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