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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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tags: |
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- Godzilla |
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- MIDI |
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- MIDI dataset |
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- MIDI music |
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- giant |
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- raw |
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- searchable |
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- comprehensive |
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- music |
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- music ai |
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- MIR |
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pretty_name: godzillamididataset |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- audio-classification |
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--- |
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# Godzilla MIDI Dataset |
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## Enormous, comprehensive, normalized and searchable MIDI dataset for MIR and symbolic music AI purposes |
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*** |
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## Dataset features |
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### 1) Over 5.8M+ unique, de-duped and normalized MIDIs |
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### 2) Each MIDI was converted to proper MIDI format specification and checked for integrity |
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### 3) Dataset was de-duped twice: by md5 hashes and by pitches-patches counts |
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### 4) Extensive and comprehansive (meta)data was collected from all MIDIs in the dataset |
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### 5) Dataset comes with a custom-designed and highly optimized GPU-accelerated search and filter code |
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*** |
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## Installation |
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### pip and setuptools |
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```sh |
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# It is recommended that you upgrade pip and setuptools prior to install for max compatibility |
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!pip install --upgrade pip |
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!pip install --upgrade setuptools |
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``` |
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### CPU-only install |
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```sh |
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# The following command will install Godzilla MIDI Dataset for CPU-only search |
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# Please note that CPU search is quite slow and it requires a minimum of 128GB RAM to work for full searches |
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!pip install -U godzillamididataset |
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``` |
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### CPU/GPU install |
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```sh |
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# The following command will install Godzilla MIDI Dataset for fast GPU search |
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# Please note that GPU search requires at least 30GB GPU VRAM for full searches at float16 precision |
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!pip install -U godzillamididataset[gpu] |
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``` |
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### Optional packages |
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#### Packages for Fast Parallel Exctract module |
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```sh |
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# The following command will install packages for Fast Parallel Extract module |
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# It will allow you to extract (untar) Godzilla MIDI Dataset much faster |
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!sudo apt update -y |
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!sudo apt install -y p7zip-full |
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!sudo apt install -y pigz |
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``` |
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#### Packages for midi_to_colab_audio module |
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```sh |
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# The following command will install packages for midi_to_colab_audio module |
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# It will allow you to render Godzilla MIDI Dataset MIDIs to audio |
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!sudo apt update -y |
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!sudo apt install fluidsynth |
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``` |
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*** |
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## Quick-start use example |
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```python |
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# Import main Godzilla MIDI Dataset module |
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import godzillamididataset |
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# Download Godzilla MIDI Dataset from Hugging Face repo |
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godzillamididataset.download_dataset() |
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# Extract Godzilla MIDI Dataset with built-in function (slow) |
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godzillamididataset.parallel_extract() |
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# Or you can extract much faster if you have installed the optional packages for Fast Parallel Extract |
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# from godzillamididataset import fast_parallel_extract |
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# fast_parallel_extract.fast_parallel_extract() |
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# Load all MIDIs basic signatures |
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sigs_data = godzillamididataset.read_jsonl() |
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# Create signatures dictionaries |
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sigs_dicts = godzillamididataset.load_signatures(sigs_data) |
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# Pre-compute signatures |
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X, global_union = godzillamididataset.precompute_signatures(sigs_dicts) |
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# Run the search |
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# IO dirs will be created on the first run of the following function |
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# Do not forget to put your master MIDIs into created Master-MIDI-Dataset folder |
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# The full search for each master MIDI takes about 2-3 sec on a GPU and 4-5 min on a CPU |
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godzillamididataset.search_and_filter(sigs_dicts, X, global_union) |
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``` |
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*** |
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## Dataset structure information |
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``` |
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Godzilla-MIDI-Dataset/ # Dataset root dir |
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├── ARTWORK/ # Concept artwork |
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│ ├── Illustrations/ # Concept illustrations |
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│ ├── Logos/ # Dataset logos |
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│ └── Posters/ # Dataset posters |
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├── CODE/ # Supplemental python code and python modules |
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├── DATA/ # Dataset (meta)data dir |
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│ ├── Averages/ # Averages data for all MIDIs and clean MIDIs |
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│ ├── Basic Features/ # All basic features for all clean MIDIs |
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│ ├── Files Lists/ # Files lists by MIDIs types and categories |
