README.md

---
license: mit
base_model: huper29/huper_recognizer
tags:
  - coreml
  - phoneme-recognition
  - ctc
  - wavlm
  - pronunciation
language:
  - en
---

# HuPER recognizer, Core ML (int8)

A Core ML build of [huper29/huper_recognizer](https://huggingface.co/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's `Wav2Vec2FeatureExtractor` with `do_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.