diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index 321f654..4d83976 100755
--- a/vall_e/models/base.py
+++ b/vall_e/models/base.py
@@ -1038,21 +1038,21 @@ class Base(nn.Module):
 				if loss_factor_text > 0.0 and target_text_list:
 					target_text = torch.cat( target_text_list ).long()
 					inputs_text = torch.cat( logits_text )
-					self.loss["text"] = F.cross_entropy( inputs_text, target_text, ignore_index=self.ignore_index )
+					self.loss["text"] = F.cross_entropy( inputs_text, target_text, ignore_index=self.ignore_index )  * loss_factor_text
 					self.stats["acc"]["text"] = self.accuracy_metric( inputs_text, target_text )
 
 				loss_factor_prom = self.loss_factor("prom")
 				if loss_factor_prom > 0.0 and target_prom_list:
 					target_prom = torch.cat( target_prom_list ).long()
 					inputs_prom = torch.cat( logits_prom )
-					self.loss["prom"] = F.cross_entropy( inputs_prom, target_prom, ignore_index=self.ignore_index )
+					self.loss["prom"] = F.cross_entropy( inputs_prom, target_prom, ignore_index=self.ignore_index ) * loss_factor_prom
 					self.stats["acc"]["prom"] = self.accuracy_metric( inputs_prom, target_prom )
 
 				loss_factor_resp = self.loss_factor("resp")
 				if loss_factor_resp > 0.0 and target_resp_list:
 					target_resp = torch.cat( target_resp_list ).long()
 					inputs_resp = torch.cat( logits_resp )
-					self.loss["resp"] = F.cross_entropy( inputs_resp, target_resp, ignore_index=self.ignore_index )
+					self.loss["resp"] = F.cross_entropy( inputs_resp, target_resp, ignore_index=self.ignore_index )  * loss_factor_resp
 					self.stats["acc"]["resp"] = self.accuracy_metric( inputs_resp, target_resp )
 
 				# to-do: compute loss per individual batch to scale per RVQ level