godzillamididataset 25.6.5
Enormous, comprehensive, normalized and searchable MIDI dataset for MIR and symbolic music AI purposes
Requires Python: >=3.6
Keywords: MIDI, music, music ai, MIDI dataset, music dataset, MIR

# Godzilla MIDI Dataset
## Enormous, comprehensive, normalized and searchable MIDI dataset for MIR and symbolic music AI purposes

![Godzilla-MIDI-Dataset](https://github.com/user-attachments/assets/8008d578-f120-4a02-a0bf-7154e9a7423d)

***

## Dataset features

### 1) Over 5.8M+ unique, de-duped and normalized MIDIs
### 2) Each MIDI was converted to proper MIDI format specification and checked for integrity
### 3) Dataset was de-duped twice: by md5 hashes and by pitches-patches counts
### 4) Extensive and comprehansive (meta)data was collected from all MIDIs in the dataset
### 5) Dataset comes with a custom-designed and highly optimized GPU-accelerated search and filter code

***

## Installation

### pip and setuptools

```sh
# It is recommended that you upgrade pip and setuptools prior to install for max compatibility
!pip install --upgrade pip
!pip install --upgrade setuptools
```

### CPU-only install

```sh
# The following command will install Godzilla MIDI Dataset for CPU-only search
# Please note that CPU search is quite slow and it requires a minimum of 128GB RAM to work for full searches

!pip install -U godzillamididataset
```

### CPU/GPU install

```sh
# The following command will install Godzilla MIDI Dataset for fast GPU search
# Please note that GPU search requires at least 30GB GPU VRAM for full searches at float16 precision

!pip install -U godzillamididataset[gpu]
```

### Optional packages

#### Packages for Fast Parallel Exctract module

```sh
# The following command will install packages for Fast Parallel Extract module
# It will allow you to extract (untar) Godzilla MIDI Dataset much faster

!sudo apt update -y
!sudo apt install -y p7zip-full
!sudo apt install -y pigz
```

#### Packages for midi_to_colab_audio module

```sh
# The following command will install packages for midi_to_colab_audio module
# It will allow you to render Godzilla MIDI Dataset MIDIs to audio

!sudo apt update -y
!sudo apt install fluidsynth
```

***

## Quick-start use example

```python
# Import main Godzilla MIDI Dataset module
import godzillamididataset

# Download Godzilla MIDI Dataset from Hugging Face repo
godzillamididataset.download_dataset()

# Extract Godzilla MIDI Dataset with built-in function (slow)
godzillamididataset.parallel_extract()

# Or you can extract much faster if you have installed the optional packages for Fast Parallel Extract
# from godzillamididataset import fast_parallel_extract
# fast_parallel_extract.fast_parallel_extract()

# Load all MIDIs basic signatures
sigs_data = godzillamididataset.read_jsonl()

# Create signatures dictionaries
sigs_dicts = godzillamididataset.load_signatures(sigs_data)

# Pre-compute signatures
X, global_union = godzillamididataset.precompute_signatures(sigs_dicts)

# Run the search
# IO dirs will be created on the first run of the following function
# Do not forget to put your master MIDIs into created Master-MIDI-Dataset folder
# The full search for each master MIDI takes about 2-3 sec on a GPU and 4-5 min on a CPU
godzillamididataset.search_and_filter(sigs_dicts, X, global_union)
```

***

## Dataset structure information

```
Godzilla-MIDI-Dataset/              # Dataset root dir
├── ARTWORK/                        # Concept artwork
│   ├── Illustrations/              # Concept illustrations
│   ├── Logos/                      # Dataset logos
│   └── Posters/                    # Dataset posters
├── CODE/                           # Supplemental python code and python modules
├── DATA/                           # Dataset (meta)data dir
│   ├── Averages/                   # Averages data for all MIDIs and clean MIDIs
│   ├── Basic Features/             # All basic features for all clean MIDIs
│   ├── Files Lists/                # Files lists by MIDIs types and categories
│   ├── Identified MIDIs/           # Comprehensive data for identified MIDIs
│   ├── Metadata/                   # Raw metadata from all MIDIs
│   ├── Mono Melodies/              # Data for all MIDIs with monophonic melodies
│   ├── Pitches Patches Counts/     # Pitches-patches counts for all MIDIs 
│   ├── Pitches Sums/               # Pitches sums for all MIDIs
│   ├── Signatures/                 # Signatures data for all MIDIs and MIDIs subsets
│   └── Text Captions/              # Music description text captions for all MIDIs
├── MIDIs/                          # Root MIDIs dir
└── SOUNDFONTS/                     # Select high-quality soundfont banks to render MIDIs
```

