optional loss masking for stoptoken predictor

TTS/tts/layers/losses.py

@@ -163,14 +163,20 @@ class BCELossMasked(nn.Module):
         """
         # mask: (batch, max_len, 1)
         target.requires_grad = False
-        mask = sequence_mask(sequence_length=length,
-                             max_len=target.size(1)).float()
+        if length is not None:
+            mask = sequence_mask(sequence_length=length,
+                                max_len=target.size(1)).float()
+            x = x * mask
+            target = target * mask
+            num_items = mask.sum()
+        else:
+            num_items = torch.numel(x)
         loss = functional.binary_cross_entropy_with_logits(
-            x * mask,
-            target * mask,
+            x,
+            target,
             pos_weight=self.pos_weight,
             reduction='sum')
-        loss = loss / mask.sum()
+        loss = loss / num_items
         return loss