PolUVR 1.3.0

Easy to use audio stem separation with a UI, using various models from UVR trained primarily by @Anjok07

PolUVR 🎶

Overview

PolUVR is a Python-based audio separation tool that leverages advanced machine learning models to separate audio tracks into different stems, such as vocals, instrumental, drums, bass, and more. This project is a fork of the python-audio-separator repository, and it aims to provide a user-friendly interface for audio separation tasks.

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Installation 🛠️

Hardware Acceleration Options

Nvidia GPU with CUDA

Supported CUDA Versions: 11.8 and 12.2

To verify successful configuration, run PolUVR --env_info. You should see the following log message:

ONNXruntime has CUDAExecutionProvider available, enabling acceleration

Installation:

pip install "PolUVR[gpu]"

Apple Silicon, macOS Sonoma+ with M1 or newer CPU (CoreML acceleration)

To verify successful configuration, run PolUVR --env_info. You should see the following log message:

ONNXruntime has CoreMLExecutionProvider available, enabling acceleration

Installation:

pip install "PolUVR[cpu]"

CPU-Only (No Hardware Acceleration)

Installation:

pip install "PolUVR[cpu]"

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FFmpeg Dependency

To check if PolUVR is correctly configured to use FFmpeg, run PolUVR --env_info. The log should show:

FFmpeg installed

If it says that FFmpeg is missing or an error occurs, install FFmpeg using the following commands:

Debian/Ubuntu:

• apt-get update; apt-get install -y ffmpeg
  

macOS:

• brew update; brew install ffmpeg
  

Windows:

• Follow this guide: Install-FFmpeg-on-Windows

If you cloned the repository, you can use the following command to install FFmpeg:

PolUVR-ffmpeg

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GPU / CUDA Specific Installation Steps

In theory, installing PolUVR with the [gpu] extra should suffice. However, sometimes PyTorch and ONNX Runtime with CUDA support can be tricky. You may need to reinstall these packages directly:

pip uninstall torch onnxruntime
pip cache purge
pip install --force-reinstall torch torchvision torchaudio
pip install --force-reinstall onnxruntime-gpu

For the latest PyTorch version, use the command recommended by the PyTorch installation wizard.

Multiple CUDA Library Versions

Depending on your environment, you may need specific CUDA library versions. For example, Google Colab uses CUDA 12 by default, but ONNX Runtime may still require CUDA 11 libraries. Install CUDA 11 libraries alongside CUDA 12:

apt update; apt install nvidia-cuda-toolkit

If you encounter errors like Failed to load library or cannot open shared object file, resolve them by running:

python -m pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/

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Usage 🚀

Gradio Interface

usage: PolUVR-app [--share] [--open]

Params:
  --share                  Opens public access to the interface (for servers, Google Colab, Kaggle, etc.).
  --open                   Automatically opens the interface in a new browser tab.

Once the following output message Running on local URL: http://127.0.0.1:7860 or Running on public URL: https://28425b3eb261b9ddc6.gradio.live appears, you can click on the link to open a tab with the WebUI.

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Requirements 📋

• Python >= 3.10
• Libraries: torch, onnx, onnxruntime, numpy, librosa, requests, six, tqdm, pydub

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Developing Locally

Prerequisites

• Python 3.10 or newer
• Conda (recommended: Miniforge)

Clone the Repository

git clone https://github.com/YOUR_USERNAME/PolUVR.git
cd PolUVR

Create and Activate the Conda Environment

conda env create
conda activate PolUVR-dev

Install Dependencies

poetry install

Install extra dependencies:

poetry install --extras "cpu"

or

poetry install --extras "gpu"

Running the CLI Locally

PolUVR path/to/your/audio-file.wav

Deactivate the Virtual Environment

conda deactivate

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Contributing 🤝

Contributions are welcome! Fork the repository, make your changes, and submit a pull request.

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Original Repository

This project is a fork of the original python-audio-separator repository.