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* Update Install.sh * Format Code * Delete dev null * Update README, Support Dark Mode in CSS/JS
33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
import sys
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import os
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import 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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from ERes2NetV2 import ERes2NetV2
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import kaldi as Kaldi
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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", weights_only=False)
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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 == 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_embedding3(self, wav):
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with torch.no_grad():
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if self.is_half == 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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