f"--coqpit.output_path {output_path} "
"--coqpit.datasets.0.name ljspeech_test "
"--coqpit.datasets.0.meta_file_train metadata.csv "
"--coqpit.datasets.0.meta_file_val metadata.csv "
"--coqpit.datasets.0.path tests/data/ljspeech "
"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
"--coqpit.test_delay_epochs 0")
run_cli(command_train)
# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")),
key=os.path.getmtime)
# Inference using TTS API
continue_config_path = os.path.join(continue_path, "config.json")
continue_restore_path, _ = get_last_checkpoint(continue_path)
out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
speaker_id = "ljspeech-1"
continue_speakers_path = os.path.join(continue_path, "speakers.json")
# Check integrity of the config
with open(continue_config_path, "r", encoding="utf-8") as f:
config_loaded = json.load(f)
assert config_loaded["characters"] is not None
assert config_loaded["output_path"] in continue_path
assert config_loaded["test_delay_epochs"] == 0
# Load the model and run inference
inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
run_cli(inference_command)
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