Upload train_vocoder.py with huggingface_hub

train_vocoder.py

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+import os
+from dataclasses import dataclass, field
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.config import load_config, register_config
+from TTS.utils.audio import AudioProcessor
+from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
+from TTS.vocoder.models import setup_model
+
+
+@dataclass
+class TrainVocoderArgs(TrainerArgs):
+    config_path: str = field(default=None, metadata={"help": "Path to the config file."})
+
+
+def main():
+    """Run `tts` model training directly by a `config.json` file."""
+    # init trainer args
+    train_args = TrainVocoderArgs()
+    parser = train_args.init_argparse(arg_prefix="")
+
+    # override trainer args from comman-line args
+    args, config_overrides = parser.parse_known_args()
+    train_args.parse_args(args)
+
+    # load config.json and register
+    if args.config_path or args.continue_path:
+        if args.config_path:
+            # init from a file
+            config = load_config(args.config_path)
+            if len(config_overrides) > 0:
+                config.parse_known_args(config_overrides, relaxed_parser=True)
+        elif args.continue_path:
+            # continue from a prev experiment
+            config = load_config(os.path.join(args.continue_path, "config.json"))
+            if len(config_overrides) > 0:
+                config.parse_known_args(config_overrides, relaxed_parser=True)
+        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(config_overrides)
+            config = register_config(config_base.model)()
+
+    # load training samples
+    if "feature_path" in config and config.feature_path:
+        # load pre-computed features
+        print(f" > Loading features from: {config.feature_path}")
+        eval_samples, train_samples = load_wav_feat_data(config.data_path, config.feature_path, config.eval_split_size)
+    else:
+        # load data raw wav files
+        eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
+
+    # setup audio processor
+    ap = AudioProcessor(**config.audio)
+
+    # init the model from config
+    model = setup_model(config)
+
+    # init the trainer and 🚀
+    trainer = Trainer(
+        train_args,
+        config,
+        config.output_path,
+        model=model,
+        train_samples=train_samples,
+        eval_samples=eval_samples,
+        training_assets={"audio_processor": ap},
+        parse_command_line_args=False,
+    )
+    trainer.fit()
+
+
+if __name__ == "__main__":
+    main()