diff --git a/codes/data/audio/fast_paired_dataset.py b/codes/data/audio/fast_paired_dataset.py
index 11d9377f..6dca25fd 100644
--- a/codes/data/audio/fast_paired_dataset.py
+++ b/codes/data/audio/fast_paired_dataset.py
@@ -2,6 +2,7 @@ import hashlib
 import os
 import random
 import sys
+import time
 from itertools import groupby
 
 import torch
@@ -69,6 +70,9 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
             self.tokenizer = CharacterTokenizer()
         self.skipped_items = 0  # records how many items are skipped when accessing an index.
 
+        self.load_times = torch.zeros((256,))
+        self.load_ind = 0
+
     def get_wav_text_pair(self, audiopath_and_text):
         # separate filename and text
         audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
@@ -148,6 +152,7 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
         }
 
     def __getitem__(self, index):
+        start = time.time()
         self.skipped_items += 1
         apt = self.load_random_line()
         try:
@@ -185,6 +190,11 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
             aligned_codes = F.pad(aligned_codes, (0, self.max_aligned_codes-aligned_codes.shape[0]))
         if tseq.shape[0] != self.max_text_len:
             tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
+
+        elapsed = time.time() - start
+        self.load_times[self.load_ind] = elapsed
+        self.load_ind = (self.load_ind + 1) % len(self.load_times)
+
         res = {
             'real_text': text,
             'padded_text': tseq,
@@ -195,12 +205,14 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
             'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
             'filenames': path,
             'skipped_items': actually_skipped_items,
+            'load_time': self.load_times.mean()
         }
         if self.load_conditioning:
             res['conditioning'] = cond
             res['conditioning_contains_self'] = cond_is_self
         if self.produce_ctc_metadata:
             res.update(self.get_ctc_metadata(raw_codes))
+
         return res
 
     def __len__(self):
@@ -213,6 +225,7 @@ class FastPairedVoiceDebugger:
         self.loaded_items = 0
         self.self_conditioning_items = 0
         self.unique_files = set()
+        self.load_time = 0
 
     def get_state(self):
         return {'total_items': self.total_items,
@@ -228,6 +241,7 @@ class FastPairedVoiceDebugger:
     def update(self, batch):
         self.total_items += batch['wav'].shape[0]
         self.loaded_items += batch['skipped_items'].sum().item()
+        self.load_time = batch['load_time'].mean().item()
         for filename in batch['filenames']:
             self.unique_files.add(hashlib.sha256(filename.encode('utf-8')))
         if 'conditioning' in batch.keys():
@@ -238,12 +252,13 @@ class FastPairedVoiceDebugger:
             'total_samples_loaded': self.total_items,
             'percent_skipped_samples': (self.loaded_items - self.total_items) / self.loaded_items,
             'percent_conditioning_is_self': self.self_conditioning_items / self.loaded_items,
-            'unique_files_loaded': len(self.unique_files)
+            'unique_files_loaded': len(self.unique_files),
+            'load_time': self.load_time,
         }
 
 
 if __name__ == '__main__':
-    batch_sz = 16
+    batch_sz = 256
     params = {
         'mode': 'fast_paired_voice_audio',
         'path': ['Y:\\libritts\\train-clean-360\\transcribed-w2v.tsv', 'Y:\\clips\\books1\\transcribed-w2v.tsv'],
@@ -255,8 +270,8 @@ if __name__ == '__main__':
         'sample_rate': 22050,
         'load_conditioning': True,
         'num_conditioning_candidates': 1,
-        'conditioning_length': 44000,
-        'use_bpe_tokenizer': False,
+        'conditioning_length': 66000,
+        'use_bpe_tokenizer': True,
         'load_aligned_codes': True,
         'produce_ctc_metadata': True,
     }
@@ -275,10 +290,11 @@ if __name__ == '__main__':
     max_pads, max_repeats = 0, 0
     for i, b in tqdm(enumerate(dl)):
         for ib in range(batch_sz):
-            max_pads = max(max_pads, b['ctc_pads'].max())
-            max_repeats = max(max_repeats, b['ctc_repeats'].max())
-            print(f'{i} {ib} {b["real_text"][ib]}')
+            #max_pads = max(max_pads, b['ctc_pads'].max())
+            #max_repeats = max(max_repeats, b['ctc_repeats'].max())
+            #print(f'{i} {ib} {b["real_text"][ib]}')
             #save(b, i, ib, 'wav')
+            pass
         #if i > 5:
         #    break
     print(max_pads, max_repeats)