def get_arguments():
parser = argparse.ArgumentParser(description='WaveNet example network')
parser.add_argument('--batch_size', type=int, default=BATCH_SIZE,
help='How many wav files to process at once.')
parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
parser.add_argument('--store_metadata', type=bool, default=False,
help='Whether to store advanced debugging information '
'(execution time, memory consumption) for use with '
'TensorBoard.')
parser.add_argument('--logdir', type=str, default=None,
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.')
parser.add_argument('--checkpoint_every', type=int, default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after')
parser.add_argument('--num_steps', type=int, default=NUM_STEPS,
help='Number of training steps.')
parser.add_argument('--learning_rate', type=float, default=LEARNING_RATE,
help='Learning rate for training.')
parser.add_argument('--wavenet_params', type=str, default=WAVENET_PARAMS,
help='JSON file with the network parameters.')
parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples.')
parser.add_argument('--l2_regularization_strength', type=float,
default=L2_REGULARIZATION_STRENGTH,
help='Coefficient in the L2 regularization. '
'Disabled by default')
parser.add_argument('--silence_threshold', type=float,
default=SILENCE_THRESHOLD,
help='Volume threshold below which to trim the start '
'and the end from the training set samples.')
parser.add_argument('--optimizer', type=str, default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option.')
parser.add_argument('--momentum', type=float,
default=MOMENTUM, help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer.')
return parser.parse_args()
def get_arguments():
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')
parser.add_argument(
'--batch_size',
type=int,
default=BATCH_SIZE,
help='How many wav files to process at once. Default: ' +
str(BATCH_SIZE) + '.')
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
parser.add_argument('--store_metadata',
type=bool,
default=METADATA,
help='Whether to store advanced debugging information '
'(execution time, memory consumption) for use with '
'TensorBoard. Default: ' + str(METADATA) + '.')
parser.add_argument('--logdir',
type=str,
default=None,
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.')
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('--num_steps',
type=int,
default=NUM_STEPS,
help='Number of training steps. Default: ' +
str(NUM_STEPS) + '.')
parser.add_argument('--learning_rate',
type=float,
default=LEARNING_RATE,
help='Learning rate for training. Default: ' +
str(LEARNING_RATE) + '.')
parser.add_argument(
'--wavenet_params',
type=str,
default=WAVENET_PARAMS,
help='JSON file with the network parameters. Default: ' +
WAVENET_PARAMS + '.')
parser.add_argument('--sample_size',
type=int,
default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples. Default: ' + str(SAMPLE_SIZE) + '.')
parser.add_argument('--l2_regularization_strength',
type=float,
default=L2_REGULARIZATION_STRENGTH,
help='Coefficient in the L2 regularization. '
'Default: False')
parser.add_argument(
'--silence_threshold',
type=float,
default=SILENCE_THRESHOLD,
help='Volume threshold below which to trim the start '
'and the end from the training set samples. Default: ' +
str(SILENCE_THRESHOLD) + '.')
parser.add_argument(
'--optimizer',
type=str,
default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. Default: adam.')
parser.add_argument('--momentum',
type=float,
default=MOMENTUM,
help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer. Default: ' + str(MOMENTUM) + '.')
parser.add_argument(
'--histograms',
type=_str_to_bool,
default=False,
help='Whether to store histogram summaries. Default: False')
parser.add_argument(
'--gc_channels',
type=int,
default=None,
help=
'Number of global condition channels. Default: None. Expecting: Int')
parser.add_argument(
'--max_checkpoints',
type=int,
default=MAX_TO_KEEP,
help='Maximum amount of checkpoints that will be kept alive. Default: '
+ str(MAX_TO_KEEP) + '.')
return parser.parse_args()
def get_arguments():
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')
parser.add_argument('--batch_size', type=int, default=BATCH_SIZE,
help='How many wav files to process at once. Default: ' + str(BATCH_SIZE) + '.')
parser.add_argument('--data_set', type=str, default=DATA_SET,
help='String id for Nottingham or JSB_Chorales.')
parser.add_argument('--store_metadata', type=bool, default=METADATA,
help='Whether to store advanced debugging information '
'(execution time, memory consumption) for use with '
'TensorBoard. Default: ' + str(METADATA) + '.')
parser.add_argument('--logdir', type=str, default=None,
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.')
