# traini
batch_size=32,
continued=False,
overwrite_workdir=True,
iterations=10000,
workdir='./temp/Fillblank/')
model = DCRNN(hparam)
model.build()
# coder = NoteDurationCoder(normalize_key='C5', first_voice=False)
coder = MultiHotCoder(normalize_key='C5', with_velocity=True)
try:
data = np.load('temp/easy.npz')['data']
except:
data = np.array(
map_dir(lambda fn: coder.encode(ms.converter.parse(fn)),
'./datasets/easymusicnotes/'))
np.savez('temp/easy.npz', data=data)
print(len(data), map(lambda x: len(x), data))
data = filter(lambda x: len(x) > 0 and x.shape[1] > hparam.timesteps, data)
# import matplotlib.pyplot as plt
# for d in data:
# print d.shape
# for v in d:
# fig, ax = plt.subplots()
# ax.imshow(v)
# plt.show()
data = map(lambda x: x.sum(0), data)
print(len(data), map(lambda x: len(x), data))
def sample(batch_size, noise=0.20):
# mode = 'train'
mode = 'generate'
if mode == 'train':
hparam = StackedLSTMHParam(batch_size=64,
cells=[256, 256],
input_dim=363,
timesteps=100,
output_dim=363,
learning_rate=5e-3,
iterations=3000,
overwrite_workdir=True)
model = StackedLSTM(hparam)
coder = ExampleCoder()
data = np.array(
map_dir(lambda fn: coder.encode(get_midi(fn)),
'./datasets/easymusicnotes'))
def fetch_data(batch_size):
x, y = [], []
for _ in range(batch_size):
ind = np.random.randint(len(data))
start = np.random.randint(data[ind].shape[0] -
hparam.timesteps - 1)
xi = data[ind][start:start + hparam.timesteps]
yi = data[ind][start + 1:start + hparam.timesteps + 1]
x.append(coder.onehot(xi))
y.append(yi)
x, y = np.array(x), np.array(y)
return x, y
model.train(fetch_data)
try:
# data = np.load('temp/easy.npz')
data = np.load('temp/piano-midi.npz')
voi, vel = data['voice'], data['velocity']
# data = (voi*vel)/127.
data = voi
except:
def read_song(filename):
return coder.encode(ms.converter.parse(filename))
data = np.array(
map_dir(
read_song,
# './datasets/piano-midi.de/', cores=6))
'./datasets/easymusicnotes/',
cores=8))
data = filter(lambda x: x is not None, data)
voi, vel = zip(*data)
np.savez('temp/easy.npz', voice=voi, velocity=vel)
# np.savez('temp/piano-midi.npz', voice=voi, velocity=vel)
print(len(data), map(lambda x: len(x), data))
data = filter(lambda x: len(x) > 0 and x.shape[0] > hparam.timesteps, data)
print(len(data), map(lambda x: len(x), data))
train_data, test_data = train_test_split(data,
test_size=0.22,
random_state=32)
if __name__ == '__main__':
timesteps = 128
batch_size = 32
mode = 'train'
mode = 'test'
coder = NoteDurationCoder(normalize_key='C5', resolution=1. / 16.)
try:
# raise Exception
data = np.load('temp/piano-midi_duration.npz')
notes = data['notes']
durations = data['durations']
except:
data = np.array(map_dir(handle, './datasets/piano-midi.de/', cores=8))
print map(lambda x: len(x[0]), data)
data = filter(lambda x: len(x[0]) > 0, data)
print map(lambda x: len(x[0]), data)
notes, durations = zip(*data)
np.savez('temp/piano-midi_duration.npz',
notes=notes,
durations=durations)
notes = np.array(notes)
durations = np.array(durations) - 1
print 'durations'
print np.min(map(lambda x: x.min(), durations))
print np.max(map(lambda x: x.max(), durations))
print 'notes'
print np.min(map(lambda x: x.min(), notes))
# only_major=True,
length_limit=np.inf)
try:
# data = np.load('temp/easy.npz')
# raise Exception
data = np.load('temp/piano-midi.npz')
voi, vel = data['voice'], data['velocity']
# data = (voi*vel)/127.
data = voi
except:
def read_song(filename):
return coder.encode(ms.converter.parse(filename))
data = np.array(map_dir(
read_song,
'./datasets/piano-midi.de/', cores=8))
# './datasets/easymusicnotes/', cores=8))
data = filter(lambda x: x is not None, data)
voi, vel = zip(*data)
np.savez('temp/easy.npz', voice=voi, velocity=vel)
data = voi
# np.savez('temp/piano-midi.npz', voice=voi, velocity=vel)
print(len(data), map(lambda x: len(x), data))
data = filter(lambda x: len(x) > 0 and x.shape[0] > hparam.timesteps, data)
print(len(data), map(lambda x: len(x), data))
train_data, test_data = train_test_split(data,
test_size=0.22,
random_state=32)
alpha=5e-3, # 5-->2, alpha*con_loss
beta=1.00,
gamma=1e-3) # gamma*q_loss
model = ContinuousSeqAE(hparam)
model.build()
# coder = ExampleCoder()
coder = NoteDurationCoder(normalize_key='C5', )
# single=True,
# first_voice=True)
try:
data = np.load('temp/easy.npz')['data']
# data = np.load('temp/1138.npz')['data']
except:
data = np.array(
map_dir(lambda fn: coder.encode(ms.converter.parse(fn))[0],
'./datasets/1138ChinesePopSongs/1138/'))
np.savez('temp/1138.npz', data=data)
print(len(data), map(lambda x: len(x), data))
data = filter(lambda x: len(x) > hparam.timesteps, data)
data = map(remove_con_dup, data)
print(len(data), map(lambda x: len(x), data))
train_data, test_data = train_test_split(data,
test_size=0.22,
random_state=32)
def fetch_tri_data_g(dataset):
def fetch_tri_data(batch_size):
src_seqs, nei_seqs, int_seqs = [], [], []
for _ in range(batch_size):
