| # 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 |