jaxcld 0.1.0

CLD: language detection heads for ASR models

jaxcld

jaxcld is a lightweight language-detection module for multilingual ASR models (Whisper / MMS). It provides an ASRModel wrapper plus pluggable language detection heads you can attach at inference time.

Install

pip install jaxcld

If you are developing from source:

pip install -e .

Using the package (minimal inference example)

import numpy as np

from jaxcld import ASRModel, CVXNNLangDetectHead, NNLangDetectHead, SVMLangDetectHead

# 1) Load the base ASR model
languages = ["en", "hi", "id", "ms", "zh"]
asr = ASRModel.from_pretrained("openai/whisper-small", config={"languages": languages})

# 2) Load a language detection head artifact (choose ONE)
# head = CVXNNLangDetectHead.load("path/to/whisper-small_trained_cvx_mlp.pkl", asr)
# head = NNLangDetectHead.load("path/to/openai_whisper-small_nn_head.pkl", asr)
# head = SVMLangDetectHead.load("path/to/openai_whisper-small_linear_svm.pkl", asr)

# 3) Attach head and run inference
asr.set_lang_detect_head(head)

audio_16k_mono: np.ndarray = ...  # shape (T,), sampling rate 16kHz
pred_langs, pred_texts = asr.predict(audio_16k_mono)
print(pred_langs[0], pred_texts[0])

Notes

• Head artifacts (*.pkl) are produced by training scripts in the source repository; this pip README intentionally focuses only on package usage.