code_ndim=2,
# hparam
trainable_gen=['generator'],
D_lr=1e-3,
G_lr=5e-4, # change to 1e-4 when finetuning
G_k=5,
D_boost=0,
G_clipnorm=None, # 1.0,
# traini
batch_size=32,
continued=False,
overwrite_workdir=True,
iterations=20000,
workdir='./temp/DCRNN_RhythmGAN/'
)
model = DCRNN(hparam)
model.build()
# coder = NoteDurationCoder(normalize_key='C5', first_voice=False)
coder = MultiHotCoder(normalize_key='C5', only_major=True)
if hparam.code_dim == 3:
def sample(batch_size):
code_s = np.random.uniform(-1., +1.,
size=(batch_size,
hparam.code_dim))
code_e = np.random.uniform(-1., +1.,
size=(batch_size,
hparam.code_dim))
res = np.zeros((batch_size,
hparam.timesteps,
hparam.code_dim))
show_grad=False,
show_input=True,
# hparam
trainable_gen=['generator'],
D_lr=1e-3,
G_lr=5e-4, # change to 1e-4 when finetuning
G_k=5,
D_boost=0,
G_clipnorm=None, # 1.0,
# 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
cond_dim=12,
# hparam
trainable_gen=['generator'],
D_lr=1e-3,
G_lr=8e-4, # change to 1e-4 when finetuning
G_k=2,
D_boost=0,
G_clipnorm=None, # 1.0,
# traini
batch_size=32,
continued=False,
overwrite_workdir=True,
iterations=40000,
workdir='./temp/DCRNN_cond/'
)
model = DCRNN(hparam)
model.build()
# coder = NoteDurationCoder(normalize_key='C5', first_voice=False)
# coder = MultiHotCoder(normalize_key='C5', only_major=True)
coder = MultiHotCoder(# normalize_key='C5',
with_velocity=True,
# 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