SER Odyssey Baseline WavLM Dominance

🚀 音頻情感支配力識別模型

本模型用於音頻情感支配力識別，基於MSP - Podcast數據集訓練，可預測音頻中情感的支配力程度，預測範圍約為0到1，為Odyssey 2024情感識別競賽提供了基線模型。

🚀 快速開始

本模型主要用於音頻情感支配力的識別，通過輸入音頻文件，可預測其情感支配力程度。以下是使用示例：

from transformers import AutoModelForAudioClassification
import librosa, torch

#load model
model = AutoModelForAudioClassification.from_pretrained("3loi/SER-Odyssey-Baseline-WavLM-Dominance", 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: 'dominance'}
#tensor([[0.3670]])

✨ 主要特性

• 單任務專業模型：專門用於預測音頻情感的支配力程度，預測範圍約為0到1。
• 基於特定數據集訓練：在[MSP - Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP - Podcast.html)數據集上進行訓練，為Odyssey 2024情感識別競賽提供基線。

📚 詳細文檔

模型基準測試

基於Odyssey競賽的Test3和開發集進行CCC（一致性相關係數）評估，結果如下：

更多詳情

• [演示](https://huggingface.co/spaces/3loi/WavLM - SER - Multi - Baseline - Odyssey2024)
• [論文](https://ecs.utdallas.edu/research/researchlabs/msp - lab/publications/Goncalves_2024.pdf)
• [GitHub](https://github.com/MSP - UTD/MSP - Podcast_Challenge/tree/main)

引用信息

@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},
}

📄 許可證

本模型採用MIT許可證。