mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-04 03:38:58 +08:00
Project cleanup
This commit is contained in:
VSlobolinskyi
parent
b598767553
commit
f536e5f4d7
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# [Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) Training notebook"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "ZFFCx5J80SGa"
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},
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"source": [
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"[](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb)"
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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": "GmFP6bN9dvOq"
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},
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"outputs": [],
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"source": [
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"# @title 查看显卡\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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"execution_count": null,
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"metadata": {
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"id": "jwu07JgqoFON"
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},
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"outputs": [],
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"source": [
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"# @title 挂载谷歌云盘\n",
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"\n",
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"from google.colab import drive\n",
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"\n",
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"drive.mount(\"/content/drive\")"
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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": "wjddIFr1oS3W"
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},
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"outputs": [],
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"source": [
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"# @title 安装依赖\n",
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"!apt-get -y install build-essential python3-dev ffmpeg\n",
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"!pip3 install --upgrade setuptools wheel\n",
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"!pip3 install --upgrade pip\n",
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"!pip3 install faiss-cpu==1.7.2 fairseq gradio==3.14.0 ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.2"
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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": "ge_97mfpgqTm"
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},
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"outputs": [],
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"source": [
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"# @title 克隆仓库\n",
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"\n",
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"!git clone --depth=1 -b stable https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI\n",
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"%cd /content/Retrieval-based-Voice-Conversion-WebUI\n",
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"!mkdir -p pretrained uvr5_weights"
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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": "BLDEZADkvlw1"
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},
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"outputs": [],
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"source": [
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"# @title 更新仓库（一般无需执行）\n",
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"!git pull"
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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": "pqE0PrnuRqI2"
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},
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"outputs": [],
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"source": [
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"# @title 安装aria2\n",
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"!apt -y install -qq aria2"
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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": "UG3XpUwEomUz"
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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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"!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",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o D48k.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/G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G32k.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/G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G40k.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/G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G48k.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/f0D32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D32k.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/f0D40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D40k.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/f0D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D48k.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/f0G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G32k.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/f0G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G40k.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/f0G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G48k.pth"
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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": "HugjmZqZRuiF"
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},
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"outputs": [],
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"source": [
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"# @title 下载人声分离模型\n",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/uvr5_weights -o HP2-人声vocals+非人声instrumentals.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/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth"
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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": "2RCaT9FTR0ej"
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},
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"outputs": [],
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"source": [
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"# @title 下载hubert_base\n",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d /content/Retrieval-based-Voice-Conversion-WebUI -o hubert_base.pt"
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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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"outputs": [],
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"source": [
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"# @title #下载rmvpe模型\n",
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"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d /content/Retrieval-based-Voice-Conversion-WebUI -o rmvpe.pt"
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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": "Mwk7Q0Loqzjx"
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},
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"outputs": [],
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"source": [
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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 = (\n",
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" \"/content/drive/MyDrive/dataset/lulu20230327_32k.zip\" # @param {type:\"string\"}\n",
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")\n",
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"\n",
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"!mkdir -p /content/dataset\n",
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"!unzip -d /content/dataset -B {DATASET}"
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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": "PDlFxWHWEynD"
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},
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"outputs": [],
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"source": [
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"# @title 重命名数据集中的重名文件\n",
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"!ls -a /content/dataset/\n",
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"!rename 's/(\\w+)\\.(\\w+)~(\\d*)/$1_$3.$2/' /content/dataset/*.*~*"
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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": "7vh6vphDwO0b"
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},
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"outputs": [],
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"source": [
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"# @title 启动web\n",
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"%cd /content/Retrieval-based-Voice-Conversion-WebUI\n",
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"# %load_ext tensorboard\n",
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"# %tensorboard --logdir /content/Retrieval-based-Voice-Conversion-WebUI/logs\n",
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"!python3 infer-web.py --colab --pycmd python3"
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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": "FgJuNeAwx5Y_"
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},
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"outputs": [],
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"source": [
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"# @title 手动将训练后的模型文件备份到谷歌云盘\n",
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"# @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",
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"\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 模型epoch\n",
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"MODELEPOCH = 9600 # @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",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/added_*.index /content/drive/MyDrive/\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/total_*.npy /content/drive/MyDrive/\n",
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"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/weights/{MODELNAME}.pth /content/drive/MyDrive/{MODELNAME}{MODELEPOCH}.pth"
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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": "OVQoLQJXS7WX"
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},
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"outputs": [],
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"source": [
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"# @title 从谷歌云盘恢复pth\n",
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"# @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",
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"\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 模型epoch\n",
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"MODELEPOCH = 7500 # @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\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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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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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"# @markdown 采样率\n",
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"BITRATE = 48000 # @param {type:\"integer\"}\n",
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"# @markdown 使用的进程数\n",
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"THREADCOUNT = 8 # @param {type:\"integer\"}\n",
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"\n",
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"!python3 trainset_preprocess_pipeline_print.py /content/dataset {BITRATE} {THREADCOUNT} logs/{MODELNAME} True"
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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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"# @markdown 使用的进程数\n",
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"THREADCOUNT = 8 # @param {type:\"integer\"}\n",
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"# @markdown 音高提取算法\n",
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"ALGO = \"harvest\" # @param {type:\"string\"}\n",
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"\n",
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"!python3 extract_f0_print.py logs/{MODELNAME} {THREADCOUNT} {ALGO}\n",
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"\n",
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"!python3 extract_feature_print.py cpu 1 0 0 logs/{MODELNAME} True"
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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 使用的GPU\n",
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"USEGPU = \"0\" # @param {type:\"string\"}\n",
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"# @markdown 批大小\n",
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"BATCHSIZE = 32 # @param {type:\"integer\"}\n",
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"# @markdown 停止的epoch\n",
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"MODELEPOCH = 3200 # @param {type:\"integer\"}\n",
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"# @markdown 保存epoch间隔\n",
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"EPOCHSAVE = 100 # @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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"# @markdown 是否缓存训练集\n",
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"CACHEDATA = 1 # @param {type:\"integer\"}\n",
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"# @markdown 是否仅保存最新的ckpt文件\n",
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"ONLYLATEST = 0 # @param {type:\"integer\"}\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 {BATCHSIZE} -g {USEGPU} -te {MODELEPOCH} -se {EPOCHSAVE} -pg pretrained/f0G{MODELSAMPLE}.pth -pd pretrained/f0D{MODELSAMPLE}.pth -l {ONLYLATEST} -c {CACHEDATA}"
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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 = 9600 # @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",
|
||||
"!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 = \"lulu\" # @param {type:\"string\"}\n",
|
||||
"# @markdown 选中模型epoch\n",
|
||||
"MODELEPOCH = 9600 # @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": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@ -1,422 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# [Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) Training notebook"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZFFCx5J80SGa"
|
||||
},
|
||||
"source": [
|
||||
"[](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI_v2.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-get -y install build-essential python3-dev ffmpeg\n",
|
||||
"!pip3 install --upgrade setuptools wheel\n",
|
||||
"!pip3 install --upgrade pip\n",
|
||||
"!pip3 install faiss-cpu==1.7.2 fairseq gradio==3.14.0 ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "ge_97mfpgqTm"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #克隆仓库\n",
|
||||
"\n",
|
||||
"!mkdir Retrieval-based-Voice-Conversion-WebUI\n",
|
||||
"%cd /content/Retrieval-based-Voice-Conversion-WebUI\n",
|
||||
"!git init\n",
|
||||
"!git remote add origin https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git\n",
|
||||
"!git fetch origin cfd984812804ddc9247d65b14c82cd32e56c1133 --depth=1\n",
|
||||
"!git reset --hard FETCH_HEAD"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "BLDEZADkvlw1"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #更新仓库（一般无需执行）\n",
|
||||
"!git pull"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "pqE0PrnuRqI2"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #安装aria2\n",
|
||||
"!apt -y install -qq aria2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "UG3XpUwEomUz"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 下载底模\n",
|
||||
"\n",
|
||||
"# v1\n",
|
||||
"!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",
|
||||
"!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",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o D48k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G40k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o G48k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D40k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0D48k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G40k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained -o f0G48k.pth\n",
|
||||
"\n",
|
||||
"# v2\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o D32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o D40k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o D48k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o G32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o G40k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o G48k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0D32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0D40k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0D48k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0G32k.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0G40k.pth\n",
|
||||
"# !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/pretrained_v2 -o f0G48k.pth"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "HugjmZqZRuiF"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #下载人声分离模型\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d /content/Retrieval-based-Voice-Conversion-WebUI/uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "2RCaT9FTR0ej"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #下载hubert_base\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d /content/Retrieval-based-Voice-Conversion-WebUI -o hubert_base.pt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title #下载rmvpe模型\n",
|
||||
"!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d /content/Retrieval-based-Voice-Conversion-WebUI -o rmvpe.pt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 = (\n",
|
||||
" \"/content/drive/MyDrive/dataset/lulu20230327_32k.zip\" # @param {type:\"string\"}\n",
|
||||
")\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webui\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 = \"lulu\" # @param {type:\"string\"}\n",
|
||||
"# @markdown #模型epoch\n",
|
||||
"MODELEPOCH = 9600 # @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 = \"lulu\" # @param {type:\"string\"}\n",
|
||||
"# @markdown 模型epoch\n",
|
||||
"MODELEPOCH = 7500 # @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 = \"lulu\" # @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 = \"lulu\" # @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 = \"lulu\" # @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 = \"lulu\" # @param {type:\"string\"}\n",
|
||||
"# @markdown 选中模型epoch\n",
|
||||
"MODELEPOCH = 9600 # @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 = \"lulu\" # @param {type:\"string\"}\n",
|
||||
"# @markdown 选中模型epoch\n",
|
||||
"MODELEPOCH = 9600 # @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": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
440
api_231006.py
440
api_231006.py
@ -1,440 +0,0 @@
|
||||
#api for 231006 release version by Xiaokai
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import re
|
||||
import time
|
||||
import librosa
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
import torchaudio.transforms as tat
|
||||
import sounddevice as sd
|
||||
from dotenv import load_dotenv
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import threading
|
||||
import uvicorn
|
||||
import logging
|
||||
|
||||
# Initialize the logger
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Define FastAPI app
|
||||
app = FastAPI()
|
||||
|
||||
class GUIConfig:
|
||||
def __init__(self) -> None:
|
||||
self.pth_path: str = ""
|
||||
self.index_path: str = ""
|
||||
self.pitch: int = 0
|
||||
self.samplerate: int = 40000
|
||||
self.block_time: float = 1.0 # s
|
||||
self.buffer_num: int = 1
|
||||
self.threhold: int = -60
|
||||
self.crossfade_time: float = 0.05
|
||||
self.extra_time: float = 2.5
|
||||
self.I_noise_reduce = False
|
||||
self.O_noise_reduce = False
|
||||
self.rms_mix_rate = 0.0
|
||||
self.index_rate = 0.3
|
||||
self.f0method = "rmvpe"
|
||||
self.sg_input_device = ""
|
||||
self.sg_output_device = ""
|
||||
|
||||
class ConfigData(BaseModel):
|
||||
pth_path: str
|
||||
index_path: str
|
||||
sg_input_device: str
|
||||
sg_output_device: str
|
||||
threhold: int = -60
|
||||
pitch: int = 0
|
||||
index_rate: float = 0.3
|
||||
rms_mix_rate: float = 0.0
|
||||
block_time: float = 0.25
|
||||
crossfade_length: float = 0.05
|
||||
extra_time: float = 2.5
|
||||
n_cpu: int = 4
|
||||
I_noise_reduce: bool = False
|
||||
O_noise_reduce: bool = False
|
||||
|
||||
class AudioAPI:
|
||||
def __init__(self) -> None:
|
||||
self.gui_config = GUIConfig()
|
||||
self.config = None # Initialize Config object as None
|
||||
self.flag_vc = False
|
||||
self.function = "vc"
|
||||
self.delay_time = 0
|
||||
self.rvc = None # Initialize RVC object as None
|
||||
|
||||
def load(self):
|
||||
input_devices, output_devices, _, _ = self.get_devices()
|
||||
try:
|
||||
with open("configs/config.json", "r", encoding='utf-8') as j:
|
||||
data = json.load(j)
|
||||
data["rmvpe"] = True # Ensure rmvpe is the only f0method
|
||||
if data["sg_input_device"] not in input_devices:
|
||||
data["sg_input_device"] = input_devices[sd.default.device[0]]
|
||||
if data["sg_output_device"] not in output_devices:
|
||||
data["sg_output_device"] = output_devices[sd.default.device[1]]
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load configuration: {e}")
|
||||
with open("configs/config.json", "w", encoding='utf-8') as j:
|
||||
data = {
|
||||
"pth_path": " ",
|
||||
"index_path": " ",
|
||||
"sg_input_device": input_devices[sd.default.device[0]],
|
||||
"sg_output_device": output_devices[sd.default.device[1]],
|
||||
"threhold": "-60",
|
||||
"pitch": "0",
|
||||
"index_rate": "0",
|
||||
"rms_mix_rate": "0",
|
||||
"block_time": "0.25",
|
||||
"crossfade_length": "0.05",
|
||||
"extra_time": "2.5",
|
||||
"f0method": "rmvpe",
|
||||
"use_jit": False,
|
||||
}
|
||||
data["rmvpe"] = True # Ensure rmvpe is the only f0method
|
||||
json.dump(data, j, ensure_ascii=False)
|
||||
return data
|
||||
|
||||
def set_values(self, values):
|
||||
logger.info(f"Setting values: {values}")
|
||||
if not values.pth_path.strip():
|
||||
raise HTTPException(status_code=400, detail="Please select a .pth file")
|
||||
if not values.index_path.strip():
|
||||
raise HTTPException(status_code=400, detail="Please select an index file")
|
||||
self.set_devices(values.sg_input_device, values.sg_output_device)
|
||||
self.config.use_jit = False
|
||||
self.gui_config.pth_path = values.pth_path
|
||||
self.gui_config.index_path = values.index_path
|
||||
self.gui_config.threhold = values.threhold
|
||||
self.gui_config.pitch = values.pitch
|
||||
self.gui_config.block_time = values.block_time
|
||||
self.gui_config.crossfade_time = values.crossfade_length
|
||||
self.gui_config.extra_time = values.extra_time
|
||||
self.gui_config.I_noise_reduce = values.I_noise_reduce
|
||||
self.gui_config.O_noise_reduce = values.O_noise_reduce
|
||||
self.gui_config.rms_mix_rate = values.rms_mix_rate
|
||||
self.gui_config.index_rate = values.index_rate
|
||||
self.gui_config.n_cpu = values.n_cpu
|
||||
self.gui_config.f0method = "rmvpe"
|
||||
return True
|
||||
|
||||
def start_vc(self):
|
||||
torch.cuda.empty_cache()
|
||||
self.flag_vc = True
|
||||
self.rvc = rvc_for_realtime.RVC(
|
||||
self.gui_config.pitch,
|
||||
self.gui_config.pth_path,
|
||||
self.gui_config.index_path,
|
||||
self.gui_config.index_rate,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
self.config,
|
||||
self.rvc if self.rvc else None,
|
||||
)
|
||||
self.gui_config.samplerate = self.rvc.tgt_sr
|
||||
self.zc = self.rvc.tgt_sr // 100
|
||||
self.block_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.block_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.block_frame_16k = 160 * self.block_frame // self.zc
|
||||
self.crossfade_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.crossfade_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.sola_search_frame = self.zc
|
||||
self.extra_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.extra_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.input_wav = torch.zeros(
|
||||
self.extra_frame + self.crossfade_frame + self.sola_search_frame + self.block_frame,
|
||||
device=self.config.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
self.input_wav_res = torch.zeros(
|
||||
160 * self.input_wav.shape[0] // self.zc,
|
||||
device=self.config.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
self.pitch = np.zeros(self.input_wav.shape[0] // self.zc, dtype="int32")
|
||||
self.pitchf = np.zeros(self.input_wav.shape[0] // self.zc, dtype="float64")
|
||||
self.sola_buffer = torch.zeros(self.crossfade_frame, device=self.config.device, dtype=torch.float32)
|
||||
self.nr_buffer = self.sola_buffer.clone()
|
||||
self.output_buffer = self.input_wav.clone()
|
||||
self.res_buffer = torch.zeros(2 * self.zc, device=self.config.device, dtype=torch.float32)
|
||||
self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0]
|
||||
self.fade_in_window = (
|
||||
torch.sin(0.5 * np.pi * torch.linspace(0.0, 1.0, steps=self.crossfade_frame, device=self.config.device, dtype=torch.float32)) ** 2
|
||||
)
|
||||
self.fade_out_window = 1 - self.fade_in_window
|
||||
self.resampler = tat.Resample(
|
||||
orig_freq=self.gui_config.samplerate,
|
||||
new_freq=16000,
|
||||
dtype=torch.float32,
|
||||
).to(self.config.device)
|
||||
self.tg = TorchGate(
|
||||
sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9
|
||||
).to(self.config.device)
|
||||
thread_vc = threading.Thread(target=self.soundinput)
|
||||
thread_vc.start()
|
||||
|
||||
def soundinput(self):
|
||||
channels = 1 if sys.platform == "darwin" else 2
|
||||
with sd.Stream(
|
||||
channels=channels,
|
||||
callback=self.audio_callback,
|
||||
blocksize=self.block_frame,
|
||||
samplerate=self.gui_config.samplerate,
|
||||
dtype="float32",
|
||||
) as stream:
|
||||
global stream_latency
|
||||
stream_latency = stream.latency[-1]
|
||||
while self.flag_vc:
|
||||
time.sleep(self.gui_config.block_time)
|
||||
logger.info("Audio block passed.")
