Spleeter vs Demucs
The two best-known open separators. Demucs wins clearly on quality; Spleeter’s remaining argument is speed on CPU and a lighter install.
Side by side
| Spleeter | Demucs | |
|---|---|---|
| Quality | Lower | Substantially better — up to 9.0 dB SDR |
| Speed | Faster on CPU | Slower, GPU helps a lot |
| Stems | 2 / 4 / 5 | 4, or 6 with htdemucs_6s |
| Backend | TensorFlow | PyTorch |
| Install weight | Heavier TF dependency | pip install demucs |
| Artefacts | More audible | Fewer, but present |
| Language | Python | Python |
| License | MIT | MIT |
| Platforms | macOS Windows Linux | macOS Windows Linux |
| First released | 2019 | 2019 |
| Maintained | Yes | Yes |
Where they actually differ
Quality is not close
Demucs’ Hybrid Transformer models reach around 9.0 dB SDR, and the difference against Spleeter is obvious on any material with dense mid-range. For anything you intend to use rather than just demonstrate, this is the whole comparison.
Spleeter is still faster on CPU
If you have no GPU and a large batch to get through, Spleeter finishes sooner. Demucs is usable on CPU at roughly 1.5× track length, but across thousands of files that adds up.
Demucs offers more control
Model choice (htdemucs, htdemucs_ft, htdemucs_6s, the mdx family), --shifts for quality, --overlap and --segment for memory. Spleeter gives you far fewer levers.
Both are MIT and both are maintained
Licensing is not a differentiator here — both are MIT on code and models, and both repositories are still active. Demucs’ development moved to adefossez/demucs, which is where current releases come from.
Which should you choose?
Choose Spleeter when…
- You have no GPU and a very large batch
- You want the lighter, simpler tool
- You need a 5-stem split specifically
- Throughput beats quality for your use
Choose Demucs when…
- You care about separation quality
- You are remixing, sampling or making backing tracks
- You want control over models and quality settings
- You want guitar and piano stems