Bs Roformer Rhythm Onnx
A host-STFT ONNX export of a 4-stem BS-RoFormer music source-separation model (bass / drums / other / vocals), used by Silverdaw as its optional higher-quality drums + bass model. Silverdaw runs the model once and...
Source du modèle
- Type
- Other
- Taille du fichier
- 257 MB
- Version
- vBs roformer 4stem rhythm fp16
- Mis à jour
- 11 oct. 2026
- Source
- Hugging Face
Description de la source
Silverdaw Rhythm Quality Pack — 4-stem BS-RoFormer (ONNX)
A host-STFT ONNX export of a 4-stem BS-RoFormer music source-separation model
(bass / drums / other / vocals), used by Silverdaw
as its optional higher-quality drums + bass model. Silverdaw runs the model
once and keeps the drums and bass outputs (vocals come from a dedicated
vocal model; other is reconstructed as the residual).
The torch.stft / torch.istft are stripped out of the graph: the ONNX core
consumes a precomputed STFT and returns the masked per-stem spectrogram, and the
host application runs the STFT and per-stem iSTFT. This keeps the graph portable
(standard ONNX ops, opset 17, no contrib operators) so it runs on the CPU and on
DirectX 12 GPUs via ONNX Runtime's DirectML execution provider.
Files
| File | Size | Notes |
|---|---|---|
bs_roformer_4stem_rhythm_fp16.onnx | ~257 MB | fp16 weights, single self-contained graph (no external .onnx.data). |
I/O contract
- Inputs —
spec_real,spec_imag, eachfloat32 [1, 2, 1025, 801]= (batch, audio channel, frequency bin, time frame): the real and imaginary parts oftorch.stft(audio, n_fft=2048, hop_length=441, win_length=2048, window=hann, center=True, normalized=False). - Outputs —
out_spec_real,out_spec_imag, eachfloat32 [1, 4, 2, 1025, 801]= (batch, stem, channel, freq, frame): the masked spectrogram per stem. Stem order:drums, bass, other, vocals. The DC bin is zeroed in-graph. - Window — traced at a fixed 8 s chunk (
T = 801frames, 352,800 samples at 44.1 kHz). The rotary embedding caches by sequence length, so the time axis is fixed; callers chunk the audio and overlap-add. Reconstruct each stem with the inverse STFT (same parameters) and a windowed overlap-add normalised by an accumulated window counter.
Provenance & license
License: MIT. This export combines three MIT-licensed sources:
- Weights — © ZFTurbo, from
ZFTurbo/Music-Source-Separation-Training(themodel_bs_roformer_ep_17_sdr_9.65684-stem checkpoint, configconfig_bs_roformer_384_8_2_485100, trained on MUSDB18-HQ; release v1.0.12). - Architecture — BS-RoFormer /
lucidrains/BS-RoFormer(MIT). - Export pipeline — the host-STFT ONNX wrapper is derived from the MIT
elicwhite/bs-roformer-webbuild pipeline.
Note on training data: the model was trained on MUSDB18-HQ, whose dataset terms are research-oriented. Whether dataset terms reach through to the resulting weights is an unsettled, industry-wide question; this export treats the trained weights as governed by the trainer's chosen MIT license, the same posture taken by other openly-distributed separation models (e.g. htdemucs).
Intended use
Designed for offline (non-real-time) stem separation inside a desktop DAW. It is not a real-time audio-callback model — run it on a worker/background thread.
Sources
1 source
Sources
1 sourceVérifié 11 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactbs_roformer_4stem_rhythm_fp16.onnx
onnx · 257 MB · SHA-256 4bac38e42c08…5d6b · Hugging Face
Download fileExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: mit library_name: onnxruntime pipeline_tag: audio-to-audio tags: - onnx - onnxruntime - audio - music - source-separation - stem-separation - bs-roformer - roformer - drums - bass - musdb18-hq --- # Silverdaw Rhythm Quality Pack — 4-stem BS-RoFormer (ONNX) A host-STFT ONNX export of a 4-stem **BS-RoFormer** music source-separation model (bass / drums / other / vocals), used by [Silverdaw](https://github.com/irarainey/silverdaw) as its optional higher-quality **drums + bass** model. Silverdaw runs the model once and keeps the **drums** and **bass** outputs (vocals come from a dedicated vocal model; `other` is reconstructed as the residual). The `torch.stft` / `torch.istft` are stripped out of the graph: the ONNX core consumes a precomputed STFT and returns the masked per-stem spectrogram, and the host application runs the STFT and per-stem iSTFT. This keeps the graph portable (standard ONNX ops, opset 17, no contrib operators) so it runs on the CPU and on DirectX 12 GPUs via ONNX Runtime's DirectML execution provider. ## Files | File | Size | Notes | | --- | --- | --- | | `bs_roformer_4stem_rhythm_fp16.onnx` | ~257 MB | fp16 weights, single self-contained graph (no external `.onnx.data`). | ## I/O contract - **Inputs** — `spec_real`, `spec_imag`, each `float32 [1, 2, 1025, 801]` = (batch, audio channel, frequency bin, time frame): the real and imaginary parts of `torch.stft(audio, n_fft=2048, hop_length=441, win_length=2048, window=hann, center=True, normalized=False)`. - **Outputs** — `out_spec_real`, `out_spec_imag`, each `float32 [1, 4, 2, 1025, 801]` = (batch, stem, channel, freq, frame): the masked spectrogram per stem. **Stem order: `drums, bass, other, vocals`.** The DC bin is zeroed in-graph. - **Window** — traced at a fixed 8 s chunk (`T = 801` frames, 352,800 samples at 44.1 kHz). The rotary embedding caches by sequence length, so the time axis is fixed; callers chunk the audio and overlap-add. Reconstruct each stem with the inverse STFT (same parameters) and a windowed overlap-add normalised by an accumulated window counter. ## Provenance & license **License: MIT.** This export combines three MIT-licensed sources: - **Weights** — © ZFTurbo, from [`ZFTurbo/Music-Source-Separation-Training`](https://github.com/ZFTurbo/Music-Source-Separation-Training) (the `model_bs_roformer_ep_17_sdr_9.6568` 4-stem ch...
Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Library onnxruntime · Repo silverdaw/bs-roformer-rhythm-onnx