|
||||
logger.info("Ending VC")
|
||||
|
||||
def audio_callback(self, indata: np.ndarray, outdata: np.ndarray, frames, times, status):
|
||||
start_time = time.perf_counter()
|
||||
indata = librosa.to_mono(indata.T)
|
||||
if self.gui_config.threhold > -60:
|
||||
rms = librosa.feature.rms(y=indata, frame_length=4 * self.zc, hop_length=self.zc)
|
||||
db_threhold = (librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold)
|
||||
for i in range(db_threhold.shape[0]):
|
||||
if db_threhold[i]:
|
||||
indata[i * self.zc : (i + 1) * self.zc] = 0
|
||||
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
|
||||
self.input_wav[-self.block_frame :] = torch.from_numpy(indata).to(self.config.device)
|
||||
self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
|
||||
if self.gui_config.I_noise_reduce and self.function == "vc":
|
||||
input_wav = self.input_wav[-self.crossfade_frame - self.block_frame - 2 * self.zc :]
|
||||
input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2 * self.zc :]
|
||||
input_wav[: self.crossfade_frame] *= self.fade_in_window
|
||||
input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window
|
||||
self.nr_buffer[:] = input_wav[-self.crossfade_frame :]
|
||||
input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame]))
|
||||
self.res_buffer[:] = input_wav[-2 * self.zc :]
|
||||
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(input_wav)[160:]
|
||||
else:
|
||||
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(self.input_wav[-self.block_frame - 2 * self.zc :])[160:]
|
||||
if self.function == "vc":
|
||||
f0_extractor_frame = self.block_frame_16k + 800
|
||||
if self.gui_config.f0method == "rmvpe":
|
||||
f0_extractor_frame = (5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160)
|
||||
infer_wav = self.rvc.infer(
|
||||
self.input_wav_res,
|
||||
self.input_wav_res[-f0_extractor_frame:].cpu().numpy(),
|
||||
self.block_frame_16k,
|
||||
self.valid_rate,
|
||||
self.pitch,
|
||||
self.pitchf,
|
||||
self.gui_config.f0method,
|
||||
)
|
||||
infer_wav = infer_wav[-self.crossfade_frame - self.sola_search_frame - self.block_frame :]
|
||||
else:
|
||||
infer_wav = self.input_wav[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].clone()
|
||||
if (self.gui_config.O_noise_reduce and self.function == "vc") or (self.gui_config.I_noise_reduce and self.function == "im"):
|
||||
self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone()
|
||||
self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :]
|
||||
infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0)
|
||||
if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
|
||||
rms1 = librosa.feature.rms(y=self.input_wav_res[-160 * infer_wav.shape[0] // self.zc :].cpu().numpy(), frame_length=640, hop_length=160)
|
||||
rms1 = torch.from_numpy(rms1).to(self.config.device)
|
||||
rms1 = F.interpolate(rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True)[0, 0, :-1]
|
||||
rms2 = librosa.feature.rms(y=infer_wav[:].cpu().numpy(), frame_length=4 * self.zc, hop_length=self.zc)
|
||||
rms2 = torch.from_numpy(rms2).to(self.config.device)
|
||||
rms2 = F.interpolate(rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True)[0, 0, :-1]
|
||||
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
|
||||
infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.gui_config.rms_mix_rate))
|
||||
conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
|
||||
cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
|
||||
cor_den = torch.sqrt(F.conv1d(conv_input**2, torch.ones(1, 1, self.crossfade_frame, device=self.config.device)) + 1e-8)
|
||||
if sys.platform == "darwin":
|
||||
_, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0])
|
||||
sola_offset = sola_offset.item()
|
||||
else:
|
||||
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
|
||||
logger.info(f"sola_offset = {sola_offset}")
|
||||
infer_wav = infer_wav[sola_offset : sola_offset + self.block_frame + self.crossfade_frame]
|
||||
infer_wav[: self.crossfade_frame] *= self.fade_in_window
|
||||
infer_wav[: self.crossfade_frame] += self.sola_buffer * self.fade_out_window
|
||||
self.sola_buffer[:] = infer_wav[-self.crossfade_frame :]
|
||||
if sys.platform == "darwin":
|
||||
outdata[:] = infer_wav[: -self.crossfade_frame].cpu().numpy()[:, np.newaxis]
|
||||
else:
|
||||
outdata[:] = infer_wav[: -self.crossfade_frame].repeat(2, 1).t().cpu().numpy()
|
||||
total_time = time.perf_counter() - start_time
|
||||
logger.info(f"Infer time: {total_time:.2f}")
|
||||
|
||||
def get_devices(self, update: bool = True):
|
||||
if update:
|
||||
sd._terminate()
|
||||
sd._initialize()
|
||||
devices = sd.query_devices()
|
||||
hostapis = sd.query_hostapis()
|
||||
for hostapi in hostapis:
|
||||
for device_idx in hostapi["devices"]:
|
||||
devices[device_idx]["hostapi_name"] = hostapi["name"]
|
||||
input_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
]
|
||||
output_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
]
|
||||
input_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
]
|
||||
output_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
]
|
||||
return (
|
||||
input_devices,
|
||||
output_devices,
|
||||
input_devices_indices,
|
||||
output_devices_indices,
|
||||
)
|
||||
|
||||
def set_devices(self, input_device, output_device):
|
||||
(
|
||||
input_devices,
|
||||
output_devices,
|
||||
input_device_indices,
|
||||
output_device_indices,
|
||||
) = self.get_devices()
|
||||
logger.debug(f"Available input devices: {input_devices}")
|
||||
logger.debug(f"Available output devices: {output_devices}")
|
||||
logger.debug(f"Selected input device: {input_device}")
|
||||
logger.debug(f"Selected output device: {output_device}")
|
||||
|
||||
if input_device not in input_devices:
|
||||
logger.error(f"Input device '{input_device}' is not in the list of available devices")
|
||||
raise HTTPException(status_code=400, detail=f"Input device '{input_device}' is not available")
|
||||
|
||||
if output_device not in output_devices:
|
||||
logger.error(f"Output device '{output_device}' is not in the list of available devices")
|
||||
raise HTTPException(status_code=400, detail=f"Output device '{output_device}' is not available")
|
||||
|
||||
sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
|
||||
sd.default.device[1] = output_device_indices[output_devices.index(output_device)]
|
||||
logger.info(f"Input device set to {sd.default.device[0]}: {input_device}")
|
||||
logger.info(f"Output device set to {sd.default.device[1]}: {output_device}")
|
||||
|
||||
audio_api = AudioAPI()
|
||||
|
||||
@app.get("/inputDevices", response_model=list)
|
||||
def get_input_devices():
|
||||
try:
|
||||
input_devices, _, _, _ = audio_api.get_devices()
|
||||
return input_devices
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get input devices: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to get input devices")
|
||||
|
||||
@app.get("/outputDevices", response_model=list)
|
||||
def get_output_devices():
|
||||
try:
|
||||
_, output_devices, _, _ = audio_api.get_devices()
|
||||
return output_devices
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get output devices: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to get output devices")
|
||||
|
||||
@app.post("/config")
|
||||
def configure_audio(config_data: ConfigData):
|
||||
try:
|
||||
logger.info(f"Configuring audio with data: {config_data}")
|
||||
if audio_api.set_values(config_data):
|
||||
settings = config_data.dict()
|
||||
settings["use_jit"] = False
|
||||
settings["f0method"] = "rmvpe"
|
||||
with open("configs/config.json", "w", encoding='utf-8') as j:
|
||||
json.dump(settings, j, ensure_ascii=False)
|
||||
logger.info("Configuration set successfully")
|
||||
return {"message": "Configuration set successfully"}
|
||||
except HTTPException as e:
|
||||
logger.error(f"Configuration error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Configuration failed: {e}")
|
||||
raise HTTPException(status_code=400, detail=f"Configuration failed: {e}")
|
||||
|
||||
@app.post("/start")
|
||||
def start_conversion():
|
||||
try:
|
||||
if not audio_api.flag_vc:
|
||||
audio_api.start_vc()
|
||||
return {"message": "Audio conversion started"}
|
||||
else:
|
||||
logger.warning("Audio conversion already running")
|
||||
raise HTTPException(status_code=400, detail="Audio conversion already running")
|
||||
except HTTPException as e:
|
||||
logger.error(f"Start conversion error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start conversion: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to start conversion: {e}")
|
||||
|
||||
@app.post("/stop")
|
||||
def stop_conversion():
|
||||
try:
|
||||
if audio_api.flag_vc:
|
||||
audio_api.flag_vc = False
|
||||
global stream_latency
|
||||
stream_latency = -1
|
||||
return {"message": "Audio conversion stopped"}
|
||||
else:
|
||||
logger.warning("Audio conversion not running")
|
||||
raise HTTPException(status_code=400, detail="Audio conversion not running")
|
||||
except HTTPException as e:
|
||||
logger.error(f"Stop conversion error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to stop conversion: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"Failed to stop conversion: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if sys.platform == "win32":
|
||||
from multiprocessing import freeze_support
|
||||
freeze_support()
|
||||
load_dotenv()
|
||||
os.environ["OMP_NUM_THREADS"] = "4"
|
||||
if sys.platform == "darwin":
|
||||
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
||||
from tools.torchgate import TorchGate
|
||||
import tools.rvc_for_realtime as rvc_for_realtime
|
||||
from configs.config import Config
|
||||
audio_api.config = Config()
|
||||
uvicorn.run(app, host="0.0.0.0", port=6242)
|
||||
565
api_240604.py
565
api_240604.py
@ -1,565 +0,0 @@
|
||||
#api for 240604 release version by Xiaokai
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import re
|
||||
import time
|
||||
import librosa
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
import torchaudio.transforms as tat
|
||||
import sounddevice as sd
|
||||
from dotenv import load_dotenv
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import threading
|
||||
import uvicorn
|
||||
import logging
|
||||
from multiprocessing import Queue, Process, cpu_count, freeze_support
|
||||
|
||||
# Initialize the logger
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Define FastAPI app
|
||||
app = FastAPI()
|
||||
|
||||
class GUIConfig:
|
||||
def __init__(self) -> None:
|
||||
self.pth_path: str = ""
|
||||
self.index_path: str = ""
|
||||
self.pitch: int = 0
|
||||
self.formant: float = 0.0
|
||||
self.sr_type: str = "sr_model"
|
||||
self.block_time: float = 0.25 # s
|
||||
self.threhold: int = -60
|
||||
self.crossfade_time: float = 0.05
|
||||
self.extra_time: float = 2.5
|
||||
self.I_noise_reduce: bool = False
|
||||
self.O_noise_reduce: bool = False
|
||||
self.use_pv: bool = False
|
||||
self.rms_mix_rate: float = 0.0
|
||||
self.index_rate: float = 0.0
|
||||
self.n_cpu: int = 4
|
||||
self.f0method: str = "fcpe"
|
||||
self.sg_input_device: str = ""
|
||||
self.sg_output_device: str = ""
|
||||
|
||||
class ConfigData(BaseModel):
|
||||
pth_path: str
|
||||
index_path: str
|
||||
sg_input_device: str
|
||||
sg_output_device: str
|
||||
threhold: int = -60
|
||||
pitch: int = 0
|
||||
formant: float = 0.0
|
||||
index_rate: float = 0.3
|
||||
rms_mix_rate: float = 0.0
|
||||
block_time: float = 0.25
|
||||
crossfade_length: float = 0.05
|
||||
extra_time: float = 2.5
|
||||
n_cpu: int = 4
|
||||
I_noise_reduce: bool = False
|
||||
O_noise_reduce: bool = False
|
||||
use_pv: bool = False
|
||||
f0method: str = "fcpe"
|
||||
|
||||
class Harvest(Process):
|
||||
def __init__(self, inp_q, opt_q):
|
||||
super(Harvest, self).__init__()
|
||||
self.inp_q = inp_q
|
||||
self.opt_q = opt_q
|
||||
|
||||
def run(self):
|
||||
import numpy as np
|
||||
import pyworld
|
||||
while True:
|
||||
idx, x, res_f0, n_cpu, ts = self.inp_q.get()
|
||||
f0, t = pyworld.harvest(
|
||||
x.astype(np.double),
|
||||
fs=16000,
|
||||
f0_ceil=1100,
|
||||
f0_floor=50,
|
||||
frame_period=10,
|
||||
)
|
||||
res_f0[idx] = f0
|
||||
if len(res_f0.keys()) >= n_cpu:
|
||||
self.opt_q.put(ts)
|
||||
|
||||
class AudioAPI:
|
||||
def __init__(self) -> None:
|
||||
self.gui_config = GUIConfig()
|
||||
self.config = None # Initialize Config object as None
|
||||
self.flag_vc = False
|
||||
self.function = "vc"
|
||||
self.delay_time = 0
|
||||
self.rvc = None # Initialize RVC object as None
|
||||
self.inp_q = None
|
||||
self.opt_q = None
|
||||
self.n_cpu = min(cpu_count(), 8)
|
||||
|
||||
def initialize_queues(self):
|
||||
self.inp_q = Queue()
|
||||
self.opt_q = Queue()
|
||||
for _ in range(self.n_cpu):
|
||||
p = Harvest(self.inp_q, self.opt_q)
|
||||
p.daemon = True
|
||||
p.start()
|
||||
|
||||
def load(self):
|
||||
input_devices, output_devices, _, _ = self.get_devices()
|
||||
try:
|
||||
with open("configs/config.json", "r", encoding='utf-8') as j:
|
||||
data = json.load(j)
|
||||
if data["sg_input_device"] not in input_devices:
|
||||
data["sg_input_device"] = input_devices[sd.default.device[0]]
|
||||
if data["sg_output_device"] not in output_devices:
|
||||
data["sg_output_device"] = output_devices[sd.default.device[1]]
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load configuration: {e}")
|
||||
with open("configs/config.json", "w", encoding='utf-8') as j:
|
||||
data = {
|
||||
"pth_path": "",
|
||||
"index_path": "",
|
||||
"sg_input_device": input_devices[sd.default.device[0]],
|
||||
"sg_output_device": output_devices[sd.default.device[1]],
|
||||
"threhold": -60,
|
||||
"pitch": 0,
|
||||
"formant": 0.0,
|
||||
"index_rate": 0,
|
||||
"rms_mix_rate": 0,
|
||||
"block_time": 0.25,
|
||||
"crossfade_length": 0.05,
|
||||
"extra_time": 2.5,
|
||||
"n_cpu": 4,
|
||||
"f0method": "fcpe",
|
||||
