[Ongoing project] : trying to apply class weights to address the class imbalance issue for higher accuracy + apply openai api for better result user friendly info

music-genre-classification

study for machine learning

Required Environment

Essential Software:

• Docker: Required to run the project in a containerized environment.
• Make: Used to automate build and execution tasks via the Makefile.
• Python 3.8 or higher: The project has been tested on Python 3.8 or above.

Required Libraries:

Libraries listed in requirements.txt are needed. These will be installed automatically within the Docker container.

Dataset:

The project normally contains music_genre_classifier_saved_model file as default pre-trained model but when it is not exist, The fma_medium dataset is used by default. If storage space is limited, the fma_small dataset can be used instead.

Usage

git clone https://github.com/coisu/music-genre-classification.git
cd music-genre-classification

before do 'make' check the Pre-trained model exist.

ls music_genre_classifier_saved_model

if it is not exist, run

wget https://os.unil.cloud.switch.ch/fma/fma_medium.zip
unzip fma_medium.zip
wget https://os.unil.cloud.switch.ch/fma/fma_metadata.zip
unzip fma_metadata.zip

if you have storage isuue, use 'fma_small.zip' instead 'fma_medium.zip' then

make

ENV

.env.gpg

encrypt environment file

make encrypt-mama

decrypt env file

make decrypt-mama

delete decrypted env file

make clean-env

docker build -t music-genre-classifier .
docker run -d -p 5000:5000 -v ~/music-genre-classification/fma_small:/app/fma_small music-genre-classifier