def process_args(args):
"""Process parsed comand line arguments.
Args:
args (argparse.Namespace or dict like): Parsed input arguments.
Returns:
c (TTS.utils.io.AttrDict): Config paramaters.
out_path (str): Path to save models and logging.
audio_path (str): Path to save generated test audios.
c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does
logging to the console.
tb_logger (TTS.utils.tensorboard.TensorboardLogger): Class that does
the TensorBoard loggind.
"""
if isinstance(args, tuple):
args, coqpit_overrides = args
if args.continue_path:
# continue a previous training from its output folder
experiment_path = args.continue_path
args.config_path = os.path.join(args.continue_path, "config.json")
args.restore_path, best_model = get_last_checkpoint(args.continue_path)
if not args.best_path:
args.best_path = best_model
# setup output paths and read configs
config = load_config(args.config_path)
# override values from command-line args
config.parse_known_args(coqpit_overrides, relaxed_parser=True)
if config.mixed_precision:
print(" > Mixed precision mode is ON")
experiment_path = args.continue_path
if not experiment_path:
experiment_path = create_experiment_folder(config.output_path,
config.run_name, args.debug)
audio_path = os.path.join(experiment_path, "test_audios")
# setup rank 0 process in distributed training
tb_logger = None
if args.rank == 0:
os.makedirs(audio_path, exist_ok=True)
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
# if model characters are not set in the config file
# save the default set to the config file for future
# compatibility.
if config.has("characters_config"):
used_characters = parse_symbols()
new_fields["characters"] = used_characters
copy_model_files(config, experiment_path, new_fields)
os.chmod(audio_path, 0o775)
os.chmod(experiment_path, 0o775)
tb_logger = TensorboardLogger(experiment_path, model_name=config.model)
# write model desc to tensorboard
tb_logger.tb_add_text("model-config", f"<pre>{config.to_json()}</pre>",
0)
c_logger = ConsoleLogger()
return config, experiment_path, audio_path, c_logger, tb_logger
OUT_PATH = args.continue_path
if args.continue_path == '':
OUT_PATH = create_experiment_folder(c.output_path, c.run_name,
args.debug)
AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
c_logger = ConsoleLogger()
if args.rank == 0:
os.makedirs(AUDIO_PATH, exist_ok=True)
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
copy_model_files(c, args.config_path,
OUT_PATH, new_fields)
os.chmod(AUDIO_PATH, 0o775)
os.chmod(OUT_PATH, 0o775)
LOG_DIR = OUT_PATH
tb_logger = TensorboardLogger(LOG_DIR, model_name='VOCODER')
# write model desc to tensorboard
tb_logger.tb_add_text('model-description', c['run_description'], 0)
try:
main(args)
except KeyboardInterrupt:
remove_experiment_folder(OUT_PATH)
def process_args(args, model_class):
"""Process parsed comand line arguments based on model class (tts or vocoder).
Args:
args (argparse.Namespace or dict like): Parsed input arguments.
model_type (str): Model type used to check config parameters and setup
the TensorBoard logger. One of ['tts', 'vocoder'].
Raises:
ValueError: If `model_type` is not one of implemented choices.
Returns:
c (TTS.utils.io.AttrDict): Config paramaters.
out_path (str): Path to save models and logging.
audio_path (str): Path to save generated test audios.
c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does
logging to the console.
tb_logger (TTS.utils.tensorboard.TensorboardLogger): Class that does
the TensorBoard loggind.
"""
if args.continue_path:
args.output_path = args.continue_path
args.config_path = os.path.join(args.continue_path, "config.json")
args.restore_path, best_model = get_last_checkpoint(args.continue_path)
if not args.best_path:
args.best_path = best_model
# setup output paths and read configs
c = load_config(args.config_path)
_ = os.path.dirname(os.path.realpath(__file__))
if 'mixed_precision' in c and c.mixed_precision:
print(" > Mixed precision mode is ON")
out_path = args.continue_path
if not out_path:
out_path = create_experiment_folder(c.output_path, c.run_name,
args.debug)
audio_path = os.path.join(out_path, "test_audios")
c_logger = ConsoleLogger()
tb_logger = None
if args.rank == 0:
os.makedirs(audio_path, exist_ok=True)
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
# if model characters are not set in the config file
# save the default set to the config file for future
# compatibility.
if model_class == 'tts' and 'characters' not in c:
used_characters = parse_symbols()
new_fields['characters'] = used_characters
copy_model_files(c, args.config_path, out_path, new_fields)
os.chmod(audio_path, 0o775)
os.chmod(out_path, 0o775)
log_path = out_path
tb_logger = TensorboardLogger(log_path, model_name=model_class.upper())
# write model desc to tensorboard
tb_logger.tb_add_text("model-description", c["run_description"], 0)
return c, out_path, audio_path, c_logger, tb_logger
def process_args(args, config=None):
"""Process parsed comand line arguments and initialize the config if not provided.
Args:
args (argparse.Namespace or dict like): Parsed input arguments.
config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None.
Returns:
c (TTS.utils.io.AttrDict): Config paramaters.
out_path (str): Path to save models and logging.
audio_path (str): Path to save generated test audios.
c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does
logging to the console.
dashboard_logger (WandbLogger or TensorboardLogger): Class that does the dashboard Logging
TODO:
- Interactive config definition.
"""
if isinstance(args, tuple):
args, coqpit_overrides = args
if args.continue_path:
# continue a previous training from its output folder
experiment_path = args.continue_path
args.config_path = os.path.join(args.continue_path, "config.json")
args.restore_path, best_model = get_last_checkpoint(args.continue_path)
if not args.best_path:
args.best_path = best_model
# init config if not already defined
if config is None:
if args.config_path:
# init from a file
config = load_config(args.config_path)
else:
# init from console args
from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel
config_base = BaseTrainingConfig()
config_base.parse_known_args(coqpit_overrides)
config = register_config(config_base.model)()
# override values from command-line args
config.parse_known_args(coqpit_overrides, relaxed_parser=True)
experiment_path = args.continue_path
if not experiment_path:
experiment_path = get_experiment_folder_path(config.output_path,
config.run_name)
audio_path = os.path.join(experiment_path, "test_audios")
config.output_log_path = experiment_path
# setup rank 0 process in distributed training
dashboard_logger = None
if args.rank == 0:
new_fields = {}
if args.restore_path:
new_fields["restore_path"] = args.restore_path
new_fields["github_branch"] = get_git_branch()
# if model characters are not set in the config file
# save the default set to the config file for future
# compatibility.
if config.has("characters") and config.characters is None:
used_characters = parse_symbols()
new_fields["characters"] = used_characters
copy_model_files(config, experiment_path, new_fields)
dashboard_logger = logger_factory(config, experiment_path)
c_logger = ConsoleLogger()
return config, experiment_path, audio_path, c_logger, dashboard_logger
