The model was trained on MSP-Podcast for the Odyssey 2024 Emotion Recognition competition baseline
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
| Multi-Task Setup |
|---|
| Test 3 | Development |
|---|
| Val | Dom | Aro | Val | Dom | Aro |
| 0.577 | 0.577 | 0.405 | 0.652 | 0.688 | 0.579 |
For more details: demo, paper, and GitHub.
@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
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]])
