Update dataset.py, formatting

src/f5_tts/model/dataset.py

@@ -133,31 +133,30 @@ class CustomDataset(Dataset):
             text = row["text"]
             duration = row["duration"]
     
-            # Check if the duration is within the acceptable range
+            # filter by given length
             if 0.3 <= duration <= 30:
-                break  # Valid sample found, exit the loop
-    
-            # Move to the next index and wrap around if necessary
+                break  # valid
+
             index = (index + 1) % len(self.data)
-    
+
         if self.preprocessed_mel:
             mel_spec = torch.tensor(row["mel_spec"])
         else:
             audio, source_sample_rate = torchaudio.load(audio_path)
-    
-            # If the audio has multiple channels, convert it to mono
+
+            # make sure mono input
             if audio.shape[0] > 1:
                 audio = torch.mean(audio, dim=0, keepdim=True)
-    
-            # Resample the audio if necessary
+
+            # resample if necessary
             if source_sample_rate != self.target_sample_rate:
                 resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
                 audio = resampler(audio)
-    
-            # Compute the mel spectrogram
+
+            # to mel spectrogram
             mel_spec = self.mel_spectrogram(audio)
-            mel_spec = mel_spec.squeeze(0)  # Convert from (1, D, T) to (D, T)
-    
+            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
         return {
             "mel_spec": mel_spec,
             "text": text,