x = to_categorical(x.flatten(), num_classes=DIM).\
reshape(x.shape[0], x.shape[1], DIM)
y = y.reshape(x.shape[0], x.shape[1], 1)
yield (x, y)
# model
model = StackedRNN(
timespan=LEN,
input_dim=DIM,
output_dim=DIM,
cells=[512, 512, 512],
)
# block_kwargs={'kernel_regularizer': l2(1e-5)})
model.build()
# model.model.load_weights('temp/stackedrnn_kernel_l2.h5')
# model.model.load_weights('temp/simple_rnn.h5')
# model.train(data_generator(),
# opt=1e-5,
# steps_per_epoch=30,
# epochs=100,
# save_path='temp/simple_rnn.h5')
model.build_generator('temp/e-comp_simple_rnn.h5')
res = model.generate(seed=32, length=2000)
mid = Song()
track = mid.add_track()
for msgi in AllInOneCoder().decode(res):
track.append(msgi)
mid.save_as('simple_rnn.mid')
from DeepSymphony.coders import AllInOneCoder
from DeepSymphony.models import StackedRNN
from DeepSymphony.utils import Song
from DeepSymphony.utils.stat import LCS
from tqdm import tqdm
from pprint import pprint
from keras import optimizers
if __name__ == '__main__':
LEN = 100 # length of input
DIM = 128+128+100+7
data, filelist = Song.load_from_dir(
"./datasets/easymusicnotes/",
encoder=AllInOneCoder(return_indices=True),
return_list=True)
def data_generator():
batch_size = 32
while True:
x = []
y = []
for _ in range(batch_size):
ind = np.random.randint(len(data))
start = np.random.randint(data[ind].shape[0]-LEN-1)
x.append(data[ind][start:start+LEN])
y.append(data[ind][start+1:start+LEN+1])
x = np.array(x)
y = np.array(y)
from __future__ import print_function
import sys
from DeepSymphony.utils import Song
import mido
if __name__ == '__main__':
if len(sys.argv) < 2:
sys.argv.append('simple_rnn.mid')
song = Song(sys.argv[1])
# run `timidity -iA` to open the daemon synthesizer
# use port to send message to the daemon synthesizer
port = mido.open_output(name='TiMidity port 0')
keyboard = ['_'] * 128
for msg in song.playback():
if msg.type == 'note_on':
keyboard[msg.note] = '^'
elif msg.type == 'note_off':
keyboard[msg.note] = '_'
print(''.join(keyboard), end='\r')
sys.stdout.flush()
port.send(msg)
from DeepSymphony.utils import Song
from DeepSymphony.utils.stat import LCS
from tqdm import tqdm
from pprint import pprint
from DeepSymphony.coders import AllInOneCoder
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
coder = AllInOneCoder()
song = Song('masterpiece/0035.mid')
song = coder.encode(song.midi)
data, filelist = Song.load_from_dir(
"./datasets/easymusicnotes/",
encoder=AllInOneCoder(return_indices=True),
return_list=True)
# LCS check
song = filter(lambda x: x < 128, song.argmax(1))
print len(song)
matches = []
for ind, ele in tqdm(enumerate(data)):
ele = filter(lambda x: x < 128, ele)
matches.append((LCS(ele, song, return_seq=True), filelist[ind]))
matches = sorted(matches, key=lambda x: x[0][0])[::-1]
# pprint(sorted(matches)[::-1])
for (count, lcs, p, q), filename in matches:
print count