Special purpose models
- (duplicated) Mesk’s Rifforge Mel-Roformer model final | MK Colab - focused on inst/voc separation for metal music
“The model can have some quirks (just like most models) but it's all-around clean for me to release.” “It kinda also fucks up in like cleans [not distorted/non-metal vocals]”
“A pretty insane instrumental (...) I can still hear the punch (...) drums punch is preserved!!!! result is 95% since it kept some deep madness vocals at the end and there is some subtle vocal reverb left but not noticed if you didn't focus” - mohammedmehditber
Training details:
“This is a dimension 512 depth 24 model (so fairly large file size at 1.9 GB!), with an SDR of 14.2436.
It's finetuned from an older Melband Roformer checkpoint with an SDR of 13.7.”
- “I think I found the (IMO) the best process for metal:
1. inferencing using the BS 07.2025 model on MVSEP
2. inferencing using my rifforge model
3. ensembling both with a min_fft ensemble” - mesk
it keeps the "fullness" of the rifforge model being an instrumental focused model but then also removes more stuff than my base model thanks to 07.2025”
- Max Spec ensemble of Deux and inst_gaboxFlowersV10 (below) - seems to yield good result for a metal album - mohammedmehditber
- Gabox inst_gaboxFlowersV10 Mel-Roformer (yaml) | MK Colab | uvronline via special link (for free/premium accounts) - duplicated
Inst. fullness 37.12, bleedless 36.68, SDR 16.95’ (all of these metrics are better than Inst_FV8b).
The yaml is unique from the previous ones. Deux model fine-tune.
For less buzzing, use phase fix with becruily voc model - prodbyluke (phase fixer Colab).
For UVR RTX 5000 patch and torch._dynamo error, edit “use_torch_checkpoint: True” to False (or delete the line).
Chunk size for the flowers model in the Colab should be 352800 (yaml value, might be not evaluated).
It might have louder constant buzzing vs even inst_fv4 without phase fixer, but the FlowersV10 fixes some issues of crossbleeding existing in previous models, and also with some distorted vocals and screams (dtn, dca100fb8, Tobias51).
“a huge improvement over what Gabox has accustomed us to! I got amazing results. It truly achieves a stability that previous models lacked; the clarity is much greater in these.” - billieoconnell.
- (old) mesk’s “Rifforge” metal Mel-Roformer 14.01 fine-tune instrumental model (focused more on bleedless).
Inst. fullness: 28.49, bleedless: 42.38, SDR 16.67
“training is still in progress, that's why it's a beta test of the model; It should work fine for a lot of things, but it HAS quirks on some tracks + to me there's some vocal stuff still audible on some tracks, I'm mostly trying to get feedback on how I could improve it” known issues.
Be aware that MVSep’s BS-Roformer 2025.07 can be better for metal both for vocal and instrumentals than these mesk’s models, a lot of the time. It was also trained on mesk’s metal dataset.
- Custom model import Colab has currently some issues with it. Probably, using that old version will work (at least locally).
"My old MSST repo I'm using, but I removed all the training stuff
pip install -r requirements.txt (u gotta have Python and PyTorch installed as well) for the script to work.
You just gotta put all the tracks you want to test on in the **"tracks"** folder then double-click on **"inference.bat"** to run the inference script
its like if you were to type in the command in cmd but its simpler, and I'm lazy" - mesk
- Older Mesk metal Mel-Roformer preview instrumental model
Inst. fullness: 28.81, bleedless: 42.16, SDR 16.66
Retrained from Mel Kim on metal dataset consisting of a few thousands of songs.
https://huggingface.co/meskvlla33/metal_roformer_preview/tree/main | Colab
(previous metrics were made on private dataset)
“Should work fine for all genres of metal, but doesn't work on:
- hard compressed screams
- some background vocals
- weird tracks (think Meshuggah's "The Ayahuasca Experience")”
P.S: Use the MK Colab “or training repo (MSST) if you want to [separate] with it. UVR will be abysmally slow (because of chunk_size [introduced since UVR Roformer beta #3])”
- Neo_InstVFX Mel-Roformer by neoculture | yaml | MK Colab | Huggingface / 2
Inst. fullness 39.88, bleedless: 32.56, SDR: 14.35
Focused on preserving vocal chops.
