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30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
import torch
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from GPT_SoVITS.eres2net import kaldi
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from GPT_SoVITS.eres2net.ERes2NetV2 import ERes2NetV2
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sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
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class SV:
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def __init__(self, device, is_half):
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pretrained_state = torch.load(sv_path, map_location="cpu")
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embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
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embedding_model.load_state_dict(pretrained_state)
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embedding_model.eval()
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self.embedding_model = embedding_model
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if is_half is False:
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self.embedding_model = self.embedding_model.to(device)
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else:
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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_embedding(self, wav):
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if self.is_half is True:
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wav = wav.half()
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feat = torch.stack(
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[kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
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)
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sv_emb = self.embedding_model.forward3(feat)
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return sv_emb
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