diff --git a/data.py b/data.py
index 214b6e5..67abb91 100644
--- a/data.py
+++ b/data.py
@@ -13,37 +13,33 @@ class AudioDataset(Dataset):
     def __len__(self):
         return len(self.input_files)
 
+
     def __getitem__(self, idx):
-        # Load audio samples using torchaudio
         high_quality_wav, sr_original = torchaudio.load(self.input_files[idx], normalize=True)
 
-        # Resample to 16000 Hz if necessary
         resample_transform = torchaudio.transforms.Resample(sr_original, 16000)
         low_quality_wav = resample_transform(high_quality_wav)
 
-        # Calculate target length in samples if target_duration is specified
+        # Calculate target length based on desired duration and 16000 Hz
         if self.target_duration is not None:
-            target_length = int(self.target_duration * 16000)  # Assuming 16000 Hz as target sample rate
+            target_length = int(self.target_duration * 44100)
         else:
-            target_length = high_quality_wav.size(1)
+            # Calculate duration of original high quality audio
+            duration_original = high_quality_wav.shape[1] / sr_original
+            target_length = int(duration_original * 16000)
 
-        # Pad high_quality_wav and low_quality_wav to target_length
+        # Pad both to the calculated target length
         high_quality_wav = self.pad_tensor(high_quality_wav, target_length)
         low_quality_wav = self.pad_tensor(low_quality_wav, target_length)
 
-        return high_quality_wav, low_quality_wav
+        return low_quality_wav, high_quality_wav
 
     def pad_tensor(self, tensor, target_length):
-        """Pad tensor to target length along the time dimension (dim=1)."""
         current_length = tensor.size(1)
-
         if current_length < target_length:
-            # Calculate padding amount for each side
             padding_amount = target_length - current_length
-            padding = (0, padding_amount)  # (left_pad, right_pad) for 1D padding
+            padding = (0, padding_amount)
             tensor = torch.nn.functional.pad(tensor, padding, mode=self.padding_mode, value=self.padding_value)
-        else:
-            # If tensor is longer than target, truncate it
+        elif current_length > target_length:
             tensor = tensor[:, :target_length]
-
         return tensor