Upload README.md

README.md

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+---
+license: mit
+tags:
+  - music-source-separation
+  - stem-separation
+  - onnx
+  - audio
+  - hot-step
+language:
+  - en
+---
+
+# HOT-Step CPP SuperSep — ONNX Stem Separation Models
+
+Pre-converted ONNX models for multi-stem audio separation in [HOT-Step CPP](https://github.com/scragnog/HOT-Step-CPP). These run natively via ONNX Runtime GPU — no Python required.
+
+## Models
+
+| File | Architecture | Size | Purpose |
+|------|-------------|------|---------|
+| `bs_roformer_sw.onnx` | BS-Roformer | 672 MB | **Stage 1**: Primary 6-stem split (vocals, drums, bass, guitar, piano, other) |
+| `mel_band_roformer_karaoke.onnx` | Mel-Band RoFormer | 875 MB | **Stage 2**: Vocal sub-separation (lead vs backing) |
+| `mdx23c_drumsep.onnx` | MDX23C | 418 MB | **Stage 3**: Drum sub-separation (kick, snare, toms, hi-hat, cymbals) |
+| `htdemucs_6s.onnx` | HTDemucs | 105 MB | **Stage 4**: "Other" stem refinement |
+
+**Total: ~2.07 GB**
+
+## Usage
+
+These models are designed for use with the HOT-Step CPP Model Manager. In the app:
+
+1. Open the **Model Manager** (click "Get More Models" in the Models dropdown)
+2. Go to the **Stem Separation** tab
+3. Click **Download** on each model (or use the Stem Separation starter pack)
+
+Models are downloaded to `models/supersep/` and loaded automatically by the SuperSep engine.
+
+### Technical Details
+
+- **Format**: ONNX (opset 18, legacy TorchScript export)
+- **Precision**: FP32
+- **Input**: Spectrogram representation (STFT performed in C++ engine)
+- **Output**: Separation masks (iSTFT performed in C++ engine)
+- **Runtime**: ONNX Runtime 1.25.1+ with CUDA Execution Provider
+
+The models export only the neural network portion — STFT/iSTFT operations are handled natively in C++ for optimal performance.
+
+## Conversion
+
+These were converted from PyTorch checkpoints using the [MSS_ONNX_TensorRT](https://github.com/ZFTurbo/MSS_ONNX_TensorRT) toolset with `dynamo=False` (legacy TorchScript exporter) for compatibility with complex attention architectures.
+
+## Attribution & Licenses
+
+### Training & Checkpoints
+
+- **BS-Roformer** checkpoint by [aufr33](https://github.com/jarredou/mss-oracle-list) — trained on the Music Source Separation framework
+- **Mel-Band RoFormer Karaoke** checkpoint by [aufr33 & viperx](https://github.com/jarredou/mss-oracle-list) — SDR 10.1956 on karaoke separation
+- **MDX23C DrumSep** checkpoint by [aufr33 & jarredou](https://github.com/jarredou/mss-oracle-list) — drum sub-component isolation
+- **HTDemucs** by [Meta / Facebook AI Research](https://github.com/facebookresearch/demucs) — Hybrid Transformer architecture
+
+### Frameworks & Tools
+
+- **[Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training)** by ZFTurbo — training framework for BS-Roformer, Mel-Band RoFormer, and MDX23C architectures
+- **[MSS_ONNX_TensorRT](https://github.com/ZFTurbo/MSS_ONNX_TensorRT)** by ZFTurbo — ONNX conversion tooling with STFT extraction and model validation
+- **[Demucs](https://github.com/facebookresearch/demucs)** by Meta Research — HTDemucs architecture and pre-trained weights (MIT License)
+
+### Architecture Papers
+
+- **BS-Roformer**: "Music Source Separation with Band-Split RoFormer" ([arXiv:2309.02612](https://arxiv.org/abs/2309.02612))
+- **Mel-Band RoFormer**: Mel-frequency variant of Band-Split RoFormer
+- **MDX23C**: Based on TFC-TDF-UNet v3 architecture
+- **HTDemucs**: "Hybrid Transformers for Music Source Separation" ([arXiv:2211.08553](https://arxiv.org/abs/2211.08553))
+
+## License
+
+The conversion and packaging is released under MIT. Individual model weights are subject to their original training licenses — see the attribution links above for details.