diff --git a/codes/data/audio/paired_voice_audio_dataset.py b/codes/data/audio/paired_voice_audio_dataset.py
index fdc63a3a..3201c967 100644
--- a/codes/data/audio/paired_voice_audio_dataset.py
+++ b/codes/data/audio/paired_voice_audio_dataset.py
@@ -6,6 +6,7 @@ import torch
 import torch.nn.functional as F
 import torch.utils.data
 import torchaudio
+from munch import munchify
 from tokenizers import Tokenizer
 from tqdm import tqdm
 from transformers import GPT2TokenizerFast
@@ -49,7 +50,7 @@ def load_voxpopuli(filename):
 
 class CharacterTokenizer:
     def encode(self, txt):
-        return text_to_sequence(txt, ['english_cleaners'])
+        return munchify({'ids': text_to_sequence(txt, ['english_cleaners'])})
 
     def decode(self, seq):
         return sequence_to_text(seq)
@@ -95,7 +96,8 @@ class TextWavLoader(torch.utils.data.Dataset):
         self.needs_collate = opt_get(hparams, ['needs_collate'], True)
         if not self.needs_collate:
             assert self.max_wav_len is not None and self.max_text_len is not None
-        if opt_get(hparams, ['use_bpe_tokenizer'], True):
+        self.use_bpe_tokenizer = opt_get(hparams, ['use_bpe_tokenizer'], True)
+        if self.use_bpe_tokenizer:
             self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json'))
         else:
             self.tokenizer = CharacterTokenizer()
@@ -110,10 +112,11 @@ class TextWavLoader(torch.utils.data.Dataset):
     def get_text(self, text):
         tokens = self.tokenizer.encode(text.strip().lower()).ids
         tokens = torch.IntTensor(tokens)
-        # Assert if any UNK,start,stop tokens encountered.
+        if self.use_bpe_tokenizer:
+            # Assert if any UNK,start tokens encountered.
+            assert not torch.any(tokens == 1)
+        # The stop token should always be sacred.
         assert not torch.any(tokens == 0)
-        assert not torch.any(tokens == 1)
-        assert not torch.any(tokens == 9999)
         return tokens
 
     def load_conditioning_candidates(self, path):
@@ -245,6 +248,7 @@ if __name__ == '__main__':
         'max_text_length': 200,
         'sample_rate': 22050,
         'load_conditioning': False,
+        'use_bpe_tokenizer': False,
     }
     from data import create_dataset, create_dataloader