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('--num_steps', type=int, default=NUM_STEPS,
help='Number of training steps. Default: ' + str(NUM_STEPS) + '.')
parser.add_argument('--learning_rate', type=float, default=LEARNING_RATE,
help='Learning rate for training. Default: ' + str(LEARNING_RATE) + '.')
parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples. Default: ' + str(SAMPLE_SIZE) + '.')
parser.add_argument('--max_dilation_pow', type=int, default=MAX_DILATION_POW,
help='Maximum dilation of causal convolutional filter'
'max_dilation_pow. Default: ' + str(MAX_DILATION_POW) + '.')
parser.add_argument('--dil_chan', type=int, default=DIL_CHAN,
help='Number of dilation channels'
'dil_chan. Default: ' + str(DIL_CHAN) + '.')
parser.add_argument('--res_chan', type=int, default=RES_CHAN,
help='Number of residual channels'
'res_chan. Default: ' + str(RES_CHAN) + '.')
parser.add_argument('--skip_chan', type=int, default=SKIP_CHAN,
help='Number of skip channels'
'skip_chan. Default: ' + str(SKIP_CHAN) + '.')
parser.add_argument('--expansion_reps', type=int, default=EXPANSION_REPS,
help='How many times to repeat dilated causal convolutional expansion'
'expansion_reps. Default: ' + str(EXPANSION_REPS) + '.')
parser.add_argument('--l2_regularization_strength', type=float,
default=L2_REGULARIZATION_STRENGTH,
help='Coefficient in the L2 regularization. '
'Default: False')
parser.add_argument('--optimizer', type=str, default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. Default: adam.')
parser.add_argument('--momentum', type=float,
default=MOMENTUM, help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer. Default: ' + str(MOMENTUM) + '.')
parser.add_argument('--histograms', type=_str_to_bool, default=False,
help='Whether to store histogram summaries. Default: False')
parser.add_argument('--gc_channels', type=int, default=None,
help='Number of global condition channels. Default: None. Expecting: Int')
parser.add_argument('--max_checkpoints', type=int, default=MAX_TO_KEEP,
help='Maximum amount of checkpoints that will be kept alive. Default: '
+ str(MAX_TO_KEEP) + '.')
return parser.parse_args()
type=float,
default=0.003,
help='Learning rate for SGD')
# WAVENET.PY
parser.add_argument('--sample_size',
type=int,
default=1000,
help='Concatenate and cut audio samples to this many '
'samples. Default: 1000')
parser.add_argument('--l2_regularization_strength', type=float,
default=0,
help='Coefficient in the L2 regularization. '
'Default: False')
parser.add_argument('--optimizer', type=str, default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. Default: adam.')
parser.add_argument('--momentum', type=float,
default=0.9, help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer. Default: 0.9.')
parser.add_argument('--reader_config', type=str,
default="reader_config.json", help='Specify the path to the config file.')
# Wavenet Params
parser.add_argument('--filter_width',
type=int,
default=8,
help='Part of Wavenet Params')
parser.add_argument('--dilations',
def get_arguments():
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 for Transcription '
'- training')
parser.add_argument('--batch_size',
type=int,
default=BATCH_SIZE,
help='How many batch samples to process at once. '
'Default: ' + str(BATCH_SIZE) + '.')
parser.add_argument('--data_dir_train',
type=str,
default=DATA_DIRECTORY_TRAIN,
help='The directory '
'containing the training data files. '
'Default: ' + DATA_DIRECTORY_TRAIN + '.')
parser.add_argument('--data_dir_valid',
type=str,
default=DATA_DIRECTORY_VALID,
help='The directory '
'containing the validation data files. '
'Default: ' + DATA_DIRECTORY_VALID + '.')
parser.add_argument('--logdir',
type=str,
default=None,
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.')
parser.add_argument(
'--model_params',
type=str,
default=MODEL_PARAMS,
help='JSON file with the architecture hyperparameters. '
'Default: ' + MODEL_PARAMS + '.')
parser.add_argument('--training_params',
type=str,
default=TRAINING_PARAMS,
help='JSON file with some training hyperparameters. '
'Default: ' + TRAINING_PARAMS + '.')
parser.add_argument('--sample_size',
type=int,
default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples. Default: ' + str(SAMPLE_SIZE) + '.')
parser.add_argument('--optimizer',
type=str,
default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. '
'Default: adam.')
