tiantiaf/wavlm-large-categorical-emotion

WavLM-Large for Categorical Emotion Classification

Model Description

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

The training pipeline used is also the top performing solution (SAILER) in INTERSPEECH 2025 - Speech Emotion Challenge (https://lab-msp.com/MSP-Podcast_Competition/IS2025/). Note that we did not use all the augmentation and and did not use the transcript to make the model simple but still effective compared to our INTERSPEECH Challenge solution. We use the MSP-Podcast data for training this model.

The included emotions are:

[
    'Anger', 
    'Contempt', 
    'Disgust', 
    'Fear', 
    'Happiness', 
    'Neutral', 
    'Sadness', 
    'Surprise', 
    'Other'
]

• 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.emotion.wavlm_emotion import WavLMWrapper

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

# Load model from Huggingface
model = WavLMWrapper.from_pretrained("tiantiaf/wavlm-large-categorical-emotion").to(device)
model.eval()

Prediction

# Label List
emotion_list = [
    'Anger', 
    'Contempt', 
    'Disgust', 
    'Fear', 
    'Happiness', 
    'Neutral', 
    'Sadness', 
    'Surprise', 
    'Other'
]
    
# 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
emotion_prob = F.softmax(logits, dim=1)
print(emotion_list[torch.argmax(emotion_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}
}