sigsep/open-unmix-pytorch

Health v2 · maintenance only

Adoption not part of the score

1503 stars · 206 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Open-Unmix is a PyTorch deep learning reference implementation for music source separation, with pre-trained models that split pop music into vocals, drums, bass, and other stems. It is designed as a research baseline with reproducible results on the MUSDB18 dataset.

Use cases

• separate a song into vocals drums bass and other stems
• remove vocals from a music track
• extract instrument stems from audio files
• baseline model for music source separation research
• train custom source separation models on own data
• speech enhancement with pretrained umxse model

When to choose

• you need a well-documented, peer-reviewed reference implementation for source separation research
• you want ready-to-use pretrained models for stem separation in Python/PyTorch
• you want reproducible results on MUSDB18

When to avoid

• you need real-time or low-latency separation in production
• you want the absolute state-of-the-art separation quality rather than a baseline
• you need a non-PyTorch framework or a GUI tool

Facets

library · maturity maintenance

audio-processing machine-learning deep-learning machine-learning deep-learning python music-source-separation pytorch stem-separation audio pretrained-models

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sigsep/open-unmix-pytorch")