parser.add_argument('--max_checkpoints',
type=int,
default=MAX_TO_KEEP,
help='Maximum amount of checkpoints that will be '
'kept alive. '
'Default: ' + str(MAX_TO_KEEP) + '.')
parser.add_argument('--velocity',
type=_str_to_bool,
default=VELOCITY,
help='Whether to train to estimate velocity of '
'present notes. '
'Default: ' + str(VELOCITY) + '.')
parser.add_argument('--threshold',
type=float,
default=THRESHOLD,
help='Threshold for post-processing. '
'Default: ' + str(THRESHOLD) + '.')
return parser.parse_args()
def get_arguments():
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')
parser.add_argument('--batch_size', type=int, default=BATCH_SIZE,
help='How many wav files to process at once. Default: ' + str(BATCH_SIZE) + '.')
parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
parser.add_argument('--store_metadata', type=bool, default=METADATA,
help='Whether to store advanced debugging information '
'(execution time, memory consumption) for use with '
'TensorBoard. Default: ' + str(METADATA) + '.')
parser.add_argument('--logdir', type=str, default=None,
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.')
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('--num_steps', type=int, default=NUM_STEPS,
help='Number of training steps. Default: ' + str(NUM_STEPS) + '.')
parser.add_argument('--learning_rate', type=float, default=LEARNING_RATE,
help='Learning rate for training. Default: ' + str(LEARNING_RATE) + '.')
parser.add_argument('--wavenet_params', type=str, default=WAVENET_PARAMS,
help='JSON file with the network parameters. Default: ' + WAVENET_PARAMS + '.')
parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples. Default: ' + str(SAMPLE_SIZE) + '.')
parser.add_argument('--l2_regularization_strength', type=float,
default=L2_REGULARIZATION_STRENGTH,
help='Coefficient in the L2 regularization. '
'Default: False')
parser.add_argument('--silence_threshold', type=float,
default=SILENCE_THRESHOLD,
help='Volume threshold below which to trim the start '
'and the end from the training set samples. Default: ' + str(SILENCE_THRESHOLD) + '.')
parser.add_argument('--optimizer', type=str, default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. Default: adam.')
parser.add_argument('--momentum', type=float,
default=MOMENTUM, help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer. Default: ' + str(MOMENTUM) + '.')
parser.add_argument('--histograms', type=_str_to_bool, default=False,
help='Whether to store histogram summaries. Default: False')
parser.add_argument('--gc_channels', type=int, default=None,
help='Number of global condition channels. Default: None. Expecting: Int')
parser.add_argument('--max_checkpoints', type=int, default=MAX_TO_KEEP,
help='Maximum amount of checkpoints that will be kept alive. Default: '
+ str(MAX_TO_KEEP) + '.')
return parser.parse_args()
def get_arguments():
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 for Transcription '
'- training')
parser.add_argument('--batch_size', type=int, default=BATCH_SIZE,
help='How many batch samples to process at once. '
'Default: ' + str(BATCH_SIZE) + '.')
parser.add_argument('--data_dir_train', type=str,
default=DATA_DIRECTORY_TRAIN, help='The directory '
'containing the training data files. '
'Default: ' + DATA_DIRECTORY_TRAIN + '.')
parser.add_argument('--data_dir_valid', type=str,
default=DATA_DIRECTORY_VALID, help='The directory '
'containing the validation data files. '
'Default: ' + DATA_DIRECTORY_VALID + '.')
parser.add_argument('--logdir', type=str, default=None,
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.')
parser.add_argument('--model_params', type=str, default=MODEL_PARAMS,
help='JSON file with the architecture hyperparameters. '
'Default: ' + MODEL_PARAMS + '.')
parser.add_argument('--training_params', type=str, default=TRAINING_PARAMS,
help='JSON file with some training hyperparameters. '
'Default: ' + TRAINING_PARAMS + '.')
parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples. Default: ' + str(SAMPLE_SIZE) + '.')
parser.add_argument('--optimizer', type=str, default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. '
'Default: adam.')
parser.add_argument('--max_checkpoints', type=int, default=MAX_TO_KEEP,
help='Maximum amount of checkpoints that will be '
'kept alive. '
'Default: ' + str(MAX_TO_KEEP) + '.')
parser.add_argument('--velocity', type=_str_to_bool, default=VELOCITY,
help='Whether to train to estimate velocity of '
'present notes. '
'Default: ' + str(VELOCITY) + '.')
parser.add_argument('--threshold', type=float, default=THRESHOLD,
help='Threshold for post-processing. '
'Default: ' + str(THRESHOLD) + '.')
return parser.parse_args()
def get_arguments():
parser = argparse.ArgumentParser(description='WaveNet example network')
parser.add_argument(
'--batch_size',
type=int,
default=BATCH_SIZE,
help='How many wav files to process at once. Default: ' +
str(BATCH_SIZE) + '.')