"use_jit": False,
|
||||
"use_pv": False,
|
||||
}
|
||||
json.dump(data, j, ensure_ascii=False)
|
||||
return data
|
||||
|
||||
def set_values(self, values):
|
||||
logger.info(f"Setting values: {values}")
|
||||
if not values.pth_path.strip():
|
||||
raise HTTPException(status_code=400, detail="Please select a .pth file")
|
||||
if not values.index_path.strip():
|
||||
raise HTTPException(status_code=400, detail="Please select an index file")
|
||||
self.set_devices(values.sg_input_device, values.sg_output_device)
|
||||
self.config.use_jit = False
|
||||
self.gui_config.pth_path = values.pth_path
|
||||
self.gui_config.index_path = values.index_path
|
||||
self.gui_config.threhold = values.threhold
|
||||
self.gui_config.pitch = values.pitch
|
||||
self.gui_config.formant = values.formant
|
||||
self.gui_config.block_time = values.block_time
|
||||
self.gui_config.crossfade_time = values.crossfade_length
|
||||
self.gui_config.extra_time = values.extra_time
|
||||
self.gui_config.I_noise_reduce = values.I_noise_reduce
|
||||
self.gui_config.O_noise_reduce = values.O_noise_reduce
|
||||
self.gui_config.rms_mix_rate = values.rms_mix_rate
|
||||
self.gui_config.index_rate = values.index_rate
|
||||
self.gui_config.n_cpu = values.n_cpu
|
||||
self.gui_config.use_pv = values.use_pv
|
||||
self.gui_config.f0method = values.f0method
|
||||
return True
|
||||
|
||||
def start_vc(self):
|
||||
torch.cuda.empty_cache()
|
||||
self.flag_vc = True
|
||||
self.rvc = rvc_for_realtime.RVC(
|
||||
self.gui_config.pitch,
|
||||
self.gui_config.pth_path,
|
||||
self.gui_config.index_path,
|
||||
self.gui_config.index_rate,
|
||||
self.gui_config.n_cpu,
|
||||
self.inp_q,
|
||||
self.opt_q,
|
||||
self.config,
|
||||
self.rvc if self.rvc else None,
|
||||
)
|
||||
self.gui_config.samplerate = (
|
||||
self.rvc.tgt_sr
|
||||
if self.gui_config.sr_type == "sr_model"
|
||||
else self.get_device_samplerate()
|
||||
)
|
||||
self.zc = self.gui_config.samplerate // 100
|
||||
self.block_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.block_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.block_frame_16k = 160 * self.block_frame // self.zc
|
||||
self.crossfade_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.crossfade_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.sola_buffer_frame = min(self.crossfade_frame, 4 * self.zc)
|
||||
self.sola_search_frame = self.zc
|
||||
self.extra_frame = (
|
||||
int(
|
||||
np.round(
|
||||
self.gui_config.extra_time
|
||||
* self.gui_config.samplerate
|
||||
/ self.zc
|
||||
)
|
||||
)
|
||||
* self.zc
|
||||
)
|
||||
self.input_wav = torch.zeros(
|
||||
self.extra_frame
|
||||
+ self.crossfade_frame
|
||||
+ self.sola_search_frame
|
||||
+ self.block_frame,
|
||||
device=self.config.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
self.input_wav_denoise = self.input_wav.clone()
|
||||
self.input_wav_res = torch.zeros(
|
||||
160 * self.input_wav.shape[0] // self.zc,
|
||||
device=self.config.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
self.rms_buffer = np.zeros(4 * self.zc, dtype="float32")
|
||||
self.sola_buffer = torch.zeros(
|
||||
self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
|
||||
)
|
||||
self.nr_buffer = self.sola_buffer.clone()
|
||||
self.output_buffer = self.input_wav.clone()
|
||||
self.skip_head = self.extra_frame // self.zc
|
||||
self.return_length = (
|
||||
self.block_frame + self.sola_buffer_frame + self.sola_search_frame
|
||||
) // self.zc
|
||||
self.fade_in_window = (
|
||||
torch.sin(
|
||||
0.5
|
||||
* np.pi
|
||||
* torch.linspace(
|
||||
0.0,
|
||||
1.0,
|
||||
steps=self.sola_buffer_frame,
|
||||
device=self.config.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
)
|
||||
** 2
|
||||
)
|
||||
self.fade_out_window = 1 - self.fade_in_window
|
||||
self.resampler = tat.Resample(
|
||||
orig_freq=self.gui_config.samplerate,
|
||||
new_freq=16000,
|
||||
dtype=torch.float32,
|
||||
).to(self.config.device)
|
||||
if self.rvc.tgt_sr != self.gui_config.samplerate:
|
||||
self.resampler2 = tat.Resample(
|
||||
orig_freq=self.rvc.tgt_sr,
|
||||
new_freq=self.gui_config.samplerate,
|
||||
dtype=torch.float32,
|
||||
).to(self.config.device)
|
||||
else:
|
||||
self.resampler2 = None
|
||||
self.tg = TorchGate(
|
||||
sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9
|
||||
).to(self.config.device)
|
||||
thread_vc = threading.Thread(target=self.soundinput)
|
||||
thread_vc.start()
|
||||
|
||||
def soundinput(self):
|
||||
channels = 1 if sys.platform == "darwin" else 2
|
||||
with sd.Stream(
|
||||
channels=channels,
|
||||
callback=self.audio_callback,
|
||||
blocksize=self.block_frame,
|
||||
samplerate=self.gui_config.samplerate,
|
||||
dtype="float32",
|
||||
) as stream:
|
||||
global stream_latency
|
||||
stream_latency = stream.latency[-1]
|
||||
while self.flag_vc:
|
||||
time.sleep(self.gui_config.block_time)
|
||||
logger.info("Audio block passed.")
|
||||
logger.info("Ending VC")
|
||||
|
||||
def audio_callback(self, indata: np.ndarray, outdata: np.ndarray, frames, times, status):
|
||||
start_time = time.perf_counter()
|
||||
indata = librosa.to_mono(indata.T)
|
||||
if self.gui_config.threhold > -60:
|
||||
indata = np.append(self.rms_buffer, indata)
|
||||
rms = librosa.feature.rms(y=indata, frame_length=4 * self.zc, hop_length=self.zc)[:, 2:]
|
||||
self.rms_buffer[:] = indata[-4 * self.zc :]
|
||||
indata = indata[2 * self.zc - self.zc // 2 :]
|
||||
db_threhold = (
|
||||
librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold
|
||||
)
|
||||
for i in range(db_threhold.shape[0]):
|
||||
if db_threhold[i]:
|
||||
indata[i * self.zc : (i + 1) * self.zc] = 0
|
||||
indata = indata[self.zc // 2 :]
|
||||
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
|
||||
self.input_wav[-indata.shape[0] :] = torch.from_numpy(indata).to(self.config.device)
|
||||
self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
|
||||
# input noise reduction and resampling
|
||||
if self.gui_config.I_noise_reduce:
|
||||
self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[self.block_frame :].clone()
|
||||
input_wav = self.input_wav[-self.sola_buffer_frame - self.block_frame :]
|
||||
input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)).squeeze(0)
|
||||
input_wav[: self.sola_buffer_frame] *= self.fade_in_window
|
||||
input_wav[: self.sola_buffer_frame] += self.nr_buffer * self.fade_out_window
|
||||
self.input_wav_denoise[-self.block_frame :] = input_wav[: self.block_frame]
|
||||
self.nr_buffer[:] = input_wav[self.block_frame :]
|
||||
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
|
||||
self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
|
||||
)[160:]
|
||||
else:
|
||||
self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = (
|
||||
self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[160:]
|
||||
)
|
||||
# infer
|
||||
if self.function == "vc":
|
||||
infer_wav = self.rvc.infer(
|
||||
self.input_wav_res,
|
||||
self.block_frame_16k,
|
||||
self.skip_head,
|
||||
self.return_length,
|
||||
self.gui_config.f0method,
|
||||
)
|
||||
if self.resampler2 is not None:
|
||||
infer_wav = self.resampler2(infer_wav)
|
||||
elif self.gui_config.I_noise_reduce:
|
||||
infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
|
||||
else:
|
||||
infer_wav = self.input_wav[self.extra_frame :].clone()
|
||||
# output noise reduction
|
||||
if self.gui_config.O_noise_reduce and self.function == "vc":
|
||||
self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone()
|
||||
self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :]
|
||||
infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0)
|
||||
# volume envelop mixing
|
||||
if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
|
||||
if self.gui_config.I_noise_reduce:
|
||||
input_wav = self.input_wav_denoise[self.extra_frame :]
|
||||
else:
|
||||
input_wav = self.input_wav[self.extra_frame :]
|
||||
rms1 = librosa.feature.rms(
|
||||
y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
|
||||
frame_length=4 * self.zc,
|
||||
hop_length=self.zc,
|
||||
)
|
||||
rms1 = torch.from_numpy(rms1).to(self.config.device)
|
||||
rms1 = F.interpolate(
|
||||
rms1.unsqueeze(0),
|
||||
size=infer_wav.shape[0] + 1,
|
||||
mode="linear",
|
||||
align_corners=True,
|
||||
)[0, 0, :-1]
|
||||
rms2 = librosa.feature.rms(
|
||||
y=infer_wav[:].cpu().numpy(),
|
||||
frame_length=4 * self.zc,
|
||||
hop_length=self.zc,
|
||||
)
|
||||
rms2 = torch.from_numpy(rms2).to(self.config.device)
|
||||
rms2 = F.interpolate(
|
||||
rms2.unsqueeze(0),
|
||||
size=infer_wav.shape[0] + 1,
|
||||
mode="linear",
|
||||
align_corners=True,
|
||||
)[0, 0, :-1]
|
||||
rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
|
||||
infer_wav *= torch.pow(
|
||||
rms1 / rms2, torch.tensor(1 - self.gui_config.rms_mix_rate)
|
||||
)
|
||||
# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
|
||||
conv_input = infer_wav[None, None, : self.sola_buffer_frame + self.sola_search_frame]
|
||||
cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
|
||||
cor_den = torch.sqrt(
|
||||
F.conv1d(
|
||||
conv_input**2,
|
||||
torch.ones(1, 1, self.sola_buffer_frame, device=self.config.device),
|
||||
)
|
||||
+ 1e-8
|
||||
)
|
||||
if sys.platform == "darwin":
|
||||
_, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0])
|
||||
sola_offset = sola_offset.item()
|
||||
else:
|
||||
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
|
||||
logger.info(f"sola_offset = {sola_offset}")
|
||||
infer_wav = infer_wav[sola_offset:]
|
||||
if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
|
||||
infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
|
||||
infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
|
||||
else:
|
||||
infer_wav[: self.sola_buffer_frame] = phase_vocoder(
|
||||
self.sola_buffer,
|
||||
infer_wav[: self.sola_buffer_frame],
|
||||
self.fade_out_window,
|
||||
self.fade_in_window,
|
||||
)
|
||||
self.sola_buffer[:] = infer_wav[
|
||||
self.block_frame : self.block_frame + self.sola_buffer_frame
|
||||
]
|
||||
if sys.platform == "darwin":
|
||||
outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
|
||||
else:
|
||||
outdata[:] = infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
|
||||
total_time = time.perf_counter() - start_time
|
||||
logger.info(f"Infer time: {total_time:.2f}")
|
||||
|
||||
def get_devices(self, update: bool = True):
|
||||
if update:
|
||||
sd._terminate()
|
||||
sd._initialize()
|
||||
devices = sd.query_devices()
|
||||
hostapis = sd.query_hostapis()
|
||||
for hostapi in hostapis:
|
||||
for device_idx in hostapi["devices"]:
|
||||
devices[device_idx]["hostapi_name"] = hostapi["name"]
|
||||
input_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
]
|
||||
output_devices = [
|
||||
f"{d['name']} ({d['hostapi_name']})"
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
]
|
||||
input_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_input_channels"] > 0
|
||||
]
|
||||
output_devices_indices = [
|
||||
d["index"] if "index" in d else d["name"]
|
||||
for d in devices
|
||||
if d["max_output_channels"] > 0
|
||||
]
|
||||
return (
|
||||
input_devices,
|
||||
output_devices,
|
||||
input_devices_indices,
|
||||
output_devices_indices,
|
||||
)
|
||||
|
||||
def set_devices(self, input_device, output_device):
|
||||
(
|
||||
input_devices,
|
||||
output_devices,
|
||||
input_device_indices,
|
||||
output_device_indices,
|
||||
) = self.get_devices()
|
||||
logger.debug(f"Available input devices: {input_devices}")
|
||||
logger.debug(f"Available output devices: {output_devices}")
|
||||
logger.debug(f"Selected input device: {input_device}")
|
||||
logger.debug(f"Selected output device: {output_device}")
|
||||
|
||||
if input_device not in input_devices:
|
||||
logger.error(f"Input device '{input_device}' is not in the list of available devices")
|
||||
raise HTTPException(status_code=400, detail=f"Input device '{input_device}' is not available")
|
||||
|
||||
if output_device not in output_devices:
|
||||
logger.error(f"Output device '{output_device}' is not in the list of available devices")
|
||||
raise HTTPException(status_code=400, detail=f"Output device '{output_device}' is not available")
|
||||
|
||||
sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
|
||||
sd.default.device[1] = output_device_indices[output_devices.index(output_device)]
|
||||
logger.info(f"Input device set to {sd.default.device[0]}: {input_device}")
|
||||
logger.info(f"Output device set to {sd.default.device[1]}: {output_device}")
|
||||
|
||||
audio_api = AudioAPI()
|
||||
|
||||
@app.get("/inputDevices", response_model=list)
|
||||
def get_input_devices():
|
||||
try:
|
||||
input_devices, _, _, _ = audio_api.get_devices()
|
||||
return input_devices
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get input devices: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to get input devices")
|
||||
|
||||
@app.get("/outputDevices", response_model=list)
|
||||
def get_output_devices():
|
||||
try:
|
||||
_, output_devices, _, _ = audio_api.get_devices()
|
||||
return output_devices
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get output devices: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to get output devices")
|
||||
|
||||
@app.post("/config")
|
||||
def configure_audio(config_data: ConfigData):
|
||||
try:
|
||||
logger.info(f"Configuring audio with data: {config_data}")
|
||||
if audio_api.set_values(config_data):
|
||||
settings = config_data.dict()
|
||||
settings["use_jit"] = False
|
||||
with open("configs/config.json", "w", encoding='utf-8') as j:
|
||||
json.dump(settings, j, ensure_ascii=False)
|
||||
logger.info("Configuration set successfully")
|
||||
return {"message": "Configuration set successfully"}
|
||||
except HTTPException as e:
|
||||
logger.error(f"Configuration error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Configuration failed: {e}")
|
