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()