| --- |
| license: mit |
| library_name: coreai |
| pipeline_tag: audio-to-audio |
| base_model: KimberleyJSN/melbandroformer |
| tags: [core-ai, coreaikit, source-separation, stem-separation, vocals, karaoke, on-device, apple] |
| --- |
| |
| # Mel-Band RoFormer (Kim Vocal) — Core AI |
|
|
| [`KimberleyJSN/melbandroformer`](https://huggingface.co/KimberleyJSN/melbandroformer) (MIT, ~228 M) |
| converted to **Apple Core AI** — the [zoo](https://github.com/john-rocky/coreai-model-zoo)'s **first |
| source-separation model**. Split any song into a **vocals (acapella)** stem and an **instrumental |
| (karaoke)** stem, entirely on device: iPhone (AOT) and Mac. |
|
|
| Mel-Band RoFormer is `STFT -> band-split (mel, overlapping) -> axial rotary transformer x6 -> mask |
| estimator -> band-average -> complex mask multiply -> iSTFT`. The neural core lowers to Core AI |
| directly (no rewrite), and two moves keep the on-device host trivial: |
|
|
| - **Real-arithmetic core** — the band-average scatter becomes a constant matmul and the complex mask |
| multiply becomes real ops, so the graph carries no complex tensors and no `scatter_add`. |
| - **STFT/iSTFT folded into the graph** as constant DFT matmuls (window baked in). The shipped graph is |
| `frames[1,2,801,2048] -> recon[1,2,801,2048]`; the host only does reflect-pad, framing and |
| overlap-add — **no FFT, no vDSP packing**. |
|
|
| Fixed shapes throughout (8 s chunk = 352 800 samples @ 44.1 kHz, 801 STFT frames), so there are no |
| dynamic-shape recompiles. The instrumental stem is `mix - vocals`. |
|
|
| ## Contents |
|
|
| - `mbr_full_fp16.aimodel` — macOS bundle (fp16, ~470 MB). |
| - `mbr_full_fp16.h18p.aimodelc` — iOS AOT specialization (A19 / h18p, GPU). |
| - `metadata.json` — sample rate, chunk size, STFT parameters, graph I/O, host recipe. |
| - `golden_raw.f32` / `golden_vocals.f32` — an 8 s demo chunk and its expected vocals stem |
| (stereo, channel-major, float32) for a host-side self-test. |
|
|
| ## Host recipe |
|
|
| Reflect-pad the chunk by `n_fft // 2`, frame with `hop_length`, feed `frames[1,2,801,2048]`, then |
| overlap-add the output, divide by the summed squared window and trim the pad. Chunks are 8 s with |
| `num_overlap` crossfade; `instrumental = mix - vocals`. |
|
|
| ## Gates |
|
|
| | gate | result | |
| |---|---| |
| | re-authored real-arithmetic core vs the reference model | cos **1.0000000** | |
| | in-graph STFT/iSTFT vs `torch.stft` reference | cos **0.9999984** | |
| | Core AI fp16, Mac GPU, framing + overlap-add round trip | cos **0.9999453** | |
| | **iPhone 17 Pro** (A19 Pro, AOT h18p, GPU) vs the Mac golden | cos **1.000000**, rms ratio **1.0000** | |
|
|
| On device (iPhone 17 Pro, GPU): **8 s chunk in 1.23 s ~ 6.5x real-time** warm (load 0.57 s); the |
| cold first run is 3.82 s ~ 2.1x real-time (load 1.24 s), before the GPU clocks ramp. |
|
|
| ## Use it |
|
|
| ```swift |
| import CoreAIKit |
| |
| let separator = try await KitSeparator(catalog: "melband-roformer-vocal") |
| let stems = try await separator.separate(contentsOf: songURL) |
| // stems.vocals / stems.instrumental — [channel][sample] at 44.1 kHz |
| ``` |
|
|
| Ships in the zoo's **[coreai-audio](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/coreai-audio)** |
| app (**Separate** tab), which pairs it with the Music tab (Stable Audio Open Small): generate a |
| track, then rip its stems. Conversion recipe and the Swift host reference: |
| [`conversion/melband_roformer`](https://github.com/john-rocky/coreai-model-zoo/tree/main/conversion/melband_roformer). |
|
|
| ## Attribution |
|
|
| Mel-Band RoFormer (Ju-Chiang Wang, Wei-Tsung Lu, Minz Won — ByteDance AI Labs); checkpoint by |
| KimberleyJensen; lucidrains BS-RoFormer implementation; ZFTurbo training code. MIT. |
|
|
| *Community port — not an Apple model.* |
|
|
