Whisper-Large v3 for Narrow Accent Classification

Model Description

This model includes the implementation of narrow accent classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648)

Github repository: https://github.com/tiantiaf0627/vox-profile-release

The included English accents are:

[
  'East Asia', 'English', 'Germanic', 'Irish', 
  'North America', 'Northern Irish', 'Oceania', 
  'Other', 'Romance', 'Scottish', 'Semitic', 'Slavic', 
  'South African', 'Southeast Asia', 'South Asia', 'Welsh'
]

Some observations we have seen for this model (List to add as we observe more):

1. TTS samples have a higher tendency to be recognized as Germanic.

• Library: https://github.com/tiantiaf0627/vox-profile-release

How to use this model

Download repo

git clone git@github.com:tiantiaf0627/vox-profile-release.git

Install the package

conda create -n vox_profile python=3.8
cd vox-profile-release
pip install -e .

Load the model

# Load libraries
import torch
import torch.nn.functional as F
from src.model.accent.whisper_accent import WhisperWrapper

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

# Load model from Huggingface
model = WhisperWrapper.from_pretrained("tiantiaf/whisper-large-v3-narrow-accent").to(device)
model.eval()

Prediction

# Label List
english_accent_list = [
    'East Asia', 'English', 'Germanic', 'Irish', 
    'North America', 'Northern Irish', 'Oceania', 
    'Other', 'Romance', 'Scottish', 'Semitic', 'Slavic', 
    'South African', 'Southeast Asia', 'South Asia', 'Welsh'
]
    
# Load data, here just zeros as the example
# Our training data filters output audio shorter than 3 seconds (unreliable predictions) and longer than 15 seconds (computation limitation)
# So you need to prepare your audio to a maximum of 15 seconds, 16kHz and mono channel
max_audio_length = 15 * 16000
data = torch.zeros([1, 16000]).float().to(device)[:, :max_audio_length]
logits, embeddings = model(data, return_feature=True)
    
# Probability and output
accent_prob = F.softmax(logits, dim=1)
print(english_accent_list[torch.argmax(accent_prob).detach().cpu().item()])

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

Kindly cite our paper if you are using our model or find it useful in your work

@article{feng2025vox,
  title={Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits},
  author={Feng, Tiantian and Lee, Jihwan and Xu, Anfeng and Lee, Yoonjeong and Lertpetchpun, Thanathai and Shi, Xuan and Wang, Helin and Thebaud, Thomas and Moro-Velazquez, Laureano and Byrd, Dani and others},
  journal={arXiv preprint arXiv:2505.14648},
  year={2025}
}

Responsible use of the Model: the Model is released under Open RAIL license, and users should respect the privacy and consent of the data subjects, and adhere to the relevant laws and regulations in their jurisdictions in using our model.

❌ Out-of-Scope Use

• Clinical or diagnostic applications
• Surveillance
• Privacy-invasive applications
• No commercial use