2.1 KiB
2.1 KiB
license, pipeline_tag, tags
| license | pipeline_tag | tags | ||||
|---|---|---|---|---|---|---|
| mit | audio-to-audio |
|
Mel-Band RoFormer (vocals) for ai-toolkit
Safetensors repack of Kimberley Jensen's Mel-Band RoFormer vocal separation model, used by
ai-toolkit to split songs into a vocals track and an
instrumental track (instrumental = mix - vocals).
These weights are not ours. All credit goes to the original authors:
- Weights: KimberleyJSN/melbandroformer (
MelBandRoformer.ckpt, MIT) by Kimberley Jensen - Training framework and config: ZFTurbo/Music-Source-Separation-Training
(
config_vocals_mel_band_roformer_kj.yaml) by Roman Solovyev - Architecture implementation: lucidrains/BS-RoFormer by Phil Wang
- Paper: Mel-Band RoFormer for Music Source Separation, Wang et al. 2023
Files
| file | notes |
|---|---|
melbandroformer_vocals_kj.safetensors |
fp32, byte-identical tensors to the original .ckpt. Model kwargs and inference defaults (chunk_size, num_overlap) are stored in the safetensors metadata, so no separate config is needed. |
Conversion script: scripts/convert_melbandroformer.py.
Usage
ai-toolkit downloads this file automatically on first use:
python -m toolkit.audio.melbandroformer song.flac
# -> song_vocals.flac, song_instrumental.flac
from toolkit.audio.melbandroformer import load_melbandroformer, separate
model = load_melbandroformer(device="cuda", compile=True)
vocals, instrumental = separate(model, wav, sample_rate) # wav: [channels, samples]
Input: mono or stereo at any sample rate (resampled to 44.1 kHz internally, output at the input rate).
License
MIT, same as the original weights and code.