***

## Dataset (meta)data information

****

### Averages

#### Averages for all MIDIs are presented in three groups:

* ##### Notes averages without drums
* ##### Notes and drums averages
* ##### Drums averages without notes

#### Each group of averages is represented by a list of four values:

* ##### Delta start-times average in ms
* ##### Durations average in ms
* ##### Pitches average
* ##### Velocities average

****

### Basic features

#### Basic features are presented in a form of a dictionary of 111 metrics
#### The features were collected from a solo piano score representation of all MIDIs with MIDI instruments below 80
#### These features are useful for music classification, analysis and other MIR tasks

****

### Files lists

#### Numerous files lists were created for convenience and easy MIDIs retrieval from the dataset
#### These include lists of all MIDIs as well as subsets of MIDIs
#### Files lists are presented in a dictionary format of two strings:

* ##### MIDI md5 hash
* ##### Full MIDI path

****

### Identified MIDIs

#### This data contains information about all MIDIs that were definitivelly identified by artist, title, and genre

****

### Metadata

#### Metadata was collected from all MIDIs in the dataset and its a list of all MIDI events preceeding first MIDI note event
#### The list also includes the last note event of the MIDI which is useful for measuring runtime of the MIDI
#### The list follows the MIDI.py score format

****

### Mono melodies

#### This data contains information about all MIDIs with at least one monophonic melody
#### The data in a form of list of tuples where first element represents monophonic melody patch/instrument
#### And the second element of the tuple represents number of notes for indicated patch/instrument
#### Please note that many MIDIs may have more than one monophonic melody

****

### Pitches patches counts

#### This data contains the pitches-patches counts for all MIDIs in the dataset
#### This information is very useful for de-duping, MIR and statistical analysis

****

### Pitches sums

#### This data contains MIDI pitches sums for all MIDIs in the dataset
#### Pitches sums can be used for de-duping, MIR and comparative analysis

****

### Signatures

#### This data contains two signatures for each MIDI in the dataset:

* ##### Full signature with 577 features
* ##### Basic signature with 392 features

#### Both signatures are presented as lists of tuples where first element is a feature and the second element is a feature count
#### Both signatures also include number of bad features indicated by -1

#### Signatures features are divided into three groups:

* ##### MIDI pitches (represented by values 0-127)
* ##### MIDI chords (represented by values 128-449 or 128-264)
* ##### MIDI drum pitches (represented by values 449-577 or 264-392)

#### Both signatures can be very effectively used for MIDI comparison or MIDI search and filtering

****

### Text captions

#### This data contains detailed textual description of music in each MIDI in the dataset
#### These captions can be used for text-to-music tasks and for MIR tasks

***

## Citations

```bibtex
@misc{GodzillaMIDIDataset2025,
  title        = {Godzilla MIDI Dataset: Enormous, comprehensive, normalized and searchable MIDI dataset for MIR and symbolic music AI purposes},
  author       = {Alex Lev},
  publisher    = {Project Los Angeles / Tegridy Code},
  year         = {2025},
  url          = {https://huggingface.co/datasets/projectlosangeles/Godzilla-MIDI-Dataset}
```

```bibtex
@misc {breadai_2025,
    author       = { {BreadAi} },
    title        = { Sourdough-midi-dataset (Revision cd19431) },
    year         = 2025,
    url          = {\url{https://huggingface.co/datasets/BreadAi/Sourdough-midi-dataset}},
    doi          = { 10.57967/hf/4743 },
    publisher    = { Hugging Face }
}
```

```bibtex
@inproceedings{bradshawaria,
  title={Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling},
  author={Bradshaw, Louis and Colton, Simon},
  booktitle={International Conference on Learning Representations},
  year={2025},
  url={https://openreview.net/forum?id=X5hrhgndxW}, 
}
```

```bibtex
@misc{TegridyMIDIDataset2025,
  title        = {Tegridy MIDI Dataset: Ultimate Multi-Instrumental MIDI Dataset for MIR and Music AI purposes},
  author       = {Alex Lev},
  publisher    = {Project Los Angeles / Tegridy Code},
  year         = {2025},
  url          = {https://github.com/asigalov61/Tegridy-MIDI-Dataset}
```

***

### Project Los Angeles
### Tegridy Code 2025