speedy speehc losses

TTS/tts/layers/losses.py

@@ -240,6 +240,23 @@ class GuidedAttentionLoss(torch.nn.Module):
         return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2)
 
 
+class Huber(nn.Module):
+    def forward(self, x, y, length=None):
+        """
+        Shapes:
+            x: B x T
+            y: B x T
+            length: B
+        """
+        mask = sequence_mask(sequence_length=length, max_len=y.size(1)).float()
+        return torch.nn.functional.smooth_l1_loss(
+            x * mask, y * mask, reduction='sum') / mask.sum()
+
+
+########################
+# MODEL LOSS LAYERS
+########################
+
 class TacotronLoss(torch.nn.Module):
     """Collection of Tacotron set-up based on provided config."""
     def __init__(self, c, stopnet_pos_weight=10, ga_sigma=0.4):
@@ -403,8 +420,27 @@ class GlowTTSLoss(torch.nn.Module):
         return_dict['log_mle'] = log_mle
         return_dict['loss_dur'] = loss_dur
 
-         # check if any loss is NaN
+        # check if any loss is NaN
         for key, loss in return_dict.items():
             if torch.isnan(loss):
                 raise RuntimeError(f" [!] NaN loss with {key}.")
         return return_dict
+
+
+class SpeedySpeechLoss(nn.Module):
+    def __init__(self, c):
+        super().__init__()
+        self.l1 = L1LossMasked(False)
+        self.ssim = SSIMLoss()
+        self.huber = Huber()
+
+        self.ssim_alpha = c.ssim_alpha
+        self.huber_alpha = c.huber_alpha
+        self.l1_alpha = c.l1_alpha
+
+    def forward(self, decoder_output, decoder_target, decoder_output_lens, dur_output, dur_target, input_lens):
+        l1_loss = self.l1(decoder_output, decoder_target, decoder_output_lens)
+        ssim_loss = self.ssim(decoder_output, decoder_target, decoder_output_lens)
+        huber_loss = self.huber(dur_output, dur_target, input_lens)
+        loss = l1_loss + ssim_loss + huber_loss
+        return {'loss': loss, 'loss_l1': l1_loss, 'loss_ssim': ssim_loss, 'loss_dur': huber_loss}