kaung-htet-myat/music-classifier-training-api

Training codes for music genre classification

Music Genre Classification

This repo contains code to train a classification model to classify music genres from a music sample. Model supports 10 genres: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae and rock.

Using the repository

• Clone the repository
• Create a Python environment using Conda or Venv
• Install requirements from requirements.txt
• Prepare the dataset (See dataset format in data preparation section)

Data Preparation

Dataset must be stored in tfrecords with:

• label (String): genre label of the sample
• waveform (FloatList): list of float values of the sample

Config Preparation

Training configs can be updated in configs/config.yaml. Hydra is used for configuration management.

Data Preprocessing

Three types of data preprocessings are used:

• Spectrogram
• Melspectrogram
• MFCC

Model Architectures

EfficientNet is used as backbone for all the architectures used in the experiments with 3 variations of head architectures.

Three types of head architectures used are:

• Stack of Linear Layers
• PRCNN
• Vision transformers

Sanity Checks

Before training, data samples, model architectures and hyperparameters can be checked using scripts provided.

• show_sample.py to visualize a training sample after applying data preprocessing
• show_config.py to check hyperparameters set in configs/config.yaml
• show_model.py to check the model architecture
• show_lrfn.py to check the learning rate schedule

Training

After configuring the hyperperameters and checking everything using sanity check scripts, python run train.py to start training. Checkpoints will be saved at the directory specified in the config.yaml.

Testing

python run test.py to evaluate the model.

Inference

python run inference.py to inference the trained model on test samples.

Model exporting

python run export.py to export the model from a trained checkpoint.