ahmedrachid/audio-semantic-search

Greenplum Audio Semantic Search

This repository provides instructions and code for setting up an Audio/Music Semantic Search engine leveraging VMware Greenplum as Vector Database.

Getting Started

Follow these steps to set up the audio search system on your local machine.

Prerequisites

• Greenplum Database
• pgvector extension
• Kaggle API Access
• Docker
• Jupyter Notebook
• Python dependencies (requirements.txt)

Step 1: Create Database Tables

1. Run the script.sql file to create tables for storing metadata and embeddings in your Greenplum database:

   $ psql -U your_username -d your_database -a -f script.sql

Step 2: Generate Embeddings

1. Use the Audio_Semantic_Search.ipynb Notebook to download the dataset and generate embeddings into Greenplum.
2. Install the required Python packages listed in requirements.txt.

Step 3: Build Docker Image

1. Build your Docker image for the Greenplum audio search system:

   $ docker build -t greenplum-audio-search .

Step 4: Run Docker Container

1. Run the Docker container for the audio search system:

   $ docker run -d -p 8501:8501 greenplum-audio-search

Step 5: Access the Web App

1. Once the container is running, access the web application by opening a web browser and navigating to:

   http://localhost:8501