diff --git a/GPT_SoVITS/onnx_export.py b/GPT_SoVITS/onnx_export.py
new file mode 100644
index 0000000..f08679f
--- /dev/null
+++ b/GPT_SoVITS/onnx_export.py
@@ -0,0 +1,314 @@
+from module.models_onnx import SynthesizerTrn, symbols
+from AR.models.t2s_lightning_module_onnx import Text2SemanticLightningModule
+import torch
+import torchaudio
+from torch import nn
+from feature_extractor import cnhubert
+cnhubert_base_path = "pretrained_models/chinese-hubert-base"
+cnhubert.cnhubert_base_path=cnhubert_base_path
+ssl_model = cnhubert.get_model()
+from text import cleaned_text_to_sequence
+import soundfile
+from my_utils import load_audio
+import os
+import json
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    hann_window = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+
+class T2SEncoder(nn.Module):
+    def __init__(self, t2s, vits):
+        super().__init__()
+        self.encoder = t2s.onnx_encoder
+        self.vits = vits
+    
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
+        codes = self.vits.extract_latent(ssl_content)
+        prompt_semantic = codes[0, 0]
+        bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1)
+        all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
+        bert = bert.unsqueeze(0)
+        prompt = prompt_semantic.unsqueeze(0)
+        return self.encoder(all_phoneme_ids, bert), prompt
+
+
+class T2SModel(nn.Module):
+    def __init__(self, t2s_path, vits_model):
+        super().__init__()
+        dict_s1 = torch.load(t2s_path, map_location="cpu")
+        self.config = dict_s1["config"]
+        self.t2s_model = Text2SemanticLightningModule(self.config, "ojbk", is_train=False)
+        self.t2s_model.load_state_dict(dict_s1["weight"])
+        self.t2s_model.eval()
+        self.vits_model = vits_model.vq_model
+        self.hz = 50
+        self.max_sec = self.config["data"]["max_sec"]
+        self.t2s_model.model.top_k = torch.LongTensor([self.config["inference"]["top_k"]])
+        self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
+        self.t2s_model = self.t2s_model.model
+        self.t2s_model.init_onnx()
+        self.onnx_encoder = T2SEncoder(self.t2s_model, self.vits_model)
+        self.first_stage_decoder = self.t2s_model.first_stage_decoder
+        self.stage_decoder = self.t2s_model.stage_decoder
+        #self.t2s_model = torch.jit.script(self.t2s_model)
+
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
+        early_stop_num = self.t2s_model.early_stop_num
+
+        #[1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
+        x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+
+        prefix_len = prompts.shape[1]
+
+        #[1,N,512] [1,N]
+        y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+
+        stop = False
+        for idx in range(1, 1500):
+            #[1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
+            enco = self.stage_decoder(y, k, v, y_emb, x_example)
+            y, k, v, y_emb, logits, samples = enco
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                stop = True
+            if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
+                stop = True
+            if stop:
+                break
+        y[0, -1] = 0
+
+        return y[:, -idx:].unsqueeze(0)
+
+    def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, dynamo=False):
+        #self.onnx_encoder = torch.jit.script(self.onnx_encoder)
+        if dynamo:
+            export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
+            onnx_encoder_export_output = torch.onnx.dynamo_export(
+                self.onnx_encoder,
+                (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
+                export_options=export_options
+            )
+            onnx_encoder_export_output.save(f"onnx/{project_name}/{project_name}_t2s_encoder.onnx")
+            return
+        torch.onnx.export(
+            self.onnx_encoder,
+            (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
+            f"onnx/{project_name}/{project_name}_t2s_encoder.onnx",
+            input_names=["ref_seq", "text_seq", "ref_bert", "text_bert", "ssl_content"],
+            output_names=["x", "prompts"],
+            dynamic_axes={
+                "ref_seq": [1],
+                "text_seq": [1],
+                "ref_bert": [0],
+                "text_bert": [0],
+                "ssl_content": [2],
+            },
+            opset_version=16
+        )
+        x, prompts = self.onnx_encoder(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+        torch.exp
+        torch.onnx.export(
+            self.first_stage_decoder,
+            (x, prompts),
+            f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx",
+            input_names=["x", "prompts"],
+            output_names=["y", "k", "v", "y_emb", "x_example"],
+            dynamic_axes={
+                "x": [1],
+                "prompts": [1],
+            },
+            verbose=True,
