def setup_model(config):
print(" > Using model: {}".format(config.model))
# fetch the right model implementation.
if "base_model" in config and config["base_model"] is not None:
MyModel = find_module("TTS.tts.models", config.base_model.lower())
else:
MyModel = find_module("TTS.tts.models", config.model.lower())
# define set of characters used by the model
if config.characters is not None:
# set characters from config
if hasattr(MyModel, "make_symbols"):
symbols = MyModel.make_symbols(config)
else:
symbols, phonemes = make_symbols(**config.characters)
else:
from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel
phonemes, symbols,
)
if config.use_phonemes:
symbols = phonemes # noqa: F811
# use default characters and assign them to config
config.characters = parse_symbols()
# consider special `blank` character if `add_blank` is set True
num_chars = len(symbols) + getattr(config, "add_blank", False)
config.num_chars = num_chars
# compatibility fix
if "model_params" in config:
config.model_params.num_chars = num_chars
if "model_args" in config:
config.model_args.num_chars = num_chars
model = MyModel(config)
return model
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
def get_characters(config: Coqpit) -> str:
# TODO: implement CharacterProcessor
if config.characters is not None:
symbols, phonemes = make_symbols(**config.characters)
else:
from TTS.tts.utils.text.symbols import parse_symbols, phonemes, symbols
config.characters = CharactersConfig(**parse_symbols())
model_characters = phonemes if config.use_phonemes else symbols
num_chars = len(model_characters) + getattr(config, "add_blank", False)
return model_characters, config, num_chars
def get_characters(config: Coqpit):
if config.characters is not None:
symbols = Vits.make_symbols(config)
else:
from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel
parse_symbols, phonemes, symbols,
)
config.characters = parse_symbols()
if config.use_phonemes:
symbols = phonemes # noqa: F811
num_chars = len(symbols) + getattr(config, "add_blank", False)
return symbols, config, num_chars
def get_characters(config: Coqpit) -> str:
# TODO: implement CharacterProcessor
if config.characters is not None:
symbols, phonemes = make_symbols(**config.characters)
else:
from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel
parse_symbols,
phonemes,
symbols,
)
config.characters = parse_symbols()
model_characters = phonemes if config.use_phonemes else symbols
return model_characters, config
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 = init_dashboard_logger(config)
c_logger = ConsoleLogger()
return config, experiment_path, audio_path, c_logger, dashboard_logger