||||
raise HTTPException(status_code=400, detail=f"Configuration failed: {e}")
|
||||
|
||||
@app.post("/start")
|
||||
def start_conversion():
|
||||
try:
|
||||
if not audio_api.flag_vc:
|
||||
audio_api.start_vc()
|
||||
return {"message": "Audio conversion started"}
|
||||
else:
|
||||
logger.warning("Audio conversion already running")
|
||||
raise HTTPException(status_code=400, detail="Audio conversion already running")
|
||||
except HTTPException as e:
|
||||
logger.error(f"Start conversion error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to start conversion: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to start conversion: {e}")
|
||||
|
||||
@app.post("/stop")
|
||||
def stop_conversion():
|
||||
try:
|
||||
if audio_api.flag_vc:
|
||||
audio_api.flag_vc = False
|
||||
global stream_latency
|
||||
stream_latency = -1
|
||||
return {"message": "Audio conversion stopped"}
|
||||
else:
|
||||
logger.warning("Audio conversion not running")
|
||||
raise HTTPException(status_code=400, detail="Audio conversion not running")
|
||||
except HTTPException as e:
|
||||
logger.error(f"Stop conversion error: {e.detail}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to stop conversion: {e}")
|
||||
raise HTTPException(status_code=500, detail="Failed to stop conversion: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if sys.platform == "win32":
|
||||
freeze_support()
|
||||
load_dotenv()
|
||||
os.environ["OMP_NUM_THREADS"] = "4"
|
||||
if sys.platform == "darwin":
|
||||
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
||||
from tools.torchgate import TorchGate
|
||||
import tools.rvc_for_realtime as rvc_for_realtime
|
||||
from configs.config import Config
|
||||
audio_api.config = Config()
|
||||
audio_api.initialize_queues()
|
||||
uvicorn.run(app, host="0.0.0.0", port=6242)
|
||||
@ -65,13 +65,15 @@ def process_lines(lines, target_gpu):
|
||||
return output_lines
|
||||
|
||||
def main():
|
||||
if len(sys.argv) != 3:
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python configure_gpu_deps.py <pyproject.toml> <gpu_type>")
|
||||
print(" where <gpu_type> is either 'nvidia' or 'amd'")
|
||||
sys.exit(1)
|
||||
|
||||
toml_path = sys.argv[1]
|
||||
gpu_type = sys.argv[2].lower()
|
||||
gpu_type = sys.argv[1].lower()
|
||||
toml_path = "pyproject.toml"
|
||||
with open(toml_path, "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()
|
||||
if gpu_type not in {"nvidia", "amd"}:
|
||||
print("gpu_type must be either 'nvidia' or 'amd'")
|
||||
sys.exit(1)
|
||||
|
||||
@ -1,186 +0,0 @@
|
||||
name: pydml
|
||||
channels:
|
||||
- pytorch
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
|
||||
- defaults
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
|
||||
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/
|
||||
dependencies:
|
||||
- abseil-cpp=20211102.0=hd77b12b_0
|
||||
- absl-py=1.3.0=py310haa95532_0
|
||||
- aiohttp=3.8.3=py310h2bbff1b_0
|
||||
- aiosignal=1.2.0=pyhd3eb1b0_0
|
||||
- async-timeout=4.0.2=py310haa95532_0
|
||||
- attrs=22.1.0=py310haa95532_0
|
||||
- blas=1.0=mkl
|
||||
- blinker=1.4=py310haa95532_0
|
||||
- bottleneck=1.3.5=py310h9128911_0
|
||||
- brotli=1.0.9=h2bbff1b_7
|
||||
- brotli-bin=1.0.9=h2bbff1b_7
|
||||
- brotlipy=0.7.0=py310h2bbff1b_1002
|
||||
- bzip2=1.0.8=he774522_0
|
||||
- c-ares=1.19.0=h2bbff1b_0
|
||||
- ca-certificates=2023.05.30=haa95532_0
|
||||
- cachetools=4.2.2=pyhd3eb1b0_0
|
||||
- certifi=2023.5.7=py310haa95532_0
|
||||
- cffi=1.15.1=py310h2bbff1b_3
|
||||
- charset-normalizer=2.0.4=pyhd3eb1b0_0
|
||||
- click=8.0.4=py310haa95532_0
|
||||
- colorama=0.4.6=py310haa95532_0
|
||||
- contourpy=1.0.5=py310h59b6b97_0
|
||||
- cryptography=39.0.1=py310h21b164f_0
|
||||
- cycler=0.11.0=pyhd3eb1b0_0
|
||||
- fonttools=4.25.0=pyhd3eb1b0_0
|
||||
- freetype=2.12.1=ha860e81_0
|
||||
- frozenlist=1.3.3=py310h2bbff1b_0
|
||||
- giflib=5.2.1=h8cc25b3_3
|
||||
- glib=2.69.1=h5dc1a3c_2
|
||||
- google-auth=2.6.0=pyhd3eb1b0_0
|
||||
- google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
|
||||
- grpc-cpp=1.48.2=hf108199_0
|
||||
- grpcio=1.48.2=py310hf108199_0
|
||||
- gst-plugins-base=1.18.5=h9e645db_0
|
||||
- gstreamer=1.18.5=hd78058f_0
|
||||
- icu=58.2=ha925a31_3
|
||||
- idna=3.4=py310haa95532_0
|
||||
- intel-openmp=2023.1.0=h59b6b97_46319
|
||||
- jpeg=9e=h2bbff1b_1
|
||||
- kiwisolver=1.4.4=py310hd77b12b_0
|
||||
- krb5=1.19.4=h5b6d351_0
|
||||
- lerc=3.0=hd77b12b_0
|
||||
- libbrotlicommon=1.0.9=h2bbff1b_7
|
||||
- libbrotlidec=1.0.9=h2bbff1b_7
|
||||
- libbrotlienc=1.0.9=h2bbff1b_7
|
||||
- libclang=14.0.6=default_hb5a9fac_1
|
||||
- libclang13=14.0.6=default_h8e68704_1
|
||||
- libdeflate=1.17=h2bbff1b_0
|
||||
- libffi=3.4.4=hd77b12b_0
|
||||
- libiconv=1.16=h2bbff1b_2
|
||||
- libogg=1.3.5=h2bbff1b_1
|
||||
- libpng=1.6.39=h8cc25b3_0
|
||||
- libprotobuf=3.20.3=h23ce68f_0
|
||||
- libtiff=4.5.0=h6c2663c_2
|
||||
- libuv=1.44.2=h2bbff1b_0
|
||||
- libvorbis=1.3.7=he774522_0
|
||||
- libwebp=1.2.4=hbc33d0d_1
|
||||
- libwebp-base=1.2.4=h2bbff1b_1
|
||||
- libxml2=2.10.3=h0ad7f3c_0
|
||||
- libxslt=1.1.37=h2bbff1b_0
|
||||
- lz4-c=1.9.4=h2bbff1b_0
|
||||
- markdown=3.4.1=py310haa95532_0
|
||||
- markupsafe=2.1.1=py310h2bbff1b_0
|
||||
- matplotlib=3.7.1=py310haa95532_1
|
||||
- matplotlib-base=3.7.1=py310h4ed8f06_1
|
||||
- mkl=2023.1.0=h8bd8f75_46356
|
||||
- mkl-service=2.4.0=py310h2bbff1b_1
|
||||
- mkl_fft=1.3.6=py310h4ed8f06_1
|
||||
- mkl_random=1.2.2=py310h4ed8f06_1
|
||||
- multidict=6.0.2=py310h2bbff1b_0
|
||||
- munkres=1.1.4=py_0
|
||||
- numexpr=2.8.4=py310h2cd9be0_1
|
||||
- numpy=1.24.3=py310h055cbcc_1
|
||||
- numpy-base=1.24.3=py310h65a83cf_1
|
||||
- oauthlib=3.2.2=py310haa95532_0
|
||||
- openssl=1.1.1t=h2bbff1b_0
|
||||
- packaging=23.0=py310haa95532_0
|
||||
- pandas=1.5.3=py310h4ed8f06_0
|
||||
- pcre=8.45=hd77b12b_0
|
||||
- pillow=9.4.0=py310hd77b12b_0
|
||||
- pip=23.0.1=py310haa95532_0
|
||||
- ply=3.11=py310haa95532_0
|
||||
- protobuf=3.20.3=py310hd77b12b_0
|
||||
- pyasn1=0.4.8=pyhd3eb1b0_0
|
||||
- pyasn1-modules=0.2.8=py_0
|
||||
- pycparser=2.21=pyhd3eb1b0_0
|
||||
- pyjwt=2.4.0=py310haa95532_0
|
||||
- pyopenssl=23.0.0=py310haa95532_0
|
||||
- pyparsing=3.0.9=py310haa95532_0
|
||||
- pyqt=5.15.7=py310hd77b12b_0
|
||||
- pyqt5-sip=12.11.0=py310hd77b12b_0
|
||||
- pysocks=1.7.1=py310haa95532_0
|
||||
- python=3.10.11=h966fe2a_2
|
||||
- python-dateutil=2.8.2=pyhd3eb1b0_0
|
||||
- pytorch-mutex=1.0=cpu
|
||||
- pytz=2022.7=py310haa95532_0
|
||||
- pyyaml=6.0=py310h2bbff1b_1
|
||||
- qt-main=5.15.2=he8e5bd7_8
|
||||
- qt-webengine=5.15.9=hb9a9bb5_5
|
||||
- qtwebkit=5.212=h2bbfb41_5
|
||||
- re2=2022.04.01=hd77b12b_0
|
||||
- requests=2.29.0=py310haa95532_0
|
||||
- requests-oauthlib=1.3.0=py_0
|
||||
- rsa=4.7.2=pyhd3eb1b0_1
|
||||
- setuptools=67.8.0=py310haa95532_0
|
||||
- sip=6.6.2=py310hd77b12b_0
|
||||
- six=1.16.0=pyhd3eb1b0_1
|
||||
- sqlite=3.41.2=h2bbff1b_0
|
||||
- tbb=2021.8.0=h59b6b97_0
|
||||
- tensorboard=2.10.0=py310haa95532_0
|
||||
- tensorboard-data-server=0.6.1=py310haa95532_0
|
||||
- tensorboard-plugin-wit=1.8.1=py310haa95532_0
|
||||
- tk=8.6.12=h2bbff1b_0
|
||||
- toml=0.10.2=pyhd3eb1b0_0
|
||||
- tornado=6.2=py310h2bbff1b_0
|
||||
- tqdm=4.65.0=py310h9909e9c_0
|
||||
- typing_extensions=4.5.0=py310haa95532_0
|
||||
- tzdata=2023c=h04d1e81_0
|
||||
- urllib3=1.26.16=py310haa95532_0
|
||||
- vc=14.2=h21ff451_1
|
||||
- vs2015_runtime=14.27.29016=h5e58377_2
|
||||
- werkzeug=2.2.3=py310haa95532_0
|
||||
- wheel=0.38.4=py310haa95532_0
|
||||
- win_inet_pton=1.1.0=py310haa95532_0
|
||||
- xz=5.4.2=h8cc25b3_0
|
||||
- yaml=0.2.5=he774522_0
|
||||
- yarl=1.8.1=py310h2bbff1b_0
|
||||
- zlib=1.2.13=h8cc25b3_0
|
||||
- zstd=1.5.5=hd43e919_0
|
||||
- pip:
|
||||
- antlr4-python3-runtime==4.8
|
||||
- appdirs==1.4.4
|
||||
- audioread==3.0.0
|
||||
- bitarray==2.7.4
|
||||
- cython==0.29.35
|
||||
- decorator==5.1.1
|
||||
- fairseq==0.12.2
|
||||
- faiss-cpu==1.7.4
|
||||
- filelock==3.12.0
|
||||
- hydra-core==1.0.7
|
||||
- jinja2==3.1.2
|
||||
- joblib==1.2.0
|
||||
- lazy-loader==0.2
|
||||
- librosa==0.10.0.post2
|
||||
- llvmlite==0.40.0
|
||||
- lxml==4.9.2
|
||||
- mpmath==1.3.0
|
||||
- msgpack==1.0.5
|
||||
- networkx==3.1
|
||||
- noisereduce==2.0.1
|
||||
- numba==0.57.0
|
||||
- omegaconf==2.0.6
|
||||
- opencv-python==4.7.0.72
|
||||
- pooch==1.6.0
|
||||
- portalocker==2.7.0
|
||||
- pysimplegui==4.60.5
|
||||
- pywin32==306
|
||||
- pyworld==0.3.3
|
||||
- regex==2023.5.5
|
||||
- sacrebleu==2.3.1
|
||||
- scikit-learn==1.2.2
|
||||
- scipy==1.10.1
|
||||
- sounddevice==0.4.6
|
||||
- soundfile==0.12.1
|
||||
- soxr==0.3.5
|
||||
- sympy==1.12
|
||||
- tabulate==0.9.0
|
||||
- threadpoolctl==3.1.0
|
||||
- torch==2.0.0
|
||||
- torch-directml==0.2.0.dev230426
|
||||
- torchaudio==2.0.1
|
||||
- torchvision==0.15.1
|
||||
- wget==3.2
|
||||
prefix: D:\ProgramData\anaconda3_\envs\pydml
|
||||
@ -1,2 +0,0 @@
|
||||
runtime\python.exe gui_v1.py --pycmd runtime\python.exe --dml
|
||||
pause
|
||||
@ -1,2 +0,0 @@
|
||||
runtime\python.exe gui_v1.py
|
||||
pause
|
||||
@ -1,2 +0,0 @@
|
||||
runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897 --dml
|
||||
pause
|
||||
@ -1,2 +0,0 @@
|
||||
runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
|
||||
pause
|
||||
578
poetry.lock
generated
578
poetry.lock
generated
@ -174,13 +174,13 @@ doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pyd
|
||||
|
||||
[[package]]
|
||||
name = "antlr4-python3-runtime"
|
||||
version = "4.8"
|
||||
description = "ANTLR 4.8 runtime for Python 3.7"
|
||||
version = "4.9.3"
|
||||
description = "ANTLR 4.9.3 runtime for Python 3.7"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "antlr4-python3-runtime-4.8.tar.gz", hash = "sha256:15793f5d0512a372b4e7d2284058ad32ce7dd27126b105fb0b2245130445db33"},
|
||||
{file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -204,23 +204,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
|
||||
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
|
||||
trio = ["trio (>=0.23)"]
|
||||
|
||||
[[package]]
|
||||
name = "aria2"
|
||||
version = "0.0.1b0"
|
||||
description = "python wheel for aria2 static build."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "aria2-0.0.1b0-py3-none-manylinux_2_17_aarch64.whl", hash = "sha256:349f9fe693d900453d2c1a885cab761a0fce6b92ac7e390df5da32edbb5018e5"},
|
||||
{file = "aria2-0.0.1b0-py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:80e6626d297e8d391f483e173abc193a7232a74b26c108c89250e697ced02596"},
|
||||
{file = "aria2-0.0.1b0-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:8bbbeaa1bc1a0b30ccdd7909e786821fb345071ba0fe14157a0f715a761e1691"},
|
||||
{file = "aria2-0.0.1b0-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:0f87a26658c44668d4aadaf50c0342c63b04814032f51bf533bdeacc024ed1e2"},
|
||||
{file = "aria2-0.0.1b0-py3-none-win32.whl", hash = "sha256:4e73cfb52fd61aedfb7e8e23942b50eebb3081c452ece8acb682622dfcdb709d"},
|
||||
{file = "aria2-0.0.1b0-py3-none-win_amd64.whl", hash = "sha256:00aa00c9f8b709d37849a84760709d31b6c7752007f645f8bea175325c0b14cb"},
|
||||
{file = "aria2-0.0.1b0.tar.gz", hash = "sha256:332b31b5caafb381c43112de01f01cade19de5c68b515c37d971bb147edf56f8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "24.2.0"
|
||||
@ -866,34 +849,39 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "fairseq"
|
||||
version = "0.12.2"
|
||||
version = "0.12.3"
|
||||
description = "Facebook AI Research Sequence-to-Sequence Toolkit"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "fairseq-0.12.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:fe65b07c5121b7cda0c7a17166994a6b0059259ce37881b6daa117b8c209b662"},
|
||||
{file = "fairseq-0.12.2-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:0543905012e39f00bd8c3f3781d9f49e76ab309801eb2eb7de250f5984df0de3"},
|
||||
{file = "fairseq-0.12.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c4877d65346797fc580a3a7e6e2364d2331a0026ef099c22eb8311441e49c2c6"},
|
||||
{file = "fairseq-0.12.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:26454f334ca705c67f898846dff34e14c148fcdaf53b4f52d64209773b509347"},
|
||||
{file = "fairseq-0.12.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:3b8c8b6dc368d2fd23a06ff613a2af05959eee275fe90846d7cffef4a43c522a"},
|
||||
{file = "fairseq-0.12.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:08fa308c760f995cdc13d9c385e2b9d923a78b48275d8b4d78f3a854c71a8f29"},
|
||||
{file = "fairseq-0.12.2.tar.gz", hash = "sha256:34f1b18426bf3844714534162f065ab733e049597476daa35fffb4d06a92b524"},
|
||||
]
|
||||
files = []
|
||||
develop = false
|
||||
|
||||
[package.dependencies]
|
||||
bitarray = "*"
|
||||
cffi = "*"
|
||||
cython = "*"
|
||||
hydra-core = ">=1.0.7,<1.1"
|
||||
numpy = {version = "*", markers = "python_version >= \"3.7\""}
|
||||
omegaconf = "<2.1"
|
||||
hydra-core = ">=1.3.2"
|
||||
numpy = ">=1.21.3"
|
||||
omegaconf = "*"
|
||||
packaging = "*"
|
||||
regex = "*"
|
||||
sacrebleu = ">=1.4.12"
|
||||
torch = "*"
|
||||
scikit-learn = "*"
|
||||
torch = ">=1.13"
|
||||
torchaudio = ">=0.8.0"
|
||||
tqdm = "*"
|
||||
|
||||
[package.extras]
|
||||
dev = ["black (==22.3.0)", "flake8", "pytest"]
|
||||
docs = ["sphinx", "sphinx-argparse"]
|
||||
|
||||
[package.source]
|
||||
type = "git"
|
||||
url = "https://github.com/One-sixth/fairseq.git"
|
||||
reference = "HEAD"
|
||||
resolved_reference = "44800430a728c2216fd1cf1e8daa672f50dfacba"
|
||||
|
||||
[[package]]
|
||||
name = "faiss-cpu"
|
||||
version = "1.7.3"
|
||||
@ -1445,19 +1433,20 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
|
||||
|
||||
[[package]]
|
||||
name = "hydra-core"
|
||||
version = "1.0.7"
|
||||
version = "1.3.2"
|
||||
description = "A framework for elegantly configuring complex applications"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "hydra-core-1.0.7.tar.gz", hash = "sha256:58cc3f7531995b6d8de162ca21f936e17bdaebd4d1e8614d63c32e17c2e41e45"},
|
||||
