Music Genre Classification with AI🎶

The music-classifier model classifies music genres based on audio signals. It was fine-tuned on the music_genres_small dataset.

This project leverages the Wav2Vec2 model from Facebook, a state-of-the-art transformer-based architecture for audio processing, to classify music genres. The model is fine-tuned on a dataset of music samples to predict the genre of a given audio file.

The repository includes:

• Preprocessing of audio data.
• Flask service for a website for try the model
• Training scripts for fine-tuning the Wav2Vec2 model.
• Model evaluation and performance analysis.
• Deployment of the trained model for real-world predictions.

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Getting Started

Prerequisites

• Python 3
• pip install transformers werkzeug flask torch librosa

Run API Flask

• Python app.py (run app.py)
• website on http://localhost:5000

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🖥️ Access the Model

➡️ Fine-Tuned Model gastonduault/music-classifier

Metrics

• Validation Accuracy: 75%
• F1 Score: 74%
• Validation Loss: 0.77

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📊 Dataset

The dataset used for training is lewtun/music_genres_small from Hugging Face's datasets library. It contains diverse audio tracks labeled with genres.

Classes:

• Electronic
• Rock
• Punk
• Experimental
• Hip-Hop
• Folk
• Chiptune / Glitch
• Instrumental
• Pop
• International

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📂 Repository Structure

• training.py: Script for training and evaluating the Wav2Vec2 model (with Google colab).
• prediction.py: Script to make predictions on new audio files using the trained model.
• app.py: Python API with Flask framework for testing the model.

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The interface of testing