diff --git a/TTS/trainer.py b/TTS/trainer.py
index 6087f1bc..63b9cd42 100644
--- a/TTS/trainer.py
+++ b/TTS/trainer.py
@@ -150,7 +150,7 @@ class TrainerTTS:
 
         # count model size
         num_params = count_parameters(self.model)
-        logging.info("\n > Model has {} parameters".format(num_params))
+        print("\n > Model has {} parameters".format(num_params))
 
     @staticmethod
     def get_model(num_chars: int, num_speakers: int, config: Coqpit,
@@ -186,7 +186,6 @@ class TrainerTTS:
                             out_path: str = "",
                             data_train: List = []) -> SpeakerManager:
         speaker_manager = SpeakerManager()
-        if config.use_speaker_embedding:
             if restore_path:
                 speakers_file = os.path.join(os.path.dirname(restore_path),
                                             "speaker.json")
@@ -196,16 +195,6 @@ class TrainerTTS:
                     )
                     speakers_file = config.external_speaker_embedding_file
 
-                if config.use_external_speaker_embedding_file:
-                    speaker_manager.load_x_vectors_file(speakers_file)
-                else:
-                    speaker_manager.load_ids_file(speakers_file)
-            elif config.use_external_speaker_embedding_file and config.external_speaker_embedding_file:
-                speaker_manager.load_x_vectors_file(
-                    config.external_speaker_embedding_file)
-            else:
-                speaker_manager.parse_speakers_from_items(data_train)
-                file_path = os.path.join(out_path, "speakers.json")
                 speaker_manager.save_ids_file(file_path)
         return speaker_manager
 
@@ -238,15 +227,15 @@ class TrainerTTS:
         print(" > Restoring from %s ..." % os.path.basename(restore_path))
         checkpoint = torch.load(restore_path)
         try:
-            logging.info(" > Restoring Model...")
+            print(" > Restoring Model...")
             model.load_state_dict(checkpoint["model"])
-            logging.info(" > Restoring Optimizer...")
+            print(" > Restoring Optimizer...")
             optimizer.load_state_dict(checkpoint["optimizer"])
             if "scaler" in checkpoint and config.mixed_precision:
-                logging.info(" > Restoring AMP Scaler...")
+                print(" > Restoring AMP Scaler...")
                 scaler.load_state_dict(checkpoint["scaler"])
         except (KeyError, RuntimeError):
-            logging.info(" > Partial model initialization...")
+            print(" > Partial model initialization...")
             model_dict = model.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint["model"], config)
             model.load_state_dict(model_dict)