Update model.py

model.py

@@ -13,8 +13,6 @@ from typing import Optional
 import torchaudio.functional as F
 import random
 
-
-
 def find_wav_files(path_to_dir: Union[Path, str]):
     paths = list(sorted(Path(path_to_dir).glob("**/*.wav")))
 
@@ -627,7 +625,7 @@ def pred_audio(path):
     audio = [path]
     
     audio_ds = AudioDataset(audio)
-    audio_ds = PadDataset(train_dataset)
+    audio_ds = PadDataset(audio_ds)
     
     audio_ds = mfcc(
         directory_or_audiodataset=audio_ds,
@@ -635,20 +633,19 @@ def pred_audio(path):
     )
     
     audio_ds = double_delta(audio_ds)
-
     
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     
     cnn_model = ShallowCNN(in_features= 1,out_dim=1).to(device)
-    cnn_checkpoint = torch.load("models/best_cnn.pt", map_location=device)
+    cnn_checkpoint = torch.load("./models/best_cnn.pt", map_location=device)
     cnn_model.load_state_dict(cnn_checkpoint['state_dict'])
     
     lstm_model = SimpleLSTM(feat_dim= 40, time_dim= 972, mid_dim= 30, out_dim= 1).to(device)
-    lstm_checkpoint = torch.load("models/best_lstm.pt", map_location=device)
+    lstm_checkpoint = torch.load("./models/best_lstm.pt", map_location=device)
     lstm_model.load_state_dict(lstm_checkpoint['state_dict'])
     
     dtdnn_model = DTDNN(feat_dim= 38880,num_classes= 1).to(device)
-    dtdnn_checkpoint = torch.load("models/best_tdnn.pt", map_location=device)
+    dtdnn_checkpoint = torch.load("./models/best_tdnn.pt", map_location=device)
     dtdnn_model.load_state_dict(dtdnn_checkpoint['state_dict'])
     
     # Set models to evaluation mode