ONNX export, int8 weights, model card

LICENSE

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+MIT License
+
+Copyright (c) 2024 starrytong
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+library_name: onnx
+pipeline_tag: audio-to-audio
+tags:
+- audio
+- source-separation
+- music-source-separation
+- onnx
+---
+
+# SCNet, ONNX (int8 weights)
+
+SCNet (Tong, Zhu, Chen, Kang, Jiang, Li, Wu, Meng, "SCNet: Sparse Compression Network for Music Source
+Separation", ICASSP 2024, [arXiv:2401.13276](https://arxiv.org/abs/2401.13276)): band-split convolutions around dual-path
+LSTMs on the complex spectrogram, 10.1 M parameters (SCNet-large at half the width), trained by its author on
+MUSDB18-HQ. It splits a song into four stems: drums, bass, other, vocals. This is the network between its STFT and iSTFT, as
+[@audio/neural-separate](https://github.com/audiojs/neural/tree/main/packages/neural-separate) runs it
+(`model: 'scnet'`).
+
+| File | Size | SHA-256 |
+|---|---|---|
+| `scnet.int8.onnx` | 12.9 MB (12,900,277 bytes) | `98228931494151762a1c4ab1ec7899a894b1f81fd4a509921a9d2f9bacc50845` |
+
+The float32 export it is made from is 42.8 MB; float16 weights would be 23.2 MB.
+
+## Source
+
+- Model and code: [starrytong/SCNet](https://github.com/starrytong/SCNet) (MIT), at `5d95bf96b19c3eede63248d171efeca8e3abb948`.
+- Checkpoint: `scnet_checkpoint_musdb18.ckpt` (SHA-256 `1bc0d1abb20bfdf966dcd07637bafd03e4bc13653d09ef18bc9b3e342eafe2aa`),
+  release v.1.0.6 of [ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training)
+  (MIT), with its `config_musdb18_scnet.yaml`; the model code that repository's `models/scnet` at
+  `84b1eac0887756b4f1a9d7a1ff49105939749ed2`.
+
+## Licence and attribution
+
+MIT, Copyright (c) 2024 starrytong ([LICENSE](LICENSE)). The weights' author, in
+[starrytong/SCNet#35](https://github.com/starrytong/SCNet/issues/35#issuecomment-4999873539) (2026-07-17):
+
+> I confirm that the released SCNet and SCNet-large pretrained weights are distributed under the MIT License,
+> consistent with the source code. You are welcome to redistribute the original checkpoints and format-converted
+> versions, including ONNX exports, as part of your MIT-licensed tool, with appropriate attribution.
+
+SCNet by its authors (starrytong/SCNet); the checkpoint as Music-Source-Separation-Training (Roman Solovyev)
+distributes it; ONNX export and compaction by audiojs. Trained on MUSDB18-HQ (Rafii et al., 2019), licensed for
+educational use; whether that reaches the weights no project has settled.
+
+```bibtex
+@inproceedings{tong2024scnet,
+  title     = {SCNet: Sparse Compression Network for Music Source Separation},
+  author    = {Tong, Weinan and Zhu, Jiaxu and Chen, Jun and Kang, Shiyin and Jiang, Tao and Li, Yang and Wu, Zhiyong and Meng, Helen},
+  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  year      = {2024},
+  eprint    = {2401.13276},
+  archivePrefix = {arXiv}
+}
+```
+
+## Graph
+
+One 11 s segment (485,100 samples at 44.1 kHz, padded to 476 frames) per run:
+
+| | name | shape | |
+|---|---|---|---|
+| input | `mix_spec` | [1, 4, 2049, 476] | STFT: n 4096, hop 1024, no window, scaled by 1/√4096, centered with reflect padding; L re, L im, R re, R im |
+| output | `stems_spec` | [1, 16, 2049, 476] | each source (drums, bass, other, vocals), channel, re and im |
+
+The segments (every 2.75 s), their fades and the input's normalization follow Music-Source-Separation-Training's
+`demix()`; @audio/neural-separate's README, Algorithm, has them.
+
+```js
+import separate from '@audio/neural-separate'
+let { stems } = await separate([left, right], { sampleRate: 44100, model: 'scnet' })
+```
+
+## Export, compaction, verification
+
+`scripts/export-scnet.py --model scnet --verify` exports the network (its rFFT over time as cosine and sine
+products, its GroupNorm statistics reduced axis by axis) and compares the graph with `SCNet.forward` on noise and tones:
+max |diff| ≤ 2.2e-6 of max |y|; the package's pipeline matches `SCNet.forward` on its segments to 123–133 dB SNR per
+stem. `scripts/compact.py --model scnet --calibrate <two MUSDB18 training previews>` makes this file from it:
+
+- Weights: 77 of 82 stored in int8 (99.2 % of the values; symmetric, a scale per output channel, an LSTM's per gate row
+  and direction), the five layers ending the decoder in float16 (`decoder.2.0`'s convolution, `decoder.1.1`'s three
+  transposed convolutions, `decoder.2.1`'s first): rounded alone to int8, each moves the output 27 to 39 dB under its
+  power; all 82 together, 23.4 dB.
+- Computed in float32 on every backend: each weight is Cast and multiplied by its scale in the graph, which onnxruntime
+  folds at load (the session holds float32 weights).
+- Folded and named short, changing no value: with the input's shape fixed, every value computable from the weights and
+  the shapes alone is stored as the graph computes it (its DFT matrices, made in float64 from a Range, which
+  onnxruntime-web's WebGPU session cannot place); node and value names are base-36 counters.
+- Against the export, on the calibration previews: max |diff| 1.1e-2 of max |y|, SNR 42.4 dB.
+
+## Quality
+
+The 50 MUSDB18 test previews, BSSEval v4 SDR (museval), the median over songs, dB:
+
+| | vocals | drums | bass | other |
+|---|---|---|---|---|
+| export (float32) | 9.88 | 9.43 | 8.35 | 6.15 |
+| this file | 9.88 | 9.44 | 8.35 | 6.14 |
+| change per song: median · the song that lost most | +0.00 · −0.29 | −0.00 · −0.04 | −0.00 · −0.10 | −0.01 · −0.07 |
+
+The −0.29 dB is a song whose vocal stem is near silence (PR - Happy Daze, −2.2 dB SDR as exported); with all 82 weights
+in int8 (12.8 MB) it lost 2.6 dB, hence the five in float16. Remixes (the input plus (g − 1) times a stem, against the
+true remix): vocals +6 dB 18.23 → 18.23, vocals −6 dB 21.05 → 21.05, drums −6 dB 20.91 → 20.92. Float16 weights
+(23.2 MB) change no median by more than 0.001 dB.

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