“Great model (at least for K-pop it achieved the clarity and quality that no other model managed to have) it should be noted that it has a bit of noise even in its latest update, its stability is impressive, how it captures vocal chops, in blank spaces it does not leave a vocal record, sometimes the voice on certain occasions tries to eliminate them confusing them with noise, but in general it was a model that impressed me. It captures the instruments very clearly” - billieoconnell.
“NOISY AF, this is probably the dumbest idea ever had for an instrumental model. Don’t use it as your main one, some vocals will leak because I added tracks with vocal chops to the dataset. Just use this model for songs that have vocal chops” - neoculture
New fine-tune is in works.
- Unwa BS-Roformer-Inst-EXP-Value-Residual (uses Mel v2 model type in UVR; If it wasn’t made compatible with MSST already, replace bs_roformer.py from this repo and
from bs_roformer.attend import attend
⇩
from models.bs_roformer.attend import attend
in bs_roformer.py file
generally not very good model, but sometimes capable: “successfully removed [vocals] and kept the digital choir atmosphere as well” vs deux, inst_gaboxFlowersV10 and HyperACE but it it’s considerably slower than deux - mohammedmehditber)
Older/other models (still less muddy than vocal models)
0) INSTV6 by Gabox | yaml | x-minus | MK Colab
Inst. fullness 37.62, bleedless 35.07, SDR 16.43
v1e still gives better fullness, but the noise in it is a problem
Opinions are divided whether v5 or v6 is better.
“Seems like a mix between brecuily and unwa's models”
“Slightly better than v5 (...) less muddy and also removes the vocals without adding that low EQ effect when the vocals would come in, so I feel it's better” zzz
Old viperx’ 12xx models have fewerproblems with sax.
- Inst_GaboxFVX | yaml | Huggingface / 2
Inst. fullness 38.25, bleedless 35.35, SDR 16.49
“instv7+3” - fuller than instv3
- Gabox instv10 (experimental) | yaml
Less noise and vocal residues than V7, but muddier
0) Gabox Mel-Roformer instrumental model “inst_gabox.ckpt” (Kim/Unwa/Becruily fine-tuned)
Gabox’ models repo | MK Colab | Huggingface / 2
inst fullness 37.07 (better than unwa inst v1 and v2), bleedless 37.40 (better than v1e by 1.8, slightly worse than unwa’s v1)
“It’s like the v1 model with phase fixer, but it gets more instruments,
like, it prevents some instruments from getting into the vocals”, “sometimes both models don't get choirs”.
_____
List of fast inference models
(small model size/potentially workable on not ancient CPUs without GPU acceleration
or slow GPUs)
_____
Older fullness models
0) Gabox F/fullness v2 | Huggingface / 2
inst fullness 37.46 | bleedless: 37.09
*) Gabox inst_Fv4 (F - fullness/v4) | (don’t confuse with vocal fv4) | yaml | MK Colab
inst fullness 39.40 | bleedless 33.49
Duplicate from the above
Others
0) intrumental_gabox | yaml | Huggingface / 2
__
0) Gabox B/bleedless v1 instrumental model | yaml | Huggingface / 2
Inst. fullness 35.03, bleedless 39.10, SDR 16.49
0) Gabox B/bleedless v2 instrumental model | yaml | Huggingface / 2
Inst. fullness 35.09, bleedless 38.38, SDR 16.49
0) Cut your song into fragments consisting from the best moments of e.g. v1e/v1/v2 into one (and optionally Mel-Roformer Bas Curtiz FT on MVSEP as it will give you even less vocal bleeding, but more muddiness if necessary)
0) Propositions of models for phase fixer to alleviate vocal residues (from the above)
a) Becruily voc with Becruily inst (muddy but very few residues if any)
b) FT3 with V1e+
c) Unwa Beta 6 with inst_gabox3 (although it might be less consistent than the top)
d) Unwa Revive model is also good with any instrumental model
e) Unwa Bigbeta5 used to be not bad either.
f) Or any of the vocal models above with e.g. V1e (it's pretty full, and it might be not enough for it nevertheless)
How to use the phase fixer in UVR?
Separate with vocal model, then with instrumental model. Go to Audio Tools>Phase swapper, and use vocal model result as reference, and instrumental as target
__
Older non-Roformers moved further with descriptions here
__