debug

nnet/xsets.py

@@ -232,6 +232,10 @@ class SideSet(Dataset):
                 self.transform["add_noise"] = self.transformation["add_noise"]
             if "add_reverb" in transforms:
                 self.transform["add_reverb"] = self.transformation["add_reverb"]
+            if "codec" in transforms:
+                self.transform["codec"] = []
+            if "phone_filtering" in transforms:
+                self.transform["phone_filtering"] = []
 
         self.noise_df = None
         if "add_noise" in self.transform:

nnet/xvector.py

@@ -1433,7 +1433,6 @@ def xtrain(speaker_number,
             pretrained_dict = checkpoint["model_state_dict"]
             for part in reset_parts:
                 pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}
-                pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}
 
             new_model_dict = model.state_dict()
             new_model_dict.update(pretrained_dict)
@@ -1936,17 +1935,10 @@ def cross_validation(model, validation_loader, device, validation_shape, tar_ind
             batch_size = target.shape[0]
             data = data.squeeze().to(device)
             with torch.cuda.amp.autocast(enabled=mixed_precision):
-                if loss_criteria == 'aam':
-                    batch_predictions, batch_embeddings = model(data, target=None, is_eval=False)
-                elif loss_criteria == 'aps':
-                    batch_predictions, batch_embeddings = model(data, target=None, is_eval=False)
-                else:
-                    batch_predictions, batch_embeddings = model(data, target=None, is_eval=False)
-                    batch_embeddings = l2_norm(batch_embeddings)
+                batch_predictions, batch_embeddings = model(data, target=None, is_eval=False)
                 accuracy += (torch.argmax(batch_predictions.data, 1) == target).sum()
                 loss += criterion(batch_predictions, target)
             embeddings[cursor:cursor + batch_size,:] = batch_embeddings.detach().cpu()
-            #classes[cursor:cursor + batch_size] = target.detach().cpu()
             cursor += batch_size
 
     local_device = "cpu" if embeddings.shape[0] > 3e4 else device