diff --git a/GPT_SoVITS/module/models_onnx.py b/GPT_SoVITS/module/models_onnx.py
index c5d96d0c..abe2a3c6 100644
--- a/GPT_SoVITS/module/models_onnx.py
+++ b/GPT_SoVITS/module/models_onnx.py
@@ -231,7 +231,7 @@ class TextEncoder(nn.Module):
 
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
-    def forward(self, y, text, ge):
+    def forward(self, y, text, ge, speed=1):
         y_mask = torch.ones_like(y[:1,:1,:])
 
         y = self.ssl_proj(y * y_mask) * y_mask
@@ -244,6 +244,9 @@ 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")
 
         stats = self.proj(y) * y_mask
         m, logs = torch.split(stats, self.out_channels, dim=1)
@@ -887,7 +890,7 @@ class SynthesizerTrn(nn.Module):
             # self.enc_p.encoder_text.requires_grad_(False)
             # self.enc_p.mrte.requires_grad_(False)
 
-    def forward(self, codes, text, refer):
+    def forward(self, codes, text, refer,noise_scale=0.5, speed=1):
         refer_mask = torch.ones_like(refer[:1,:1,:])
         if (self.version == "v1"):
             ge = self.ref_enc(refer * refer_mask, refer_mask)
@@ -900,10 +903,10 @@ class SynthesizerTrn(nn.Module):
             quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
 
         x, m_p, logs_p, y_mask = self.enc_p(
-            quantized, text, ge
+            quantized, text, ge, speed
         )
         
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p)
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
 
         z = self.flow(z_p, y_mask, g=ge, reverse=True)