diff --git a/codes/models/gpt_voice/gpt_asr_hf.py b/codes/models/gpt_voice/gpt_asr_hf.py
index a685a4a1..9b487441 100644
--- a/codes/models/gpt_voice/gpt_asr_hf.py
+++ b/codes/models/gpt_voice/gpt_asr_hf.py
@@ -1,14 +1,9 @@
-from time import time
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from munch import munchify
 from transformers import GPT2Model, GPT2Config
 
-from models.gpt_voice.lucidrains_gpt import Transformer
-from models.tacotron2.taco_utils import get_mask_from_lengths
-from models.tacotron2.text import symbols, sequence_to_text
+from models.tacotron2.text import symbols
 from trainer.networks import register_model
 from utils.util import opt_get
 
@@ -17,10 +12,10 @@ class ResBlock(nn.Module):
     def __init__(self, chan):
         super().__init__()
         self.net = nn.Sequential(
-            nn.Conv1d(chan, chan, kernel_size=5, padding = 2),
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
             nn.BatchNorm1d(chan),
             nn.ReLU(),
-            nn.Conv1d(chan, chan, kernel_size=5, padding = 2),
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
             nn.BatchNorm1d(chan)
         )
 
@@ -32,17 +27,15 @@ class MelEncoder(nn.Module):
     def __init__(self, channels, mel_channels=80):
         super().__init__()
         self.channels = channels
-        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=7, padding=3),
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=5, padding=2),
                                      ResBlock(channels//4),
                                      ResBlock(channels//4),
-                                     nn.Conv1d(channels//4, channels//2, kernel_size=5, stride=2, padding=2),
+                                     nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
                                      nn.BatchNorm1d(channels//2),
                                      nn.ReLU(),
                                      ResBlock(channels//2),
                                      ResBlock(channels//2),
-                                     ResBlock(channels//2),
-                                     nn.Conv1d(channels//2, channels, kernel_size=5, stride=2, padding=2),
-                                     ResBlock(channels),
+                                     nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
                                      ResBlock(channels),
                                      ResBlock(channels)
                                      )