AEmotionStudio/roformer-models

RoFormer Stem Separation Models (Safetensors)

BS-RoFormer & MelBand RoFormer — State-of-the-art music source separation

Pretrained weights converted to safetensors format for use with Mæstræa AI Workstation.

Models

BS-RoFormer (Band-Split RoPE Transformer)

Variant	SDR	Task	Path
Vocals (viperx)	12.97	Vocal/instrumental separation	bs_roformer/vocals_viperx/
Multi-stem	9.65	4-stem (bass/drums/vocals/other)	bs_roformer/multistem/

MelBand RoFormer (Mel-Band RoPE Transformer)

Variant	SDR	Task	Path
Vocals (KimberleyJensen)	10.98	Best vocal isolation	mel_band_roformer/vocals_kj/
Vocals (viperx)	11.43	Vocal/instrumental separation	mel_band_roformer/vocals_viperx/
Dereverb (anvuew)	19.17	Remove reverb from audio	mel_band_roformer/dereverb/
Denoise (aufr33)	27.99	Remove noise from audio	mel_band_roformer/denoise/

Architecture

Both models use the Band-Split RoPE Transformer architecture from lucidrains/BS-RoFormer:

• BS-RoFormer: Splits spectrogram into uniform-width subbands
• MelBand RoFormer: Splits using mel-scale (perceptually-weighted) overlapping bands

Both significantly outperform HTDemucs on vocal separation tasks.

Usage

Each model directory contains:

• model.safetensors — Model weights
• config.yaml — Architecture configuration (required for model instantiation)

Requires bs-roformer Python package: pip install bs-roformer

Credits

• Architecture: lucidrains/BS-RoFormer
• Training framework: ZFTurbo/Music-Source-Separation-Training
• BS-RoFormer vocals: viperx via TRvlvr
• MelBand vocals: KimberleyJensen, viperx
• MelBand dereverb: anvuew
• MelBand denoise: aufr33
• Conversion & Mirror by: AEmotionStudio

License

MIT — same as all upstream model releases.