Create README.md

README.md

@@ -0,0 +1,94 @@
+# 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:
+research.obvious@gmail.com