def main():
args = get_arguments()
try:
# directories返回:{'logdir':logdir, 'restore_from':restore_from}
directories = validate_directories(args)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
if args.l2_regularization_strength == 0:
args.l2_regularization_strength = None # ???默认为0
train(directories, args) # 此时data_dir = test_dir
return
def main():
args = get_arguments()
try:
directories = validate_directories(args)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
if args.l2_regularization_strength == 0:
args.l2_regularization_strength = None
train(directories,args)
return
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError(
'Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = './data/kss,./data/son'
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
LOGDIR = None
#LOGDIR = './/logdir-wavenet//train//2018-12-21T22-58-10'
parser.add_argument(
'--logdir',
type=str,
default=LOGDIR,
help=
'Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.'
)
parser.add_argument(
'--logdir_root',
type=str,
default=None,
help=
'Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.'
)
parser.add_argument(
'--restore_from',
type=str,
default=None,
help=
'Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.'
)
CHECKPOINT_EVERY = 1000 # checkpoint 저장 주기
parser.add_argument(
'--checkpoint_every',
type=int,
default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after. Default: ' +
str(CHECKPOINT_EVERY) + '.')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
config.data_dir = config.data_dir.split(",")
try:
directories = validate_directories(config, hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
log_path = os.path.join(logdir, 'train.log')
infolog.init(log_path, logdir)
global_step = tf.Variable(0, name='global_step', trainable=False)
# Create coordinator.
coord = tf.train.Coordinator()
num_speakers = len(config.data_dir)
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = hparams.silence_threshold if hparams.silence_threshold > EPSILON else None
gc_enable = num_speakers > 1
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = DataFeederWavenet(
coord,
config.data_dir,
batch_size=hparams.wavenet_batch_size,
receptive_field=WaveNetModel.calculate_receptive_field(
hparams.filter_width, hparams.dilations, hparams.scalar_input,
hparams.initial_filter_width),
gc_enable=gc_enable)
if gc_enable:
audio_batch, lc_batch, gc_id_batch = reader.inputs_wav, reader.local_condition, reader.speaker_id
else:
print("didn't work")
#audio_batch, lc_batch = reader.inputs_wav, local_condition
# Create network.
net = WaveNetModel(
batch_size=hparams.wavenet_batch_size,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels=hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases, # True
scalar_input=hparams.scalar_input,
initial_filter_width=hparams.initial_filter_width,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=num_speakers,
local_condition_channels=hparams.num_mels,
upsample_factor=hparams.upsample_factor,
train_mode=True)
if hparams.l2_regularization_strength == 0:
hparams.l2_regularization_strength = None
net.add_loss(input_batch=audio_batch,
local_condition=lc_batch,
global_condition_batch=gc_id_batch,
l2_regularization_strength=hparams.l2_regularization_strength)
net.add_optimizer(hparams, global_step)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)
) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
start_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or start_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
except:
print(
"Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model."
)
raise
###########
start_step = sess.run(global_step)
last_saved_step = start_step
try:
reader.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
if hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
log('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize],
options=run_options,
run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize])
duration = time.time() - start_time
log('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
if step >= hparams.num_steps:
# error message가 나오지만, 여기서 멈춘 것은 맞다.
raise Exception('End xxx~~~yyy')
except Exception as e:
print('finally')
#if step > last_saved_step:
# save(saver, sess, logdir, step)
coord.request_stop(e)
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError('Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = '/home/kjm/Tacotron2-Wavenet-Korean-TTS/data/monika,/home/kjm/Tacotron2-Wavenet-Korean-TTS/data/kss'
#DATA_DIRECTORY = 'D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\moon'
parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY, help='The directory containing the VCTK corpus.')
#LOGDIR = None
LOGDIR = './logdir-wavenet/train/2021-03-10T02-58-23'
parser.add_argument('--logdir', type=str, default=LOGDIR,help='Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.')
parser.add_argument('--logdir_root', type=str, default=None,help='Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.')
parser.add_argument('--restore_from', type=str, default=None,help='Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.')
