diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py
index f07851ac..8256c0f7 100644
--- a/TTS/tts/layers/losses.py
+++ b/TTS/tts/layers/losses.py
@@ -2,6 +2,7 @@ import math
 import numpy as np
 import torch
 from torch import nn
+from inspect import signature
 from torch.nn import functional
 from TTS.tts.utils.generic_utils import sequence_mask
 
@@ -142,7 +143,11 @@ class DifferentailSpectralLoss(nn.Module):
     def forward(self, x, target, length):
         x_diff = x[:, 1:] - x[:, :-1]
         target_diff = target[:, 1:] - target[:, :-1]
-        return self.loss_func(x_diff, target_diff, length-1)
+        if len(signature(self.loss_func).parameters) > 2:
+            return self.loss_func(x_diff, target_diff, length-1)
+        else:
+            # if loss masking is not enabled
+            return self.loss_func(x_diff, target_diff)
 
 
 class GuidedAttentionLoss(torch.nn.Module):
@@ -262,8 +267,11 @@ class TacotronLoss(torch.nn.Module):
 
         # double decoder consistency loss (if enabled)
         if self.config.double_decoder_consistency:
-            decoder_b_loss = self.criterion(decoder_b_output, mel_input,
-                                            output_lens)
+            if self.config.loss_masking:
+                decoder_b_loss = self.criterion(decoder_b_output, mel_input,
+                                                output_lens)
+            else:
+                decoder_b_loss = self.criterion(decoder_b_output, mel_input)
             # decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
             attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
             loss += self.decoder_alpha * (decoder_b_loss + attention_c_loss)