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')
seq = get_openning(LEN, mode='borrow')
notes = [] # deepcopy(seq)
accumulate = np.zeros((dim, )).astype('int')
for _ in range(SONG_LEN):
note = model.predict(np.array([seq]))[0][-1]
seq.pop(0)
# sustain too long
accumulate = accumulate * (note >= THRESHOLD) + (note >= THRESHOLD)
note[accumulate >= MAX_SUSTAIN] = 0.
accumulate[accumulate >= MAX_SUSTAIN] = 0.
print ''.join([('x' if char >= THRESHOLD else '_') for char in note])
notes.append(note)
if (note >= THRESHOLD).sum() == 0:
# too less notes
print 'no note, max =', note.max()
# note = np.random.rand(dim,)
# seq = get_openning(LEN)
if note.max() > 0:
note /= note.max()
seq.append(note)
else:
# use output as input
note = note * np.random.uniform(0.5, 2.0, size=(dim, ))
seq.append(note)
mid.compose(track, np.array(notes), deltat=200, threshold=THRESHOLD)
mid.save_as('simple_rnn.mid')
THRESHOLD = 0.85
hots = Song(FILE).\
encode_onehot(
{'filter_f': lambda x: x.type in ['note_on', 'note_off'],
'unit': 'beat'},
{'resolution': 0.25})
print hots.shape
model = load_model('temp/memorize.h5')
mid = Song()
track = mid.add_track()
seq = [hots[i] for i in range(20)]
# seq = [np.random.binomial(1, 0.3, (dim,)) for _ in range(LEN)]
notes = [] # deepcopy(seq)
accumulate = np.zeros((dim,)).astype('int')
for _ in range(SONG_LEN):
note = model.predict(np.array([seq]))[0]
seq.pop(0)
print ''.join([('x' if char >= THRESHOLD else '_') for char in note])
notes.append(note)
# use output as input
# note = note * np.random.uniform(0.75, 1.2, size=(dim, ))
seq.append(note)
mid._compose(track, np.array(notes), deltat=100, threshold=THRESHOLD)
mid.save_as('memorize.mid')
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)
# hots = encoder(midi.midi).astype('bool')
# np.savez_compressed
# (os.path.join(tmppath, filename.split('/')[-1]+'.npz'),