---
library_name: coreml
tags:
  - audio
  - music-source-separation
  - bs-roformer
  - coreml
base_model: noblebarkrr/BS-Roformer-MVSep-Mega-53-stems
---

# BSRoformer-Wind-CoreML

The wind stem (brass and woodwinds) of the [MVSep Mega 53-stem BS-RoFormer](https://github.com/ZFTurbo/Music-Source-Separation-Training/releases/tag/v1.0.21), converted to Core ML for [slurper](https://github.com/trevors/slurper), where it produces the "horns" stem.

- `bsr_wind_fp16.mlpackage`: the graph `frames[1,2,690,2048] -> recon[1,2,690,2048]` for one 8 s chunk at 44.1 kHz, framed with a 2048-sample periodic Hann window at hop 512: windowed DFT, band split, the axial rotary transformer, mask estimator, complex mask multiply and inverse DFT, all inside the model in float16. The host reflect-pads and frames the chunk and overlap-adds the output divided by the summed squared window.
- `golden_raw.f32`, `golden_horns.f32`: an 8 s stereo chunk (channel-major float32) and PyTorch's output for it, used by slurper's tests. Core ML matches PyTorch on it at cosine 0.99998 (44.7 dB SDR).

Produced by `scripts/convert_bs_roformer.py` in the slurper repository from the single-stem repack [noblebarkrr/BS-Roformer-MVSep-Mega-53-stems](https://huggingface.co/noblebarkrr/BS-Roformer-MVSep-Mega-53-stems) (`v1/bs_mega_53stem_wind_mvsep.ckpt`), with the model code from [Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training) (MIT). See the upstream release for the weights' terms.

The golden chunk is 8 s from 0:32 of "Blues for Mundy" by The Airmen of Note, The United States Air Force Band (composed by Rick Whitehead), from *60 Years of the Airmen of Note* (2011), via [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Blues_for_Mundy_-_Airmen_of_Note_-_United_States_Air_Force_Band.mp3). As a work of the US federal government the recording is in the public domain.