CHECKPOINT_EVERY = 1000 # checkpoint 저장 주기
parser.add_argument('--checkpoint_every', type=int, default=CHECKPOINT_EVERY,help='How many steps to save each checkpoint after. Default: ' + str(CHECKPOINT_EVERY) + '.')
parser.add_argument('--eval_every', type=int, default=1000,help='Steps between eval on test data')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
config.data_dir = config.data_dir.split(",")
try:
directories = validate_directories(config,hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
log_path = os.path.join(logdir, 'train.log')
infolog.init(log_path, logdir)
global_step = tf.Variable(0, name='global_step', trainable=False)
if hparams.l2_regularization_strength == 0:
hparams.l2_regularization_strength = None
# Create coordinator.
coord = tf.train.Coordinator()
num_speakers = len(config.data_dir)
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = hparams.silence_threshold if hparams.silence_threshold > EPSILON else None
gc_enable = True # Before: num_speakers > 1 After: 항상 True
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = DataFeederWavenet(coord,config.data_dir,batch_size=hparams.wavenet_batch_size,gc_enable= gc_enable,test_mode=False)
# test를 위한 DataFeederWavenet를 하나 만들자. 여기서는 딱 1개의 파일만 가져온다.
reader_test = DataFeederWavenet(coord,config.data_dir,batch_size=1,gc_enable= gc_enable,test_mode=True,queue_size=1)
audio_batch, lc_batch, gc_id_batch = reader.inputs_wav, reader.local_condition, reader.speaker_id
# Create train network.
net = create_network(hparams,hparams.wavenet_batch_size,num_speakers,is_training=True)
net.add_loss(input_batch=audio_batch,local_condition=lc_batch, global_condition_batch=gc_id_batch, l2_regularization_strength=hparams.l2_regularization_strength,upsample_type=hparams.upsample_type)
net.add_optimizer(hparams,global_step)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
start_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or start_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
start_step=0
except:
print("Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model.")
raise
###########
reader.start_in_session(sess,start_step)
reader_test.start_in_session(sess,start_step)
################### Create test network. <---- Queue 생성 때문에, sess restore후 test network 생성
net_test = create_network(hparams,1,num_speakers,is_training=False)
if hparams.scalar_input:
samples = tf.placeholder(tf.float32,shape=[net_test.batch_size,None])
waveform = 2*np.random.rand(net_test.batch_size).reshape(net_test.batch_size,-1)-1
else:
samples = tf.placeholder(tf.int32,shape=[net_test.batch_size,None]) # samples: mu_law_encode로 변환된 것. one-hot으로 변환되기 전. (batch_size, 길이)
waveform = np.random.randint(hparams.quantization_channels,size=net_test.batch_size).reshape(net_test.batch_size,-1)
upsampled_local_condition = tf.placeholder(tf.float32,shape=[net_test.batch_size,hparams.num_mels])
speaker_id = tf.placeholder(tf.int32,shape=[net_test.batch_size])
next_sample = net_test.predict_proba_incremental(samples,upsampled_local_condition,speaker_id) # Fast Wavenet Generation Algorithm-1611.09482 algorithm 적용
sess.run(net_test.queue_initializer)
# test를 위한 placeholder는 모두 3개: samples,speaker_id,upsampled_local_condition
# test용 mel-spectrogram을 하나 뽑자. 그것을 고정하지 않으면, thread가 계속 돌아가면서 data를 읽어온다. reader_test의 역할은 여기서 끝난다.
mel_input_test, speaker_id_test = sess.run([reader_test.local_condition,reader_test.speaker_id])
with tf.variable_scope('wavenet',reuse=tf.AUTO_REUSE):
upsampled_local_condition_data = net_test.create_upsample(mel_input_test,upsample_type=hparams.upsample_type)
upsampled_local_condition_data_ = sess.run(upsampled_local_condition_data) # upsampled_local_condition_data_ 을 feed_dict로 placehoder인 upsampled_local_condition에 넣어준다.
