--- title: Frugal AI Challenge Submission emoji: 🌍 colorFrom: blue colorTo: green sdk: docker pinned: false --- ## 🔊 Audio classification ### Strategy for solving the problem To minimize energy consumption, we deliberately **chose not to use deep learning techniques** such as CNN-based spectrogram analysis, LSTM on raw audio signals or transformer models, which are generally **more computationally intensive**. Instead, a more **lightweight approach** was adopted: - Feature extraction from the audio signal (MFCCs and spectral contrast) - Training a simple machine learning model (decision tree) on these extracted features Potential Improvements (Not Yet Tested) - Hyperparameter tuning for better performance - Exploring alternative lightweight ML models, such as logistic regression or k-nearest neighbors - Feature extraction without Librosa, using NumPy directly to compute basic signal properties, further reducing dependencies and overhead. The model is exported from the notebook `notebooks\Audio_Challenge.ipynb` and saved as `model_audio.pkl` ## 📚 Text classification ### Evaluate locally To evaluate the model locally, you can use the following command: ```bash python main.py --config config_evaluation_{model_name}.json ``` where `{model_name}` is either `distilBERT` or `embeddingML`. ### Models Description #### DistilBERT Model The model uses the `distilbert-base-uncased` model from the Hugging Face Transformers library, fine-tuned on the training dataset (see below). #### Embedding + ML Model The model uses a simple embedding layer followed by a classic ML model. Currently, the embedding layer is a simple TF-IDF vectorizer, and the ML model is a logistic regression.