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---
license: mit
language:
- en
pipeline_tag: audio-classification
tags:
- wavlm
- msp-podcast
- emotion-recognition
- audio
- speech
- valence
- arousal
- dominance
- lucas
- speech-emotion-recognition
---
The model was trained on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) for the Odyssey 2024 Emotion Recognition competition baseline<br>
This particular model is the multi-attributed based model which predict arousal, dominance and valence in a range of approximately 0...1.
# Benchmarks
CCC based on Test3 and Development sets of the Odyssey Competition
<table style="width:500px">
<tr><th colspan=6 align="center" >Multi-Task Setup</th></tr>
<tr><th colspan=3 align="center">Test 3</th><th colspan=3 align="center">Development</th></tr>
<tr> <td>Val</td> <td>Dom</td> <td>Aro</td> <td>Val</td> <td>Dom</td> <td>Aro</td> </tr>
<tr> <td> 0.577</td> <td>0.577</td> <td>0.405</td> <td>0.652</td> <td>0.688</td> <td>0.579</td> </tr>
</table>
For more details: [demo](https://huggingface.co/spaces/3loi/WavLM-SER-Multi-Baseline-Odyssey2024), [paper/soon]() and [GitHub](https://github.com/MSP-UTD/MSP-Podcast_Challenge/tree/main).
```
@InProceedings{Goncalves_2024,
author={L. Goncalves and A. N. Salman and A. {Reddy Naini} and L. Moro-Velazquez and T. Thebaud and L. {Paola Garcia} and N. Dehak and B. Sisman and C. Busso},
title={Odyssey2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results},
booktitle={Odyssey 2024: The Speaker and Language Recognition Workshop)},
volume={To appear},
year={2024},
month={June},
address = {Quebec, Canada},
}
```
# Usage
```python
from transformers import AutoModelForAudioClassification
import librosa, torch
#load model
model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Multi-Attributes", trust_remote_code=True)
#get mean/std
mean = model.config.mean
std = model.config.std
#load an audio file
audio_path = "/path/to/audio.wav"
raw_wav, _ = librosa.load(audio_path, sr=model.config.sampling_rate)
#normalize the audio by mean/std
norm_wav = (raw_wav - mean) / (std+0.000001)
#generate the mask
mask = torch.ones(1, len(norm_wav))
#batch it (add dim)
wavs = torch.tensor(norm_wav).unsqueeze(0)
#predict
with torch.no_grad():
pred = model(wavs, mask)
print(model.config.id2label)
print(pred)
#{0: 'arousal', 1: 'dominance', 2: 'valence'}
#tensor([[0.3670, 0.4553, 0.4240]])
``` |