---
license: mit
tags:
  - coreml
  - apple-silicon
  - ios
  - efficientat
  - mobilenetv3
  - audioset
  - audio-classification
  - schism-coreml
---

# EfficientAT mn40 (AudioSet, mn40_as_ext) — Core ML

EfficientAT `mn40_as_ext` (width-4.0 MobileNetV3 distilled from AudioSet transformers, 527-class tagging, mAP .487 — the strongest released EfficientAT tagger; Schmid et al. 2022, [fschmid56/EfficientAT](https://github.com/fschmid56/EfficientAT)) for Core ML on Apple devices. 68.4M params.

Converted from the same verified reference used by the
[schism-mlx](https://github.com/schism-audio/schism-mlx) MLX ports. Two variants per model:

| File | Precision | Compute units | max logit diff |
|---|---|---|---|
| `EfficientAT_mn40_fp16.mlpackage` | FLOAT16 | ALL (ANE) | 3.5e-2, top-5 identical on tested clips |
| `EfficientAT_mn40_fp32.mlpackage` | FLOAT32 | CPU+GPU | 5.7e-6 |

Verified on-device-equivalently via coremltools on an M5 Max, against the
reference implementation on real audio. fp16 is ANE-eligible and recommended
for iPhone / iPad; fp32 is the tight-parity fallback.

## Download

`.mlpackage` bundles must be materialized as real files — the Core ML
compiler rejects the symlinks that a default `snapshot_download` creates in
the Hugging Face cache:

```python
from huggingface_hub import snapshot_download
path = snapshot_download("schism-audio/efficient-at-mn40-coreml", local_dir="./efficient-at-mn40-coreml")
```

(or `hf download schism-audio/efficient-at-mn40-coreml --local-dir ./efficient-at-mn40-coreml`). Swift hosts
downloading files directly are unaffected.

## I/O contract

- input `logmel`: `(1, 1, 128, 1000)` float32 — EfficientAT mel frontend (32 kHz, n_fft 1024, win 800 symmetric hann zero-padded, hop 320, pre-emphasis 0.97, Kaldi mel 128 bins 0–15000 Hz, `ln(x + 1e-5)`, `(x + 4.5) / 5`) of a 10 s window, transposed to (mel, time) — the frontend emits (frames, mels)
- 10 s at 32 kHz is exactly 1000 frames (pre-emphasis drops one sample: `1 + 319999 // 320`)
- output `logits`: `(1, 527)` float32 — apply sigmoid; multi-label
- longer audio: 1000-frame windows, aggregate scores; shorter: zero-pad the waveform to 10 s before the frontend

## DSP frontend (host-side)

The Core ML graph contains the network only. The host implements the audio
frontend and must match `schism_mlx.audio` numerically —
`test_vectors_*.npz` in this repo holds deterministic input/output pairs
(float32; match within ~1e-4 relative to be interchangeable with what these
models were verified against). The architecture is fully convolutional up to the global average pool, but this fixed-shape export takes exactly 10 s windows — window the full-file mel and aggregate scores. A validated Swift
implementation (Accelerate; modules `SchismDSP` and `SchismPipeline`) is
available at
[schism-audio/schism-dsp](https://github.com/schism-audio/schism-dsp),
tested against these exact vectors.

## License

MIT, inherited from [fschmid56/EfficientAT](https://github.com/fschmid56/EfficientAT) (Schmid, Koutini, Widmer — CP JKU; arXiv:2211.04772). Core ML conversion by [schism-audio](https://huggingface.co/schism-audio).