cleaning

nnet/xvector.py

@@ -516,12 +516,12 @@ class Xtractor(torch.nn.Module):
                 ("lin_be", torch.nn.Linear(in_features = 5120,
                                            out_features = self.embedding_size,
                                            bias=False)),
-                ("bn_be", torch.nn.BatchNorm1d(self.embedding_size))
+                #("bn_be", torch.nn.BatchNorm1d(self.embedding_size))
             ]))
 
             #self.before_speaker_embedding = torch.nn.Linear(in_features = 5120,
             #                                                out_features = self.embedding_size)
-            self.stat_pooling = AttentivePooling(256, 80, global_context=True)
+            self.stat_pooling = AttentivePooling(self.embedding_size, 80, global_context=True)
 
             self.loss = loss
             if self.loss == "aam":