def config():
debug = False
model_path = ''
assert len(model_path) > 0, 'Set the model path on the command line.'
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = get_storage_dir()
datasets = ["mix_2_spk_min_cv", "mix_2_spk_min_tt"]
export_audio = False
sample_rate = 8000
target = 'speech_source'
database_json = None
oracle_num_spk = False # If true, the model is forced to perform the correct (oracle) number of iterations
max_iterations = 4 # The number of iterations is limited to this number
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
locals() # Fix highlighting
ex.observers.append(
FileStorageObserver(Path(Path(experiment_dir) / 'sacred')))
def config():
debug = False
database_json = None
if database_json is None and 'NT_DATABASE_JSONS_DIR' in os.environ:
database_json = Path(
os.environ.get('NT_DATABASE_JSONS_DIR')) / 'wsj0_2mix_8k.json'
assert len(database_json) > 0, (
'Set path to database Json on the command line or set environment '
'variable "NT_DATABASE_JSONS_DIR"')
model_path = ''
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = None
if experiment_dir is None:
experiment_dir = pt.io.get_new_subdir(Path(model_path) / 'evaluation',
consider_mpi=True)
batch_size = 1
datasets = ["mix_2_spk_min_cv", "mix_2_spk_min_tt"]
locals() # Fix highlighting
ex.observers.append(
FileStorageObserver(Path(Path(experiment_dir) / 'sacred')))
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
def config():
debug = False
dump_audio = False
# Model config
model_path = ''
assert len(model_path) > 0, 'Set the model path on the command line.'
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = None
if experiment_dir is None:
experiment_dir = pt.io.get_new_subdir(Path(model_path) / 'evaluation',
consider_mpi=True)
# Database config
database_json = None
if database_json is None and JSON_BASE:
database_json = Path(JSON_BASE) / 'wsj0_2mix_8k.json'
sample_rate = 8000
datasets = ['mix_2_spk_min_cv', 'mix_2_spk_min_tt']
target = 'speech_source'
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
locals() # Fix highlighting
ex.observers.append(
FileStorageObserver(Path(Path(experiment_dir) / 'sacred')))
def _assert_no_missing_args(self, args, kwargs, bound):
free_params = self.get_free_parameters(args, kwargs, bound)
missing_args = [m for m in free_params if m not in self.kwargs]
if missing_args:
raise MissingConfigError('{} is missing value(s):'.format(
self.name),
missing_configs=missing_args)
def config():
debug = False
batch_size = 4 # Runs on 4GB GPU mem. Can safely be set to 12 on 12 GB (e.g., GTX1080)
chunk_size = 32000 # 4s chunks @8kHz
train_dataset = "mix_2_spk_min_tr"
validate_dataset = "mix_2_spk_min_cv"
target = 'speech_source'
lr_scheduler_step = 2
lr_scheduler_gamma = 0.98
load_model_from = None
database_json = None
if database_json is None and JSON_BASE:
database_json = Path(JSON_BASE) / 'wsj0_2mix_8k.json'
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
feat_size = 64
encoder_window_size = 16
trainer = {
"model": {
"factory": padertorch.contrib.examples.source_separation.tasnet.TasNet,
'encoder': {
'factory': padertorch.contrib.examples.source_separation.tasnet.tas_coders.TasEncoder,
'window_length': encoder_window_size,
'feature_size': feat_size,
},
'decoder': {
'factory': padertorch.contrib.examples.source_separation.tasnet.tas_coders.TasDecoder,
'window_length': encoder_window_size,
'feature_size': feat_size,
},
},
"storage_dir": None,
"optimizer": {
"factory": pt.optimizer.Adam,
"gradient_clipping": 1
},
"summary_trigger": (1000, "iteration"),
"stop_trigger": (100, "epoch"),
"loss_weights": {
"si-sdr": 1.0,
"log-mse": 0.0,
"log1p-mse": 0.0,
}
}
pt.Trainer.get_config(trainer)
if trainer['storage_dir'] is None:
trainer['storage_dir'] = pt.io.get_new_storage_dir(experiment_name)
ex.observers.append(FileStorageObserver(
Path(trainer['storage_dir']) / 'sacred')
)
def config():
debug = False
batch_size = 6
database_json = None # Path to WSJ0_2mix .json
if database_json is None and JSON_BASE:
database_json = Path(JSON_BASE) / 'wsj0_2mix_8k.json'
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
train_dataset = "mix_2_spk_min_tr"
validate_dataset = "mix_2_spk_min_cv"
