FMA-A-Dataset-For-Music-Analysis

Overview

This repository contains scripts for analyzing music features, training recommendation models, and building a real-time recommendation system using Apache Kafka.

Features

• load1.py: Extracts audio features using Librosa and prints them for each audio file. Also includes plotting of normalized MFCCs, Spectral Centroid, and Zero-Crossing Rate.
• mongodb1.py: Inserts audio features into MongoDB for storage and retrieval.
• connector.py: Connects Apache Spark with MongoDB to read data into Spark DataFrames.
• PHASE2.py: Trains a music recommendation model using Annoy and performs nearest neighbor search.
• producer.py: Streams music features to Kafka for real-time processing.
• consumer.py: Consumes music recommendations from Kafka and applies them.
• app.py: A web application to upload audio files and get insights.
• index.html: A simple web interface for uploading files and displaying insights.

Data

All metadata and features for all tracks are distributed in fma_metadata.zip (342 MiB). The below tables can be used with pandas or any other data analysis tool. See the [paper] or the [usage.ipynb] notebook for a description.

• tracks.csv: per track metadata such as ID, title, artist, genres, tags and play counts, for all 106,574 tracks.
• genres.csv: all 163 genres with name and parent (used to infer the genre hierarchy and top-level genres).
• features.csv: common features extracted with librosa.
• echonest.csv: audio features provided by Echonest (now Spotify) for a subset of 13,129 tracks.

Then, you got various sizes of MP3-encoded audio data:

1. fma_small.zip: 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB)
2. fma_medium.zip: 25,000 tracks of 30s, 16 unbalanced genres (22 GiB)
3. fma_large.zip: 106,574 tracks of 30s, 161 unbalanced genres (93 GiB)
4. fma_full.zip: 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB)

Setup

1. Clone the Repository: Clone or download the repository to your local machine.

   git clone <https://github.com/tashi-2004/FMA-A-Dataset-For-Music-Analysis>

2. Set Up MongoDB: Ensure MongoDB is installed and running on your system. Update MongoDB connection strings in the relevant scripts.
3. Set Up Kafka: Install and run Apache Kafka on your system. Update the Kafka broker address in producer.py and consumer.py.
4. Run Scripts: Execute the scripts in the following order:
   • Extract and Visualize Audio Features: Run load1.py to extract and visualize audio features from your audio files.
   • Store Audio Features in MongoDB: Run mongodb1.py to store the extracted audio features in MongoDB.
   • Data Analysis with Spark: Run connector.py to connect Spark with MongoDB and perform data analysis using Spark DataFrames.
   • Train Recommendation Models: Run PHASE2.py to train music recommendation models using Annoy and perform nearest neighbor searches.
   • Stream Music Features to Kafka: Run producer.py to stream music features to Kafka for real-time processing.
   • Consume Music Recommendations from Kafka: Run consumer.py to consume music recommendations from Kafka and apply them.
   • Web Interface for Audio Files: Use app.py and index.html to upload audio files via a web interface and get insights.

Usage

1. Extract and Visualize Audio Features: Run load1.py to extract and visualize audio features from your audio files.
2. Store Audio Features in MongoDB: Run mongodb1.py to store the extracted audio features in MongoDB.
3. Data Analysis with Spark: Run connector.py to connect Spark with MongoDB and perform data analysis using Spark DataFrames.
4. Train Recommendation Models: Run PHASE2.py to train music recommendation models using Annoy and perform nearest neighbor searches.
5. Stream Music Features to Kafka: Run producer.py to stream music features to Kafka for real-time processing.
6. Consume Music Recommendations from Kafka: Run consumer.py to consume music recommendations from Kafka and apply them.
7. Web Interface for Audio Files: Use app.py and index.html to upload audio files via a web interface and get insights.

Notes (Apache Spark)

Customization

You can customize the scripts according to your requirements, such as adjusting feature extraction parameters, changing MongoDB or Kafka configurations, or modifying recommendation model algorithms.

Contributors

• Tashfeen Abbasi
• Laiba Mazhar
• Rafia Khan

Feel free to contribute to this project by submitting issues or pull requests. Enjoy analyzing and recommending music with this comprehensive toolkit!