{file = "hydra_core-1.0.7-py3-none-any.whl", hash = "sha256:e800c6deb8309395508094851fa93bc13408f2285261eb97e626d37193b58a9f"},
|
||||
{file = "hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824"},
|
||||
{file = "hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
antlr4-python3-runtime = "4.8"
|
||||
omegaconf = ">=2.0.5,<2.1"
|
||||
antlr4-python3-runtime = "==4.9.*"
|
||||
omegaconf = ">=2.2,<2.4"
|
||||
packaging = "*"
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
@ -1665,34 +1654,56 @@ files = [
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "librosa"
|
||||
version = "0.9.1"
|
||||
description = "Python module for audio and music processing"
|
||||
name = "lazy-loader"
|
||||
version = "0.4"
|
||||
description = "Makes it easy to load subpackages and functions on demand."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "librosa-0.9.1-py3-none-any.whl", hash = "sha256:c2bb61a8008367cca89a3f1dad352d8e55fe5ca5f7414fb5d5258eb52765db33"},
|
||||
{file = "librosa-0.9.1.tar.gz", hash = "sha256:7ed5d6e3f4546e5e3c2840691f9ddc56878f914a35a50060df5fca2b26d4b614"},
|
||||
{file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
|
||||
{file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
audioread = ">=2.1.5"
|
||||
decorator = ">=4.0.10"
|
||||
packaging = "*"
|
||||
|
||||
[package.extras]
|
||||
dev = ["changelist (==0.5)"]
|
||||
lint = ["pre-commit (==3.7.0)"]
|
||||
test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "librosa"
|
||||
version = "0.10.2"
|
||||
description = "Python module for audio and music processing"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "librosa-0.10.2-py3-none-any.whl", hash = "sha256:469470592c51533e22d3caa1356c735de9bb0a2e9d29345ab4a1ed52e85f7dc8"},
|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
audioread = ">=2.1.9"
|
||||
decorator = ">=4.3.0"
|
||||
joblib = ">=0.14"
|
||||
numba = ">=0.45.1"
|
||||
numpy = ">=1.17.0"
|
||||
packaging = ">=20.0"
|
||||
pooch = ">=1.0"
|
||||
resampy = ">=0.2.2"
|
||||
scikit-learn = ">=0.19.1"
|
||||
lazy-loader = ">=0.1"
|
||||
msgpack = ">=1.0"
|
||||
numba = ">=0.51.0"
|
||||
numpy = ">=1.20.3,<1.22.0 || >1.22.0,<1.22.1 || >1.22.1,<1.22.2 || >1.22.2"
|
||||
pooch = ">=1.1"
|
||||
scikit-learn = ">=0.20.0"
|
||||
scipy = ">=1.2.0"
|
||||
soundfile = ">=0.10.2"
|
||||
soundfile = ">=0.12.1"
|
||||
soxr = ">=0.3.2"
|
||||
typing-extensions = ">=4.1.1"
|
||||
|
||||
[package.extras]
|
||||
display = ["matplotlib (>=3.3.0)"]
|
||||
docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (<0.50)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (==0.5.*)", "sphinxcontrib-svg2pdfconverter"]
|
||||
tests = ["contextlib2", "matplotlib (>=3.3.0)", "pytest", "pytest-cov", "pytest-mpl", "samplerate", "soxr"]
|
||||
docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (>=0.51)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=1.2.0)", "sphinxcontrib-svg2pdfconverter"]
|
||||
tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
|
||||
|
||||
[[package]]
|
||||
name = "linkify-it-py"
|
||||
@ -2147,6 +2158,80 @@ docs = ["sphinx"]
|
||||
gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
|
||||
tests = ["pytest (>=4.6)"]
|
||||
|
||||
[[package]]
|
||||
name = "msgpack"
|
||||
version = "1.1.0"
|
||||
description = "MessagePack serializer"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
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|
||||
{file = "msgpack-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:4d1b7ff2d6146e16e8bd665ac726a89c74163ef8cd39fa8c1087d4e52d3a2325"},
|
||||
{file = "msgpack-1.1.0.tar.gz", hash = "sha256:dd432ccc2c72b914e4cb77afce64aab761c1137cc698be3984eee260bcb2896e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "multidict"
|
||||
version = "6.0.5"
|
||||
@ -2363,7 +2448,7 @@ description = "CUBLAS native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
|
||||
{file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
|
||||
@ -2376,7 +2461,7 @@ description = "CUDA profiling tools runtime libs."
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
|
||||
{file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
|
||||
@ -2389,7 +2474,7 @@ description = "NVRTC native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
|
||||
{file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
|
||||
@ -2402,27 +2487,12 @@ description = "CUDA Runtime native Libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
|
||||
{file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cudnn-cu12"
|
||||
version = "8.9.2.26"
|
||||
description = "cuDNN runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform == \"win32\""
|
||||
files = [
|
||||
{file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
nvidia-cublas-cu12 = "*"
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-cudnn-cu12"
|
||||
version = "9.1.0.70"
|
||||
@ -2430,7 +2500,7 @@ description = "cuDNN runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"win32\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"},
|
||||
{file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"},
|
||||
@ -2446,7 +2516,7 @@ description = "CUFFT native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
|
||||
{file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
|
||||
@ -2459,7 +2529,7 @@ description = "CURAND native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
|
||||
{file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
|
||||
@ -2472,7 +2542,7 @@ description = "CUDA solver native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
|
||||
{file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
|
||||
@ -2490,7 +2560,7 @@ description = "CUSPARSE native runtime libraries"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
|
||||
{file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
|
||||
@ -2499,18 +2569,6 @@ files = [
|
||||
[package.dependencies]
|
||||
nvidia-nvjitlink-cu12 = "*"
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-nccl-cu12"
|
||||
version = "2.18.1"
|
||||
description = "NVIDIA Collective Communication Library (NCCL) Runtime"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform == \"win32\""
|
||||
files = [
|
||||
{file = "nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:1a6c4acefcbebfa6de320f412bf7866de856e786e0462326ba1bac40de0b5e71"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "nvidia-nccl-cu12"
|
||||
version = "2.20.5"
|
||||
@ -2518,7 +2576,7 @@ description = "NVIDIA Collective Communication Library (NCCL) Runtime"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"win32\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
|
||||
{file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
|
||||
@ -2531,7 +2589,7 @@ description = "Nvidia JIT LTO Library"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_aarch64.whl", hash = "sha256:84fb38465a5bc7c70cbc320cfd0963eb302ee25a5e939e9f512bbba55b6072fb"},
|
||||
{file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl", hash = "sha256:562ab97ea2c23164823b2a89cb328d01d45cb99634b8c65fe7cd60d14562bd79"},
|
||||
@ -2545,7 +2603,7 @@ description = "NVIDIA Tools Extension"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
|
||||
{file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
|
||||
@ -2553,19 +2611,19 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "omegaconf"
|
||||
version = "2.0.6"
|
||||
version = "2.3.0"
|
||||
description = "A flexible configuration library"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "omegaconf-2.0.6-py3-none-any.whl", hash = "sha256:9e349fd76819b95b47aa628edea1ff83fed5b25108608abdd6c7fdca188e302a"},
|
||||
{file = "omegaconf-2.0.6.tar.gz", hash = "sha256:92ca535a788d21651bf4c2eaf5c1ca4c7a8003b2dab4a87cbb09109784268806"},
|
||||
{file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"},
|
||||
{file = "omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
PyYAML = ">=5.1"
|
||||
typing-extensions = "*"
|
||||
antlr4-python3-runtime = "==4.9.*"
|
||||
PyYAML = ">=5.1.0"
|
||||
|
||||
[[package]]
|
||||
name = "onnxruntime"
|
||||
@ -3834,6 +3892,44 @@ cffi = ">=1.0"
|
||||
[package.extras]
|
||||
numpy = ["numpy"]
|
||||
|
||||
[[package]]
|
||||
name = "soxr"
|
||||
version = "0.5.0.post1"
|
||||
description = "High quality, one-dimensional sample-rate conversion library"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "soxr-0.5.0.post1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:7406d782d85f8cf64e66b65e6b7721973de8a1dc50b9e88bc2288c343a987484"},
|
||||
{file = "soxr-0.5.0.post1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fa0a382fb8d8e2afed2c1642723b2d2d1b9a6728ff89f77f3524034c8885b8c9"},
|
||||
{file = "soxr-0.5.0.post1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b01d3efb95a2851f78414bcd00738b0253eec3f5a1e5482838e965ffef84969"},
|
||||
{file = "soxr-0.5.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fcc049b0a151a65aa75b92f0ac64bb2dba785d16b78c31c2b94e68c141751d6d"},
|
||||
{file = "soxr-0.5.0.post1-cp310-cp310-win_amd64.whl", hash = "sha256:97f269bc26937c267a2ace43a77167d0c5c8bba5a2b45863bb6042b5b50c474e"},
|
||||
{file = "soxr-0.5.0.post1-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:6fb77b626773a966e3d8f6cb24f6f74b5327fa5dc90f1ff492450e9cdc03a378"},
|
||||
{file = "soxr-0.5.0.post1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:39e0f791ba178d69cd676485dbee37e75a34f20daa478d90341ecb7f6d9d690f"},
|
||||
{file = "soxr-0.5.0.post1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f0b558f445ba4b64dbcb37b5f803052eee7d93b1dbbbb97b3ec1787cb5a28eb"},
|
||||
{file = "soxr-0.5.0.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ca6903671808e0a6078b0d146bb7a2952b118dfba44008b2aa60f221938ba829"},
|
||||
{file = "soxr-0.5.0.post1-cp311-cp311-win_amd64.whl", hash = "sha256:c4d8d5283ed6f5efead0df2c05ae82c169cfdfcf5a82999c2d629c78b33775e8"},
|
||||
{file = "soxr-0.5.0.post1-cp312-abi3-macosx_10_14_x86_64.whl", hash = "sha256:fef509466c9c25f65eae0ce1e4b9ac9705d22c6038c914160ddaf459589c6e31"},
|
||||
{file = "soxr-0.5.0.post1-cp312-abi3-macosx_11_0_arm64.whl", hash = "sha256:4704ba6b13a3f1e41d12acf192878384c1c31f71ce606829c64abdf64a8d7d32"},
|
||||
{file = "soxr-0.5.0.post1-cp312-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bd052a66471a7335b22a6208601a9d0df7b46b8d087dce4ff6e13eed6a33a2a1"},
|
||||
{file = "soxr-0.5.0.post1-cp312-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3f16810dd649ab1f433991d2a9661e9e6a116c2b4101039b53b3c3e90a094fc"},
|
||||
{file = "soxr-0.5.0.post1-cp312-abi3-win_amd64.whl", hash = "sha256:b1be9fee90afb38546bdbd7bde714d1d9a8c5a45137f97478a83b65e7f3146f6"},
|
||||
{file = "soxr-0.5.0.post1-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:c5af7b355959061beb90a1d73c4834ece4549f07b708f8c73c088153cec29935"},
|
||||
{file = "soxr-0.5.0.post1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e1dda616fc797b1507b65486f3116ed2c929f13c722922963dd419d64ada6c07"},
|
||||
{file = "soxr-0.5.0.post1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94de2812368e98cb42b4eaeddf8ee1657ecc19bd053f8e67b9b5aa12a3592012"},
|
||||
{file = "soxr-0.5.0.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c8e9c980637e03d3f345a4fd81d56477a58c294fb26205fa121bc4eb23d9d01"},
|
||||
{file = "soxr-0.5.0.post1-cp39-cp39-win_amd64.whl", hash = "sha256:7e71b0b0db450f36de70f1047505231db77a713f8c47df9342582ae8a4b828f2"},
|
||||
{file = "soxr-0.5.0.post1.tar.gz", hash = "sha256:7092b9f3e8a416044e1fa138c8172520757179763b85dc53aa9504f4813cff73"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
|
||||
[package.extras]
|
||||
docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
|
||||
test = ["pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.22.0"
|
||||
@ -3951,101 +4047,32 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.1.1+cu118"
|
||||
version = "2.4.1"
|
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@ -4064,6 +4091,7 @@ nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"
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nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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@ -4073,94 +4101,80 @@ opt-einsum = ["opt-einsum (>=3.3)"]
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[[package]]
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|
||||
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||||
|
||||
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|
||||
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||||
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||||
|
||||
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|
||||
{file = "torchaudio-2.4.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:1796a8961decb522c47daab0fbe27c057d6d143ee22bb6ae0d5eb9b2a038c7b6"},
|
||||
{file = "torchaudio-2.4.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:5b62fc7b16ed708b0c07d4393137797e92f63fc3bd5705607d97ba6a9a7cf3f0"},
|
||||
{file = "torchaudio-2.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:d721b186aae7bd8752c9ad95213f5d650926597bb9060728dfe476986a1ff570"},
|
||||
{file = "torchaudio-2.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4ea0fd00142fe795c75bcc20a303981b56f2327c7f7d321b42a8fef1d78aafa9"},
|
||||
{file = "torchaudio-2.4.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:375d8740c8035a50faca7a5afe2fbdb712aa8733715b971b2af61b4003fa1c41"},
|
||||
{file = "torchaudio-2.4.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:74d19cf9ca3dad394afcabb7e6f7ed9ab9f59f2540d502826c7ec3e33985251d"},
|
||||
{file = "torchaudio-2.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:40e9fa8fdc8d328ea4aa90be65fd34c5ef975610dbd707545e3664393a8a2497"},
|
||||
{file = "torchaudio-2.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3adce550850902b9aa6cd2378ccd720ac9ec8cf31e2eba9743ccc84ffcbe76d6"},
|
||||
{file = "torchaudio-2.4.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:98d8e03703f96b13a8d172d1ccdc7badb338227fd762985fdcea6b30f6697bdb"},
