Rework wavfile dataset to be usable for things other than augments

codes/data/audio/wavfile_dataset.py

@@ -30,13 +30,14 @@ class WavfileDataset(torch.utils.data.Dataset):
 
         # Parse options
         self.sampling_rate = opt_get(opt, ['sampling_rate'], 24000)
-        self.augment = opt_get(opt, ['do_augmentation'], False)
         self.pad_to = opt_get(opt, ['pad_to_seconds'], None)
         if self.pad_to is not None:
             self.pad_to *= self.sampling_rate
 
-        self.window = 2 * self.sampling_rate
+        self.augment = opt_get(opt, ['do_augmentation'], False)
         if self.augment:
+            # The "window size" for the clips produced in seconds.
+            self.window = 2 * self.sampling_rate
             self.augmentor = WavAugmentor()
 
     def get_audio_for_index(self, index):
@@ -57,15 +58,21 @@ class WavfileDataset(torch.utils.data.Dataset):
         return audio, audiopath
 
     def __getitem__(self, index):
-        clip1, clip2 = None, None
+        success = False
+        # This "success" thing is a hack: This dataset is randomly failing for no apparent good reason and I don't know why.
+        # Symptoms are it complaining about being unable to read a nonsensical filename that is clearly corrupted. Memory corruption? I don't know..
+        while not success:
+            try:
+                # Split audio_norm into two tensors of equal size.
+                audio_norm, filename = self.get_audio_for_index(index)
+                success = True
+            except:
+                print(f"Failed to load {index} {self.audiopaths[index]}")
 
-        while clip1 is None and clip2 is None:
-            # Split audio_norm into two tensors of equal size.
-            audio_norm, filename = self.get_audio_for_index(index)
+        if self.augment:
             if audio_norm.shape[1] < self.window * 2:
                 # Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this.
-                index = (index + 1) % len(self)
-                continue
+                return self[(index + 1) % len(self)]
             j = random.randint(0, audio_norm.shape[1] - self.window)
             clip1 = audio_norm[:, j:j+self.window]
             if self.augment:
@@ -83,12 +90,16 @@ class WavfileDataset(torch.utils.data.Dataset):
                 #print(f"Warning! Truncating clip {filename} from {audio_norm.shape[-1]} to {self.pad_to}")
                 audio_norm = audio_norm[:, :self.pad_to]
 
-        return {
+        output = {
             'clip': audio_norm,
-            'clip1': clip1[0, :].unsqueeze(0),
-            'clip2': clip2[0, :].unsqueeze(0),
             'path': filename,
         }
+        if self.augment:
+            output.update({
+                'clip1': clip1[0, :].unsqueeze(0),
+                'clip2': clip2[0, :].unsqueeze(0),
+            })
+        return output
 
     def __len__(self):
         return len(self.audiopaths)
@@ -104,6 +115,7 @@ if __name__ == '__main__':
         'phase': 'train',
         'n_workers': 0,
         'batch_size': 16,
+        'do_augmentation': False
     }
     from data import create_dataset, create_dataloader, util