Add files via upload

TTS/encoder/utils/training.py

@@ -0,0 +1,99 @@
+import os
+from dataclasses import dataclass, field
+
+from coqpit import Coqpit
+from trainer import TrainerArgs, get_last_checkpoint
+from trainer.io import copy_model_files
+from trainer.logging import logger_factory
+from trainer.logging.console_logger import ConsoleLogger
+
+from TTS.config import load_config, register_config
+from TTS.tts.utils.text.characters import parse_symbols
+from TTS.utils.generic_utils import get_experiment_folder_path, get_git_branch
+
+
+@dataclass
+class TrainArgs(TrainerArgs):
+    config_path: str = field(default=None, metadata={"help": "Path to the config file."})
+
+
+def getarguments():
+    train_config = TrainArgs()
+    parser = train_config.init_argparse(arg_prefix="")
+    return parser
+
+
+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
+
+
+def init_arguments():
+    train_config = TrainArgs()
+    parser = train_config.init_argparse(arg_prefix="")
+    return parser
+
+
+def init_training(config: Coqpit = None):
+    """Initialization of a training run."""
+    parser = init_arguments()
+    args = parser.parse_known_args()
+    config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger = process_args(args, config)
+    return args[0], config, OUT_PATH, AUDIO_PATH, c_logger, dashboard_logger