mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
7394dc7b0c
* modified: GPT_SoVITS/TTS_infer_pack/TTS.py modified: GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py modified: GPT_SoVITS/inference_webui_fast.py * 适配V3版本 * api_v2.py和inference_webui_fast.py的v3适配 * 修改了个远古bug,增加了更友好的提示信息 * 优化webui * 修改为正确的path * 修复v3 lora模型的载入问题 * 修复读取tts_infer.yaml文件时遇到的编码不匹配的问题
57 lines
2.4 KiB
Python
57 lines
2.4 KiB
Python
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')
|
|
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:
|
|
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
|