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https://github.com/RVC-Boss/GPT-SoVITS.git
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48 lines
2.2 KiB
Python
48 lines
2.2 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import sys
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import os
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AP_BWE_main_dir_path=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'AP_BWE_main')
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sys.path.append(AP_BWE_main_dir_path)
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import json
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import torch
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import torchaudio.functional as aF
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# from attrdict import AttrDict####will be bug in py3.10
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from datasets1.dataset import amp_pha_stft, amp_pha_istft
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from models.model import APNet_BWE_Model
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class AP_BWE():
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def __init__(self,device,DictToAttrRecursive,checkpoint_file=None):
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if checkpoint_file==None:
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checkpoint_file="%s/24kto48k/g_24kto48k.zip"%(AP_BWE_main_dir_path)
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if os.path.exists(checkpoint_file)==False:
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raise FileNotFoundError
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config_file = os.path.join(os.path.split(checkpoint_file)[0], 'config.json')
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with open(config_file) as f:data = f.read()
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json_config = json.loads(data)
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# h = AttrDict(json_config)
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h = DictToAttrRecursive(json_config)
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model = APNet_BWE_Model(h).to(device)
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state_dict = torch.load(checkpoint_file,map_location="cpu",weights_only=False)
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model.load_state_dict(state_dict['generator'])
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model.eval()
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self.device=device
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self.model=model
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self.h=h
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def to(self, *arg, **kwargs):
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self.model.to(*arg, **kwargs)
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self.device = self.model.conv_pre_mag.weight.device
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return self
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def __call__(self, audio,orig_sampling_rate):
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with torch.no_grad():
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# audio, orig_sampling_rate = torchaudio.load(inp_path)
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# audio = audio.to(self.device)
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audio = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.h.hr_sampling_rate)
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amp_nb, pha_nb, com_nb = amp_pha_stft(audio, self.h.n_fft, self.h.hop_size, self.h.win_size)
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amp_wb_g, pha_wb_g, com_wb_g = self.model(amp_nb, pha_nb)
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audio_hr_g = amp_pha_istft(amp_wb_g, pha_wb_g, self.h.n_fft, self.h.hop_size, self.h.win_size)
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# sf.write(opt_path, audio_hr_g.squeeze().cpu().numpy(), self.h.hr_sampling_rate, 'PCM_16')
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return audio_hr_g.squeeze().cpu().numpy(),self.h.hr_sampling_rate
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