Bass
- MVSEP Bass SCNet XL (the best 13.81 SDR, “it passes Food Mart - Tomodachi Life test. That's the first model to”
- MVSep Bass ensemble of SCNet XL, BS and HTDemucs4 models (SDR 14.07); single SCNet can be sometimes worse than Demucs which “considers not only spectrograms but also waveforms” - Unwa)
- MVSep Bass ensemble of SCNet XL + BS-Roformer SW (SDR 14.87); might be not better than the 14.07 ensemble above - isling
- MVSep Ensemble (vocals, instrum, bass, drums, other) (2025.06.30) (SDR 14.85) - premium only
After separation, you might want to then apply Mel-RoFormer De-noise to remove the high noise, and finish with Apollo Universal by Lew (model | Colab) to get more clarity (Tobias51).
SDR doesn't always tell the whole picture. Check out also Log WMSE metric on the leaderboard which is good for “anything rich in low frequency content” - jarredou
- MVSEP Bass BS Roformer 12.49 (integrated to 2 stem and multi/All-in stem ensemble too, worse other stem vs the below - more “empty” than below, problems with getting even results when bass contains a low pass filter with high resonances, picks up more “actual bass guitar than x-minus” model - drypaintdealerundr)
- x-minus.pro BS (much better at treble-heavy bass tones than demucs, better other stem than above, “catches higher end and synth basses. It makes it sound cleaner” although might sound “weird and muddy” compared to demucs_ft at times, and often when it does not capture synth bass, the mvsep bass will do - isling)
- MVSEP Bass BS Roformer SW 14.06 (might be worse than the above, at least the ensemble with the SW model is not better than the 14.07 ensemble - isling [which is based on older MVSep BS bass model, not the SW])
- https://twoshot.app/model/548 (paid)
- MVSep Double Bass model
“The BS-Roformer SW bass model should probably be used first to extract the double bass. Creates a better sound” - dynamic64
- MVSep Synth (also, it can sometimes pick up bass in places where regular models can’t)
- xlancelab Synth (Inference | models | HF inference / #2)
- BS-Roformer “MVSep Mega 53 Stems” model - 1.27GB | single models (less VRAM-hungry) - 1.27GB | MVSepless HF / HF CPU / Colab / newer (you can pick which stems you want there)
More 4 stems all-in-one models
- Demucs_ft (4 stem) - the best single Demucs’ model (Colab / MVSEP / UVR5 GUI)
Multisong dataset SDR: bass: 12.24, drums: 11.41, other: 5.84, vocals: 8.43 (inst. 9.48?)
(shifts=1, overlap=0.95)
Better drums and vocals than in Demucs 6 stem model, decent acoustic guitar results in 6s. Good bass stem as Demucs “considers not only spectrograms but also waveforms”.
For 4 stems alternatively check MDX_extra, generally Demucs 6 stem model is worse than MDX-B (a.k.a. Leaderbord B) 4 stem model released with MDX-Net arch from MDX21 competition (kuielab_b_x.onnx in this Colab), and is also faster than Demucs 6s.
For Demucs use overlap 0.1 if you have instrumental instead of mixture mixed with vocals as input (at least it works with ft model) and shifts 10 or higher. For normal use case (not instrumentals input) it will give more vocal residues, overlap 0.75 is max reasonable speed-wise, as a last resort 0.95, with shifts 10-20 max.
- SCNet-large_starrytong model (4 stems) (Colab or MSST-GUI)
Multisong dataset SDR 9.29: bass: 11.28, drums: 11.24, other: 5.58, vocals: 9.06 (overlap: 4)
It’s 3x faster than Demucs (Nvidia GPU or CPU-only) and sounds better for some people, except for bass. SDR-wise, vocals are better than in demucs_ft (which is low vs single vocal/inst models anyway). Better SDR than starrytong’s MUSDB18 and Mamba models.
- Ableton 12.3 update’s high quality option separation (4 stems) - slow, works only on CPU, probably utilizes BS-Roformer. Better bleedless than ZFTurbo SCNet XL undertrained public model below, and better SDR. Might take 20 minutes for 2 minutes separation on slower CPUs (iirc mobile Sandy/Ivy). The default separation mode has very low metrics.
