82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
'''
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Thomas Matlak
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CS 310 Final Project
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Takes directory containing midi files as input, produces a text file containing only the midi note values for the first 10 seconds of each musical piece.
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Usage:
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python midiNoteSegments.py /path/to/midi/folder/ [/path/to/output/file.txt]
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'''
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import sys, glob
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from mido import MidiFile, MidiTrack, Message
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from keras.layers import LSTM, Dense, Activation, Dropout
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.optimizers import RMSprop
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from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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import mido
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import csv
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indir = sys.argv[1]
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outfile_name = indir + "/out.txt"
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if 2 < len(sys.argv):
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outfile_name = sys.argv[2]
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midi_files = glob.glob(indir + "/*.mid")
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transposition_intervals = {
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'Cb': -11,
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'Gb': -6,
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'Db': -1,
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'Ab': -8,
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'Eb': -3,
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'Bb': -10,
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'F': -5,
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'C': 0,
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'G': -7,
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'D': -2,
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'A': -9,
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'E': -4,
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'B': -11,
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'F#': -6,
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'C#':-1
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}
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with open(outfile_name, 'wb') as outfile:
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writer = csv.writer(outfile, delimiter=' ')
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for midi_file in midi_files:
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mid = MidiFile(midi_file)
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notes = []
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time = float(0)
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prev = float(0)
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key = "C"
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for msg in mid:
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if time >= 10:
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break
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### this time is in seconds, not ticks
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time += msg.time
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if msg.type == "key_signature":
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key = msg.key
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if not msg.is_meta:
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### only interested in piano channel
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if msg.channel == 0:
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if msg.type == 'note_on':
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# note in vector form to train on
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note = msg.bytes()
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# only interested in the note #and velocity. note message is in the form of [type, note, velocity]
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note = note[1] #:3]
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# note.append(time - prev)
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prev = time
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notes.append(note + transposition_intervals[key]) # this preserves the intervlas, but transposes a;; samples to C
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writer.writerow(notes)
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