diff --git a/vall_e/train.py b/vall_e/train.py
index 4c2943d..e6d5a6e 100755
--- a/vall_e/train.py
+++ b/vall_e/train.py
@@ -207,40 +207,42 @@ def run_eval(engines, eval_name, dl, args=None):
 				training=False,
 			)
 
-			if engine.hyper_config.version >= 7:
-				kwargs = base_kwargs | cfg.evaluation.kwargs
-				# sample for NAR demask
-				if random.random() < engine.hyper_config.experimental.masking_train_p:
-					kwargs["len_list"] = [ resp.shape[0] for resp in batch["resps"] ]
-				# inference
-				resps_list = engine( **kwargs )
-			else:
-				if "len" in engine.hyper_config.capabilities:
+			with torch.autocast("cuda", dtype=cfg.trainer.dtype, enabled=cfg.trainer.amp):
+				if engine.hyper_config.version >= 7:
 					kwargs = base_kwargs | cfg.evaluation.kwargs
-					max_steps = kwargs.pop("max_steps", 500)
-
-					if "denoise_start" in kwargs:
-						len_list = [ resp.shape[0] for resp in batch["resps"] ]
-						kwargs["resps_list"] = [ resp[:, :1] for resp in batch["resps"] ]
-					else:
-						len_list = engine( max_steps=5, **kwargs )
-						len_list = [ min( l, max_steps ) for l in len_list ]
-					
-					kwargs = base_kwargs | cfg.evaluation.kwargs
-					resps_list = engine( **kwargs, len_list=len_list )
+					# sample for NAR demask
+					if random.random() < engine.hyper_config.experimental.masking_train_p:
+						kwargs["len_list"] = [ resp.shape[0] for resp in batch["resps"] ]
+					# inference
+					resps_list = engine( **kwargs )
 				else:
-					if "ar" in engine.hyper_config.capabilities:
+					if "len" in engine.hyper_config.capabilities:
 						kwargs = base_kwargs | cfg.evaluation.kwargs
-						resps_list = engine( **kwargs )
-					else:
-						resps_list = [ resp[:, 0] for resp in batch["resps"] ]
+						max_steps = kwargs.pop("max_steps", 500)
 
-					if "nar" in engine.hyper_config.capabilities:
+						if "denoise_start" in kwargs:
+							len_list = [ resp.shape[0] for resp in batch["resps"] ]
+							kwargs["resps_list"] = [ resp[:, :1] for resp in batch["resps"] ]
+						else:
+							len_list = engine( max_steps=5, **kwargs )
+							len_list = [ min( l, max_steps ) for l in len_list ]
+						
 						kwargs = base_kwargs | cfg.evaluation.kwargs
-						resps_list = engine( **kwargs, resps_list=resps_list )
+						resps_list = engine( **kwargs, len_list=len_list )
+					else:
+						if "ar" in engine.hyper_config.capabilities:
+							kwargs = base_kwargs | cfg.evaluation.kwargs
+							resps_list = engine( **kwargs )
+						else:
+							resps_list = [ resp[:, 0] for resp in batch["resps"] ]
+
+						if "nar" in engine.hyper_config.capabilities:
+							kwargs = base_kwargs | cfg.evaluation.kwargs
+							resps_list = engine( **kwargs, resps_list=resps_list )
 
 			process( name, batch, resps_list )
 
+			"""
 			# evaluate why it's so slow
 			if has_stt:
 				max_steps = max( [ text.shape[0] for text in batch["text"] ] )
@@ -254,6 +256,7 @@ def run_eval(engines, eval_name, dl, args=None):
 				text_list = [ cfg.tokenizer.decode( text ) for i, text in enumerate( text_list ) ]
 
 				_logger.info(f"Validation Metrics (STT): {text_list}")
+			"""
 
 	stats = {k: sum(v) / len(v) for k, v in stats.items() if v}
 	engines_stats = {