|
||||
{file = "torchaudio-2.4.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:36c7e7bc6b358cbf42b769c80206780fa1497d141a985c6b3e7768de44524e9a"},
|
||||
{file = "torchaudio-2.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:f46e34ab3866ad8d8ace0673cd11e697c5cde6a3b7a4d8d789207d4d8badbb6e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
torch = "2.1.1+cu118"
|
||||
|
||||
[package.source]
|
||||
type = "url"
|
||||
url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1%2Bcu118-cp311-cp311-win_amd64.whl#sha256=79b5afa556063be18de4a1964339242301fe04e782e1030a22695257dd9afbd2"
|
||||
|
||||
[[package]]
|
||||
name = "torchaudio"
|
||||
version = "2.4.0"
|
||||
description = "An audio package for PyTorch"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"win32\" and sys_platform != \"linux\""
|
||||
files = [
|
||||
{file = "torchaudio-2.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:733e9d859b88dabefeaf008e3ab2b8c7885b29466068b4b79a42766be4619e46"},
|
||||
{file = "torchaudio-2.4.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:c48bab82a9ee0c67b9323c2ebbe0890a34c5815d1ff1ace77b1c9df4e6fdbbff"},
|
||||
{file = "torchaudio-2.4.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:17cb73d4336771d455cd8dda8b4891307a5346b890a4e6b1d4b73d565258fee1"},
|
||||
{file = "torchaudio-2.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:af19edc1c3c0ac626f594fc67f087db401016d9216af8d62b6c6ff731efbae43"},
|
||||
{file = "torchaudio-2.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:acbcf9129ffcfce808254e2cbff103363c505ce06ed4c4231b3f436a10679d4d"},
|
||||
{file = "torchaudio-2.4.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:393ee8c24110ccc8030482c10cd9d5d0b5e528f6a9dd3d60557e1151aa951b13"},
|
||||
{file = "torchaudio-2.4.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:be969c09466db35e0d79b8b09dff66caedbb9569b42c903a2d5e0db2af760e3c"},
|
||||
{file = "torchaudio-2.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:2993a3288b2b451bf90c7c4d65991b5769e2614d923e295f08a10066ce79d3c0"},
|
||||
{file = "torchaudio-2.4.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ae13a95ef6fabcadb0eff36d85f5048d70474a2e9704fa9c86e9903cbcec0d4a"},
|
||||
{file = "torchaudio-2.4.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:4782a49171d94431bb815a55aa72733f5fe38034bdf6adeced28c226e2cc791b"},
|
||||
{file = "torchaudio-2.4.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:534d1907bb252ecd2ba9e1d61cff7220fd66090e63df7b3c109cea77a19d4cb8"},
|
||||
{file = "torchaudio-2.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:473c149c1c5288f4ce7b609c5ecb7b2528e7958ea701147a20413d65e5a8a59c"},
|
||||
{file = "torchaudio-2.4.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:fc3f8ecd6f0bbfc654d3bc52756a7ca359f1d88b4fa0290e1cdb763a3131b7b9"},
|
||||
{file = "torchaudio-2.4.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:2bcd9700f8ec70804cc9c48d4f6f3fa7372f52421eebb64d02c04bf805ad284d"},
|
||||
{file = "torchaudio-2.4.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:d7fe9e7f2fe8250fde07b20356c44d770d5faa3ca277abdcda3af7d484048fba"},
|
||||
{file = "torchaudio-2.4.0-cp38-cp38-win_amd64.whl", hash = "sha256:6a10d3c29097a4d81533ab79e351c93d6d91eb1584671d5eee59ba3c259be796"},
|
||||
{file = "torchaudio-2.4.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1fd670c808e322c101957a07651e29935f86ec389243c0c43a24edd7a1854841"},
|
||||
{file = "torchaudio-2.4.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:1eecb83c123577779a45381de3a38e4add132a80104cff4afd816913f51ca17b"},
|
||||
{file = "torchaudio-2.4.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:c840894de12a6dd3ea57cbb0d0086123aaa48001ba3ad99ef714fe009eae8eb9"},
|
||||
{file = "torchaudio-2.4.0-cp39-cp39-win_amd64.whl", hash = "sha256:608fd609cdd8323ef4a50c1e984a0be7282a6c630fad22e040e957f8e376950e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
torch = "2.4.0"
|
||||
torch = "2.4.1"
|
||||
|
||||
[[package]]
|
||||
name = "torchcrepe"
|
||||
version = "0.0.20"
|
||||
version = "0.0.23"
|
||||
description = "Pytorch implementation of CREPE pitch tracker"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "torchcrepe-0.0.20-py3-none-any.whl", hash = "sha256:a8803df94cd7675c0ee1f8f6ca6ea78075bca0825497632914b76e0dd5b61d31"},
|
||||
{file = "torchcrepe-0.0.20.tar.gz", hash = "sha256:b4eac23d0a85582739fe69eeb3136e2fb6ebb7e5363a6556f7c9fe83b11f8e2c"},
|
||||
{file = "torchcrepe-0.0.23-py3-none-any.whl", hash = "sha256:6e104465b89e763ba7fd0d1b228162783ed4b5a6c5735772baca286c20d7ae2c"},
|
||||
{file = "torchcrepe-0.0.23.tar.gz", hash = "sha256:8f7e75638a5ab3fbb9cfc1704c173adbcb61de73dbdadcf428b3d93b54dfe57f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
librosa = "0.9.1"
|
||||
librosa = ">=0.9.1"
|
||||
resampy = "*"
|
||||
scipy = "*"
|
||||
torch = "*"
|
||||
torchaudio = "*"
|
||||
tqdm = "*"
|
||||
|
||||
[[package]]
|
||||
@ -4181,6 +4195,45 @@ numpy = "*"
|
||||
torch = "*"
|
||||
torchaudio = "*"
|
||||
|
||||
[[package]]
|
||||
name = "torchvision"
|
||||
version = "0.19.1"
|
||||
description = "image and video datasets and models for torch deep learning"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "torchvision-0.19.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:54e8513099e6f586356c70f809d34f391af71ad182fe071cc328a28af2c40608"},
|
||||
{file = "torchvision-0.19.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:20a1f5e02bfdad7714e55fa3fa698347c11d829fa65e11e5a84df07d93350eed"},
|
||||
{file = "torchvision-0.19.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:7b063116164be52fc6deb4762de7f8c90bfa3a65f8d5caf17f8e2d5aadc75a04"},
|
||||
{file = "torchvision-0.19.1-cp310-cp310-win_amd64.whl", hash = "sha256:f40b6acabfa886da1bc3768f47679c61feee6bde90deb979d9f300df8c8a0145"},
|
||||
{file = "torchvision-0.19.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:40514282b4896d62765b8e26d7091c32e17c35817d00ec4be2362ea3ba3d1787"},
|
||||
{file = "torchvision-0.19.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:5a91be061ae5d6d5b95e833b93e57ca4d3c56c5a57444dd15da2e3e7fba96050"},
|
||||
{file = "torchvision-0.19.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:d71a6a6fe3a5281ca3487d4c56ad4aad20ff70f82f1d7c79bcb6e7b0c2af00c8"},
|
||||
{file = "torchvision-0.19.1-cp311-cp311-win_amd64.whl", hash = "sha256:70dea324174f5e9981b68e4b7cd524512c106ba64aedef560a86a0bbf2fbf62c"},
|
||||
{file = "torchvision-0.19.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:27ece277ff0f6cdc7fed0627279c632dcb2e58187da771eca24b0fbcf3f8590d"},
|
||||
{file = "torchvision-0.19.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:c659ff92a61f188a1a7baef2850f3c0b6c85685447453c03d0e645ba8f1dcc1c"},
|
||||
{file = "torchvision-0.19.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:c07bf43c2a145d792ecd9d0503d6c73577147ece508d45600d8aac77e4cdfcf9"},
|
||||
{file = "torchvision-0.19.1-cp312-cp312-win_amd64.whl", hash = "sha256:b4283d283675556bb0eae31d29996f53861b17cbdcdf3509e6bc050414ac9289"},
|
||||
{file = "torchvision-0.19.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4c4e4f5b24ea6b087b02ed492ab1e21bba3352c4577e2def14248cfc60732338"},
|
||||
{file = "torchvision-0.19.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:9281d63ead929bb19143731154cd1d8bf0b5e9873dff8578a40e90a6bec3c6fa"},
|
||||
{file = "torchvision-0.19.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:4d10bc9083c4d5fadd7edd7b729700a7be48dab4f62278df3bc73fa48e48a155"},
|
||||
{file = "torchvision-0.19.1-cp38-cp38-win_amd64.whl", hash = "sha256:ccf085ef1824fb9e16f1901285bf89c298c62dfd93267a39e8ee42c71255242f"},
|
||||
{file = "torchvision-0.19.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:731f434d91586769e255b5d70ed1a4457e0a1394a95f4aacf0e1e7e21f80c098"},
|
||||
{file = "torchvision-0.19.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:febe4f14d4afcb47cc861d8be7760ab6a123cd0817f97faf5771488cb6aa90f4"},
|
||||
{file = "torchvision-0.19.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e328309b8670a2e889b2fe76a1c2744a099c11c984da9a822357bd9debd699a5"},
|
||||
{file = "torchvision-0.19.1-cp39-cp39-win_amd64.whl", hash = "sha256:6616f12e00a22e7f3fedbd0fccb0804c05e8fe22871668f10eae65cf3f283614"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
|
||||
torch = "2.4.1"
|
||||
|
||||
[package.extras]
|
||||
gdown = ["gdown (>=4.7.3)"]
|
||||
scipy = ["scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "tornado"
|
||||
version = "6.4.1"
|
||||
@ -4239,33 +4292,6 @@ files = [
|
||||
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
|
||||
test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "2.1.0"
|
||||
description = "A language and compiler for custom Deep Learning operations"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform == \"win32\" or sys_platform == \"linux\""
|
||||
files = [
|
||||
{file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
|
||||
{file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
|
||||
{file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
|
||||
{file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
|
||||
{file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
|
||||
{file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
|
||||
{file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
|
||||
{file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
|
||||
[package.extras]
|
||||
build = ["cmake (>=3.18)", "lit"]
|
||||
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
|
||||
tutorials = ["matplotlib", "pandas", "tabulate"]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.0.0"
|
||||
@ -4273,7 +4299,7 @@ description = "A language and compiler for custom Deep Learning operations"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and sys_platform != \"win32\" and sys_platform != \"linux\""
|
||||
markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
|
||||
{file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
|
||||
@ -4573,4 +4599,4 @@ multidict = ">=4.0"
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.11,<3.12"
|
||||
content-hash = "11dff0dae7badee83fcbe401167de506736348a975eebf3f9e3015f3b3c78584"
|
||||
content-hash = "5803c6efb57eca486961794f233953c199047b36c5e823e7310488a6adc91fdd"
|
||||
|
||||
@ -60,18 +60,18 @@ av = "*"
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# --- NVIDIA GPU configuration ---
|
||||
torch = [
|
||||
{ url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=d99be44487d3ed0f7e6ef5d6689a37fb4a2f2821a9e7b59e7e04002a876a667a", markers = "sys_platform == 'win32'" },
|
||||
{ url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=f3c0ba02b50d0021ff26f030e22d4c45965537cf91f322e52a65b8c58396f81c", markers = "sys_platform == 'linux'" }
|
||||
]
|
||||
torchaudio = [
|
||||
{ url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=79b5afa556063be18de4a1964339242301fe04e782e1030a22695257dd9afbd2", markers = "sys_platform == 'win32'" },
|
||||
{ url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=2b077639f240176bb27e964e2e9b3a5c2a8d560a3a7bc1ffd0a024e81f2e10b4", markers = "sys_platform == 'linux'" }
|
||||
]
|
||||
# torch = [
|
||||
# { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=d99be44487d3ed0f7e6ef5d6689a37fb4a2f2821a9e7b59e7e04002a876a667a", markers = "sys_platform == 'win32'" },
|
||||
# { url = "https://download.pytorch.org/whl/cu118/torch-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=f3c0ba02b50d0021ff26f030e22d4c45965537cf91f322e52a65b8c58396f81c", markers = "sys_platform == 'linux'" }
|
||||
# ]
|
||||
# torchaudio = [
|
||||
# { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-win_amd64.whl#sha256=79b5afa556063be18de4a1964339242301fe04e782e1030a22695257dd9afbd2", markers = "sys_platform == 'win32'" },
|
||||
# { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.1.1+cu118-cp311-cp311-linux_x86_64.whl#sha256=2b077639f240176bb27e964e2e9b3a5c2a8d560a3a7bc1ffd0a024e81f2e10b4", markers = "sys_platform == 'linux'" }
|
||||
# ]
|
||||
# --- AMD GPU configuration ---
|
||||
# torch = "2.4.1"
|
||||
# torchaudio = "2.4.1"
|
||||
# torch-directml = "^0.2.5.dev240914"
|
||||
torch = "2.4.1"
|
||||
torchaudio = "2.4.1"
|
||||
torch-directml = "^0.2.5.dev240914"
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
||||
62
run.sh
62
run.sh
@ -1,62 +0,0 @@
|
||||
#!/bin/sh
|
||||
|
||||
if [ "$(uname)" = "Darwin" ]; then
|
||||
# macOS specific env:
|
||||
export PYTORCH_ENABLE_MPS_FALLBACK=1
|
||||
export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
|
||||
elif [ "$(uname)" != "Linux" ]; then
|
||||
echo "Unsupported operating system."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ -d ".venv" ]; then
|
||||
echo "Activate venv..."
|
||||
. .venv/bin/activate
|
||||
else
|
||||
echo "Create venv..."
|
||||
requirements_file="requirements.txt"
|
||||
|
||||
# Check if Python 3.8 is installed
|
||||
if ! command -v python3.8 >/dev/null 2>&1 || pyenv versions --bare | grep -q "3.8"; then
|
||||
echo "Python 3 not found. Attempting to install 3.8..."
|
||||
if [ "$(uname)" = "Darwin" ] && command -v brew >/dev/null 2>&1; then
|
||||
brew install python@3.8
|
||||
elif [ "$(uname)" = "Linux" ] && command -v apt-get >/dev/null 2>&1; then
|
||||
sudo apt-get update
|
||||
sudo apt-get install python3.8
|
||||
else
|
||||
echo "Please install Python 3.8 manually."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
python3.8 -m venv .venv
|
||||
. .venv/bin/activate
|
||||
|
||||
# Check if required packages are installed and install them if not
|
||||
if [ -f "${requirements_file}" ]; then
|
||||
installed_packages=$(python3.8 -m pip freeze)
|
||||
while IFS= read -r package; do
|
||||
expr "${package}" : "^#.*" > /dev/null && continue
|
||||
package_name=$(echo "${package}" | sed 's/[<>=!].*//')
|
||||
if ! echo "${installed_packages}" | grep -q "${package_name}"; then
|
||||
echo "${package_name} not found. Attempting to install..."
