---
license: mit
language:
- en
pipeline_tag: audio-classification
tags:
- wavlm
- msp-podcast
- emotion-recognition
- audio
- speech
- valence
- 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 single-task specialized valence model, which predict 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" >Sinle-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>Val</td>  </tr>
  <tr>  <td> 0.607</td>  <td>0.709</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-Valence", 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: 'valence'}
#tensor([[0.3670]])
```