mirror of https://github.com/coqui-ai/TTS.git
remove merge conflicts
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@ -254,17 +254,9 @@ class TacotronLoss(torch.nn.Module):
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torch.flip(decoder_b_output, dims=(1, )), mel_input,
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output_lens)
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else:
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<<<<<<< HEAD
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decoder_b_loss = self.criterion(
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torch.flip(decoder_b_output, dims=(1, )), mel_input)
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decoder_c_loss = torch.nn.functional.l1_loss(
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torch.flip(decoder_b_output, dims=(1, )), decoder_output)
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loss += decoder_b_loss + decoder_c_loss
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=======
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decoder_b_loss = self.criterion(torch.flip(decoder_b_output, dims=(1, )), mel_input)
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decoder_c_loss = torch.nn.functional.l1_loss(torch.flip(decoder_b_output, dims=(1, )), decoder_output)
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loss += self.decoder_alpha * (decoder_b_loss + decoder_c_loss)
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>>>>>>> differential spectral loss
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return_dict['decoder_b_loss'] = decoder_b_loss
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return_dict['decoder_c_loss'] = decoder_c_loss
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@ -273,14 +265,8 @@ class TacotronLoss(torch.nn.Module):
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decoder_b_loss = self.criterion(decoder_b_output, mel_input,
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output_lens)
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# decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
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<<<<<<< HEAD
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attention_c_loss = torch.nn.functional.l1_loss(
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alignments, alignments_backwards)
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loss += decoder_b_loss + attention_c_loss
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=======
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attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
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loss += self.decoder_alpha * (decoder_b_loss + attention_c_loss)
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>>>>>>> differential spectral loss
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return_dict['decoder_coarse_loss'] = decoder_b_loss
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return_dict['decoder_ddc_loss'] = attention_c_loss
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