freeze pretrained kor w2v

evaluate.py

@@ -17,7 +17,7 @@ def load_metadata(file_path):
         lines = f.readlines()
         for line in lines:
             line = line.strip()
-            label = line.split(" ")[5]
+            label = line.split(" ")[2]
             labels.append(label)
     return labels
 
@@ -40,8 +40,8 @@ def load_metadata_from_proto(meta_file_path, proto_file_path):
         lines = f.readlines()
         for line in lines:
             line = line.strip()
-            file_name = line.split(" ")[1]
-            label = line.split(" ")[5]
+            file_name = line.split(" ")[0]
+            label = line.split(" ")[2]
             if file_name in protos:
                 index = protos.index(file_name)
                 labels[index] = label
@@ -167,8 +167,8 @@ def calculate_EER(scores, labels):
     # and bonafide otherwise
 
     # create two lists: one for the labels and one for the predictions
-    # labels = metadata
-    labels = load_metadata_from_proto(args.metadata_file, args.protocol_file)
+    labels = metadata
+    # labels = load_metadata_from_proto(args.metadata_file, args.protocol_file)
     predictions = []
     for i, file_name in enumerate(proto):
         score = scores[i]
@@ -194,5 +194,5 @@ def calculate_EER(scores, labels):
     print(f"TN = {cm[1][1]}")
     print(f"FP = {cm[0][1]}")
     print(f"FN = {cm[1][0]}")
-    
+
     calculate_EER(scores, labels)

oc_classifier.py

@@ -73,9 +73,11 @@ def __init__(self, protocol_file, dataset_dir, eval=False):
                 for line in lines:
                     line = line.strip()
                     line = line.split(" ")
-                    if line[4] == "bonafide":
-                        self.file_list.append(line[1])
-                        self.label_list.append(line[4]) # bonafide only
+                    self.file_list.append(line[0])
+                    self.label_list.append("bonafide")
+                    # if line[4] == "bonafide":
+                    #     self.file_list.append(line[1])
+                    #     self.label_list.append(line[4]) # bonafide only
 
         self._length = len(self.file_list)    
 

oc_training.py

@@ -318,6 +318,11 @@ def collate_fn(self, batch):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     aasist = AModel(None, device).to(device)
+    aasist.train()
+
+    aasist.ssl_model.eval()
+    for param in aasist.ssl_model.parameters():
+        param.requires_grad = False
     # ssl = SSLModel(device)
     # senet34 = se_resnet34().to(device)
     # lcnn = lcnn_net(asoftmax=False).to(device)
@@ -349,7 +354,8 @@ def collate_fn(self, batch):
         print(f"Epoch {epoch + 1}\n-------------------------------")
 
         # Training phase
-        aasist.train()
+
+        #aasist.train()
         # ssl.eval()
         # senet34.train()
         # lcnn.train()