+            opset_version=16
+        )
+        y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
+
+        torch.onnx.export(
+            self.stage_decoder,
+            (y, k, v, y_emb, x_example),
+            f"onnx/{project_name}/{project_name}_t2s_sdec.onnx",
+            input_names=["iy", "ik", "iv", "iy_emb", "ix_example"],
+            output_names=["y", "k", "v", "y_emb", "logits", "samples"],
+            dynamic_axes={
+                "iy": [1],
+                "ik": [1],
+                "iv": [1],
+                "iy_emb": [1],
+                "ix_example": [1],
+            },
+            verbose=True,
+            opset_version=16
+        )
+
+
+class VitsModel(nn.Module):
+    def __init__(self, vits_path):
+        super().__init__()
+        dict_s2 = torch.load(vits_path,map_location="cpu")
+        self.hps = dict_s2["config"]
+        self.hps = DictToAttrRecursive(self.hps)
+        self.hps.model.semantic_frame_rate = "25hz"
+        self.vq_model = SynthesizerTrn(
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model
+        )
+        self.vq_model.eval()
+        self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
+        
+    def forward(self, text_seq, pred_semantic, ref_audio):
+        refer = spectrogram_torch(
+            ref_audio,
+            self.hps.data.filter_length,
+            self.hps.data.sampling_rate,
+            self.hps.data.hop_length,
+            self.hps.data.win_length,
+            center=False
+        )
+        return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
+
+
+class GptSoVits(nn.Module):
+    def __init__(self, vits, t2s):
+        super().__init__()
+        self.vits = vits
+        self.t2s = t2s
+    
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content):
+        pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+        return self.vits(text_seq, pred_semantic, ref_audio)
+
+    def export(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, project_name):
+        self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name)
+        pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
+        torch.onnx.export(
+            self.vits,
+            (text_seq, pred_semantic, ref_audio),
+            f"onnx/{project_name}/{project_name}_vits.onnx",
+            input_names=["text_seq", "pred_semantic", "ref_audio"],
+            output_names=["audio"],
+            dynamic_axes={
+                "text_seq": [1],
+                "pred_semantic": [2],
+                "ref_audio": [1],
+            },
+            opset_version=17
+        )
+
+
+class SSLModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.ssl = ssl_model
+
+    def forward(self, ref_audio_16k):
+        return self.ssl.model(ref_audio_16k)["last_hidden_state"].transpose(1, 2)
+
+
+def export(vits_path, gpt_path, project_name):
+    vits = VitsModel(vits_path)
+    gpt = T2SModel(gpt_path, vits)
+    gpt_sovits = GptSoVits(vits, gpt)
+    ssl = SSLModel()
+    ref_seq = torch.LongTensor([cleaned_text_to_sequence(["n", "i2", "h", "ao3", ",", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
+    text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4"])])
+    ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
+    text_bert = torch.randn((text_seq.shape[1], 1024)).float()
+    ref_audio = torch.randn((1, 48000 * 5)).float()
+    # ref_audio = torch.tensor([load_audio("rec.wav", 48000)]).float()
+    ref_audio_16k = torchaudio.functional.resample(ref_audio,48000,16000).float()
+    ref_audio_sr = torchaudio.functional.resample(ref_audio,48000,vits.hps.data.sampling_rate).float()
+
+    try:
+        os.mkdir(f"onnx/{project_name}")
+    except:
+        pass
+
+    ssl_content = ssl(ref_audio_16k).float()
+
+    a = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content).detach().cpu().numpy()
+
+    # soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
+
+    gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, project_name)
+
+    MoeVSConf = {
+            "Folder" : f"{project_name}",
+            "Name" : f"{project_name}",
+            "Type" : "GPT-SoVits",
+            "Rate" : vits.hps.data.sampling_rate,
+            "NumLayers": gpt.t2s_model.num_layers,
+            "EmbeddingDim": gpt.t2s_model.embedding_dim,
+            "Dict": "BasicDict",
+            "BertPath": "chinese-roberta-wwm-ext-large",
+            "Symbol": symbols,
+            "AddBlank": False
+        }
+    
+    MoeVSConfJson = json.dumps(MoeVSConf)
+    with open(f"onnx/{project_name}.json", 'w') as MoeVsConfFile:
+        json.dump(MoeVSConf, MoeVsConfFile, indent = 4)
+
+
+if __name__ == "__main__":
+    try:
+        os.mkdir("onnx")
+    except:
+        pass
+
+    gpt_path = "pt_model/koharu-e20.ckpt"
+    vits_path = "pt_model/koharu_e20_s4960.pth"
+    exp_path = "koharu"
+    export(vits_path, gpt_path, exp_path)
+
+    # soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
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