support sovits v2Pro v2ProPlus

support sovits v2Pro v2ProPlus

GPT_SoVITS/sv.py

@@ -0,0 +1,24 @@
+import sys,os,torch
+sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
+sv_path = "GPT_SoVITS\pretrained_models\sv\pretrained_eres2netv2w24s4ep4.ckpt"
+from ERes2NetV2 import ERes2NetV2
+import kaldi as Kaldi
+class SV:
+    def __init__(self,device,is_half):
+        pretrained_state = torch.load(sv_path, map_location='cpu', weights_only=False)
+        embedding_model = ERes2NetV2(baseWidth=24,scale=4,expansion=4)
+        embedding_model.load_state_dict(pretrained_state)
+        embedding_model.eval()
+        self.embedding_model=embedding_model
+        if is_half == False:
+            self.embedding_model=self.embedding_model.to(device)
+        else:
+            self.embedding_model=self.embedding_model.half().to(device)
+        self.is_half=is_half
+
+    def compute_embedding3(self,wav):#(1,x)#-1~1
+        with torch.no_grad():
+            if self.is_half==True:wav=wav.half()
+            feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
+            sv_emb = self.embedding_model.forward3(feat)
+        return sv_emb