diff --git a/codes/models/gpt_voice/unified_voice.py b/codes/models/gpt_voice/unified_voice.py
index ec447a3a..20254c8e 100644
--- a/codes/models/gpt_voice/unified_voice.py
+++ b/codes/models/gpt_voice/unified_voice.py
@@ -41,7 +41,7 @@ class UnifiedGptVoice(nn.Module):
     - Voice conditioned on text
     """
 
-    def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=120, 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_total_tokens=370, max_conditioning_inputs=3,
                  checkpointing=True, mel_length_compression=1024, max_conditioning_length=60, number_text_tokens=256,
                  start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
                  stop_mel_token=8193):
@@ -56,12 +56,15 @@ class UnifiedGptVoice(nn.Module):
 
         self.max_mel_tokens = max_mel_tokens
         self.max_symbols_per_phrase = max_symbols_per_phrase
+        self.max_total_tokens = max_total_tokens
         self.model_dim = model_dim
         self.max_conditioning_inputs = max_conditioning_inputs
         self.mel_length_compression = mel_length_compression
         self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
         self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
-        seq_length = 2+self.max_symbols_per_phrase+self.max_conditioning_inputs+self.max_mel_tokens
+        self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
+        self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 1, model_dim)
+        seq_length = 2+self.max_total_tokens+self.max_conditioning_inputs
         self.gpt_config = GPT2Config(vocab_size=self.number_mel_codes,
                                      n_positions=seq_length,
                                      n_ctx=seq_length,
@@ -145,9 +148,10 @@ class UnifiedGptVoice(nn.Module):
         speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
 
         text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
-        text_emb = self.text_embedding(text_inputs)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
         mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.start_mel_token, self.stop_mel_token)
         mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
         if text_first:
             text_logits, mel_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
         else:
@@ -168,7 +172,7 @@ class UnifiedGptVoice(nn.Module):
         speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
 
         text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
-        text_emb = self.text_embedding(text_inputs)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
         text_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head)
         loss_text = F.cross_entropy(text_logits, text_targets.long())
         return loss_text.mean()
@@ -183,17 +187,18 @@ class UnifiedGptVoice(nn.Module):
 
         mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.start_mel_token, self.stop_mel_token)
         mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
         mel_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head)
         loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
         return loss_mel.mean()
 
     def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
         if not hasattr(self, 'inference_model'):
-            self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, None, self.final_norm, self.mel_head)
+            self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
 
         text_inputs = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_inputs.shape[1]), value=self.stop_text_token)
         text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
-        text_emb = self.text_embedding(text_inputs)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
 
         # Randomly permute the conditioning spectrogram, to destroy any structure present.
         speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)