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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "WHBMn6dOWm-S"
},
"source": [
"# [Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) Training notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZFFCx5J80SGa"
},
"source": [
"[![RVC v1](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/tools/colab/v1.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GmFP6bN9dvOq"
},
"outputs": [],
"source": [
"# @title 查看显卡\n",
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jwu07JgqoFON"
},
"outputs": [],
"source": [
"# @title 挂载谷歌云盘\n",
"\n",
"from google.colab import drive\n",
"\n",
"drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wjddIFr1oS3W"
},
"outputs": [],
"source": [
"# @title 安装依赖\n",
"!apt -y install build-essential python3-dev ffmpeg\n",
"!pip3 install --upgrade setuptools wheel\n",
"!pip3 install --upgrade pip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ge_97mfpgqTm"
},
"outputs": [],
"source": [
"# @title 克隆仓库\n",
"\n",
"!git clone --depth=1 -b v1 https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI\n",
"%cd /content/Retrieval-based-Voice-Conversion-WebUI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BLDEZADkvlw1"
},
"outputs": [],
"source": [
"# @title 安装依赖\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pqE0PrnuRqI2"
},
"outputs": [],
"source": [
"# @title 下载安装 RVC-Models-Downloader\n",
"!wget https://github.com/RVC-Project/RVC-Models-Downloader/releases/download/v0.2.2/rvcmd_linux_amd64.deb\n",
"!apt install ./rvcmd_linux_amd64.deb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UG3XpUwEomUz"
},
"outputs": [],
"source": [
"# @title 下载所需资源\n",
"!rvcmd -notrs -w 1 -notui assets/v1\n",
"!rvcmd -notrs -w 1 -notui assets/rmvpe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Mwk7Q0Loqzjx"
},
"outputs": [],
"source": [
"# @title 从谷歌云盘加载打包好的数据集到/content/dataset\n",
"\n",
"# @markdown 数据集位置\n",
"DATASET = \"/content/drive/MyDrive/mydataset.zip\" # @param {type:\"string\"}\n",
"\n",
"!mkdir -p /content/dataset\n",
"!unzip -d /content/dataset -B {DATASET}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PDlFxWHWEynD"
},
"outputs": [],
"source": [
"# @title 重命名数据集中的重名文件\n",
"!ls -a /content/dataset/\n",
"!rename 's/(\\w+)\\.(\\w+)~(\\d*)/$1_$3.$2/' /content/dataset/*.*~*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7vh6vphDwO0b"
},
"outputs": [],
"source": [
"# @title 启动web\n",
"%cd /content/Retrieval-based-Voice-Conversion-WebUI\n",
"# %load_ext tensorboard\n",
"# %tensorboard --logdir /content/Retrieval-based-Voice-Conversion-WebUI/logs\n",
"!python3 infer-web.py --colab --pycmd python3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FgJuNeAwx5Y_"
},
"outputs": [],
"source": [
"# @title 手动将训练后的模型文件备份到谷歌云盘\n",
"# @markdown 需要自己查看logs文件夹下模型的文件名,手动修改下方命令末尾的文件名\n",
"\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 模型epoch\n",
"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
"\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_D_{MODELEPOCH}.pth\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/added_*.index /content/drive/MyDrive/\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/total_*.npy /content/drive/MyDrive/\n",
"\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/weights/{MODELNAME}.pth /content/drive/MyDrive/{MODELNAME}{MODELEPOCH}.pth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OVQoLQJXS7WX"
},
"outputs": [],
"source": [
"# @title 从谷歌云盘恢复pth\n",
"# @markdown 需要自己查看logs文件夹下模型的文件名,手动修改下方命令末尾的文件名\n",
"\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 模型epoch\n",
"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
"\n",
"!mkdir -p /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
"\n",
"!cp /content/drive/MyDrive/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth\n",
