diff --git a/TTS/bin/train_tts.py b/TTS/bin/train_tts.py
index 863bd3b9..cfd092f1 100644
--- a/TTS/bin/train_tts.py
+++ b/TTS/bin/train_tts.py
@@ -1,12 +1,62 @@
-import sys
+import os
 
-from TTS.trainer import Trainer, init_training
+from TTS.config import load_config, register_config
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models import setup_model
+from TTS.utils.audio import AudioProcessor
 
 
 def main():
-    """Run 🐸TTS trainer from terminal. This is also necessary to run DDP training by ```distribute.py```"""
-    args, config, output_path, _, c_logger, dashboard_logger = init_training(sys.argv)
-    trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=False)
+    """Run `tts` model training directly by a `config.json` file."""
+    # init trainer args
+    train_args = TrainingArgs()
+    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
+    train_samples, eval_samples = load_tts_samples(config.datasets, eval_split=True)
+
+    # 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()
 
 
diff --git a/TTS/bin/train_vocoder.py b/TTS/bin/train_vocoder.py
index 000083e0..cd665f29 100644
--- a/TTS/bin/train_vocoder.py
+++ b/TTS/bin/train_vocoder.py
@@ -1,26 +1,69 @@
 import os
-import sys
-import traceback
 
-from TTS.trainer import Trainer, init_training
-from TTS.utils.generic_utils import remove_experiment_folder
+from TTS.config import load_config, register_config
+from TTS.trainer import Trainer, TrainingArgs
+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
 
 
 def main():
-    try:
-        args, config, output_path, _, c_logger, dashboard_logger = init_training(sys.argv)
-        trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
-        trainer.fit()
-    except KeyboardInterrupt:
-        remove_experiment_folder(output_path)
-        try:
-            sys.exit(0)
-        except SystemExit:
-            os._exit(0)  # pylint: disable=protected-access
-    except Exception:  # pylint: disable=broad-except
-        remove_experiment_folder(output_path)
-        traceback.print_exc()
-        sys.exit(1)
+    """Run `tts` model training directly by a `config.json` file."""
+    # init trainer args
+    train_args = TrainingArgs()
+    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__":