README.md

---
license: mit
tags:
  - audio
  - pitch-estimation
  - crepe
  - core-ai
  - apple-silicon
  - macos
library_name: swift-pitch-tracker
---

# tune-crepe — CREPE for Core AI (macOS 27)

CREPE (Kim, Salamon, Li and Bello, "CREPE: A Convolutional Representation for Pitch
Estimation", ICASSP 2018; MIT) exported to Apple Core AI `.aimodel` assets for on-device pitch
tracking. The weights are the original Keras ones via [torchcrepe](https://github.com/maxrmorrison/torchcrepe)
(Max Morrison, MIT). Read by [swift-pitch-tracker](https://github.com/arraypress/swift-pitch-tracker)
and the [`tune`](https://github.com/arraypress/swift-tune-cli) command-line tool.

## Files

| Path | What |
|---|---|
| `tune-crepe-full-float32.aimodel/` | The paper's model, 22 M parameters, 89 MB. `frames [256, 1024]` → `activations [256, 360]`. |
| `tune-crepe-tiny-float32.aimodel/` | The small model, 0.5 M parameters, 2 MB. Same shapes. |

Input: 1024-sample frames of 16 kHz audio, each normalised to zero mean and unit (biased)
standard deviation, as `crepe.core.get_activation` builds them (centre zero-pad 512, hop 160).
Output: sigmoid activations over 360 bins 20 cents apart from 1997.38 cents above 10 Hz.
Decoding (local average around the argmax, or Viterbi) is the host's, reproducing `marl/crepe`.

## Fidelity

The exported network matches the original Keras `crepe` at 145–151 dB PSNR on the activations
(argmax identical); on Core AI it runs at 141–145 dB (full) and 145 dB (tiny) against
torchcrepe; whole clips decode to within 0.001 cents of `crepe.predict`.

## Use

```sh
hf download arraypress/tune-crepe --local-dir models
tune model install models
tune bass_01.wav            # E1 −4 cents
```

Requires macOS 27 (Core AI) on Apple silicon. Converted with coreai-torch 0.4.2 / torch 2.13.