https://github.com/hayatiyrtgl/music_genre_classification

https://github.com/hayatiyrtgl/music_genre_classification

ai audio-classification audio-processing classification keras machine-learning music python tensorflow

Last synced: 3 days ago
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• Host: GitHub
• URL: https://github.com/hayatiyrtgl/music_genre_classification
• Owner: HayatiYrtgl
• License: mit
• Created: 2024-06-26T09:15:03.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-06-26T09:19:44.000Z (4 months ago)
• Last Synced: 2024-06-26T10:45:48.214Z (4 months ago)
• Topics: ai, audio-classification, audio-processing, classification, keras, machine-learning, music, python, tensorflow
• Language: Python
• Homepage:
• Size: 50.8 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

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# Music Genre Classifier

This project involves building a deep learning model to classify music genres from audio clips using TensorFlow and Keras. The classifier uses mel spectrograms as input features.

## Requirements

- TensorFlow

- Keras

- numpy

- scipy

- matplotlib

- pydub

## Setup

1. **Dataset:** Ensure your dataset is organized in directories by genre under `../DATASET/sesler/Data/genres_original/`.

2. **Preprocessing:**

   - `load_wav_16k_mono(filename)`: Loads a WAV file as mono 16kHz.

   - `audio_to_melspectogram(wav, sr=16000, n_mels=128, n_fft=2048, hop_length=512)`: Converts audio to mel spectrogram.

3. **Data Preparation:**

   - Load and preprocess audio files from the dataset.

   - Concatenate and prepare datasets for training.

4. **Model Training:**

   - Define and train a Convolutional Neural Network (CNN) model using TensorFlow and Keras.

   - Model architecture:

     - Input layer: (28, 128, 1)

     - Convolutional layers with MaxPooling

     - Dense layers with dropout

     - Output layer with softmax activation (11 classes)

   - Optimizer: Adam with custom parameters.

   - Loss function: Categorical Crossentropy.

5. **Model Evaluation:**

   - Evaluate the model on test data.

   - Plot training history using matplotlib.

   - ![alt text](https://github.com/HayatiYrtgl/Music_Genre_Classification/blob/main/music_classifier.png?raw=true)

6. **Save Model:**

   - Save the trained model as `Music_Classifier.h5`.

7. **Audio Prediction:**

   - Use `split_audio(file_path)` to split an MP3 file into chunks.

   - Preprocess chunks into mel spectrograms for prediction.

   - Load the saved model and predict genres for each chunk.

## Usage

- Ensure dependencies are installed (`pip install -r requirements.txt`).

- Run scripts sequentially for dataset preparation, model training, and audio prediction.

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