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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...

Sourcehuggingface.co/silverdaw/bs-roformer-rhythm-onnx/tree/9fa8c3acdbd488b97309e4b78c9c605389cec777
KindOtherVersionBs roformer 4stem rhythm fp16Cgrade

Model source

Kind
Other
File size
257 MB
Version
vBs roformer 4stem rhythm fp16
Updated
Oct 11, 2026
Source
Hugging Face

Source description

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

FileSizeNotes
bs_roformer_4stem_rhythm_fp16.onnx~257 MBfp16 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:

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
Hugging FacePrimaryVersion 9fa8c3acdbd488b97309e4b78c9c605389cec777
huggingface.co/silverdaw/bs-roformer-rhythm-onnx/tree/9fa8c3acdbd488b97309e4b78c9c605389cec777

Verified Oct 11

Model artifacts

1 artifact

bs_roformer_4stem_rhythm_fp16.onnx

onnx · 257 MB · SHA-256 4bac38e42c08…5d6b · Hugging Face

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Source excerpts

2 excerpts
ReadmeAug 2huggingface.co/silverdaw/bs-roformer-rhythm-onnx

--- 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...

NoteAug 2huggingface.co/silverdaw/bs-roformer-rhythm-onnx

Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Library onnxruntime · Repo silverdaw/bs-roformer-rhythm-onnx