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the training corpus.')
parser.add_argument('--logdir',
type=str,
default=None,
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.')
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('--num_steps',
type=int,
default=NUM_STEPS,
help='Number of training steps. Default: ' +
str(NUM_STEPS) + '.')
parser.add_argument(
'--wavenet_params',
type=str,
default=WAVENET_PARAMS,
help='JSON file with the network parameters. Default: ' +
WAVENET_PARAMS + '.')
parser.add_argument('--optimizer',
type=str,
default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option. '
'Default: adam.')
parser.add_argument('--momentum',
type=float,
default=MOMENTUM,
help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer. Default: ' + str(MOMENTUM) + '.')
parser.add_argument('--gc_channels',
type=int,
default=None,
help='Number of global condition channels. '
'Default: None. Expecting: Int')
parser.add_argument('--lc_channels',
type=int,
default=None,
help='Number of local condition channels. '
'Default: None. Expecting: Int')
return parser.parse_args()
def get_arguments():
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')
parser.add_argument('--batch_size',
type=int,
default=BATCH_SIZE,
help='How many wav files to process at once.')
parser.add_argument(
'--file_list',
type=str,
default=FILE_LIST,
help=
'The list that contains the base names of the training audio and label files.'
)
parser.add_argument('--audio_dir',
type=str,
default=DATA_DIRECTORY + 'wav/',
help='The directory containing the audio samples.')
parser.add_argument(
'--label_dir',
type=str,
default=DATA_DIRECTORY + 'binary_label_norm/',
help='The directory containing the full context labels.')
parser.add_argument(
'--label_dim',
type=int,
default=LABEL_DIM,
help=
'The dimension of the min-max normalized binary full context labels.')
parser.add_argument('--audio_ext',
type=str,
default=AUDIO_EXT,
help='The extention of the audio filenames.')
parser.add_argument('--label_ext',
type=str,
default=LABEL_EXT,
help='The extention of the label filenames.')
parser.add_argument(
'--frame_shift',
type=float,
default=FRAME_SHIFT,
help='The shift of the window in label files. Usually 0.005 sec')
parser.add_argument('--store_metadata',
type=bool,
default=False,
help='Whether to store advanced debugging information '
'(execution time, memory consumption) for use with '
'TensorBoard.')
parser.add_argument('--logdir',
type=str,
default=None,
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.')
parser.add_argument('--checkpoint_every',
type=int,
default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after')
parser.add_argument('--num_steps',
type=int,
default=NUM_STEPS,
help='Number of training steps.')
parser.add_argument('--learning_rate',
type=float,
default=LEARNING_RATE,
help='Learning rate for training.')
parser.add_argument('--wavenet_params',
type=str,
default=WAVENET_PARAMS,
help='JSON file with the network parameters.')
parser.add_argument('--sample_size',
type=int,
default=SAMPLE_SIZE,
help='Concatenate and cut audio samples to this many '
'samples.')
parser.add_argument('--l2_regularization_strength',
type=float,
default=L2_REGULARIZATION_STRENGTH,
help='Coefficient in the L2 regularization. '
'Disabled by default')
parser.add_argument('--silence_threshold',
type=float,
default=SILENCE_THRESHOLD,
help='Volume threshold below which to trim the start '
'and the end from the training set samples.')
parser.add_argument('--optimizer',
type=str,
default='adam',
choices=optimizer_factory.keys(),
help='Select the optimizer specified by this option.')
parser.add_argument('--momentum',
type=float,
default=MOMENTUM,
help='Specify the momentum to be '
'used by sgd or rmsprop optimizer. Ignored by the '
'adam optimizer.')
parser.add_argument('--histograms',
type=_str_to_bool,
default=False,
help='Whether to store histogram summaries.')
return parser.parse_args()