diff --git a/codes/trainer/injectors/audio_injectors.py b/codes/trainer/injectors/audio_injectors.py
index 24a83bee..3120cb39 100644
--- a/codes/trainer/injectors/audio_injectors.py
+++ b/codes/trainer/injectors/audio_injectors.py
@@ -5,7 +5,7 @@ import torch.nn.functional as F
 import torchaudio
 
 from trainer.inject import Injector
-from utils.util import opt_get, load_model_from_config
+from utils.util import opt_get, load_model_from_config, pad_or_truncate
 
 TACOTRON_MEL_MAX = 2.3143386840820312
 TACOTRON_MEL_MIN = -11.512925148010254
@@ -173,17 +173,16 @@ class GptVoiceLatentInjector(Injector):
     def forward(self, state):
         with torch.no_grad():
             mel_inputs = self.to_mel(state[self.input])
-            mel_cond = self.to_mel(state[self.conditioning_key])
+            state_cond = pad_or_truncate(state[self.conditioning_key], 88000)
+            mel_conds = []
+            for k in range(state_cond.shape[1]):
+                mel_conds.append(self.to_mel(state_cond[:, k]))
+            mel_conds = torch.stack(mel_conds, dim=1)
 
-            # Use the input as a conditioning input as well. This is fine because we are not actually training the GPT network so it can't learn to cheat.
-            max_mel_len = max(mel_inputs.shape[-1], mel_cond.shape[-1])
-            mel_cond = F.pad(mel_cond, (0, max_mel_len-mel_cond.shape[-1]))
-            mel_cond2 = F.pad(mel_inputs, (0, max_mel_len-mel_inputs.shape[-1]))
-            mel_cond = torch.cat([mel_cond.unsqueeze(1), mel_cond2.unsqueeze(1)], dim=1)
             self.dvae = self.dvae.to(mel_inputs.device)
             codes = self.dvae.get_codebook_indices(mel_inputs)
             self.gpt = self.gpt.to(codes.device)
-            latents = self.gpt.forward(mel_cond, state[self.text_input_key],
+            latents = self.gpt.forward(mel_conds, state[self.text_input_key],
                                        state[self.text_lengths_key], codes, state[self.input_lengths_key],
                                        text_first=True, raw_mels=None, return_attentions=False, return_latent=True)
             return {self.output: latents}