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prev = None
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new_line =[]
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for y in x:
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this = y
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if prev != None:
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if this > 0 and prev > 0:
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new_line.append(0)
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else:
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new_line.append(y)
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else:
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new_line.append(y)
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prev = this
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now_block.append(new_line)
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return np.array(now_block)
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print('Start convertion')
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for midi_file in os.listdir(MIDI_DIRECTORY):
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try:
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print('Reading file: {}'.format(midi_file))
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song = roll.Multitrack('{}/{}'.format(MIDI_DIRECTORY, midi_file))
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# no_drums_mt = roll.Multitrack(tempo=120.0, downbeat=[0, 96, 192, 288], beat_resolution=24)
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intruments_only = roll.Multitrack(tempo=120.0, beat_resolution=24)
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for track in song.tracks:
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if track.is_drum == False:
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print(track.name, track.program)
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intruments_only.append_track(track=track, pianoroll=track.pianoroll)
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instrument_track = track.pianoroll
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# plt.imshow(instrument_track[24*8:24*24].T)
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# plt.savefig('data/0_{}.png'.format(midi_file))
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instrument_track = erase_note_lenth(instrument_track.T).T
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# plt.imshow(instrument_track[24*8:24*24].T)
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# plt.savefig('data/1_{}.png'.format(midi_file))
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# instruments = no_drums_mt.get_merged_pianoroll(mode='sum')
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beats = floor( (instrument_track.shape[0] / MIDI_RESOLUTION) / BEAT_PER_BATCH) * BEAT_PER_BATCH
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notes_for_beats = beats * MIDI_RESOLUTION
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print('beats: ', beats)
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samples_of_song = np.asarray(np.split(instrument_track[:notes_for_beats], beats))
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samples_of_song = samples_of_song.reshape(int(beats/BEAT_PER_BATCH),BEAT_PER_BATCH,96,128)
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print('Converted samples: {}'.format(samples_of_song.shape))
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samples = np.concatenate([samples_of_song,samples], axis=0)
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np.savez_compressed(SAMPLES_DIRECTORY,samples)
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except Exception as error:
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print('Convertion faild: {}'.format(error))
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pass
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finally:
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print('Done!')
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