diff --git a/GPT_SoVITS/module/models_onnx.py b/GPT_SoVITS/module/models_onnx.py
index b62b8b71..9ee78d60 100644
--- a/GPT_SoVITS/module/models_onnx.py
+++ b/GPT_SoVITS/module/models_onnx.py
@@ -205,6 +205,8 @@ class TextEncoder(nn.Module):
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
     def forward(self, y, text, ge, speed=1):
+        if type(speed) == float:
+            speed = torch.FloatTensor([speed])
         y_mask = torch.ones_like(y[:1, :1, :])
 
         y = self.ssl_proj(y * y_mask) * y_mask
@@ -217,9 +219,8 @@ class TextEncoder(nn.Module):
         y = self.mrte(y, y_mask, text, text_mask, ge)
 
         y = self.encoder2(y * y_mask, y_mask)
-        if speed != 1:
-            y = F.interpolate(y, size=int(y.shape[-1] / speed) + 1, mode="linear")
-            y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
+        y = F.interpolate(y, size=(y.shape[-1] / speed).to(torch.int) + 1, mode="linear")
+        y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
 
         stats = self.proj(y) * y_mask
         m, logs = torch.split(stats, self.out_channels, dim=1)
diff --git a/GPT_SoVITS/onnx_export_v1v2.py b/GPT_SoVITS/onnx_export_v1v2.py
index d2d444e1..45f4679b 100644
--- a/GPT_SoVITS/onnx_export_v1v2.py
+++ b/GPT_SoVITS/onnx_export_v1v2.py
@@ -233,7 +233,7 @@ class VitsModel(nn.Module):
 
         self.hps = DictToAttrRecursive(self.hps)
         self.hps.model.semantic_frame_rate = "25hz"
-        self.vq_model = SynthesizerTrn(
+        self.vq_model:SynthesizerTrn = 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,
@@ -243,32 +243,27 @@ class VitsModel(nn.Module):
         self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
         # print(f"filter_length:{self.hps.data.filter_length} sampling_rate:{self.hps.data.sampling_rate} hop_length:{self.hps.data.hop_length} win_length:{self.hps.data.win_length}")
         #v2 filter_length: 2048 sampling_rate: 32000 hop_length: 640 win_length: 2048
-    def forward(self, text_seq, pred_semantic, spectrum, sv_emb):
+    def forward(self, text_seq, pred_semantic, spectrum, sv_emb, speed):
         if self.is_v2p:
-            return self.vq_model(pred_semantic, text_seq, spectrum, sv_emb=sv_emb)[0, 0]
+            return self.vq_model(pred_semantic, text_seq, spectrum, sv_emb=sv_emb, speed=speed)[0, 0]
         else:
-            return self.vq_model(pred_semantic, text_seq, spectrum)[0, 0]
+            return self.vq_model(pred_semantic, text_seq, spectrum, speed=speed)[0, 0]
 
 
-class GptSoVits(nn.Module):
+class GptSoVits():
     def __init__(self, vits, t2s):
         super().__init__()
         self.vits = vits
         self.t2s = t2s
 
-    def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
-        pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
-        audio = self.vits(text_seq, pred_semantic, spectrum, sv_emb)
-        return audio
-
-    def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, project_name, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
+    def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, spectrum, sv_emb, speed, project_name, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
         self.t2s.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
         pred_semantic = self.t2s(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
         torch.onnx.export(
             self.vits,
-            (text_seq, pred_semantic, spectrum, sv_emb),
+            (text_seq, pred_semantic, spectrum, sv_emb, speed),
             f"onnx/{project_name}/{project_name}_vits.onnx",
-            input_names=["input_text_phones", "pred_semantic", "spectrum", "sv_emb"],
+            input_names=["input_text_phones", "pred_semantic", "spectrum", "sv_emb", "speed"],
             output_names=["audio"],
             dynamic_axes={
                 "input_text_phones": {1: "text_length"},
@@ -379,13 +374,12 @@ def export(vits_path, gpt_path, project_name, voice_model_version, export_audio_
     top_p = torch.FloatTensor([1.0])
     repetition_penalty = torch.FloatTensor([1.0])
     temperature = torch.FloatTensor([1.0])
+    speed = torch.FloatTensor([1.0])
 
     os.makedirs(f"onnx/{project_name}", exist_ok=True)
 
     [ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
-    gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
-    # exit()
-    gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
+    gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), speed, project_name, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
 
     if export_audio_preprocessor:
         torch.onnx.export(preprocessor, (ref_audio32k,), f"onnx/{project_name}/{project_name}_audio_preprocess.onnx",