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fumiamafumiama
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为笔记本添加更多功能
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‎Retrieval_based_Voice_Conversion_WebUI.ipynb

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@@ -20,11 +20,6 @@
2020
"!nvidia-smi"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -75,6 +70,7 @@
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},
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"outputs": [],
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"source": [
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"#@title 下载底模\n",
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"!apt -y install -qq aria2\n",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o D32k.pth\n",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o D40k.pth\n",
@@ -106,7 +102,7 @@
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"#@title 从谷歌云盘加载打包好的数据集到/content/dataset\n",
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"\n",
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"#@markdown 数据集位置\n",
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"DATASET = \"/content/drive/MyDrive/dataset/lulu20230327.zip\" #@param {type:\"string\"}\n",
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"DATASET = \"/content/drive/MyDrive/dataset/lulu20230327_32k.zip\" #@param {type:\"string\"}\n",
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"\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')\n",
@@ -141,7 +137,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 模型epoch\n",
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"MODELEPOCH = 3540 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 6600 #@param {type:\"integer\"}\n",
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"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth\n",
@@ -165,10 +161,15 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 模型epoch\n",
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"MODELEPOCH = 730 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 250 #@param {type:\"integer\"}\n",
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"\n",
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"!mkdir -p /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
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"\n",
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"!cp /content/drive/MyDrive/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth\n",
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"!cp /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth"
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"!cp /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
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"!cp /content/drive/MyDrive/*.index /content/\n",
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"!cp /content/drive/MyDrive/*.npy /content/\n",
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"!cp /content/drive/MyDrive/{MODELNAME}{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/weights/{MODELNAME}.pth"
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]
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},
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{
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"id": "ZKAyuKb9J6dz"
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},
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"outputs": [],
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"source": [
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"#@title 手动预处理（不推荐）\n",
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"\n",
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"!python3 trainset_preprocess_pipeline_print.py /content/dataset 32000 8 logs/{MODELNAME} True\n"
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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": "CrxJqzAUKmPJ"
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},
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"outputs": [],
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"source": [
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"#@title 手动提取特征（不推荐）\n",
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"\n",
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"!python3 extract_feature_print.py 1 0 0 logs/{MODELNAME}\n"
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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": "IMLPLKOaKj58"
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},
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"outputs": [],
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"source": [
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"#@title 手动训练（不推荐）\n",
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 停止的epoch\n",
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"MODELEPOCH = 700 #@param {type:\"integer\"}\n",
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"#@markdown 保存epoch间隔\n",
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"EPOCHSAVE = 20 #@param {type:\"integer\"}\n",
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"#@markdown 采样率\n",
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"MODELSAMPLE = \"48k\" #@param {type:\"string\"}\n",
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"\n",
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"!python3 train_nsf_sim_cache_sid_load_pretrain.py -e lulu -sr {MODELSAMPLE} -f0 1 -bs 32 -g 0 -te {MODELEPOCH} -se {EPOCHSAVE} -pg pretrained/f0G{MODELSAMPLE}.pth -pd pretrained/f0D{MODELSAMPLE}.pth -l 0 -c 1\n"
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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": "haYA81hySuDl"
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},
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"outputs": [],
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"source": [
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"#@title 删除其它pth，只留选中的（慎点，仔细看代码）\n",
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 选中模型epoch\n",
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"MODELEPOCH = 6600 #@param {type:\"integer\"}\n",
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"\n",
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"!echo \"备份选中的模型。。。\"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/{MODELNAME}_G_{MODELEPOCH}.pth\n",
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"\n",
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"!echo \"正在删除。。。\"\n",
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"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
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"!rm /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/*.pth\n",
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"\n",
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"!echo \"恢复选中的模型。。。\"\n",
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"!mv /content/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth \n",
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"!mv /content/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
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"\n",
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"!echo \"删除完成\"\n",
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"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}"
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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": "QhSiPTVPoIRh"
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},
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"outputs": [],
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"source": [
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"#@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",
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 选中模型epoch\n",
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"MODELEPOCH = 1500 #@param {type:\"integer\"}\n",
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"\n",
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"!echo \"备份选中的模型。。。\"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/{MODELNAME}_G_{MODELEPOCH}.pth\n",
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"\n",
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"!python3 trainset_preprocess_pipeline_print.py /content/dataset 32000 8 logs/{MODELNAME} True\n",
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"!echo \"正在删除。。。\"\n",
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"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
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"!rm -rf /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/*\n",
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"\n",
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"!python3 extract_feature_print.py 1 0 0 logs/{MODELNAME}\n",
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"!echo \"恢复选中的模型。。。\"\n",
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"!mv /content/{MODELNAME}_D_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth \n",
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"!mv /content/{MODELNAME}_G_{MODELEPOCH}.pth /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth\n",
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"\n",
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"!python3 train_nsf_sim_cache_sid_load_pretrain.py -e lulu -sr 32k -f0 0 -g 0 -bs 4 -te 10 -se 5 -pg pretrained/G32k.pth -pd pretrained/D32k.pth -l 0 -c 0\n"
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"!echo \"删除完成\"\n",
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"!ls /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}"
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]
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}
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],

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