# Dict describing the model parameters, to allow changing the parameters from the command line.
# Configurable automatically inserts the default values of not mentioned parameters to the config.json
trainer = {
"model": {
"factory": pt.contrib.examples.source_separation.pit.model.
PermutationInvariantTrainingModel,
"dropout_input": 0.,
"dropout_hidden": 0.,
"dropout_linear": 0.
},
"storage_dir": None,
"optimizer": {
"factory": pt.optimizer.Adam,
"gradient_clipping": 1
},
"summary_trigger": (1000, "iteration"),
"stop_trigger": (300_000, "iteration"),
"loss_weights": {
"pit_ips_loss": 1.0,
"pit_mse_loss": 0.0,
}
}
pt.Trainer.get_config(trainer)
if trainer['storage_dir'] is None:
trainer['storage_dir'] = pt.io.get_new_storage_dir(experiment_name)
ex.observers.append(
FileStorageObserver(Path(trainer['storage_dir']) / 'sacred'))
def config():
debug = False
batch_size = 4 # Runs on 4GB GPU mem. Can safely be set to 12 on 12 GB (e.g., GTX1080)
chunk_size = 32000 # 4s chunks @8kHz
train_dataset = "mix_2_spk_min_tr"
validate_dataset = "mix_2_spk_min_cv"
target = 'speech_source'
lr_scheduler_step = 2
lr_scheduler_gamma = 0.98
load_model_from = None
database_json = None
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
# Start with an empty dict to allow tracking by Sacred
trainer = {
"model": {
"factory": 'padertorch.contrib.examples.tasnet.tasnet.TasNet',
},
"storage_dir": None,
"optimizer": {
"factory": pt.optimizer.Adam,
"gradient_clipping": 1
},
"summary_trigger": (1000, "iteration"),
"stop_trigger": (100_000, "iteration"),
"loss_weights": {
"si-sdr": 1.0,
"log-mse": 0.0,
"si-sdr-grad-stop": 0.0,
}
}
pt.Trainer.get_config(trainer)
if trainer['storage_dir'] is None:
trainer['storage_dir'] = get_storage_dir()
ex.observers.append(
FileStorageObserver(Path(trainer['storage_dir']) / 'sacred'))
def config():
debug = False
# Model config
model_path = '' # Required path to the model to evaluate
assert len(model_path) > 0, 'Set the model path on the command line.'
checkpoint_name = 'ckpt_best_loss.pth'
experiment_dir = None
if experiment_dir is None:
experiment_dir = pt.io.get_new_subdir(
Path(model_path) / 'evaluation', consider_mpi=True)
# Data config
database_json = None
if database_json is None and JSON_BASE:
database_json = Path(JSON_BASE) / 'wsj0_2mix_8k.json'
datasets = ["mix_2_spk_min_cv", "mix_2_spk_min_tt"]
target = 'speech_source'
sample_rate = 8000
if database_json is None:
raise MissingConfigError(
'You have to set the path to the database JSON!', 'database_json')
if not Path(database_json).exists():
raise InvalidConfigError('The database JSON does not exist!',
'database_json')
# Evaluation options
dump_audio = False # If true, exports the separated audio files into a sub-directory "audio"
oracle_num_spk = False # If true, the model is forced to perform the correct (oracle) number of iterations
max_iterations = 4 # The number of iterations is limited to this number
locals() # Fix highlighting
ex.observers.append(FileStorageObserver(
Path(Path(experiment_dir) / 'sacred')
))