def demo_random_new(argv):
mid = Song()
track = mid.add_track()
# header = MidiFile('AutumnL.mid')
for i in range(1000):
note = np.random.randint(60, 100)
# key down
track.append(
Message(
'note_on',
note=note,
velocity=np.random.randint(30, 70),
time=0 if np.random.rand() > 0.8 else np.random.randint(
100, 300),
))
# key up
track.append(
Message(
'note_on',
note=note,
velocity=0,
time=np.random.randint(100, 300),
))
mid.save('new_song.mid')
def demo_copy(argv):
mid = Song()
track = mid.add_track()
source = MidiFile('playground/AutumnL.mid')
# only copy notes
Song._copy(source, track, filter_f=lambda x: x.type == 'note_on')
mid.save('playground/AutumnL_copy.mid')
from midiwrapper import Song
from keras.models import load_model
import numpy as np
if __name__ == '__main__':
m = load_model('./temp/gan2.h5')
m.summary()
bs, seq_len, note_dim, _ = m.output_shape
code_dim = m.input_shape[-1]
def code_generator(bs):
Z = np.random.uniform(-1., 1., size=(bs, code_dim))
return Z
mid = Song()
track = mid.add_track()
notes = m.predict(code_generator(1))[0, :, :, 0]
mid._compose(track, notes, deltat=100, threshold=1.)
mid.save_as('gan2.mid')
# testing
if __name__ == '__main__':
from mido import MidiFile
midi = MidiFile('./datasets/e-comp/2002chan01.mid')
encoder = AllInOneEncoder()
code = encoder.encode(midi)
msg = encoder.decode(code)
print code.dtype
print code.shape
# print msg[-10:]
from midiwrapper import Song
s = Song()
track = s.add_track()
for msgi in msg:
track.append(msgi)
s.save_as("decode.mid")
# import os
# from tqdm import tqdm
# dirpath = './datasets/e-comp/'
# tmppath = './datasets/e-comp-allinone/'
# encoder = AllInOneEncoder()
# filelist = []
# for root, _, files in os.walk(dirpath):
# for name in files:
# filelist.append(os.path.join(root, name))
# for filename in tqdm(filelist):
# midi = Song(filename)