diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py
index 338e88d..623da80 100644
--- a/GPT_SoVITS/module/models.py
+++ b/GPT_SoVITS/module/models.py
@@ -1251,7 +1251,7 @@ class SynthesizerTrnV3(nn.Module):
         return cfm_loss
 
     @torch.no_grad()
-    def decode_encp(self, codes,text, refer,ge=None):
+    def decode_encp(self, codes,text, refer,ge=None,speed=1):
         # print(2333333,refer.shape)
         # ge=None
         if(ge==None):
@@ -1259,13 +1259,17 @@ class SynthesizerTrnV3(nn.Module):
             refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
             ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
         y_lengths = torch.LongTensor([int(codes.size(2)*2)]).to(codes.device)
-        y_lengths1 = torch.LongTensor([int(codes.size(2)*2.5*1.5)]).to(codes.device)
+        if speed==1:
+            sizee=int(codes.size(2)*2.5*1.5)
+        else:
+            sizee=int(codes.size(2)*2.5*1.5/speed)+1
+        y_lengths1 = torch.LongTensor([sizee]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
 
         quantized = self.quantizer.decode(codes)
         if self.semantic_frame_rate == '25hz':
             quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
-        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge,speed)
         fea=self.bridge(x)
         fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
         ####more wn paramter to learn mel