Some of the below can be inferenced using HF inference / #2
- SCNet XL IHF model (4 stems) by ZFTurbo
Multisong dataset SDR 9.93: bass: 11.94, drums: 11.58, other: 6.49, vocals: 9.69
- SCNet XL model (4 stems) by ZFTurbo (Colab | MSST-GUI | MSST | UVR beta patch)
Multisong dataset SDR 9.72: bass: 11.87, drums: 11.49, other: 6.19, vocals: 9.32
Better metrics than the starrytong model but “downgrade to the Large model since it produces a f*** ton of buzzing” due to undertraining.
Only bass is better in Demucs_ft - 12.24, although drums might be still better in demucs_ft.
2 stem model on MVSEP is further trained iirc.
- BS-Roformer model (4 stems) by ZFTurbo | MSST-GUI
Multisong dataset SDR 9.38: bass: 11.08, drums: 11.29, other: 5.96, vocals: 9.19
Trained on MUSDB18HQ
- Mel-Roformer models (4 stems) by Aname
a) Large (4GB): multisong dataset SDR drums: 9.72, bass: 9.40, other: 5.11
b) XL (7GB):
Despite lower AVG SDR on musdb18 dataset (8.54 vs 9), it seems to outperform demucs_ft model (only other stem has better SDR in demucs_ft - all other metrics are better in SCNet/XL/BS-Roformer).
XL is “heavy and slow, without giving a quality boost compared to existing public 4 stems models trained on musdb18 by ZFTurbo and starrytong (BsRofo and SCNet large/XL [above])” - jarredou (maybe minus buzzing in the public XL model).
“Drums are sounding really good in particular, tested a couple songs with the large model after using unwa's v1e+ for instrumental” “drums are absolutely the standout”.
“The bass stem is definitely the weakest one from this new model. Very, very muddy and inconsistent.” - santilli_
“Large [variant] works in like 99% use case” “Large split sounds amazing so far tho”
XL “result would take so much longer, but the large results sounded better IMO” - 5B
XL model won’t work with default settings on Colab, and very slow on e.g. RTX 3060, “on 4070 Super it took like 6 mins on XL 4 stems compared to 30 seconds on Large 4 stems”
- SCNet Tran a.k.a. small model (4 stems) by ZFTurbo
Multisong dataset SDR bass: 10.99, drums: 10.87, other: 5.63, vocals: 8.42
Outperformed by the above models at least SDR-wise. Cannot be used in UVR.
- KUIELab-MDXNET23C (4 stems) - its first scores were probably from ensemble of its five models, and in that configuration it had better SDR than demucs_ft on its own, and drums had better SDR than “SCNet-large_starrytong” above (so single models’ score of any of these MDX23C models is probably lower than in demucs_ft).
- Lighter “model1” drums sounds surprisingly better than htdemucs non_ft v4 on previously separated instrumental. It handles trap really well and preserves hi-hats correctly, but in cost of other stem bleeding. v4 model can be used to clean it a bit further, but at least using GPU Conversion on AMD and older directml.dll for some GPUs, it adds more noise/artefacts, so use CPU in that case (tested on Roformer as preprocessor for instrumental). It’s relatively fast, but not as mdx_extra (which sounds rather lo-fi in that case).
- Bigger “model2” is heavier and doesn’t work on at least AMD 4GB VRAM GPUs on Beta Roformer patch #2 (before the introduction of the new overlap code).
To run model1/2 in UVR “You must change the model names in "mdx_C" from "ckpt" [name] to model1.ckpt, model2.ckpt, & model3.ckpt. [so simply add the name to the extension]” and then copy the ckpts to models\MDX-Net without yamls. There are actually 3 mdx23c models there (and 2 demucs), but model3 seems to be only for vocals (and with low SDR). So the two of three most important were explained above.
OG KUIELab’s repo.
- model_mdx23c_ep_168_sdr_7.0207 (4 stems)
Multisong dataset SDR bass: 8.40, drums: 7.73, other: 4.57, vocals: 7.36
4 stems, also trained on MUSDB18HQ, but by ZFTurbo, it’s different from the above, similar size to “model2”.
- Aname 4 stem BS-Roformer model | yaml
Multisong dataset SDR bass: 9.79, drums: 10.21, other: 5.27, vocals: 9.13
It has better SDR than the 7.0207 above (as in the SDR metrics link below), but worse than demucs_ft and BS-Roformer 4 stem ZFTurbo model above.
