diff --git a/codes/models/gpt_voice/gpt_tts_hf.py b/codes/models/gpt_voice/gpt_tts_hf.py
new file mode 100644
index 00000000..1e6f3611
--- /dev/null
+++ b/codes/models/gpt_voice/gpt_tts_hf.py
@@ -0,0 +1,116 @@
+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 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
+
+
+class GptTtsHf(nn.Module):
+    NUMBER_TEXT_TOKENS = len(symbols)+1
+    START_TEXT_TOKEN = len(symbols)
+    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=200, max_mel_tokens=250, max_conditioning_inputs=3, checkpointing=True):
+        super().__init__()
+        self.max_mel_tokens = max_mel_tokens
+        self.max_symbols_per_phrase = max_symbols_per_phrase
+
+        self.model_dim = model_dim
+        self.max_mel_tokens = max_mel_tokens
+        self.max_conditioning_inputs = max_conditioning_inputs
+        self.conditioning_encoder = AudioMiniEncoder(80, model_dim)
+        self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
+        self.conditioning_embedding = nn.Embedding(self.max_conditioning_inputs, model_dim)
+        self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 2, model_dim)
+        seq_length = 2+self.max_symbols_per_phrase+self.max_conditioning_inputs+self.max_mel_tokens
+        self.gpt_config = GPT2Config(vocab_size=self.NUMBER_MEL_CODES,
+                                        n_positions=seq_length,
+                                        n_ctx=seq_length,
+                                        n_embd=model_dim,
+                                        n_layer=layers,
+                                        n_head=heads,
+                                        gradient_checkpointing=checkpointing,
+                                        use_cache=not checkpointing)
+        self.gpt = GPT2Model(self.gpt_config)
+        self.final_norm = nn.LayerNorm(model_dim)
+        self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
+        self.mel_head = nn.Linear(model_dim, self.NUMBER_MEL_CODES)
+
+
+    def get_logits(self, text_inputs, cond_inputs, mel_targets, get_attns=False):
+        assert text_inputs.shape[1] <= self.max_symbols_per_phrase
+        assert cond_inputs.shape[1] <= self.max_conditioning_inputs
+        assert mel_targets.shape[1] <= self.max_mel_tokens
+
+        mel_targets = F.pad(mel_targets, (1,0), value=self.START_MEL_TOKEN)
+        mel_targets = F.pad(mel_targets, (0, self.max_mel_tokens - mel_targets.shape[1]), value=self.STOP_MEL_TOKEN)
+        mel_emb = self.gpt.get_input_embeddings()(mel_targets)
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_targets.device))
+
+        text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN)
+        text_targets = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_targets.shape[1]), value=self.STOP_TEXT_TOKEN)
+        text_emb = self.gpt.get_input_embeddings()(text_targets)
+        text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
+
+        conds = []
+        for k in range(cond_inputs.shape[1]):
+            conds.append(self.conditioning_encoder(cond_inputs[:, k]))
+        while len(conds) < self.max_conditioning_inputs:
+            conds.append(conds[-1])
+        conds = torch.stack(conds, dim=1)
+        conds = conds + self.conditioning_embedding(torch.arange(conds.shape[1], device=conds.device))
+
+        emb = torch.cat([mel_emb, conds, text_emb], dim=1)
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        if get_attns:
+            return gpt_out.attentions
+        enc = gpt_out.last_hidden_state
+
+        text_logits = self.final_norm(enc[:, :self.max_symbols_per_phrase])
+        text_logits = self.text_head(text_logits)
+        text_logits = text_logits.permute(0,2,1)
+        mel_logits = self.final_norm(enc[:, -self.max_mel_tokens:])
+        mel_logits = self.mel_head(mel_logits)
+        mel_logits = mel_logits.permute(0,2,1)
+
+        return text_logits, mel_logits
+
+    def forward(self, text_inputs, cond_inputs, mel_targets, return_attentions=False):
+        """
+        Forward pass
+        text_inputs: long tensor, (b,t)
+        cond_inputs: MEL float tensor, (b,c,80,s)
+        mel_targets: long tensor, (b,m)
+        """
+        text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_targets, get_attns=return_attentions)
+        if return_attentions:
+            return mel_logits
+
+        text_targets = F.pad(text_inputs, (0,self.max_symbols_per_phrase-text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN)
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        mel_targets = F.pad(mel_targets, (0,self.max_mel_tokens-mel_targets.shape[1]), value=self.STOP_MEL_TOKEN)
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        return loss_text.mean(), loss_mel.mean(), mel_logits
+
+
+@register_model
+def register_gpt_tts_hf(opt_net, opt):
+    return GptTtsHf(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    gpt = GptTtsHf()
+    l = gpt(torch.randint(high=len(symbols), size=(2,100)),
+            torch.randn(2,2,80,800),
+            torch.randint(high=8192, size=(2,200)))