Demucs vs Spleeter: Demucs wins on quality. In its authors' own table, Hybrid Transformer Demucs scores 9.0 dB SDR on the MUSDB HQ test set against Spleeter's 5.9, while Spleeter is the faster, "100x faster than real-time" on a GPU. Veena, a free AI DAW with a personal music producer built in, splits any track you upload into four stems in your browser, with nothing to install.
Most separation tools are wrappers around a handful of models. Knowing which is which tells you what quality to expect. Every figure below comes from the Demucs and Spleeter READMEs on GitHub, read on 23 September 2026.
| September 2026 | Veena | Demucs v4 (run it yourself) | Spleeter (run it yourself) |
|---|
| What you install | Nothing: a browser tab | Python 3.8 or later and PyTorch; runs on CPU, far faster with a GPU | Python, TensorFlow and ffmpeg; "known issues with Apple M1 chips" |
| How you split | Right-click a clip and choose "Separate into Stems", or ask CoProducer | A command line, such as demucs -n htdemucs_ft song.mp3 | A command line, such as spleeter separate -p spleeter:4stems |
| Where the stems land | On new tracks in your project, lined up with the original | Audio files in a folder | Audio files in a folder |
| What you do next | Keep producing: CoProducer builds around a stem or turns it into MIDI | Open another app | Open another app |
Deezer's release that made separation mainstream: a library "with pretrained models written in Python" that "uses Tensorflow". A spectrogram-based U-Net predicts a mask for each source, with models for 2 stems (vocals and accompaniment), 4 stems (vocals, drums, bass, other) and 5 stems (adding piano).
Strengths: fast, light, easy to run, still fine for rough work — Deezer says it separates "to 4 stems 100x faster than real-time when run on a GPU".
Weaknesses: noticeable artifacts, especially watery vocals. It works on magnitude spectrograms and reuses the original phase, which is a significant part of the artifact, and it scores 5.9 dB SDR in the Demucs authors' comparison.
Still widely embedded in older tools.
The model family its author built at Meta, and the significant advance: it started out working directly on the waveform rather than the spectrogram.
That matters because waveform models handle phase natively rather than reconstructing it. The result sounds meaningfully more natural — less of the phasey quality that characterises spectrogram separation.
Hybrid Demucs (v3) added a spectrogram branch alongside the waveform branch, taking advantage of both. Hybrid Transformer Demucs (HTDemucs, v4), released in November 2022, added Transformer layers for better long-range context; its README says it "achieves a SDR of 9.00 dB on the MUSDB HQ test set".
Strengths: the highest-scoring widely available open model in its authors' own table, and natural-sounding output.
Weaknesses: computationally heavier, slow on CPU without a GPU, and the original repository now carries a notice that it "is not maintained anymore"; its author's fork is "not actively maintained anymore" either.
The Demucs README lists its pretrained models, which you pick with the -n flag:
- htdemucs — the default: the first Hybrid Transformer model, "Trained on MusDB + 800 songs".
- htdemucs_ft — the fine-tuned version: "separation will take 4 times more time but might be a bit better". The pick when quality matters more than time.
- htdemucs_6s — six sources, adding guitar and piano as their own stems; the README itself warns that "the piano source is not working great at the moment".
- hdemucs_mmi — Hybrid Demucs v3, retrained on MusDB plus 800 songs.
- mdx and mdx_extra — the 2021 Music Demixing Challenge models; mdx_q and mdx_extra_q are quantized versions with a smaller download, though "quality can be slightly worse".
The short version: htdemucs_ft for a final split, htdemucs when you need speed, and htdemucs_6s only when you need a guitar stem.
Newer models split the spectrum into bands and process each with dedicated subnetworks, recognising that vocals, bass and cymbals need different treatment. Strong results, particularly on vocals: in the Demucs authors' own table, Band-Split RNN reaches 8.2 dB SDR without extra data and 9.0 with it.
An open reference implementation designed for research reproducibility, scoring 5.3 dB SDR in the same table. Not the best-sounding, but valuable as a well-documented baseline.
Most separation products build on Demucs variants or proprietary models trained on similar principles, often with:
- Larger private training sets — the single biggest quality factor
- More stems — separating guitar and piano rather than lumping them into "other"
- Post-processing to reduce artifacts
- Server GPUs for speed
The differences between good commercial tools are usually smaller than their marketing suggests, because they share architectural lineage.
Demucs is open source, free, and runs locally — which also means your audio never uploads. It needs at least Python 3.8 and works far faster with a GPU, but CPU works.
For anyone processing a lot of material, or working with confidential audio, this is the option worth knowing about. Just know you are adopting research code its own author no longer maintains.
Veena, the AI DAW with a personal music producer built in, has a free stem splitter on every plan. Right-click any audio clip and choose "Separate into Stems", or ask CoProducer. A full song takes about 45 to 120 seconds, and the four stems — vocals, drums, bass and other — land on new tracks lined up with the original, which is muted so nothing doubles. Separating by right-click doesn't use any of your daily CoProducer allowance.
Need just one part? Isolate mode gives you one stem plus the rest of the mix, such as a vocal and an instrumental. Files up to 95 MiB work, about eight and a half to nine and a half minutes of stereo audio. The step-by-step lives in the guide to stem separation in Veena.
Separation quality across good tools is closer than it looks. The differentiator is usually what happens next: whether you get four files in a folder, or stems in a project you can immediately edit. In Veena, the next step is the producer's: ask CoProducer to build a new beat under your vocal, or turn the bass stem into MIDI you can rewrite note by note.
Related reading: stem splitter · how stem separation works · best stem separation tools · AI music tools and stem bleed