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@ -1,6 +1,6 @@
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import sys,os,torch
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sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
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sv_path = "GPT_SoVITS\pretrained_models\sv\pretrained_eres2netv2w24s4ep4.ckpt"
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sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
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from ERes2NetV2 import ERes2NetV2
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import kaldi as Kaldi
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class SV:
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@ -16,9 +16,9 @@ class SV:
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self.embedding_model=self.embedding_model.half().to(device)
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self.is_half=is_half
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def compute_embedding3(self,wav):#(1,x)#-1~1
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def compute_embedding3(self,wav):
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with torch.no_grad():
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if self.is_half==True:wav=wav.half()
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feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
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sv_emb = self.embedding_model.forward3(feat)
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return sv_emb
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return sv_emb
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