PolUVR 1.3.3

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 distinct stems, such as vocals, instrumental, drums, bass, and more. Built as a fork of the python-audio-separator, PolUVR offers enhanced usability, hardware acceleration, and a user-friendly Gradio interface.

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Key Features

• Audio Separation: Extract vocals, instrumental, drums, bass, and other stems.
• Hardware Acceleration: Supports CUDA (Nvidia GPUs) and CoreML (Apple Silicon).
• Cross-Platform: Works on Linux, macOS, and Windows.
• Gradio Interface: Easy-to-use web interface for audio separation.

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Installation 🛠️

Hardware Acceleration Options

PolUVR supports multiple hardware acceleration options for optimal performance. To verify successful configuration, run:

PolUVR --env_info

Command	Expected Log Message
pip install "PolUVR[gpu]"	ONNXruntime has CUDAExecutionProvider available, enabling acceleration
pip install "PolUVR[cpu]"	ONNXruntime has CoreMLExecutionProvider available, enabling acceleration
pip install "PolUVR[cpu]"	No hardware acceleration enabled

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FFmpeg Dependency

PolUVR relies on FFmpeg for audio processing. To check if FFmpeg is installed, run:

PolUVR --env_info

The log should show: FFmpeg installed

If FFmpeg is missing, install it using the following commands:

OS	Command
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 install FFmpeg with:

PolUVR-ffmpeg

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GPU / CUDA Specific Installation Steps

While installing PolUVR with the [gpu] extra should suffice, sometimes PyTorch and ONNX Runtime with CUDA support require manual intervention. If you encounter issues, follow these steps:

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

If you need to install multiple CUDA versions (e.g., CUDA 11 alongside CUDA 12), use:

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

To launch the Gradio interface, use:

PolUVR-app [--share] [--open]

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

As soon as one of the following messages appears:

Running on local URL:  http://127.0.0.1:7860

Running on public URL: https://28425b3eb261b9ddc6.gradio.live

you can click on the link to open 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/Bebra777228/PolUVR.git
cd PolUVR

Create and Activate the Conda Environment

conda env create
conda activate PolUVR-dev

Install Dependencies

poetry install

For extra dependencies, use:

poetry install --extras "cpu"

or

poetry install --extras "gpu"

Running the Gradio interface Locally

PolUVR-app --open

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. For major changes, please open an issue first to discuss what you would like to add.

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Acknowledgments

This project is a fork of the original python-audio-separator repository. Special thanks to the contributors of the original project for their foundational work.