music-ds

Data Science for Music 🎸🎶

Final Project for the Código Facilito's 2023 Data Science Bootcamp

Installing the Streamlit App

Open up a Terminal (macOS/Linux) or PowerShell (Windows) and enter the following commands:

Cloning the repository

git clone https://github.com/underdoge/music-ds

cd music-ds

Creating the virtual environment

python -m venv venv

Activating the virtual environment on macOS / Linux

source venv/bin/activate

Activating the virtual environment on Windows (PowerShell)

.\venv\Scripts\Activate.ps1

Installing requirements

pip install -r requirements.txt

Running the Streamlit App

Running the program on macOS / Linux

streamlit run app.py

Hosted Streamlit App

A hosted version of the app can be found on Streamlit here.

Requirements

• Python 3.11 or greater
• Git (to clone the repo)

Dataset Sources

• My Winamp music library's Media Library Export - can be found under data/music_library_export.xml
• My Last.fm account's scrobbles (time series of when each song was played) extracted from here - can be found under data/lastfm-scrobbles-edchapa.csv
• GTZAN Dataset - Music Genre Classification from Kaggle.

Not Supported

At the moment, there's a bug while loading h5 keras models on Windows created on macOS/Linux. You may encounter this bug when running the Streamlit app directly on Windows right after cloning the repo if you don't run the Jupyter Notekbook first. The workaround is to run the Streamlit app from Windows Subsystem for Linux, or running the Jupyter Notebook on Windows first (which will create new model files that don't have the issue on Windows) before running the Streamlit app.