tiantiaf/childvox-percept_r-babyhubert

BabyHuBERT for PERCEPT-R Classification (Audio classification of /ɹ/ in children)

Model Description

This model includes the implementation for audio classification of /ɹ/ in children described in ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood (Accepted to EMNLP 2026 Main)

Github repository: https://github.com/tiantiaf0627/childvox-release

The model is fine-tuned on the PERCEPT-R dataset, a large-scale corpus for audio classification of /ɹ/ in children.

The included categories are:

[
  'Derhotic',
  'Rhotic'
]

How to use this model

Download repo

git clone git@github.com:tiantiaf0627/childvox-release

Install the package

conda create -n childvox python=3.10
cd childvox
pip install -e .

Load the model

# Load libraries
import torch
import torch.nn.functional as F
from src.model.childvox.hubert_audio import BabyHuBERTWrapper

# Find device
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"

# Load model from Huggingface
# We provide model with different folds, and specify the fold from 1, 2, 3, 4, 5
model = BabyHuBERTWrapper.from_pretrained("tiantiaf/childvox-percet_r-babyhubert", fold_idx=1).to(device)
model.eval()

Prediction

# Label List
label_list = [
  'Derhotic',
  'Rhotic'
]

# Load data, here just zeros as the example
# The child word reading segments used in training are short, so we cap the input at 2 seconds
# You need to prepare your audio to a length of 2 seconds, 16kHz and mono channel
max_audio_length = 2 * 16000
data = torch.zeros([1, 160000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)

# Probability and output
r_prob = F.softmax(logits, dim=1)
print(label_list[torch.argmax(r_prob).detach().cpu().item()])

Responsible Use: Child speech data is highly sensitive. Users should respect the privacy and consent of the children and families whose recordings are processed, obtain approval from the appropriate ethics/IRB body, and adhere to the relevant laws and regulations in their jurisdictions when using ChildVox.

If you have any questions, please contact: Tiantian Feng (tiantiaf@usc.edu)

❌ Out-of-Scope Use

• Clinical or diagnostic applications (e.g., screening for developmental or language disorders)
• Individual-level developmental assessment without expert human review
• Surveillance
• Privacy-invasive applications
• No commercial use

If you like our work or use the models in your work, kindly cite the following. We appreciate your recognition!

@article{feng2026childvox,
  title={ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood},
  author={Feng, Tiantian and Xu, Anfeng and Shi, Xuan and Kommineni, Aditya and Siam, Shakhrul Iman and Micheletti, Megan and Shi, Zhonghao and Tager-Flusberg, Helen and Zhang, Mi and Perry, Lynn K and others},
  journal={arXiv preprint arXiv:2605.29257},
  year={2026}
}