README.md

# V2MIDI Dataset

## Overview

The V2MIDI dataset pairs 40,000 MIDI files with AI-generated videos, connecting music and visual art in a new way. It's designed to help researchers and artists explore how to synchronize music and visuals using AI. This dataset isn't just a collection of files – it's a tool that could change how we create and experience audio-visual content.

## Dataset Description

- **Size**: About 257GB
- **Contents**: 40,000 pairs of MIDI files and MP4 videos
- **Video Details**: 256x256 pixels, 16 seconds long, 24 frames per second
- **Music Focus**: House music drum patterns
- **Visual Variety**: AI-generated visuals based on diverse text prompts

## How We Created the Dataset

We built the V2MIDI dataset through several key steps:

1. **Gathering MIDI Data**:
   We started with a large archive of drum and percussion MIDI files, focusing on house music. We picked files based on their rhythm quality and how well they might match with visuals.

2. **Standardizing MIDI Files**:
   We processed each chosen MIDI file to make a 16-second sequence. We focused on five main drum sounds: kick, snare, closed hi-hat, open hi-hat, and pedal hi-hat. This helped keep things consistent across the dataset.

3. **Linking Music to Visuals**:
   We created a system to turn MIDI events into visual changes. For example, a kick drum might make a peak of strength in the visuals, while hi-hats might make things rotate. This is the core of how we sync the music and visuals.

4. **Creating Visual Ideas**:
   We came up with 10,000 text prompts across 100 themes. We used AI to help generate ideas, then went through and refined them by hand. This gave us a wide range of visual styles that fit well with electronic music.

5. **Making the Videos**:
   We used our MIDI-to-visual system and tools such as Parseq, Deforum and Automatic1111 (Stable Diffusion web UI) to create videos for each MIDI file.

6. **Organizing and Checking**:
   Finally, we paired each video with its MIDI file and organized everything neatly. We carefully made sure the visuals matched the music well and looked good.

## Why It's Useful

The V2MIDI dataset is special because it precisely matches MIDI events to visual changes. This opens up some exciting possibilities:

- **See the music**: Train AI to create visuals that match music in real-time.
- **Hear the visuals**: Explore whether AI can "guess" the music just by watching the video.
- **New creative tools**: Develop apps that let musicians visualize their music or let artists "hear" their visual creations.
- **Better live shows**: Create live visuals that perfectly sync with the music.

## Flexible and Customizable

We've built the V2MIDI creation process to be flexible. Researchers and artists can:

- Adjust how MIDI files are processed
- Change how music events are mapped to visual effects
- Create different styles of visuals
- Experiment with video settings like resolution and frame rate
- Adapt the process to work on different computer setups

This flexibility means the V2MIDI approach could be extended to other types of music or visual styles.

## Training AI Models

One of the most important aspects of the V2MIDI dataset is its potential for training AI models. Researchers can use this dataset to develop models that:

- Predict musical features from video content
- Create cross-modal representations linking audio and visual domains
- Develop more sophisticated audio-visual generation models

The size and quality of the dataset make it particularly valuable for deep learning approaches.

## How to Get the Dataset

The dataset is quite big so we've split it into 257 parts of about 1GB each. Here's how to put it back together:

1. Download all the parts (they're named `img2img_part_aa` to `img2img_part_jw`)
2. Stick them together with this command: `cat img2img_part_* > img2img-images_clean.tar`
3. Unpack it: `tar -xvf img2img-images_clean.tar`

Make sure you have at least 257GB of free space on your computer for this.

## What's Next?

We see the V2MIDI dataset as just the beginning. Future work could:

- Include more types of music
- Work with more complex musical structures
- Try generating music from videos (not just videos from music)
- Create tools for live performances

## Thank You

We couldn't have made this without the people who created the original MIDI archive and the open-source communities behind Stable Diffusion, Deforum, and AUTOMATIC1111.

## Get in Touch

If you have questions or want to know more about the V2MIDI dataset, email us at:
[email protected]