diff --git a/GPT_SoVITS/onnx_export.py b/GPT_SoVITS/onnx_export.py
index f08679f..b82e987 100644
--- a/GPT_SoVITS/onnx_export.py
+++ b/GPT_SoVITS/onnx_export.py
@@ -140,6 +140,7 @@ class T2SModel(nn.Module):
             )
             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),
@@ -147,16 +148,16 @@ class T2SModel(nn.Module):
             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],
+                "ref_seq": {1 : "ref_length"},
+                "text_seq": {1 : "text_length"},
+                "ref_bert": {0 : "ref_length"},
+                "text_bert": {0 : "text_length"},
+                "ssl_content": {2 : "ssl_length"},
             },
             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),
@@ -164,10 +165,10 @@ class T2SModel(nn.Module):
             input_names=["x", "prompts"],
             output_names=["y", "k", "v", "y_emb", "x_example"],
             dynamic_axes={
-                "x": [1],
-                "prompts": [1],
+                "x": {1 : "x_length"},
+                "prompts": {1 : "prompts_length"},
             },
-            verbose=True,
+            verbose=False,
             opset_version=16
         )
         y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
@@ -179,13 +180,13 @@ class T2SModel(nn.Module):
             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],
+                "iy": {1 : "iy_length"},
+                "ik": {1 : "ik_length"},
+                "iv": {1 : "iv_length"},
+                "iy_emb": {1 : "iy_emb_length"},
+                "ix_example": {1 : "ix_example_length"},
             },
-            verbose=True,
+            verbose=False,
             opset_version=16
         )
 
@@ -224,9 +225,19 @@ class GptSoVits(nn.Module):
         self.vits = vits
         self.t2s = t2s
     
-    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content):
+    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ref_audio, ssl_content, debug=False):
         pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
-        return self.vits(text_seq, pred_semantic, ref_audio)
+        audio = self.vits(text_seq, pred_semantic, ref_audio)
+        if debug:
+            import onnxruntime
+            sess = onnxruntime.InferenceSession("onnx/koharu/koharu_vits.onnx", providers=["CPU"])
+            audio1 = sess.run(None, {
+                "text_seq" : text_seq.detach().cpu().numpy(),
+                "pred_semantic" : pred_semantic.detach().cpu().numpy(), 
+                "ref_audio" : ref_audio.detach().cpu().numpy()
+            })
+            return audio, audio1
+        return 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)
@@ -238,11 +249,12 @@ class GptSoVits(nn.Module):
             input_names=["text_seq", "pred_semantic", "ref_audio"],
             output_names=["audio"],
             dynamic_axes={
-                "text_seq": [1],
-                "pred_semantic": [2],
-                "ref_audio": [1],
+                "text_seq": {1 : "text_length"},
+                "pred_semantic": {2 : "pred_length"},
+                "ref_audio": {1 : "audio_length"},
             },
-            opset_version=17
+            opset_version=17,
+            verbose=False
         )
 
 
@@ -261,7 +273,7 @@ def export(vits_path, gpt_path, project_name):
     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"])])
+    text_seq = torch.LongTensor([cleaned_text_to_sequence(["w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "w", "o3", "sh", "i4", "b", "ai2", "y", "e4", "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()
@@ -275,10 +287,18 @@ def export(vits_path, gpt_path, project_name):
         pass
 
     ssl_content = ssl(ref_audio_16k).float()
+    
+    debug = False
 
+    if debug:
+        a, b = gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ref_audio_sr, ssl_content, debug=debug)
+        soundfile.write("out1.wav", a.cpu().detach().numpy(), vits.hps.data.sampling_rate)
+        soundfile.write("out2.wav", b[0], vits.hps.data.sampling_rate)
+        return
+    
     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)
+    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)
 
@@ -306,9 +326,9 @@ if __name__ == "__main__":
     except:
         pass
 
-    gpt_path = "pt_model/koharu-e20.ckpt"
-    vits_path = "pt_model/koharu_e20_s4960.pth"
-    exp_path = "koharu"
+    gpt_path = "GPT_weights/nahida-e25.ckpt"
+    vits_path = "SoVITS_weights/nahida_e30_s3930.pth"
+    exp_path = "nahida"
     export(vits_path, gpt_path, exp_path)
 
     # soundfile.write("out.wav", a, vits.hps.data.sampling_rate)
\ No newline at end of file