callmesanfornow/fsl-audio-abuse

Code repository for COLING 2025 Paper on Few Shot Audio Abuse Detection in Low Resource Languages # Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning This is the code repository for our paper published as part of the proceedings of COLING 2025. ## Abstract Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts. ## Overview of the codebase ### Folders 1. ```data```: Contains the script to download the dataset with the extracted embeddings. 2. ```feature-extraction```: Contains scripts to extract features from audio files of the ADIMA dataset. 3. ```plots```: Contains plots of Accuracy Scores and tSNE experiments. 3. ```results```: Contains csv files with FSL experiment results. 3. ```utils```: Contains code for the FSL experiments. ### Code files 1. ```fsl-whisper.py```: To run the Whisper-Related Experiments 2. ```fsl-wav2vec.py```: To run the Wav2Vec-Related Experiments 3. ```results-plot.ipynb```: Contains code to the plots presented in the paper. Refer to the Original Authors Repository ADIMA for the audio files and annotations. ## Requirements We performed the experiments with Pytho …