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GPT_SoVITS/module/models.py

@@ -223,7 +223,7 @@ class TextEncoder(nn.Module):
 
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
-    def forward(self, y, y_lengths, text, text_lengths, ge, test=None):
+    def forward(self, y, y_lengths, text, text_lengths, ge, speed=1,test=None):
         y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
             y.dtype
         )
@@ -240,9 +240,10 @@ class TextEncoder(nn.Module):
         text = self.text_embedding(text).transpose(1, 2)
         text = self.encoder_text(text * text_mask, text_mask)
         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)
         return y, m, logs, y_mask
@@ -966,7 +967,7 @@ class SynthesizerTrn(nn.Module):
         return o, y_mask, (z, z_p, m_p, logs_p)
 
     @torch.no_grad()
-    def decode(self, codes, text, refer, noise_scale=0.5):
+    def decode(self, codes, text, refer, noise_scale=0.5,speed=1):
         ge = None
         if refer is not None:
             refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
@@ -983,9 +984,8 @@ class SynthesizerTrn(nn.Module):
             quantized = F.interpolate(
                 quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
             )
-
         x, m_p, logs_p, y_mask = self.enc_p(
-            quantized, y_lengths, text, text_lengths, ge
+            quantized, y_lengths, text, text_lengths, ge,speed
         )
         z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale