Audio source separation has gone from a niche research problem to something you can do in a few lines of Python. The tooling has improved dramatically in the past two years, but the documentation is scattered. Here's a curated list of the resources actually worth reading in 2026.

Understanding the technology first

Before you write any code, it's worth understanding what you're working with. Modern stem separation uses neural networks trained on large datasets of music with known stems. The current state-of-the-art open source model is HTDemucs from Meta AI Research — a hybrid transformer architecture that processes both the waveform and spectrogram simultaneously.

HTDemucs GitHub — the model itself, with installation docs and usage examples

HTDemucs paper (arXiv) — the research behind the architecture

MDX Workshop benchmarks — the most rigorous public comparison of separation models

The practical Python guides

For the full implementation comparison:

Demucs, Spleeter & API Compared (Hashnode) — covers…