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vsep is a lightning-fast audio stem separator powered by 100+ AI models from the Ultimate Vocal Remover ecosystem. It splits music into vocals, drums, bass, and other instruments using state-of-the-art architectures.

Quick Links

• 🚀 Installation — Get started in 2 minutes
• 📋 CLI Reference — Full command-line docs
• 🎵 Model Catalog — All models with recommendations
• 📖 Python API — Use vsep as a Python library
• 🔧 Configuration — All settings explained
• 🏗️ Architecture — How it works internally
• 📓 Google Colab — Run in your browser
• 🔧 Troubleshooting — Fix common issues

Features

• 100+ models across 4 architectures (VR, MDX-Net, Demucs, Roformer/MDXC)
• Automatic architecture detection from model filename
• GPU acceleration (NVIDIA CUDA, Apple Silicon MPS, AMD DirectML)
• 11 ensemble algorithms for combining multiple models
• Audio chunking for arbitrarily long files
• Parallel model downloads with resume support
• Google Colab notebook — no local setup needed
• CLI and Python API for integration
• Remote deployment (Modal, Google Cloud Run)

Example

# Separate vocals from a song
python utils/cli.py song.mp3

# Use a specific model
python utils/cli.py song.mp3 -m Kim_Vocal_2.onnx

# List all available models
python utils/cli.py --list_models

from separator import Separator

separator = Separator()
output_files = separator.separate("song.mp3")

Supported Architectures

Architecture	Models	Extension	Best For
VR (Band Split)	29	.pth	Lightweight separation, TTA support
MDX-Net	39	.onnx	Fast inference, community models
MDXC (Roformer)	83+	.ckpt	Best vocal quality, high SDR
Demucs	24	.th / .yaml	4-stem separation

License

MIT License. Individual AI models may have their own licenses — check before commercial use.