GPT-SoVITS/GPT_SoVITS_Inference.ipynb

Credits for bubarino giving me the huggingface import code (感谢 bubarino 给了我 huggingface 导入代码)

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!git clone https://github.com/RVC-Boss/GPT-SoVITS.git
%cd GPT-SoVITS
!apt-get update && apt-get install -y --no-install-recommends tzdata ffmpeg libsox-dev parallel aria2 git git-lfs && git lfs install
!pip install -r extra-req.txt --no-deps
!pip install -r requirements.txt

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# @title Download pretrained models 下载预训练模型
!mkdir -p /content/GPT-SoVITS/GPT_SoVITS/pretrained_models
!mkdir -p /content/GPT-SoVITS/tools/damo_asr/models
!mkdir -p /content/GPT-SoVITS/tools/uvr5
%cd /content/GPT-SoVITS/GPT_SoVITS/pretrained_models
!git clone https://huggingface.co/lj1995/GPT-SoVITS
%cd /content/GPT-SoVITS/tools/damo_asr/models
!git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git
!git clone https://www.modelscope.cn/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch.git
!git clone https://www.modelscope.cn/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git
# @title UVR5 pretrains 安装uvr5模型
%cd /content/GPT-SoVITS/tools/uvr5
!git clone https://huggingface.co/Delik/uvr5_weights
!git config core.sparseCheckout true
!mv /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/GPT-SoVITS/* /content/GPT-SoVITS/GPT_SoVITS/pretrained_models/

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#@title Create folder models 创建文件夹模型
import os
base_directory = "/content/GPT-SoVITS"
folder_names = ["SoVITS_weights", "GPT_weights"]

for folder_name in folder_names:
  if os.path.exists(os.path.join(base_directory, folder_name)):
    print(f"The folder '{folder_name}' already exists. (文件夹'{folder_name}'已经存在。)")
  else:
    os.makedirs(os.path.join(base_directory, folder_name))
    print(f"The folder '{folder_name}' was created successfully! (文件夹'{folder_name}'已成功创建！)")

print("All folders have been created. (所有文件夹均已创建。)")

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import requests
import zipfile
import shutil
import os

#@title Import model 导入模型 (HuggingFace)
hf_link = 'https://huggingface.co/modelloosrvcc/Nagisa_Shingetsu_GPT-SoVITS/resolve/main/Nagisa.zip' #@param {type: "string"}

output_path = '/content/'

response = requests.get(hf_link)
with open(output_path + 'file.zip', 'wb') as file:
    file.write(response.content)

with zipfile.ZipFile(output_path + 'file.zip', 'r') as zip_ref:
    zip_ref.extractall(output_path)

os.remove(output_path + "file.zip")

source_directory = output_path
SoVITS_destination_directory = '/content/GPT-SoVITS/SoVITS_weights'
GPT_destination_directory = '/content/GPT-SoVITS/GPT_weights'

for filename in os.listdir(source_directory):
    if filename.endswith(".pth"):
        source_path = os.path.join(source_directory, filename)
        destination_path = os.path.join(SoVITS_destination_directory, filename)
        shutil.move(source_path, destination_path)

for filename in os.listdir(source_directory):
    if filename.endswith(".ckpt"):
        source_path = os.path.join(source_directory, filename)
        destination_path = os.path.join(GPT_destination_directory, filename)
        shutil.move(source_path, destination_path)

print(f'Model downloaded. (模型已下载。)')

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# @title launch WebUI 启动WebUI
!/usr/local/bin/pip install ipykernel
!sed -i '10s/False/True/' /content/GPT-SoVITS/config.py
%cd /content/GPT-SoVITS/
!/usr/local/bin/python  webui.py