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‎colab.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"https://github.com/PlayVoice/so-vits-svc-5.0/\n",
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"\n",
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"↑原仓库\n",
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"\n",
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"*《colab保持连接的方法》*https://zhuanlan.zhihu.com/p/144629818\n",
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"\n",
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"预览版本，可使用预设模型进行推理"
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],
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"metadata": {
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"id": "SggegFslkbbK"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# **环境配置&必要文件下载**\n"
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],
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"metadata": {
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"id": "M1MdDryJP73G"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "xfJWCr_EkO2i"
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},
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"outputs": [],
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"source": [
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"#@title 看看抽了个啥卡~~基本都是T4~~\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 克隆github仓库\n",
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"!git clone https://github.com/PlayVoice/so-vits-svc-5.0/ -b bigvgan"
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],
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"metadata": {
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"id": "nMspj8t3knR6"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 安装依赖&下载必要文件\n",
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"%cd /content/so-vits-svc-5.0\n",
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"\n",
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"!apt install ffmpeg\n",
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"!pip install -r requirements.txt\n",
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"!pip install --upgrade pip setuptools numpy numba\n",
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"!gdown --id \"1UPjQ2LVSIt3o-9QMKMJcdzT8aZRZCI-E\" --output /content/so-vits-svc-5.0/speaker_pretrain/best_model.pth.tar\n",
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"!wget -P whisper_pretrain/ https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt\n",
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"!wget -P chkpt/sovits5.0/ https://github.com/PlayVoice/so-vits-svc-5.0/releases/download/bigvgan_release/sovits5.0_bigvgan_555.pth"
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],
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"metadata": {
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"id": "Kj2j81K6kubj"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 加载Google云端硬盘\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"metadata": {
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"id": "v9zHS9VXly9b"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 包含多说话人的推理预览"
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],
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"metadata": {
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"id": "hZ5KH8NgQ7os"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取内容编码\n",
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"\n",
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"#@markdown **将处理好的\" .wav \"输入源文件上传到云盘根目录，并修改以下选项**\n",
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"\n",
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"#@markdown **\" .wav \"文件【文件名】**\n",
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"input = \"\\u30AE\\u30BF\\u30FC\\u3068\\u5B64\\u72EC\\u3068\\u84BC\\u3044\\u60D1\\u661F\" #@param {type:\"string\"}\n",
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"input_path = \"/content/drive/MyDrive/\"\n",
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"input_name = input_path + input\n",
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"!PYTHONPATH=. python whisper/inference.py -w {input_name}.wav -p test.ppg.npy"
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],
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"metadata": {
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"id": "2o6m3D0IsphU"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 推理\n",
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"\n",
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"#@markdown **将处理好的\" .wav \"输入源文件上传到云盘根目录，并修改以下选项**\n",
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"\n",
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"#@markdown **\" .wav \"文件【文件名】**\n",
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"input = \"\\u30AE\\u30BF\\u30FC\\u3068\\u5B64\\u72EC\\u3068\\u84BC\\u3044\\u60D1\\u661F\" #@param {type:\"string\"}\n",
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"input_path = \"/content/drive/MyDrive/\"\n",
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"input_name = input_path + input\n",
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"#@markdown **指定说话人（0001~0056）（推荐0022、0030、0047、0051）**\n",
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"speaker = \"0002\" #@param {type:\"string\"}\n",
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"!PYTHONPATH=. python svc_inference.py --config configs/base.yaml --model sovits5.0.pretrain.pth --spk ./configs/singers/singer{speaker}.npy --wave {input_name}.wav --ppg test.ppg.npy"
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],
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"metadata": {
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"id": "A7nvX5mRlwJ7"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"推理结果保存在根目录，文件名为svc_out.wav"
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],
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"metadata": {
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"id": "F8oerogXyd3u"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 训练"
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],
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"metadata": {
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"id": "qKX17GElPuso"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"将音频剪裁为小于30秒的音频段，响度匹配并修改为单声道，预处理时会进行重采样所以对采样率无要求。（但是降低采样率的操作会降低你的数据质量）\n",
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"\n",
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"**使用Adobe Audition™的响度匹配功能可以一次性完成重采样修改声道和响度匹配。**\n",
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"\n",
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"之后将音频文件保存为以下文件结构：\n",
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"```\n",
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"dataset_raw\n",
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"├───speaker0\n",
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"│ ├───xxx1-xxx1.wav\n",
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"│ ├───...\n",
