HuPER recognizer, Core ML (int8)
A Core ML build of huper29/huper_recognizer (WavLM-large with a CTC head over 39 ARPAbet phones plus DX), made for Accent Lab, a macOS app for English pronunciation practice, to check each sound on the Mac.
HuPER.mlmodelc: compiled Core ML model, macOS 15 or later. Weights are int8 (linear, symmetric, per channel); the math runs in FP32.vocab.json,added_tokens.json: the output labels, unchanged from the original.
Input and output
audio: Float32[1, samples], 16 kHz mono, 1 to 20 s, normalized to zero mean and unit variance (as the original'sWav2Vec2FeatureExtractorwithdo_normalize).logprobs: Float32[1, frames, 46], log-softmax over the labels, one frame per 20 ms.
How close it is to the original
On 17 lines of real speech (845 phones), goodness-of-pronunciation scores differ from the PyTorch model by 0.34 on average, and 4 of the 845 phones change sides of a −2.0 threshold. The best label per frame matches 98 % of the time.
License
MIT, as the original model. All credit for the model goes to the HuPER authors.
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