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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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46
tools/audio_sr.py
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@@ -1,50 +1,44 @@
from __future__ import absolute_import, division, print_function, unicode_literals
import sys,os
import traceback
AP_BWE_main_dir_path=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'AP_BWE_main')
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 glob
import argparse
import json
from re import S
import torch
import numpy as np
import torchaudio
import time
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
import soundfile as sf
import matplotlib.pyplot as plt
from rich.progress import track
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:
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()
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'])
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
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):
def __call__(self, audio, orig_sampling_rate):
with torch.no_grad():
# audio, orig_sampling_rate = torchaudio.load(inp_path)
# audio = audio.to(self.device)
@@ -53,4 +47,4 @@ class AP_BWE():
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
return audio_hr_g.squeeze().cpu().numpy(), self.h.hr_sampling_rate