diff --git a/codes/models/gpt_voice/gpt_asr_hf2.py b/codes/models/gpt_voice/gpt_asr_hf2.py
index 993f9a82..abfd086e 100644
--- a/codes/models/gpt_voice/gpt_asr_hf2.py
+++ b/codes/models/gpt_voice/gpt_asr_hf2.py
@@ -45,7 +45,9 @@ class MelEncoder(nn.Module):
                                      )
 
     def forward(self, x):
-        return self.encoder(x)
+        for e in self.encoder:
+            x = e(x)
+        return x
 
 
 class GPT2InferenceModel(GPT2PreTrainedModel):
@@ -262,22 +264,21 @@ class GptAsrHf2(nn.Module):
 
         mel_emb = self.mel_encoder(mel_inputs)
         assert mel_emb.shape[-1] <= self.max_mel_frames
-        mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
         mel_emb = mel_emb.permute(0,2,1).contiguous()
         mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
         self.inference_model.store_mel_emb(mel_emb)
 
         # "fake_inputs" are stand-ins for the MEL frames, which will be injected with the prep_inputs function above.
         if cond_text is None:
-            fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
-            fake_inputs[:,-1] = self.NUMBER_SYMBOLS
+            fake_inputs = torch.full((mel_emb.shape[0],mel_emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
+            fake_inputs[:,-1] = self.START_TOKEN
         else:
             cond_used = 10
-            fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
-            fake_inputs[:,-1-cond_used] = self.NUMBER_SYMBOLS
+            fake_inputs = torch.full((mel_emb.shape[0],mel_emb.shape[1]+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
+            fake_inputs[:,-1-cond_used] = self.START_TOKEN
             fake_inputs[:, -cond_used:] = cond_text[:, :cond_used]
-        gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.NUMBER_SYMBOLS, pad_token_id=0, eos_token_id=0,
-                          max_length=self.max_symbols_per_phrase+self.max_mel_frames, temperature=temperature, num_beams=num_beams, use_cache=True)
+        gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_TOKEN, pad_token_id=0, eos_token_id=0,
+                          max_length=self.max_symbols_per_phrase+mel_emb.shape[1], temperature=temperature, num_beams=num_beams, use_cache=True)
         return gen[:, self.max_mel_frames:]
 
 
diff --git a/codes/scripts/audio/asr_eval.py b/codes/scripts/audio/asr_eval.py
index 96605a17..7aa9976c 100644
--- a/codes/scripts/audio/asr_eval.py
+++ b/codes/scripts/audio/asr_eval.py
@@ -44,7 +44,7 @@ if __name__ == "__main__":
     torch.backends.cudnn.benchmark = True
     want_metrics = False
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_hf.yml')
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_hf2.yml')
     opt = option.parse(parser.parse_args().opt, is_train=False)
     opt = option.dict_to_nonedict(opt)
     utils.util.loaded_options = opt