From 2b142405b81fd2b57b49b0d5a7682b6e46e1db36 Mon Sep 17 00:00:00 2001
From: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
Date: Wed, 7 Aug 2024 11:28:05 +0800
Subject: [PATCH] support timebre mixing

support timebre mixing
---
 GPT_SoVITS/module/models.py | 33 +++++++++++++++++++++------------
 1 file changed, 21 insertions(+), 12 deletions(-)

diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py
index ec7407e..6bfee08 100644
--- a/GPT_SoVITS/module/models.py
+++ b/GPT_SoVITS/module/models.py
@@ -1,8 +1,7 @@
-import warnings
-warnings.filterwarnings("ignore")
 import copy
 import math
 import os
+import pdb
 
 import torch
 from torch import nn
@@ -984,16 +983,26 @@ class SynthesizerTrn(nn.Module):
 
     @torch.no_grad()
     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)
-            refer_mask = torch.unsqueeze(
-                commons.sequence_mask(refer_lengths, refer.size(2)), 1
-            ).to(refer.dtype)
-            if (self.version == "v1"):
-                ge = self.ref_enc(refer * refer_mask, refer_mask)
-            else:
-                ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
+        def get_ge(refer):
+            ge = None
+            if refer is not None:
+                refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+                refer_mask = torch.unsqueeze(
+                    commons.sequence_mask(refer_lengths, refer.size(2)), 1
+                ).to(refer.dtype)
+                if (self.version == "v1"):
+                    ge = self.ref_enc(refer * refer_mask, refer_mask)
+                else:
+                    ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
+            return ge
+        if(type(refer)==list):
+            ges=[]
+            for _refer in refer:
+                ge=get_ge(_refer)
+                ges.append(ge)
+            ge=torch.stack(ges,0).mean(0)
+        else:
+            ge=get_ge(refer)
 
         y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)