---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- speech_emotion_recognition
license: bsd-2-clause
language:
- en
metrics:
- accuracy
base_model:
- openai/whisper-large-v3
pipeline_tag: audio-classification
---
# Whisper-Large V3 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 did not use the transcript compared to our official challenge submission system, but we created a speech-only system to make the model simple but still effective.

We use the MSP-Podcast data to train this model, noting that the model might be sensitive to content information when making emotion predictions. However, this could be a good feature for classifying emotions from online content.


The included emotions are: 
<pre>
[
    'Anger', 
    'Contempt', 
    'Disgust', 
    'Fear', 
    'Happiness', 
    'Neutral', 
    'Sadness', 
    'Surprise', 
    'Other'
]
</pre>

- 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
```python
# Load libraries
import torch
import torch.nn.functional as F
from src.model.emotion.whisper_emotion 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-msp-podcast-emotion").to(device)
model.eval()
```

## Prediction
```python
# Label List
emotion_label_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, embedding, _, _, _, _ = model(
    data, return_feature=True
)
    
# Probability and output
emotion_prob = F.softmax(logits, dim=1)
print(emotion_label_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}
}
```