######################################################
start_step = sess.run(global_step)
step = last_saved_step = start_step
try:
while not coord.should_stop():
start_time = time.time()
if hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
log('Storing metadata')
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
step, loss_value, _ = sess.run([global_step, net.loss, net.optimize],options=run_options,run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
step, loss_value, _ = sess.run([global_step,net.loss, net.optimize])
duration = time.time() - start_time
log('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
if step % config.eval_every == 0: # config.eval_every
eval_step(sess,logdir,step,waveform,upsampled_local_condition_data_,speaker_id_test,mel_input_test,samples,speaker_id,upsampled_local_condition,next_sample)
if step >= hparams.num_steps:
# error message가 나오지만, 여기서 멈춘 것은 맞다.
raise Exception('End xxx~~~yyy')
except Exception as e:
print('finally')
log('Exiting due to exception: %s' % e, slack=True)
#if step > last_saved_step:
# save(saver, sess, logdir, step)
traceback.print_exc()
coord.request_stop(e)
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError('Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = 'D:\\hccho\\multi-speaker-tacotron-tensorflow-master\\datasets\son\\audio' # './VCTK-Corpus'
parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY, help='The directory containing the VCTK corpus.')
LOGDIR = None
#LOGDIR = './/logdir//train//2018-11-25T14-10-48' # son
#LOGDIR = './/logdir//train//2018-11-30T22-22-58' # test
parser.add_argument('--logdir', type=str, default=LOGDIR,help='Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.')
parser.add_argument('--logdir_root', type=str, default=None,help='Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.')
parser.add_argument('--restore_from', type=str, default=None,help='Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.')
CHECKPOINT_EVERY = 20 # checkpoint 저장 주기
parser.add_argument('--checkpoint_every', type=int, default=CHECKPOINT_EVERY,help='How many steps to save each checkpoint after. Default: ' + str(CHECKPOINT_EVERY) + '.')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
try:
directories = validate_directories(config,hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
# Create coordinator.
coord = tf.train.Coordinator()
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = hparams.silence_threshold if hparams.silence_threshold > EPSILON else None
gc_enabled = hparams.gc_channels is not None
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = AudioReader(config.data_dir,coord,sample_rate=hparams.sample_rate,gc_enabled=gc_enabled,
receptive_field=WaveNetModel.calculate_receptive_field(hparams.filter_width, hparams.dilations,hparams.scalar_input, hparams.initial_filter_width),
sample_size=hparams.sample_size,silence_threshold=silence_threshold)
if gc_enabled:
audio_batch, gc_id_batch = reader.dequeue(hparams.batch_size) # (batch_size, ?, 1)
else:
audio_batch = reader.dequeue(hparams.batch_size)
# Create network.
net = WaveNetModel(
batch_size=hparams.batch_size,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels =hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases, # True
scalar_input=hparams.scalar_input,
initial_filter_width=hparams.initial_filter_width,
histograms=hparams.histograms,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=reader.gc_category_cardinality,
train_mode=True)
if hparams.l2_regularization_strength == 0:
hparams.l2_regularization_strength = None
loss = net.loss(input_batch=audio_batch, global_condition_batch=gc_id_batch, l2_regularization_strength=hparams.l2_regularization_strength)
optimizer = optimizer_factory[hparams.optimizer](learning_rate=hparams.learning_rate,momentum=hparams.momentum)
trainable = tf.trainable_variables()
optim = optimizer.minimize(loss, var_list=trainable)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
saved_global_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
except:
print("Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model.")
raise
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
step = None
last_saved_step = saved_global_step
try:
for step in range(saved_global_step + 1, hparams.num_steps+1):
start_time = time.time()
if hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
print('Storing metadata')
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
loss_value, _ = sess.run([loss, optim],options=run_options,run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
loss_value, _ = sess.run([loss, optim])
duration = time.time() - start_time
print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
except KeyboardInterrupt:
# Introduce a line break after ^C is displayed so save message
# is on its own line.
print()
finally:
if step > last_saved_step:
save(saver, sess, logdir, step)
coord.request_stop()
coord.join(threads)