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"│ └───Lxx-0xx8.wav\n",
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"└───speaker1\n",
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" ├───xx2-0xxx2.wav\n",
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" ├───...\n",
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" └───xxx7-xxx007.wav\n",
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"```\n",
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"\n",
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"打包为zip格式，命名为data.zip，上传到网盘根目录。"
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],
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"metadata": {
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"id": "sVe0lEGWQBLU"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 从云盘获取数据集\n",
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"!unzip -d /content/so-vits-svc-5.0/ /content/drive/MyDrive/data.zip #自行修改路径与文件名"
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],
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"metadata": {
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"id": "vC8IthV8VYgy"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 重采样\n",
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"# 生成采样率16000Hz音频, 存储路径为：./data_svc/waves-16k\n",
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"!python prepare/preprocess_a.py -w ./data_raw -o ./data_svc/waves-16k -s 16000\n",
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"# 生成采样率48000Hz音频, 存储路径为：./data_svc/waves-48k\n",
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"!python prepare/preprocess_a.py -w ./data_raw -o ./data_svc/waves-32k -s 32000"
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],
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"metadata": {
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"id": "J101PiFUSL1N"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取f0\n",
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"!python prepare/preprocess_f0.py -w data_svc/waves-16k/ -p data_svc/pitch"
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],
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"metadata": {
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"id": "ZpxeYJCBSbgf"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取内容特征\n",
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"!PYTHONPATH=. python prepare/preprocess_ppg.py -w data_svc/waves-16k/ -p data_svc/whisper"
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],
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"metadata": {
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"id": "7VasDGhDSlP5"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取音色特征\n",
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"!PYTHONPATH=. python prepare/preprocess_speaker.py data_svc/waves-16k/ data_svc/speaker"
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],
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"metadata": {
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"id": "ovRqQUINSoII"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#（解决“.ipynb_checkpoints”相关的错）\n",
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"!rm -rf \"find -type d -name .ipynb_checkpoints\""
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],
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"metadata": {
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"id": "s8Ba8Fd1bzzX"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#（解决“.ipynb_checkpoints”相关的错）\n",
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"!rm -rf .ipynb_checkpoints\n",
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"!find . -name \".ipynb_checkpoints\" -exec rm -rf {} \\;"
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],
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"metadata": {
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"id": "ic9q599_b0Ae"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取平均音色\n",
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"!PYTHONPATH=. python prepare/preprocess_speaker_ave.py data_svc/speaker/ data_svc/singer"
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],
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"metadata": {
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"id": "QamG3_B6o3vF"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 提取spec\n",
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"!PYTHONPATH=. python prepare/preprocess_spec.py -w data_svc/waves-32k/ -s data_svc/specs"
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],
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"metadata": {
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"id": "3wBmyQHvSs6K"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 生成索引\n",
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"!python prepare/preprocess_train.py"
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],
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"metadata": {
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"id": "tUcljCLbS5O3"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 训练文件调试\n",
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"!PYTHONPATH=. python prepare/preprocess_zzz.py"
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],
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"metadata": {
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"id": "30fXnscFS7Wo"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 设定模型备份\n",
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"#@markdown **是否备份模型到云盘，colab随时爆炸建议备份，默认保存到云盘根目录Sovits5.0文件夹**\n",
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"Save_to_drive = True #@param {type:\"boolean\"}\n",
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"if Save_to_drive:\n",
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" !mkdir -p /content/so-vits-svc-5.0/chkpt/\n",
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" !rm -rf /content/so-vits-svc-5.0/chkpt/\n",
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" !mkdir -p /content/drive/MyDrive/Sovits5.0\n",
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" !ln -s /content/drive/MyDrive/Sovits5.0 /content/so-vits-svc-5.0/chkpt/"
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],
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"metadata": {
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"id": "hacR8qDFVOWo"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 开始训练\n",
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"%load_ext tensorboard\n",
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"%tensorboard --logdir /content/so-vits-svc-5.0/logs/\n",
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"\n",
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"!PYTHONPATH=. python svc_trainer.py -c configs/base.yaml -n sovits5.0"
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],
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"metadata": {
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"id": "5BIiKIAoU3Kd"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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