thepatch/uvr-vr-GGUF

UVR VR denoise and de-echo models, GGUF

No license is stated for these weights anywhere upstream. They are distributed with Ultimate Vocal Remover's model downloads and mirrored without one. They are republished here in GGUF form with that caveat. Do not assume any license, and if you hold the rights and want them removed, open a discussion on this repository.

Five of Ultimate Vocal Remover's VR-architecture (UVR 5.1 CascadedNet) models, converted to GGUF for stems.cpp, a C++/ggml stem separator that runs on CPU, CUDA, Vulkan and Metal with no Python. Each returns two stems: the one the network's mask selects (primary) and its complement.

model id	file	outputs (primary, secondary)	size
uvr_denoise_lite	uvr_denoise_lite-4M-v1.0-F32.gguf	noise, denoised	16.8 MiB
uvr_denoise	uvr_denoise-32M-v1.0-F32.gguf	noise, denoised	120.8 MiB
uvr_deecho_normal	uvr_deecho_normal-32M-v1.0-F32.gguf	no_echo, echo	120.8 MiB
uvr_deecho_aggressive	uvr_deecho_aggressive-32M-v1.0-F32.gguf	echo, no_echo	120.8 MiB
uvr_deecho_dereverb	uvr_deecho_dereverb-56M-v1.0-F32.gguf	no_reverb, reverb	212.8 MiB

F32 only. SHA256SUMS lists every file.

Download and run

git clone --recursive https://github.com/betweentwomidnights/stems.cpp && cd stems.cpp
./build.sh vulkan                          # or cpu | cuda | metal   (Windows: build.cmd)
./models.sh uvr_denoise uvr_deecho_normal
build-vulkan/bin/stems-split -m models/uvr_denoise-32M-v1.0-F32.gguf -i recording.wav -o clean/ --stems denoised
build-vulkan/bin/stems-split -m models/uvr_deecho_normal-32M-v1.0-F32.gguf -i vocal.wav -o dry/ --stems no_echo

stems-server finds each by model id ("model": "uvr_denoise"). The stems.cpp release packages (v0.1.3 and later) load them through the same C ABI as the other models.

What the runtime does

The network is UVR's 5.1 CascadedNet, unchanged. The spectral path around it is defined explicitly so every platform computes the same thing: periodic Hann STFT with centred zero padding, UVR's band layout (4band_v3 for the 32M/56M models, 1band_sr44100_hl1024 for Lite), polyphase resampling between bands with the filters stored in the GGUF, UVR's prefilter and synthesis masks, and 256-frame windows with 64 frames discarded at each edge. It is not byte-identical to the UVR GUI: UVR's Windows synthesis normally uses sinc_fastest, and its optional aggressiveness, test-time augmentation, artifact merging and high-end mirroring are not implemented. See stems.cpp's docs/VR.md.

Parity

SNR of the C++ output against the unchanged UVR network and stems.cpp's portable Python pipeline, on a 4 s stereo music excerpt, measured 2026-10-07/09 with ggml 9d0d910b. CPU: Core Ultra 9 275HX. Vulkan and CUDA: RTX 5070 Laptop. Every run passes stems.cpp's check (cosine >= 0.99999, SNR >= 50 dB).

model	backend	mask	primary	secondary
DeNoise-Lite	CPU / Vulkan / CUDA	90.8 / 104.9 / 106.3 dB	110.3 / 112.0 / 114.2 dB	138.9 / 139.2 / 139.4 dB
DeNoise	CPU / Vulkan / CUDA	112.3 / 114.1 / 111.4 dB	115.1 / 115.8 / 112.7 dB	135.1 / 135.1 / 135.1 dB
De-Echo Normal	CPU / Vulkan / CUDA	118.7 / 119.3 / 110.8 dB	123.1 / 122.7 / 115.7 dB	110.7 / 110.2 / 103.0 dB
De-Echo Aggressive	CPU / Vulkan / CUDA	114.8 / 116.1 / 110.2 dB	122.0 / 121.3 / 117.1 dB	112.9 / 112.2 / 108.0 dB
DeEcho-DeReverb	CPU / Vulkan / CUDA	119.5 / 121.7 / 77.6 dB	120.6 / 119.9 / 76.4 dB	106.1 / 105.4 / 61.8 dB

DeEcho-DeReverb on CUDA is the one outlier, still far below audibility.

On Apple Metal (M4, 32 GB, references dumped there) every model passes too, with cosine 1.000000000. Mask / primary / secondary SNR: De-Echo Normal 118.8 / 122.5 / 110.0 dB, De-Echo Aggressive 116.0 / 121.8 / 112.7 dB, DeEcho-DeReverb 121.9 / 120.3 / 105.8 dB (no sign of the CUDA gap), each in under 1.8 s for the 4 s excerpt. DeNoise passed Metal parity when ggml's im2col change was validated.

SNR here is against reference outputs, not against clean recordings: it shows the port computes what UVR's network computes, not how well a model denoises.

Sources

Checkpoints from the seanghay/uvr_models mirror at revision 6f4fc0c, SHA256-verified by the converter; UVR modelparams and network code from Anjok07/ultimatevocalremovergui at a5f88453bfb2b38b05a965bcf67727243e0cbf19.

file	sha256
UVR-DeNoise-Lite.pth	0023492fe98c406817b5253965de19ede65d1c147db015a3a428f07602e99571
UVR-DeNoise.pth	5addf43ece5bddd18da9f575a02d7ffdb32342414e6ad7ac8d1dd7a04138a628
UVR-De-Echo-Normal.pth	b849dd575643b075c257fb7a96c2ef5a79d7a5e7df74a2b319ad47118f1ee769
UVR-De-Echo-Aggressive.pth	1bd1d79d9c5d1b17d20f96f8a9f8aff1b55a83014f70712446bf420c0188e0a0
UVR-DeEcho-DeReverb.pth	e644028ec82865dc0fe082bc6fea85a43f7c71cfe375caee2da2d154aa661ee7

Converted with tools/convert_vr.py --preset <denoise_lite|denoise|deecho_normal|deecho_aggressive|deecho_dereverb>. BatchNorm is folded into the convolution and dense weights, the training-only auxiliary head is dropped, and the band layout and resampling filters are written as metadata. The architecture and weights are otherwise unchanged. Conversion is deterministic: re-running it reproduces these files byte for byte.