diff --git a/TTS/trainer.py b/TTS/trainer.py
index 7a38616d..40d1ab6f 100644
--- a/TTS/trainer.py
+++ b/TTS/trainer.py
@@ -362,7 +362,7 @@ class Trainer:
         # override config values from command-line args
         # TODO: Maybe it is better to do it outside
         if len(coqpit_overrides) > 0:
-            config.parse_known_args(coqpit_overrides, relaxed_parser=True)
+            config.parse_known_args(coqpit_overrides, arg_prefix="coqpit", relaxed_parser=True)
         experiment_path = args.continue_path
 
         # update the config.json fields and copy it to the output folder
@@ -618,10 +618,8 @@ class Trainer:
         else:
             grad_clip = 0.0  # meaning no gradient clipping
 
-        if grad_clip <= 0:
-            grad_norm = 0
-
         # optimizer step
+        grad_norm = 0
         update_lr_scheduler = True
         if self.use_amp_scaler:
             if self.use_apex:
@@ -636,13 +634,11 @@ class Trainer:
                 if grad_clip > 0:
                     scaler.unscale_(optimizer)
                     grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params(optimizer), grad_clip)
-                    # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN
-                    if torch.isnan(grad_norm) or torch.isinf(grad_norm):
-                        grad_norm = 0
                 scale_prev = scaler.get_scale()
                 scaler.step(optimizer)
                 scaler.update()
                 update_lr_scheduler = scale_prev <= scaler.get_scale()
+                loss_dict["amp_scaler"] = scaler.get_scale()  # for logging
         else:
             # main model optimizer step
             loss_dict["loss"].backward()
@@ -650,6 +646,10 @@ class Trainer:
                 grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
             optimizer.step()
 
+        # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN
+        if isinstance(grad_norm ,torch.Tensor) and (torch.isnan(grad_norm) or torch.isinf(grad_norm)):
+            grad_norm = 0
+
         step_time = time.time() - step_start_time
 
         # setup lr
@@ -1147,7 +1147,7 @@ class Trainer:
             if isinstance(value, (int, float)):
                 loss_dict_detached[key] = value
             else:
-                loss_dict_detached[key] = value.detach()
+                loss_dict_detached[key] = value.detach().item()
         return loss_dict_detached
 
     def _pick_target_avg_loss(self, keep_avg_target: KeepAverage) -> Dict: