Mel-Band RoFormer (vocals) — GGUF
GGUF conversion of the Mel-Band RoFormer vocal-separation model for use with
CrispASR's --separate task — no
Python, no PyTorch at runtime, just the CrispASR binary and this file.
Separates a music mix into vocals and other (instrumental) stems.
Usage
crispasr --separate -m mel-band-roformer-vocals-f16.gguf -f song.flac
# -> song_vocals.wav, song_other.wav (stereo, 44.1 kHz)
Provenance & license
- Weights: KimberleyJSN/melbandroformer (Kimberley Jensen), released under the MIT license.
- Architecture: Mel-Band RoFormer, from the MIT-licensed lucidrains/BS-RoFormer implementation (Phil Wang). Paper: Mel-Band RoFormer for Music Source Separation.
- This GGUF: converted with CrispASR's
models/convert-mel-band-roformer-to-gguf.py. MIT.
The CrispASR C++ port was validated stage-by-stage against the PyTorch reference at cosine = 1.000000 (STFT, band split, all 6 axial RoFormer layers, mask estimator, iSTFT), with the reconstructed waveform bit-exact.
Files
| file | dtype | size |
|---|---|---|
mel-band-roformer-vocals-f16.gguf |
F16 | ~436 MB |
Provenance and EU AI Act Art. 53 note
- Upstream model: KimberleyJSN/melbandroformer — published by
KimberleyJSN. - Upstream licence:
mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.