|
||||
python3.8 -m pip install --upgrade "${package}"
|
||||
fi
|
||||
done < "${requirements_file}"
|
||||
else
|
||||
echo "${requirements_file} not found. Please ensure the requirements file with required packages exists."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
# Download models
|
||||
chmod +x tools/dlmodels.sh
|
||||
./tools/dlmodels.sh
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Run the main script
|
||||
python3.8 infer-web.py --pycmd python3.8
|
||||
@ -1,362 +0,0 @@
|
||||
@echo off && chcp 65001
|
||||
|
||||
echo working dir is %cd%
|
||||
echo downloading requirement aria2 check.
|
||||
echo=
|
||||
dir /a:d/b | findstr "aria2" > flag.txt
|
||||
findstr "aria2" flag.txt >nul
|
||||
if %errorlevel% ==0 (
|
||||
echo aria2 checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. please downloading aria2 from webpage!
|
||||
echo unzip it and put in this directory!
|
||||
timeout /T 5
|
||||
start https://github.com/aria2/aria2/releases/tag/release-1.36.0
|
||||
echo=
|
||||
goto end
|
||||
)
|
||||
|
||||
echo envfiles checking start.
|
||||
echo=
|
||||
|
||||
for /f %%x in ('findstr /i /c:"aria2" "flag.txt"') do (set aria2=%%x)&goto endSch
|
||||
:endSch
|
||||
|
||||
set d32=f0D32k.pth
|
||||
set d40=f0D40k.pth
|
||||
set d48=f0D48k.pth
|
||||
set g32=f0G32k.pth
|
||||
set g40=f0G40k.pth
|
||||
set g48=f0G48k.pth
|
||||
|
||||
set d40v2=f0D40k.pth
|
||||
set g40v2=f0G40k.pth
|
||||
|
||||
set dld32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth
|
||||
set dld40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth
|
||||
set dld48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth
|
||||
set dlg32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth
|
||||
set dlg40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth
|
||||
set dlg48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth
|
||||
|
||||
set dld40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth
|
||||
set dlg40v2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth
|
||||
|
||||
set hp2_all=HP2_all_vocals.pth
|
||||
set hp3_all=HP3_all_vocals.pth
|
||||
set hp5_only=HP5_only_main_vocal.pth
|
||||
set VR_DeEchoAggressive=VR-DeEchoAggressive.pth
|
||||
set VR_DeEchoDeReverb=VR-DeEchoDeReverb.pth
|
||||
set VR_DeEchoNormal=VR-DeEchoNormal.pth
|
||||
set onnx_dereverb=vocals.onnx
|
||||
|
||||
set dlhp2_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth
|
||||
set dlhp3_all=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth
|
||||
set dlhp5_only=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth
|
||||
set dlVR_DeEchoAggressive=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth
|
||||
set dlVR_DeEchoDeReverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth
|
||||
set dlVR_DeEchoNormal=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth
|
||||
set dlonnx_dereverb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx
|
||||
|
||||
set hb=hubert_base.pt
|
||||
|
||||
set dlhb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt
|
||||
|
||||
set rmvpe=rmvpe.pt
|
||||
set dlrmvpe=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt
|
||||
|
||||
echo dir check start.
|
||||
echo=
|
||||
|
||||
if exist "%~dp0assets\pretrained" (
|
||||
echo dir .\assets\pretrained checked.
|
||||
) else (
|
||||
echo failed. generating dir .\assets\pretrained.
|
||||
mkdir pretrained
|
||||
)
|
||||
if exist "%~dp0assets\pretrained_v2" (
|
||||
echo dir .\assets\pretrained_v2 checked.
|
||||
) else (
|
||||
echo failed. generating dir .\assets\pretrained_v2.
|
||||
mkdir pretrained_v2
|
||||
)
|
||||
if exist "%~dp0assets\uvr5_weights" (
|
||||
echo dir .\assets\uvr5_weights checked.
|
||||
) else (
|
||||
echo failed. generating dir .\assets\uvr5_weights.
|
||||
mkdir uvr5_weights
|
||||
)
|
||||
if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy" (
|
||||
echo dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
|
||||
) else (
|
||||
echo failed. generating dir .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy.
|
||||
mkdir uvr5_weights\onnx_dereverb_By_FoxJoy
|
||||
)
|
||||
|
||||
echo=
|
||||
echo dir check finished.
|
||||
|
||||
echo=
|
||||
echo required files check start.
|
||||
|
||||
echo checking D32k.pth
|
||||
if exist "%~dp0assets\pretrained\D32k.pth" (
|
||||
echo D32k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d %~dp0assets\pretrained -o D32k.pth
|
||||
if exist "%~dp0assets\pretrained\D32k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking D40k.pth
|
||||
if exist "%~dp0assets\pretrained\D40k.pth" (
|
||||
echo D40k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d %~dp0assets\pretrained -o D40k.pth
|
||||
if exist "%~dp0assets\pretrained\D40k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking D40k.pth
|
||||
if exist "%~dp0assets\pretrained_v2\D40k.pth" (
|
||||
echo D40k.pth in .\assets\pretrained_v2 checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d %~dp0assets\pretrained_v2 -o D40k.pth
|
||||
if exist "%~dp0assets\pretrained_v2\D40k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking D48k.pth
|
||||
if exist "%~dp0assets\pretrained\D48k.pth" (
|
||||
echo D48k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d %~dp0assets\pretrained -o D48k.pth
|
||||
if exist "%~dp0assets\pretrained\D48k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking G32k.pth
|
||||
if exist "%~dp0assets\pretrained\G32k.pth" (
|
||||
echo G32k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d %~dp0assets\pretrained -o G32k.pth
|
||||
if exist "%~dp0assets\pretrained\G32k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking G40k.pth
|
||||
if exist "%~dp0assets\pretrained\G40k.pth" (
|
||||
echo G40k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d %~dp0assets\pretrained -o G40k.pth
|
||||
if exist "%~dp0assets\pretrained\G40k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking G40k.pth
|
||||
if exist "%~dp0assets\pretrained_v2\G40k.pth" (
|
||||
echo G40k.pth in .\assets\pretrained_v2 checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d %~dp0assets\pretrained_v2 -o G40k.pth
|
||||
if exist "%~dp0assets\pretrained_v2\G40k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking G48k.pth
|
||||
if exist "%~dp0assets\pretrained\G48k.pth" (
|
||||
echo G48k.pth in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d %~dp0assets\pretrained -o G48k.pth
|
||||
if exist "%~dp0assets\pretrained\G48k.pth" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
|
||||
echo checking %d32%
|
||||
if exist "%~dp0assets\pretrained\%d32%" (
|
||||
echo %d32% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld32% -d %~dp0assets\pretrained -o %d32%
|
||||
if exist "%~dp0assets\pretrained\%d32%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %d40%
|
||||
if exist "%~dp0assets\pretrained\%d40%" (
|
||||
echo %d40% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40% -d %~dp0assets\pretrained -o %d40%
|
||||
if exist "%~dp0assets\pretrained\%d40%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %d40v2%
|
||||
if exist "%~dp0assets\pretrained_v2\%d40v2%" (
|
||||
echo %d40v2% in .\assets\pretrained_v2 checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40v2% -d %~dp0assets\pretrained_v2 -o %d40v2%
|
||||
if exist "%~dp0assets\pretrained_v2\%d40v2%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %d48%
|
||||
if exist "%~dp0assets\pretrained\%d48%" (
|
||||
echo %d48% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld48% -d %~dp0assets\pretrained -o %d48%
|
||||
if exist "%~dp0assets\pretrained\%d48%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %g32%
|
||||
if exist "%~dp0assets\pretrained\%g32%" (
|
||||
echo %g32% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg32% -d %~dp0assets\pretrained -o %g32%
|
||||
if exist "%~dp0assets\pretrained\%g32%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %g40%
|
||||
if exist "%~dp0assets\pretrained\%g40%" (
|
||||
echo %g40% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40% -d %~dp0assets\pretrained -o %g40%
|
||||
if exist "%~dp0assets\pretrained\%g40%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %g40v2%
|
||||
if exist "%~dp0assets\pretrained_v2\%g40v2%" (
|
||||
echo %g40v2% in .\assets\pretrained_v2 checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40v2% -d %~dp0assets\pretrained_v2 -o %g40v2%
|
||||
if exist "%~dp0assets\pretrained_v2\%g40v2%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %g48%
|
||||
if exist "%~dp0assets\pretrained\%g48%" (
|
||||
echo %g48% in .\assets\pretrained checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg48% -d %~dp0assets\pretrained -o %g48%
|
||||
if exist "%~dp0assets\pretrained\%g48%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
|
||||
echo checking %hp2_all%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp2_all%" (
|
||||
echo %hp2_all% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp2_all% -d %~dp0assets\uvr5_weights -o %hp2_all%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp2_all%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %hp3_all%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp3_all%" (
|
||||
echo %hp3_all% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp3_all% -d %~dp0assets\uvr5_weights -o %hp3_all%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp3_all%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %hp5_only%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp5_only%" (
|
||||
echo %hp5_only% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp5_only% -d %~dp0assets\uvr5_weights -o %hp5_only%
|
||||
if exist "%~dp0assets\uvr5_weights\%hp5_only%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %VR_DeEchoAggressive%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoAggressive%" (
|
||||
echo %VR_DeEchoAggressive% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoAggressive% -d %~dp0assets\uvr5_weights -o %VR_DeEchoAggressive%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoAggressive%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %VR_DeEchoDeReverb%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoDeReverb%" (
|
||||
echo %VR_DeEchoDeReverb% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoDeReverb% -d %~dp0assets\uvr5_weights -o %VR_DeEchoDeReverb%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoDeReverb%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %VR_DeEchoNormal%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoNormal%" (
|
||||
echo %VR_DeEchoNormal% in .\assets\uvr5_weights checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlVR_DeEchoNormal% -d %~dp0assets\uvr5_weights -o %VR_DeEchoNormal%
|
||||
if exist "%~dp0assets\uvr5_weights\%VR_DeEchoNormal%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
echo checking %onnx_dereverb%
|
||||
if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (
|
||||
echo %onnx_dereverb% in .\assets\uvr5_weights\onnx_dereverb_By_FoxJoy checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlonnx_dereverb% -d %~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy -o %onnx_dereverb%
|
||||
if exist "%~dp0assets\uvr5_weights\onnx_dereverb_By_FoxJoy\%onnx_dereverb%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
|
||||
echo checking %hb%
|
||||
if exist "%~dp0assets\hubert\%hb%" (
|
||||
echo %hb% in .\assets\hubert checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhb% -d %~dp0assets\hubert\ -o %hb%
|
||||
if exist "%~dp0assets\hubert\%hb%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
|
||||
echo checking %rmvpe%
|
||||
if exist "%~dp0assets\rmvpe\%rmvpe%" (
|
||||
echo %rmvpe% in .\assets\rmvpe checked.
|
||||
echo=
|
||||
) else (
|
||||
echo failed. starting download from huggingface.
|
||||
%~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlrmvpe% -d %~dp0assets\rmvpe\ -o %rmvpe%
|
||||
if exist "%~dp0assets\rmvpe\%rmvpe%" (echo download successful.) else (echo please try again!
|
||||
echo=)
|
||||
)
|
||||
|
||||
echo required files check finished.
|
||||
echo envfiles check complete.
|
||||
pause
|
||||
:end
|
||||
del flag.txt
|
||||
@ -1,81 +0,0 @@
|
||||
#!/bin/sh
|
||||
|
||||
printf "working dir is %s\n" "$PWD"
|
||||
echo "downloading requirement aria2 check."
|
||||
|
||||
if command -v aria2c > /dev/null 2>&1
|
||||
then
|
||||
echo "aria2 command found"
|
||||
else
|
||||
echo "failed. please install aria2"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "dir check start."
|
||||
|
||||
check_dir() {
|
||||
[ -d "$1" ] && printf "dir %s checked\n" "$1" || \
|
||||
printf "failed. generating dir %s\n" "$1" && mkdir -p "$1"
|
||||
}
|
||||
|
||||
check_dir "./assets/pretrained"
|
||||
check_dir "./assets/pretrained_v2"
|
||||
check_dir "./assets/uvr5_weights"
|
||||
check_dir "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy"
|
||||
|
||||
echo "dir check finished."
|
||||
|
||||
echo "required files check start."
|
||||
check_file_pretrained() {
|
||||
printf "checking %s\n" "$2"
|
||||
if [ -f "./assets/""$1""/""$2""" ]; then
|
||||
printf "%s in ./assets/%s checked.\n" "$2" "$1"
|
||||
else
|
||||
echo failed. starting download from huggingface.
|
||||
if command -v aria2c > /dev/null 2>&1; then
|
||||
aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"$1"/"$2" -d ./assets/"$1" -o "$2"
|
||||
[ -f "./assets/""$1""/""$2""" ] && echo "download successful." || { echo "please try again!" && exit 1; }
|
||||
else
|
||||
echo "aria2c command not found. Please install aria2c and try again."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
check_file_special() {
|
||||
printf "checking %s\n" "$2"
|
||||
if [ -f "./assets/""$1""/""$2""" ]; then
|
||||
printf "%s in ./assets/%s checked.\n" "$2" "$1"
|
||||
else
|
||||
echo failed. starting download from huggingface.
|
||||
if command -v aria2c > /dev/null 2>&1; then
|
||||
aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/"$2" -d ./assets/"$1" -o "$2"
|
||||
[ -f "./assets/""$1""/""$2""" ] && echo "download successful." || { echo "please try again!" && exit 1; }
|
||||
else
|
||||
echo "aria2c command not found. Please install aria2c and try again."
