diff --git a/codes/data/audio/paired_voice_audio_dataset.py b/codes/data/audio/paired_voice_audio_dataset.py
index dbdf0f98..b7ea203b 100644
--- a/codes/data/audio/paired_voice_audio_dataset.py
+++ b/codes/data/audio/paired_voice_audio_dataset.py
@@ -86,7 +86,7 @@ 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
-        self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/custom_lowercase_gptvoice_tokenizer_r2.json'))
+        self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json'))
 
     def get_wav_text_pair(self, audiopath_and_text):
         # separate filename and text
diff --git a/codes/data/audio/voice_tokenizer_builder.py b/codes/data/audio/voice_tokenizer_builder.py
index c2dd3edc..813fdf5e 100644
--- a/codes/data/audio/voice_tokenizer_builder.py
+++ b/codes/data/audio/voice_tokenizer_builder.py
@@ -33,7 +33,7 @@ def build_text_file_from_priors(priors, output):
 def train():
     with open('all_texts.txt', 'r', encoding='utf-8') as at:
         ttsd = at.readlines()
-    bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train']
+    #bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train']
 
     allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—–ʼ]+$')
     def preprocess_word(word, report=False):
@@ -49,14 +49,14 @@ def train():
         for i in range(0, len(ttsd), batch_size):
             yield [preprocess_word(t, True) for t in ttsd[i:i+batch_size]]
 
-        print("Processing bookcorpus.")
-        for i in range(0, len(bcd), batch_size):
-            yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']]
+        #print("Processing bookcorpus.")
+        #for i in range(0, len(bcd), batch_size):
+        #    yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']]
 
-    trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=9999, continuing_subword_prefix='$$$')
+    trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=511, continuing_subword_prefix='$$$')
     tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
     tokenizer.pre_tokenizer = Whitespace()
-    tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd)+len(bcd))
+    tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd))#+len(bcd))
 
     print(tokenizer.decode(tokenizer.encode("i was traveling throughhadslfghds the woods in 1235375t137{{}}").ids))
 
diff --git a/codes/models/gpt_voice/unified_voice.py b/codes/models/gpt_voice/unified_voice.py
index cc57b847..558a8d58 100644
--- a/codes/models/gpt_voice/unified_voice.py
+++ b/codes/models/gpt_voice/unified_voice.py
@@ -1,16 +1,10 @@
-import random
-from time import time
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel
-from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
-from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
+from transformers import GPT2Model, GPT2Config
 
 from models.arch_util import AttentionBlock
 from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel
-from models.gpt_voice.mini_encoder import AudioMiniEncoder
 from models.tacotron2.text import symbols
 from trainer.networks import register_model
 from utils.util import opt_get
@@ -47,14 +41,14 @@ class UnifiedGptVoice(nn.Module):
     - Voice conditioned on text
     """
 
-    NUMBER_TEXT_TOKENS = 10000  # The number of tokens produced by our bespoke BPE tokenizer.
-    START_TEXT_TOKEN = 9999
+    NUMBER_TEXT_TOKENS = 256  # The number of tokens produced by our bespoke BPE tokenizer.
+    START_TEXT_TOKEN = 255
     STOP_TEXT_TOKEN = 0
     NUMBER_MEL_CODES = 8194
     START_MEL_TOKEN = 8192
     STOP_MEL_TOKEN = 8193
 
-    def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=80, max_mel_tokens=250, max_conditioning_inputs=3,
+    def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=120, max_mel_tokens=250, max_conditioning_inputs=3,
                  checkpointing=True, mel_length_compression=1024, max_conditioning_length=60):
         super().__init__()
 
@@ -222,7 +216,7 @@ def register_unified_gpt_voice(opt_net, opt):
 
 if __name__ == '__main__':
     gpt = UnifiedGptVoice(model_dim=256, heads=4)
-    l = gpt(torch.randn(2, 80, 800),
+    l = gpt(torch.randn(2, 120, 800),
             torch.randint(high=len(symbols), size=(2,80)),
             torch.randint(high=8192, size=(2,250)),
             torch.tensor([150*256,195*256]))