diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py
index 79654c07..ec5f0811 100644
--- a/TTS/tts/layers/losses.py
+++ b/TTS/tts/layers/losses.py
@@ -200,7 +200,7 @@ class TacotronLoss(torch.nn.Module):
             self.criterion = nn.L1Loss() if c.model in ["Tacotron"
                                                         ] else nn.MSELoss()
         # differential spectral loss
-        if c.diff_spec_loss_alpha > 0:
+        if c.diff_spec_alpha > 0:
             self.criterion_diff_spec = DifferentailSpectralLoss(loss_func=self.criterion)
         # guided attention loss
         if c.ga_alpha > 0:
@@ -254,11 +254,17 @@ class TacotronLoss(torch.nn.Module):
                     torch.flip(decoder_b_output, dims=(1, )), mel_input,
                     output_lens)
             else:
+<<<<<<< HEAD
                 decoder_b_loss = self.criterion(
                     torch.flip(decoder_b_output, dims=(1, )), mel_input)
             decoder_c_loss = torch.nn.functional.l1_loss(
                 torch.flip(decoder_b_output, dims=(1, )), decoder_output)
             loss += decoder_b_loss + decoder_c_loss
+=======
+                decoder_b_loss = self.criterion(torch.flip(decoder_b_output, dims=(1, )), mel_input)
+            decoder_c_loss = torch.nn.functional.l1_loss(torch.flip(decoder_b_output, dims=(1, )), decoder_output)
+            loss += self.decoder_alpha * (decoder_b_loss + decoder_c_loss)
+>>>>>>> differential spectral loss
             return_dict['decoder_b_loss'] = decoder_b_loss
             return_dict['decoder_c_loss'] = decoder_c_loss
 
@@ -267,9 +273,14 @@ class TacotronLoss(torch.nn.Module):
             decoder_b_loss = self.criterion(decoder_b_output, mel_input,
                                             output_lens)
             # decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
+<<<<<<< HEAD
             attention_c_loss = torch.nn.functional.l1_loss(
                 alignments, alignments_backwards)
             loss += decoder_b_loss + attention_c_loss
+=======
+            attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
+            loss += self.decoder_alpha * (decoder_b_loss + attention_c_loss)
+>>>>>>> differential spectral loss
             return_dict['decoder_coarse_loss'] = decoder_b_loss
             return_dict['decoder_ddc_loss'] = attention_c_loss
 
@@ -280,7 +291,7 @@ class TacotronLoss(torch.nn.Module):
             return_dict['ga_loss'] = ga_loss * self.ga_alpha
 
         # differential spectral loss
-        if self.config.diff_spec_loss_alpha > 0:
+        if self.config.diff_spec_alpha > 0:
             diff_spec_loss = self.criterion_diff_spec(postnet_output, mel_input, output_lens)
             loss += diff_spec_loss * self.diff_spec_alpha
             return_dict['diff_spec_loss'] = diff_spec_loss