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
}
|
||||
|
||||
check_file_pretrained pretrained D32k.pth
|
||||
check_file_pretrained pretrained D40k.pth
|
||||
check_file_pretrained pretrained D48k.pth
|
||||
check_file_pretrained pretrained G32k.pth
|
||||
check_file_pretrained pretrained G40k.pth
|
||||
check_file_pretrained pretrained G48k.pth
|
||||
check_file_pretrained pretrained_v2 f0D40k.pth
|
||||
check_file_pretrained pretrained_v2 f0G40k.pth
|
||||
check_file_pretrained pretrained_v2 D40k.pth
|
||||
check_file_pretrained pretrained_v2 G40k.pth
|
||||
check_file_pretrained uvr5_weights HP2_all_vocals.pth
|
||||
check_file_pretrained uvr5_weights HP3_all_vocals.pth
|
||||
check_file_pretrained uvr5_weights HP5_only_main_vocal.pth
|
||||
check_file_pretrained uvr5_weights VR-DeEchoAggressive.pth
|
||||
check_file_pretrained uvr5_weights VR-DeEchoDeReverb.pth
|
||||
check_file_pretrained uvr5_weights VR-DeEchoNormal.pth
|
||||
check_file_pretrained uvr5_weights "onnx_dereverb_By_FoxJoy/vocals.onnx"
|
||||
check_file_special rmvpe rmvpe.pt
|
||||
check_file_special hubert hubert_base.pt
|
||||
|
||||
echo "required files check finished."
|
||||
@ -1,445 +0,0 @@
|
||||
from io import BytesIO
|
||||
import os
|
||||
import pickle
|
||||
import sys
|
||||
import traceback
|
||||
from infer.lib import jit
|
||||
from infer.lib.jit.get_synthesizer import get_synthesizer
|
||||
from time import time as ttime
|
||||
import fairseq
|
||||
import faiss
|
||||
import numpy as np
|
||||
import parselmouth
|
||||
import pyworld
|
||||
import scipy.signal as signal
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchcrepe
|
||||
|
||||
from infer.lib.infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid,
|
||||
SynthesizerTrnMs256NSFsid_nono,
|
||||
SynthesizerTrnMs768NSFsid,
|
||||
SynthesizerTrnMs768NSFsid_nono,
|
||||
)
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from multiprocessing import Manager as M
|
||||
|
||||
from configs.config import Config
|
||||
|
||||
# config = Config()
|
||||
|
||||
mm = M()
|
||||
|
||||
|
||||
def printt(strr, *args):
|
||||
if len(args) == 0:
|
||||
print(strr)
|
||||
else:
|
||||
print(strr % args)
|
||||
|
||||
|
||||
# config.device=torch.device("cpu")########强制cpu测试
|
||||
# config.is_half=False########强制cpu测试
|
||||
class RVC:
|
||||
def __init__(
|
||||
self,
|
||||
key,
|
||||
pth_path,
|
||||
index_path,
|
||||
index_rate,
|
||||
n_cpu,
|
||||
inp_q,
|
||||
opt_q,
|
||||
config: Config,
|
||||
last_rvc=None,
|
||||
) -> None:
|
||||
"""
|
||||
初始化
|
||||
"""
|
||||
try:
|
||||
if config.dml == True:
|
||||
|
||||
def forward_dml(ctx, x, scale):
|
||||
ctx.scale = scale
|
||||
res = x.clone().detach()
|
||||
return res
|
||||
|
||||
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
|
||||
# global config
|
||||
self.config = config
|
||||
self.inp_q = inp_q
|
||||
self.opt_q = opt_q
|
||||
# device="cpu"########强制cpu测试
|
||||
self.device = config.device
|
||||
self.f0_up_key = key
|
||||
self.f0_min = 50
|
||||
self.f0_max = 1100
|
||||
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
|
||||
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
|
||||
self.n_cpu = n_cpu
|
||||
self.use_jit = self.config.use_jit
|
||||
self.is_half = config.is_half
|
||||
|
||||
if index_rate != 0:
|
||||
self.index = faiss.read_index(index_path)
|
||||
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
||||
printt("Index search enabled")
|
||||
self.pth_path: str = pth_path
|
||||
self.index_path = index_path
|
||||
self.index_rate = index_rate
|
||||
self.cache_pitch: torch.Tensor = torch.zeros(
|
||||
1024, device=self.device, dtype=torch.long
|
||||
)
|
||||
self.cache_pitchf = torch.zeros(
|
||||
1024, device=self.device, dtype=torch.float32
|
||||
)
|
||||
|
||||
if last_rvc is None:
|
||||
models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
|
||||
["assets/hubert/hubert_base.pt"],
|
||||
suffix="",
|
||||
)
|
||||
hubert_model = models[0]
|
||||
hubert_model = hubert_model.to(self.device)
|
||||
if self.is_half:
|
||||
hubert_model = hubert_model.half()
|
||||
else:
|
||||
hubert_model = hubert_model.float()
|
||||
hubert_model.eval()
|
||||
self.model = hubert_model
|
||||
else:
|
||||
self.model = last_rvc.model
|
||||
|
||||
self.net_g: nn.Module = None
|
||||
|
||||
def set_default_model():
|
||||
self.net_g, cpt = get_synthesizer(self.pth_path, self.device)
|
||||
self.tgt_sr = cpt["config"][-1]
|
||||
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
|
||||
self.if_f0 = cpt.get("f0", 1)
|
||||
self.version = cpt.get("version", "v1")
|
||||
if self.is_half:
|
||||
self.net_g = self.net_g.half()
|
||||
else:
|
||||
self.net_g = self.net_g.float()
|
||||
|
||||
def set_jit_model():
|
||||
jit_pth_path = self.pth_path.rstrip(".pth")
|
||||
jit_pth_path += ".half.jit" if self.is_half else ".jit"
|
||||
reload = False
|
||||
if str(self.device) == "cuda":
|
||||
self.device = torch.device("cuda:0")
|
||||
if os.path.exists(jit_pth_path):
|
||||
cpt = jit.load(jit_pth_path)
|
||||
model_device = cpt["device"]
|
||||
if model_device != str(self.device):
|
||||
reload = True
|
||||
else:
|
||||
reload = True
|
||||
|
||||
if reload:
|
||||
cpt = jit.synthesizer_jit_export(
|
||||
self.pth_path,
|
||||
"script",
|
||||
None,
|
||||
device=self.device,
|
||||
is_half=self.is_half,
|
||||
)
|
||||
|
||||
self.tgt_sr = cpt["config"][-1]
|
||||
self.if_f0 = cpt.get("f0", 1)
|
||||
self.version = cpt.get("version", "v1")
|
||||
self.net_g = torch.jit.load(
|
||||
BytesIO(cpt["model"]), map_location=self.device
|
||||
)
|
||||
self.net_g.infer = self.net_g.forward
|
||||
self.net_g.eval().to(self.device)
|
||||
|
||||
def set_synthesizer():
|
||||
if self.use_jit and not config.dml:
|
||||
if self.is_half and "cpu" in str(self.device):
|
||||
printt(
|
||||
"Use default Synthesizer model. \
|
||||
Jit is not supported on the CPU for half floating point"
|
||||
)
|
||||
set_default_model()
|
||||
else:
|
||||
set_jit_model()
|
||||
else:
|
||||
set_default_model()
|
||||
|
||||
if last_rvc is None or last_rvc.pth_path != self.pth_path:
|
||||
set_synthesizer()
|
||||
else:
|
||||
self.tgt_sr = last_rvc.tgt_sr
|
||||
self.if_f0 = last_rvc.if_f0
|
||||
self.version = last_rvc.version
|
||||
self.is_half = last_rvc.is_half
|
||||
if last_rvc.use_jit != self.use_jit:
|
||||
set_synthesizer()
|
||||
else:
|
||||
self.net_g = last_rvc.net_g
|
||||
|
||||
if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
|
||||
self.model_rmvpe = last_rvc.model_rmvpe
|
||||
if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
|
||||
self.device_fcpe = last_rvc.device_fcpe
|
||||
self.model_fcpe = last_rvc.model_fcpe
|
||||
except:
|
||||
printt(traceback.format_exc())
|
||||
|
||||
def change_key(self, new_key):
|
||||
self.f0_up_key = new_key
|
||||
|
||||
def change_index_rate(self, new_index_rate):
|
||||
if new_index_rate != 0 and self.index_rate == 0:
|
||||
self.index = faiss.read_index(self.index_path)
|
||||
self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
|
||||
printt("Index search enabled")
|
||||
self.index_rate = new_index_rate
|
||||
|
||||
def get_f0_post(self, f0):
|
||||
if not torch.is_tensor(f0):
|
||||
f0 = torch.from_numpy(f0)
|
||||
f0 = f0.float().to(self.device).squeeze()
|
||||
f0_mel = 1127 * torch.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
|
||||
self.f0_mel_max - self.f0_mel_min
|
||||
) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = torch.round(f0_mel).long()
|
||||
return f0_coarse, f0
|
||||
|
||||
def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
|
||||
n_cpu = int(n_cpu)
|
||||
if method == "crepe":
|
||||
return self.get_f0_crepe(x, f0_up_key)
|
||||
if method == "rmvpe":
|
||||
return self.get_f0_rmvpe(x, f0_up_key)
|
||||
if method == "fcpe":
|
||||
return self.get_f0_fcpe(x, f0_up_key)
|
||||
x = x.cpu().numpy()
|
||||
if method == "pm":
|
||||
p_len = x.shape[0] // 160 + 1
|
||||
f0_min = 65
|
||||
l_pad = int(np.ceil(1.5 / f0_min * 16000))
|
||||
r_pad = l_pad + 1
|
||||
s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), 16000).to_pitch_ac(
|
||||
time_step=0.01,
|
||||
voicing_threshold=0.6,
|
||||
pitch_floor=f0_min,
|
||||
pitch_ceiling=1100,
|
||||
)
|
||||
assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
|
||||
f0 = s.selected_array["frequency"]
|
||||
if len(f0) < p_len:
|
||||
f0 = np.pad(f0, (0, p_len - len(f0)))
|
||||
f0 = f0[:p_len]
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
if n_cpu == 1:
|
||||
f0, t = pyworld.harvest(
|
||||
x.astype(np.double),
|
||||
fs=16000,
|
||||
f0_ceil=1100,
|
||||
f0_floor=50,
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
f0bak = np.zeros(x.shape[0] // 160 + 1, dtype=np.float64)
|
||||
length = len(x)
|
||||
part_length = 160 * ((length // 160 - 1) // n_cpu + 1)
|
||||
n_cpu = (length // 160 - 1) // (part_length // 160) + 1
|
||||
ts = ttime()
|
||||
res_f0 = mm.dict()
|
||||
for idx in range(n_cpu):
|
||||
tail = part_length * (idx + 1) + 320
|
||||
if idx == 0:
|
||||
self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
|
||||
else:
|
||||
self.inp_q.put(
|
||||
(idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
|
||||
)
|
||||
while 1:
|
||||
res_ts = self.opt_q.get()
|
||||
if res_ts == ts:
|
||||
break
|
||||
f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
|
||||
for idx, f0 in enumerate(f0s):
|
||||
if idx == 0:
|
||||
f0 = f0[:-3]
|
||||
elif idx != n_cpu - 1:
|
||||
f0 = f0[2:-3]
|
||||
else:
|
||||
f0 = f0[2:]
|
||||
f0bak[part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]] = (
|
||||
f0
|
||||
)
|
||||
f0bak = signal.medfilt(f0bak, 3)
|
||||
f0bak *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0bak)
|
||||
|
||||
def get_f0_crepe(self, x, f0_up_key):
|
||||
if "privateuseone" in str(
|
||||
self.device
|
||||
): ###不支持dml，cpu又太慢用不成，拿fcpe顶替
|
||||
return self.get_f0(x, f0_up_key, 1, "fcpe")
|
||||
# printt("using crepe,device:%s"%self.device)
|
||||
f0, pd = torchcrepe.predict(
|
||||
x.unsqueeze(0).float(),
|
||||
16000,
|
||||
160,
|
||||
self.f0_min,
|
||||
self.f0_max,
|
||||
"full",
|
||||
batch_size=512,
|
||||
# device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
|
||||
device=self.device,
|
||||
return_periodicity=True,
|
||||
)
|
||||
pd = torchcrepe.filter.median(pd, 3)
|
||||
f0 = torchcrepe.filter.mean(f0, 3)
|
||||
f0[pd < 0.1] = 0
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def get_f0_rmvpe(self, x, f0_up_key):
|
||||
if hasattr(self, "model_rmvpe") == False:
|
||||
from infer.lib.rmvpe import RMVPE
|
||||
|
||||
printt("Loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
"assets/rmvpe/rmvpe.pt",
|
||||
is_half=self.is_half,
|
||||
device=self.device,
|
||||
use_jit=self.config.use_jit,
|
||||
)
|
||||
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def get_f0_fcpe(self, x, f0_up_key):
|
||||
if hasattr(self, "model_fcpe") == False:
|
||||
from torchfcpe import spawn_bundled_infer_model
|
||||
|
||||
printt("Loading fcpe model")
|
||||
if "privateuseone" in str(self.device):
|
||||
self.device_fcpe = "cpu"
|
||||
else:
|
||||
self.device_fcpe = self.device
|
||||
self.model_fcpe = spawn_bundled_infer_model(self.device_fcpe)
|
||||
f0 = self.model_fcpe.infer(
|
||||
x.to(self.device_fcpe).unsqueeze(0).float(),
|
||||
sr=16000,
|
||||
decoder_mode="local_argmax",
|
||||
threshold=0.006,
|
||||
)
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
return self.get_f0_post(f0)
|
||||
|
||||
def infer(
|
||||
self,
|
||||
input_wav: torch.Tensor,
|
||||
block_frame_16k,
|
||||
skip_head,
|
||||
return_length,
|
||||
f0method,
|
||||
) -> np.ndarray:
|
||||
t1 = ttime()
|
||||
with torch.no_grad():
|
||||
if self.config.is_half:
|
||||
feats = input_wav.half().view(1, -1)
|
||||
else:
|
||||
feats = input_wav.float().view(1, -1)
|
||||
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
||||
inputs = {
|
||||
"source": feats,
|
||||
"padding_mask": padding_mask,
|
||||
"output_layer": 9 if self.version == "v1" else 12,
|
||||
}
|
||||
logits = self.model.extract_features(**inputs)
|
||||
feats = (
|
||||
self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
|
||||
)
|
||||
feats = torch.cat((feats, feats[:, -1:, :]), 1)
|
||||
t2 = ttime()
|
||||
try:
|
||||
if hasattr(self, "index") and self.index_rate != 0:
|
||||
npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
|
||||
score, ix = self.index.search(npy, k=8)
|
||||
if (ix >= 0).all():
|
||||
weight = np.square(1 / score)
|
||||
weight /= weight.sum(axis=1, keepdims=True)
|
||||
npy = np.sum(
|
||||
self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
|
||||
)
|
||||
if self.config.is_half:
|
||||
npy = npy.astype("float16")
|
||||
feats[0][skip_head // 2 :] = (
|
||||
torch.from_numpy(npy).unsqueeze(0).to(self.device)
|
||||
* self.index_rate
|
||||
+ (1 - self.index_rate) * feats[0][skip_head // 2 :]
|
||||
)
|
||||
else:
|
||||
printt(
|
||||
"Invalid index. You MUST use added_xxxx.index but not trained_xxxx.index!"
|
||||
)
|
||||
else:
|
||||
printt("Index search FAILED or disabled")
|
||||
except:
|
||||
traceback.print_exc()
|
||||
printt("Index search FAILED")
|
||||
t3 = ttime()
|
||||
p_len = input_wav.shape[0] // 160
|
||||
if self.if_f0 == 1:
|
||||
f0_extractor_frame = block_frame_16k + 800
|
||||
if f0method == "rmvpe":
|
||||
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
|
||||
pitch, pitchf = self.get_f0(
|
||||
input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
|
||||
)
|
||||
shift = block_frame_16k // 160
|
||||
self.cache_pitch[:-shift] = self.cache_pitch[shift:].clone()
|
||||
self.cache_pitchf[:-shift] = self.cache_pitchf[shift:].clone()
|
||||
self.cache_pitch[4 - pitch.shape[0] :] = pitch[3:-1]
|
||||
self.cache_pitchf[4 - pitch.shape[0] :] = pitchf[3:-1]
|
||||
cache_pitch = self.cache_pitch[None, -p_len:]
|
||||
cache_pitchf = self.cache_pitchf[None, -p_len:]
|
||||
t4 = ttime()
|
||||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||||
feats = feats[:, :p_len, :]
|
||||
p_len = torch.LongTensor([p_len]).to(self.device)
|
||||
sid = torch.LongTensor([0]).to(self.device)
|
||||
skip_head = torch.LongTensor([skip_head])
|
||||
return_length = torch.LongTensor([return_length])
|
||||
with torch.no_grad():
|
||||
if self.if_f0 == 1:
|
||||
infered_audio, _, _ = self.net_g.infer(
|
||||
feats,
|
||||
p_len,
|
||||
cache_pitch,
|
||||
cache_pitchf,
|
||||
sid,
|
||||
skip_head,
|
||||
return_length,
|
||||
)
|
||||
else:
|
||||
infered_audio, _, _ = self.net_g.infer(
|
||||
feats, p_len, sid, skip_head, return_length
|
||||
)
|
||||
t5 = ttime()
|
||||
printt(
|
||||
"Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
|
||||
t2 - t1,
|
||||
t3 - t2,
|
||||
t4 - t3,
|
||||
t5 - t4,
|
||||
)
|
||||
return infered_audio.squeeze().float()
|
||||
Loading…
x
Reference in New Issue
Block a user