This repository contains a Jupyter Notebook for performing audio classification using a zero-shot audio classification model 'laion' from huggingface. The notebook demonstrates the process of loading, preprocessing, and inferencing of the neural network model to classify audio samples into various categories.
Audio classification is a crucial task in the field of machine learning, where the goal is to categorize audio signals into predefined classes. This notebook provides a comprehensive guide to building an audio classification system using a deep learning model. It covers data loading, preprocessing, model training, and evaluation steps.
- Data loading and preprocessing
- Feature extraction from audio signals
- Inferencing a neural network for classification
- Evaluation and visualization of model performance
To run this notebook, you need to have the following dependencies installed:
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Librosa
- TensorFlow or PyTorch
- Matplotlib
You can install the required packages using pip:
pip install numpy pandas librosa tensorflow matplotlib- Clone the repository:
git clone https://github.com/saadtariq001s/zero-shot-audio-classification.git
cd audio_classification- Open the Jupyter Notebook:
jupyter notebook audio_classification.ipynb- Follow the steps in the notebook to load your audio data, preprocess it, train the model, and evaluate its performance.
The notebook provides visualizations and metrics to evaluate the performance of the trained model. You can visualize the accuracy, loss, and confusion matrix to gain insights into the model's performance.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.