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from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import os
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
# from attrdict import AttrDict####will be bug in py3.10
from datasets1.dataset import amp_pha_stft, amp_pha_istft
from models.model import APNet_BWE_Model
class AP_BWE:
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)
# h = AttrDict(json_config)
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"])
model.eval()
self.device = device
self.model = model
self.h = h
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):
with torch.no_grad():
# audio, orig_sampling_rate = torchaudio.load(inp_path)
# audio = audio.to(self.device)
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)
audio_hr_g = amp_pha_istft(amp_wb_g, pha_wb_g, self.h.n_fft, self.h.hop_size, self.h.win_size)
# sf.write(opt_path, audio_hr_g.squeeze().cpu().numpy(), self.h.hr_sampling_rate, 'PCM_16')
return audio_hr_g.squeeze().cpu().numpy(), self.h.hr_sampling_rate
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