🎶 Song similarity search API based on lyrics

This API is no longer deployed for its public usage. Run your own deployment for using it.

Contents

1. Motivation
2. Requirements
3. Recommendations
4. Usage
5. Run tests
6. Development
   1. Development mode
   2. Logging
   3. Scripts
   4. How to add a new test
7. Authors
8. License

Motivation

This project was built in order to create an API for searching similarities based on song lyrics. There are a lot of songs in the industry and most of them are talking about the same topic. What I wanted to prove with SearchLy was to estimate how similar are two songs between them based on the meaning of their lyrics.

SearchLy is using a database of 150k songs from AZLyrics, using this scraper, which is being updated periodically. Then, using word2vec and NMSLIB, it was possible to create an index where you can search similarities using the k-nearest neighbors (KNN) algorithm. For having a visual image of this index, check the visualization NMSLIB tool.

The API is available here along with its documentation. Test it on this website demo.

Note: I am currently using a micro-instance from DigitalOcean where the API is deployed, so you should expect a bad performance. However, if this API becomes popular I will deploy it in a bigger instance.

Requirements

1. Python 3.7+
2. docker-ce (as provided by docker package repos)
3. docker-compose (as provided by PyPI)

Recommendations

Usage of virtualenv is recommended for package library / runtime isolation.

Usage

To run the API, please execute the following commands from the root directory:

1. Setup virtual environment

2. Install dependencies

   pip3 install -r requirements.lock

3. Initialize database (if is not initialized)

   source db/deploy.sh

4. Run the server as a docker container with docker-compose

   docker-compose up -d --build

   or as a Python module (after enabling the Development mode)

   python3 -m src.searchly

Run tests

1. Run Searchly locally with the Development mode enabled.

2. Run tests

   python3 -m unittest discover -v
   

Development

Development mode

Edit src/searchly/__init__.py and switch DEVELOPMENT_MODE flag from False to True for enabling development mode.

# DEVELOPMENT_MODE = False
DEVELOPMENT_MODE = True

Logging

For checking the logs of the whole stack in real time, the following command is recommend it:

docker-compose logs -f

Scripts

The module src/searchly/scripts contains a bunch of scripts whose allow to create and build the needed index for searching the similarity between song lyrics. It's needed to have the Development mode enabled for using the scripts.

1. Fill database (fill_database.py): from a zip file extracted from the AZLyrics scraper, found on this repository, fills the database with all the data on it.
2. Train (train.py): given the data of the database, extracts all the features from the song lyrics and trains a word2vec model. The results will be saved on the datafolder.
3. Build (build.py): given the trained word2vec model, builds an NMSLIB index for allowing searchs on the API. The index file will be saved on the data folder.
4. Extract maximum distance (extract_maximum_distance.py): given the trained word2vec model and the built index, searchs across all the database for getting the maximum distance between two points. This is needed for computing the percentage of similarity instead of returning a raw distance on the API response. The result will be saved on the data folder.

How to add a new test

Create a new Python file called test_*.py in test.searchly with the following structure:

import unittest


class NewTest(unittest.TestCase):
    
    def test_v0(self):
        expected = 5
        result = 2 + 3
        self.assertEqual(expected, result)

# ...

if __name__ == '__main__':
    unittest.main()

Authors

• Albert Suàrez

License

MIT © SearchLy