"!cp /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
"!cp /content/drive/MyDrive/*.index /content/\n",
"!cp /content/drive/MyDrive/*.npy /content/\n",
"!cp /content/drive/MyDrive/{MODELNAME}{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/weights/{MODELNAME}.pth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZKAyuKb9J6dz"
},
"outputs": [],
"source": [
"# @title 手动预处理(不推荐)\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 采样率\n",
"BITRATE = 48000 # @param {type:\"integer\"}\n",
"# @markdown 使用的进程数\n",
"THREADCOUNT = 8 # @param {type:\"integer\"}\n",
"\n",
"!python3 trainset_preprocess_pipeline_print.py /content/dataset {BITRATE} {THREADCOUNT} logs/{MODELNAME} True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CrxJqzAUKmPJ"
},
"outputs": [],
"source": [
"# @title 手动提取特征(不推荐)\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 使用的进程数\n",
"THREADCOUNT = 8 # @param {type:\"integer\"}\n",
"# @markdown 音高提取算法\n",
"ALGO = \"harvest\" # @param {type:\"string\"}\n",
"\n",
"!python3 extract_f0_print.py logs/{MODELNAME} {THREADCOUNT} {ALGO}\n",
"\n",
"!python3 extract_feature_print.py cpu 1 0 0 logs/{MODELNAME} True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IMLPLKOaKj58"
},
"outputs": [],
"source": [
"# @title 手动训练(不推荐)\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 使用的GPU\n",
"USEGPU = \"0\" # @param {type:\"string\"}\n",
"# @markdown 批大小\n",
"BATCHSIZE = 32 # @param {type:\"integer\"}\n",
"# @markdown 停止的epoch\n",
"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
"# @markdown 保存epoch间隔\n",
"EPOCHSAVE = 100 # @param {type:\"integer\"}\n",
"# @markdown 采样率\n",
"MODELSAMPLE = \"48k\" # @param {type:\"string\"}\n",
"# @markdown 是否缓存训练集\n",
"CACHEDATA = 1 # @param {type:\"integer\"}\n",
"# @markdown 是否仅保存最新的ckpt文件\n",
"ONLYLATEST = 0 # @param {type:\"integer\"}\n",
"\n",
"!python3 train_nsf_sim_cache_sid_load_pretrain.py -e lulu -sr {MODELSAMPLE} -f0 1 -bs {BATCHSIZE} -g {USEGPU} -te {MODELEPOCH} -se {EPOCHSAVE} -pg pretrained/f0G{MODELSAMPLE}.pth -pd pretrained/f0D{MODELSAMPLE}.pth -l {ONLYLATEST} -c {CACHEDATA}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "haYA81hySuDl"
},
"outputs": [],
"source": [
"# @title 删除其它pth,只留选中的(慎点,仔细看代码)\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 选中模型epoch\n",
"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
"\n",
"!echo \"备份选中的模型。。。\"\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/{MODELNAME}_G_{MODELEPOCH}.pth\n",
"\n",
"!echo \"正在删除。。。\"\n",
"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
"!rm /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/*.pth\n",
"\n",
"!echo \"恢复选中的模型。。。\"\n",
"!mv /content/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth\n",
"!mv /content/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
"\n",
"!echo \"删除完成\"\n",
"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QhSiPTVPoIRh"
},
"outputs": [],
"source": [
"# @title 清除项目下所有文件,只留选中的模型(慎点,仔细看代码)\n",
"# @markdown 模型名\n",
"MODELNAME = \"mymodel\" # @param {type:\"string\"}\n",
"# @markdown 选中模型epoch\n",
"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
"\n",
"!echo \"备份选中的模型。。。\"\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/{MODELNAME}_G_{MODELEPOCH}.pth\n",
"\n",
"!echo \"正在删除。。。\"\n",
"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
"!rm -rf /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/*\n",
"\n",
"!echo \"恢复选中的模型。。。\"\n",
"!mv /content/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth\n",
"!mv /content/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
"\n",
"!echo \"删除完成\"\n",
"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}"
]
}
],
"metadata": {
"colab": {
"private_outputs": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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