childvox-ReCANVo-whisper-base

Whisper-Base for Child Vocalization Classification (ReCANVo Dataset)

Model Description

This model includes the implementation of child speech vocalization classification described in <a href="https://arxiv.org/abs/2605.29257"><strong>ChildVox: A Speech, Audio, and Large Audio-Language Model Benchmark in Understanding and Characterizing Sound across Childhood</strong></a> (Accepted to EMNLP 2026 Main)

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

The model is fine-tuned on the ReCANVo dataset, a large-scale corpus of child vocalizations (including young adults too).

The included child vocalization categories are:

[
  "delighted",
  "dysregulated",
  "frustrated",
  "request",
  "self-talk",
  "social",
]

How to use this model

Download repo

git clone [email protected]: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.whisper_audio import WhisperWrapper

# 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 = WhisperWrapper.from_pretrained("tiantiaf/childvox-ReCANVo-whisper-base", fold_idx=1).to(device)
model.eval()

Prediction

# Label List
label_list = [
  "delighted",
  "dysregulated",
  "frustrated",
  "request",
  "self-talk",
  "social",
]

# Load data, here just zeros as the example
# The child vocalization segments used in training are short, so we cap the input at 5 seconds
# You need to prepare your audio to a length of 5 seconds, 16kHz and mono channel
max_audio_length = 5 * 16000
data = torch.zeros([1, 160000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)

# Probability and output
maturity_prob = F.softmax(logits, dim=1)
print(label_list[torch.argmax(maturity_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 ([email protected])

❌ 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}
}

Please also cite the following dataset if you use this model:

@article{johnson2023recanvo,
  title={ReCANVo: A database of real-world communicative and affective nonverbal vocalizations},
  author={Johnson, Kristina T and Narain, Jaya and Quatieri, Thomas and Maes, Pattie and Picard, Rosalind W},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={523},
  year={2023},
  publisher={Nature Publishing Group UK London}
}