def main():
''' NOTE: The input is rescaled to [-1, 1] '''
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/xmax.npf'),
xmin=np.fromfile('./etc/xmin.npf'),
)
image, label = read(
file_pattern=arch['training']['datadir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
)
machine = MODEL(arch)
loss = machine.loss(image, label)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main():
''' NOTE: The input is rescaled to [-1, 1] '''
dirs = validate_log_dirs(args)
if args.restore_from is None:
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/xmax.npf'),
xmin=np.fromfile('./etc/xmin.npf'),
)
image, label = read(
file_pattern=arch['training']['datadir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
) #image format NHWC
print "image shape:",image
print "label shape:",label
machine = MODEL(arch, args, True, False)
loss = machine.loss(image, label, True)
#sample = machine.sample()#np.asarray([SPEAKERS.index(args.trg)]))
# sample,
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], n_unroll=arch['training']['n_unroll'], machine=machine)
def main():
''' NOTE: The input is rescaled to [-1, 1] '''
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/xmax.npf'),
xmin=np.fromfile('./etc/xmin.npf'),
)
image, label = read(
file_pattern=arch['training']['datadir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
)
machine = MODEL(arch)
loss = machine.loss(image, label)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main():
""" NOTE: The input is rescaled to [-1, 1] """
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
# dirs = dict()
# dirs['logdir'] = '.'
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/xmax.npf'),
xmin=np.fromfile('./etc/xmin.npf'),
)
machine = MODEL(arch)
if args.model in ['VAWGAN_I']:
image, label, i_vec = read_i_all(
file_pattern=arch['training']['datadir'],
file_pattern2=arch['training']['ivectordir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
)
# loss = machine.loss(image, label, text_emb)
loss = machine.loss(image, label, i_vec)
elif args.model in ['VAWGAN_S', 'SentWGAN']:
image, label, text_emb = read_all(
file_pattern=arch['training']['datadir'],
file_pattern2=arch['training']['textdir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
)
loss = machine.loss(image, label, text_emb)
else:
image, label = read(
file_pattern=arch['training']['datadir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
)
loss = machine.loss(image, label)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main(unused_args=None):
''' NOTE: The input is rescaled to [-1, 1] '''
#module = import_module(args.model_module, package=None)
#MODEL = getattr(module, args.model)
#print("=== ",MODEL,"")
#module = import_module(args.trainer_module, package=None)
#TRAINER = getattr(module, args.trainer)
dirs = validate_log_dirs(args)
try:
os.makedirs(dirs['logdir'])
except OSError as e:
if e.errno != errno.EEXIST:
raise
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/{}_xmax.npf'.format(args.corpus_name)),
xmin=np.fromfile('./etc/{}_xmin.npf'.format(args.corpus_name)),
)
s_x_y = read(
file_pattern=arch['training']['src_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
data_format='NHWC',
)
t_x_y = read(
file_pattern=arch['training']['trg_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
data_format='NHWC',
)
machine = Trainer()
machine.load_data(s_x_y, t_x_y)
machine.train()
def main(unused_args=None):
''' NOTE: The input is rescaled to [-1, 1] '''
module = import_module(args.model_module, package=None)
MODEL = getattr(module, args.model)
module = import_module(args.trainer_module, package=None)
TRAINER = getattr(module, args.trainer)
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/{}_xmax.npf'.format(args.corpus_name)),
xmin=np.fromfile('./etc/{}_xmin.npf'.format(args.corpus_name)),
)
x_s, y_s, f0_s = read(
file_pattern=arch['training']['src_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
data_format='NHWC',
)
x_t, y_t, f0_t = read(
file_pattern=arch['training']['trg_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=normalizer,
data_format='NHWC',
)
machine = MODEL(arch, is_training=True)
# y_s_new = tf.stack([y_s,f0_s],axis=1)
# y_t_new = tf.stack([y_t,f0_t],axis=1)
loss = machine.loss(x_s, y_s, f0_s, x_t, y_t, f0_t)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main(_):
speaker_list = txt2list(args.speaker_list)
dirs = validate_log_dirs(args)
arch = json2dict(args.arch)
arch.update(dirs)
arch.update({'ckpt': args.ckpt})
copy_arch_file(args.arch, arch['logdir'])
net = VQVAE(arch)
P = net.n_padding()
print('Receptive field: {} samples ({:.2f} sec)\n'.format(P, P / arch['fs']))
data = ByteWavReader(
speaker_list,
args.file_pattern,
T=arch['T'],
batch_size=arch['training']['batch_size'],
buffer_size=5000
)
net.train(data)
def main(unused_args):
''' NOTE: The input is rescaled to [-1, 1] '''
if args.model is None or args.trainer is None:
raise ValueError(
'\n Both `model` and `trainer` should be assigned.' +\
'\n Use `python main.py --help` to see applicable options.'
)
module = import_module(args.model_module, package=None)
MODEL = getattr(module, args.model)
module = import_module(args.trainer_module, package=None)
TRAINER = getattr(module, args.trainer)
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
normalizer = Tanhize(
xmax=np.fromfile('./etc/xmax.npf'),
xmin=np.fromfile('./etc/xmin.npf'),
)
x, y = read(
file_pattern=arch['training']['datadir'],
batch_size=arch['training']['batch_size'],
capacity=4096,
min_after_dequeue=3000,
normalizer=normalizer,
)
machine = MODEL(arch, is_training=True)
loss = machine.loss(x, y)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main(unused_args=None):
''' NOTE: The input is rescaled to [-1, 1] '''
module = import_module(args.model_module, package=None)
MODEL = getattr(module, args.model)
module = import_module(args.trainer_module, package=None)
TRAINER = getattr(module, args.trainer)
dirs = validate_log_dirs(args)
tf.gfile.MakeDirs(dirs['logdir'])
with open(args.architecture) as f:
arch = json.load(f)
with open(os.path.join(dirs['logdir'], args.architecture), 'w') as f:
json.dump(arch, f, indent=4)
#Todo: normalize x_s, x_y
x_s, y_s = read_f0(
file_pattern=arch['training']['src_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=None,
data_format='NHWC',
)
x_t, y_t = read_f0(
file_pattern=arch['training']['trg_dir'],
batch_size=arch['training']['batch_size'],
capacity=2048,
min_after_dequeue=1024,
normalizer=None,
data_format='NHWC',
)
machine = MODEL(arch, is_training=True)
loss = machine.loss(x_s, y_s, x_t, y_t)
trainer = TRAINER(loss, arch, args, dirs)
trainer.train(nIter=arch['training']['max_iter'], machine=machine)
def main(_):
"""Train the model based on the command-line arguments."""
# Parse command-line arguments
speaker_list = txt2list(args.speaker_list)
dirs = validate_log_dirs(args)
arch = json2dict(args.arch)
arch.update(dirs)
arch.update({'ckpt': args.ckpt})
copy_arch_file(args.arch, arch['logdir'])
# Initialize the model
net = VQVAE(arch)
P = net.n_padding()
print('Receptive field: {} samples ({:.2f} sec)'.format(P, P / arch['fs']))
# Read the input data as specified by the command line arguments
data = ByteWavReader(speaker_list,
args.file_pattern,
T=arch['T'],
batch_size=arch['training']['batch_size'],
buffer_size=5000)
# Train the model on the input data
net.train(data)
