| | from __future__ import absolute_import, division, print_function, unicode_literals
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| | import sys
|
| | import os
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| |
|
| | AP_BWE_main_dir_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "AP_BWE_main")
|
| | sys.path.append(AP_BWE_main_dir_path)
|
| | import json
|
| | import torch
|
| | import torchaudio.functional as aF
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| |
|
| |
|
| | from datasets1.dataset import amp_pha_stft, amp_pha_istft
|
| | 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):
|
| | if checkpoint_file == None:
|
| | checkpoint_file = "%s/24kto48k/g_24kto48k.zip" % (AP_BWE_main_dir_path)
|
| | if os.path.exists(checkpoint_file) == False:
|
| | raise FileNotFoundError
|
| | config_file = os.path.join(os.path.split(checkpoint_file)[0], "config.json")
|
| | with open(config_file) as f:
|
| | data = f.read()
|
| | json_config = json.loads(data)
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| |
|
| | h = DictToAttrRecursive(json_config)
|
| | model = APNet_BWE_Model(h).to(device)
|
| | state_dict = torch.load(checkpoint_file, map_location="cpu", weights_only=False)
|
| | model.load_state_dict(state_dict["generator"])
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| | model.eval()
|
| | self.device = device
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| | self.model = model
|
| | self.h = h
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| |
|
| | def to(self, *arg, **kwargs):
|
| | self.model.to(*arg, **kwargs)
|
| | self.device = self.model.conv_pre_mag.weight.device
|
| | return self
|
| |
|
| | def __call__(self, audio, orig_sampling_rate):
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| | with torch.no_grad():
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| |
|
| |
|
| | audio = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.h.hr_sampling_rate)
|
| | amp_nb, pha_nb, com_nb = amp_pha_stft(audio, self.h.n_fft, self.h.hop_size, self.h.win_size)
|
| | 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)
|
| |
|
| | return audio_hr_g.squeeze().cpu().numpy(), self.h.hr_sampling_rate
|
| |
|
