diff --git a/codes/data/audio/nv_tacotron_dataset.py b/codes/data/audio/nv_tacotron_dataset.py
index 59e2ea9a..2623ad3c 100644
--- a/codes/data/audio/nv_tacotron_dataset.py
+++ b/codes/data/audio/nv_tacotron_dataset.py
@@ -81,6 +81,9 @@ class TextMelLoader(torch.utils.data.Dataset):
                     print(f'{filename} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.input_sample_rate}. This is not a good idea.')
                 audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='area', recompute_scale_factor=False)
                 audio = (audio.squeeze().clip(-1,1)+1)/2
+            if (audio.min() < -1).any() or (audio.max() > 1).any():
+                print(f"Error with audio ranging for {filename}; min={audio.min()} max={audio.max()}")
+                return None
             audio_norm = audio.unsqueeze(0)
             audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
             if self.input_sample_rate != self.sampling_rate:
@@ -105,15 +108,16 @@ class TextMelLoader(torch.utils.data.Dataset):
 
     def __getitem__(self, index):
         t, m, p = self.get_mel_text_pair(self.audiopaths_and_text[index])
-        orig_output = m.shape[-1]
-        orig_text_len = t.shape[0]
-        mel_oversize = self.max_mel_len is not None and m.shape[-1] > self.max_mel_len
-        text_oversize = self.max_text_len is not None and t.shape[0] > self.max_text_len
-        if mel_oversize or text_oversize:
-            print(f"Exception {index} mel_len:{m.shape[-1]} text_len:{t.shape[0]} fname: {p}")
+        if m is None or \
+            (self.max_mel_len is not None and m.shape[-1] > self.max_mel_len) or \
+            (self.max_text_len is not None and t.shape[0] > self.max_text_len):
+            if m is not None:
+                print(f"Exception {index} mel_len:{m.shape[-1]} text_len:{t.shape[0]} fname: {p}")
             # It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result.
             rv = random.randint(0,len(self)-1)
             return self[rv]
+        orig_output = m.shape[-1]
+        orig_text_len = t.shape[0]
         if not self.needs_collate:
             if m.shape[-1] != self.max_mel_len:
                 m = F.pad(m, (0, self.max_mel_len - m.shape[-1]))
@@ -191,9 +195,9 @@ class TextMelCollate():
 if __name__ == '__main__':
     params = {
         'mode': 'nv_tacotron',
-        'path': 'E:\\audio\\MozillaCommonVoice\\en\\test.tsv',
+        'path': 'E:\\audio\\MozillaCommonVoice\\en\\train.tsv',
         'phase': 'train',
-        'n_workers': 0,
+        'n_workers': 12,
         'batch_size': 32,
         'fetcher_mode': 'mozilla_cv',
         'needs_collate': False,
@@ -209,9 +213,10 @@ if __name__ == '__main__':
     dl = create_dataloader(ds, params, collate_fn=c)
     i = 0
     m = None
-    for i, b in tqdm(enumerate(dl)):
-        continue
-        pm = b['padded_mel']
-        pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
-        m = pm if m is None else torch.cat([m, pm], dim=0)
-        print(m.mean(), m.std())
+    for k in range(1000):
+        for i, b in tqdm(enumerate(dl)):
+            continue
+            pm = b['padded_mel']
+            pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
+            m = pm if m is None else torch.cat([m, pm], dim=0)
+            print(m.mean(), m.std())