- BS-RoFormer 4 stems model by yukunelatyh / SYH99999 added on x-minus
https://uvronline.app/ai?discordtest
Multisong dataset SDR bass: 8.68, drums: 10.37, other: 5.05, vocals: 8.57
Some people like it more than Demucs, but “it's like demucs v4 but worse, I think.
The vocals have a ton of bleed, the bass is disappointing tbh.
The other stem has a ton of bgv and adlib bleed in it” Isling
It has SDR metrics for all stems worse than 4 stem BS-Roformer by ZFTurbo and demuics_ft.
- v2 of it was added on site with lower metrics for all stems in later period.
Smaller public 4 stem models and all metrics:
- kuielab_b - lighting-fast, but mediocre results compared to the above (can be installed in e.g. UVR)
Online services
- GSEP AI (2-4-6 stems, sonically it used to have the best other stem vs Demucs, also piano in Demucs is worse, and it picks up e-piano more frequently, GSEP electric guitar model doesn't include acoustic, it's only electric). In general, it used to have a very good piano model before many alternatives existed
- Ripple (defunct; and used to be for US iOS, at one point best SDR for all-in one single 4 stem (besides the other stem), but bad, bleedy other stem. Back then could be the best bass stem, and/or kick in drums, but not the best drums in overall vs demucs_ft, “you need something to get the rest of the drums out of the ‘other’ stem and at that point might as well use a proper drum model”, good vocals. You can minimize residues in Ripple by providing an already well separated instrumental from the section above, and/or minimizing volume of the input file by 3/6dB.
- Bandlab Splitter (4-6 stem - guitar, piano, web and iOS/Android app) - previously could be used e.g. for cleaning stems from other services, 48kHz output, stems can be misaligned, the quality got worse since one of the updates (rather not worth using anymore)
- Audioshake (paid, only non-copyrightes music, or slowed down songs [see workaround in "paid" above]) - sometimes better results than Demucs ft model.
- Spectralayers 10 - mainly for bass and drum separation -
“I think I've got some really comparable samples out of jarredou's MDX23 Colab fork”, but for vocals and instrumentals it’s mediocre [in Spectralayers 10].
- music.ai - “Bass was a fair bit better than Demucs HT, Drums about the same. Guitars were very good though. Vocal was almost the same as my cleaned up work. (...) I'd say a little clearer than MVSEP 4 ensemble. It seems to get the instrument bleed out quite well, (...) An engineer I've worked with demixed to almost the same results, it took me a few hours and achieve it [in] 39 seconds” Sam Hocking
- dango.ai (https://tuanziai.com/en-US) - also has 4 or more stems separation (expensive)
- (old) MDX23 1.0 by ZFTurbo 4 stems (Colab, desktop app, as above, much cleaner vs demucs_ft, less aggressive, but in 1.0 more low volume vocal residues in completely quiet places in instrumentals vs e.g. HQ_3, instrumentals as input should sound similar to the current v. 2.4 fork as only 4 stem separation code didn’t change much since then)
- MVSEP has also single piano, guitar and bass models (in many cases, guitar model can pick up piano better than piano model;
"works great for songs with grand piano, but only grand piano, since that’s what it was trained on.
Same with guitar, which catches more piano than piano model does, ironically").
- BS-Roformer “MVSep Mega 53 Stems” model (1.27GB) | single models (less VRAM-hungry) - 1.27GB | MVSepless HF / HF CPU / Colab / newer (you can pick which stems you want there)
- x-minus/uvronline.app has also single acoustic and guitar models by viperx
- viperx’ piano model is also on x-minus/uvronline.app.
(More on piano and guitar models later below)
To enhance 4 stem results, you can use good instrumental obtained from other source as input for the above (before instrumental Roformers it could be e.g. KaraFan, and its different presets ensembled in UVR5 app with Audio Tools>Manual Ensemble)
For the best results for piano or guitar models, use other stem from 4 stems from e.g. “Ensemble 8 models” or MDX23 Colab or htdemucs_ft as input.
Moises.ai - although drums might be better using e.g. “MVSep Drums” already, probably vs Mel-Roformer on MVSEP or x-minus (not sure) [Moises] can give “better results (...) if the input material is for example cassette-tape sourced or post-FM).
FL Studio - rather nothing better than the solutions above
Older 4 stem models in UVR (for some specific songs, e.g. while trying to fix bleeding across stems):
htdemucs
htdemucs_mmi
mdx_extra
kuielab_b