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
(Max Morrison, MIT). Read by swift-pitch-tracker
and the tune 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
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.