fix bug

preprocess.py

@@ -34,7 +34,7 @@ def preprocess(audio_file):
     win_len = int(3*sr)
     last_sample = len(y) - win_len
     # start_sample_list = np.linspace(0, max(0, last_sample), num=num_eval)
-    start_sample_list = [random.randint(0, last_sample) for _ in range(num_eval)]
+    start_sample_list = [random.randint(0, max(0, last_sample)) for _ in range(num_eval)]
 
     frames = []
     for start_sample in start_sample_list:

script.py

@@ -51,32 +51,32 @@ print('Define Model')
 # model_path = './checkpoints/RAWNET_ASVSPOOF_FOR_INTHEWILD_PURDUE.pth'
 # model.load_state_dict(torch.load(model_path, map_location=device))
 
-# # LCNN MODEL
-# model = LCNN(return_emb=False).to(device)
-# # model_path = './checkpoints/LCNN_ASVSPOOF_FOR_INTHEWILD_PURDUE.pth'
-# # model_path = './checkpoints/LCNN_ALL_DATA.pth'
-# model_path = './checkpoints/LCNN_ALL_DATA_AUG.pth'
-# model.load_state_dict(torch.load(model_path, map_location=device))
+# LCNN MODEL
+model = LCNN(return_emb=False).to(device)
+# model_path = './checkpoints/LCNN_ASVSPOOF_FOR_INTHEWILD_PURDUE.pth'
+# model_path = './checkpoints/LCNN_ALL_DATA.pth'
+model_path = './checkpoints/LCNN_ALL_DATA_AUG.pth'
+model.load_state_dict(torch.load(model_path, map_location=device))
 
-# MOE MODEL
-expert_1 = LCNN(return_emb=True).to(device)
-expert_2 = LCNN(return_emb=True).to(device)
-expert_3 = LCNN(return_emb=True).to(device)
-expert_4 = LCNN(return_emb=True).to(device)
-expert_5 = LCNN(return_emb=True).to(device)
-expert_6 = LCNN(return_emb=True).to(device)
-
-# # model = UltimateMOE(experts=[expert_1, expert_2, expert_3, expert_4])
-# # model_path = './checkpoints/MOE_ULTIMATE.pth'
-
-# model = MOE_attention(experts=[expert_1, expert_2, expert_3, expert_4, expert_5, expert_6], device=device)
-# # model_path = './checkpoints/MOE_ATTENTION.pth'
-# model_path = './checkpoints/MOE_TRANSF.pth'
-
-expert_7 = LCNN(return_emb=True).to(device)
-model = MOE_attention(experts=[expert_1, expert_2, expert_3, expert_4, expert_5, expert_6, expert_7], device=device, freezing=True)
-# model_path = './checkpoints/MOE_TRANSF_7EXP.pth'
-model_path = './checkpoints/MOE_TRANSF_7EXP_AUG.pth'
+# # MOE MODEL
+# expert_1 = LCNN(return_emb=True).to(device)
+# expert_2 = LCNN(return_emb=True).to(device)
+# expert_3 = LCNN(return_emb=True).to(device)
+# expert_4 = LCNN(return_emb=True).to(device)
+# expert_5 = LCNN(return_emb=True).to(device)
+# expert_6 = LCNN(return_emb=True).to(device)
+#
+# # # model = UltimateMOE(experts=[expert_1, expert_2, expert_3, expert_4])
+# # # model_path = './checkpoints/MOE_ULTIMATE.pth'
+#
+# # model = MOE_attention(experts=[expert_1, expert_2, expert_3, expert_4, expert_5, expert_6], device=device)
+# # # model_path = './checkpoints/MOE_ATTENTION.pth'
+# # model_path = './checkpoints/MOE_TRANSF.pth'
+#
+# expert_7 = LCNN(return_emb=True).to(device)
+# model = MOE_attention(experts=[expert_1, expert_2, expert_3, expert_4, expert_5, expert_6, expert_7], device=device, freezing=True)
+# # model_path = './checkpoints/MOE_TRANSF_7EXP.pth'
+# model_path = './checkpoints/MOE_TRANSF_7EXP_AUG.pth'
 
 
 model = (model).to(device)