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│ ├── Identified MIDIs/ # Comprehensive data for identified MIDIs |
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│ ├── Metadata/ # Raw metadata from all MIDIs |
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│ ├── Mono Melodies/ # Data for all MIDIs with monophonic melodies |
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│ ├── Pitches Patches Counts/ # Pitches-patches counts for all MIDIs |
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│ ├── Pitches Sums/ # Pitches sums for all MIDIs |
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│ ├── Signatures/ # Signatures data for all MIDIs and MIDIs subsets |
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│ └── Text Captions/ # Music description text captions for all MIDIs |
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├── MIDIs/ # Root MIDIs dir |
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└── SOUNDFONTS/ # Select high-quality soundfont banks to render MIDIs |
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``` |
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*** |
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## Dataset (meta)data information |
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**** |
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### Averages |
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#### Averages for all MIDIs are presented in three groups: |
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* ##### Notes averages without drums |
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* ##### Notes and drums averages |
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* ##### Drums averages without notes |
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#### Each group of averages is represented by a list of four values: |
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* ##### Delta start-times average in ms |
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* ##### Durations average in ms |
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* ##### Pitches average |
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* ##### Velocities average |
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**** |
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### Basic features |
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#### Basic features are presented in a form of a dictionary of 111 metrics |
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#### The features were collected from a solo piano score representation of all MIDIs with MIDI instruments below 80 |
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#### These features are useful for music classification, analysis and other MIR tasks |
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**** |
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### Files lists |
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#### Numerous files lists were created for convenience and easy MIDIs retrieval from the dataset |
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#### These include lists of all MIDIs as well as subsets of MIDIs |
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#### Files lists are presented in a dictionary format of two strings: |
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* ##### MIDI md5 hash |
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* ##### Full MIDI path |
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**** |
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### Identified MIDIs |
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#### This data contains information about all MIDIs that were definitivelly identified by artist, title, and genre |
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**** |
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### Metadata |
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#### Metadata was collected from all MIDIs in the dataset and its a list of all MIDI events preceeding first MIDI note event |
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#### The list also includes the last note event of the MIDI which is useful for measuring runtime of the MIDI |
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#### The list follows the MIDI.py score format |
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**** |
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### Mono melodies |
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#### This data contains information about all MIDIs with at least one monophonic melody |
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#### The data in a form of list of tuples where first element represents monophonic melody patch/instrument |
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#### And the second element of the tuple represents number of notes for indicated patch/instrument |
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#### Please note that many MIDIs may have more than one monophonic melody |
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**** |
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### Pitches patches counts |
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#### This data contains the pitches-patches counts for all MIDIs in the dataset |
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#### This information is very useful for de-duping, MIR and statistical analysis |
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**** |
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### Pitches sums |
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#### This data contains MIDI pitches sums for all MIDIs in the dataset |
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#### Pitches sums can be used for de-duping, MIR and comparative analysis |
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**** |
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### Signatures |
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#### This data contains two signatures for each MIDI in the dataset: |
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* ##### Full signature with 577 features |
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* ##### Basic signature with 392 features |
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#### Both signatures are presented as lists of tuples where first element is a feature and the second element is a feature count |
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#### Both signatures also include number of bad features indicated by -1 |
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#### Signatures features are divided into three groups: |
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* ##### MIDI pitches (represented by values 0-127) |
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* ##### MIDI chords (represented by values 128-449 or 128-264) |
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* ##### MIDI drum pitches (represented by values 449-577 or 264-392) |
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#### Both signatures can be very effectively used for MIDI comparison or MIDI search and filtering |
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**** |
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### Text captions |
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#### This data contains detailed textual description of music in each MIDI in the dataset |
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#### These captions can be used for text-to-music tasks and for MIR tasks |
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*** |
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## Citations |
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```bibtex |
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@misc{GodzillaMIDIDataset2025, |
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title = {Godzilla MIDI Dataset: Enormous, comprehensive, normalized and searchable MIDI dataset for MIR and symbolic music AI purposes}, |
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author = {Alex Lev}, |
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publisher = {Project Los Angeles / Tegridy Code}, |
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year = {2025}, |
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url = {https://huggingface.co/datasets/projectlosangeles/Godzilla-MIDI-Dataset} |
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``` |
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```bibtex |
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@misc {breadai_2025, |
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author = { {BreadAi} }, |
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title = { Sourdough-midi-dataset (Revision cd19431) }, |
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year = 2025, |
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url = {\url{https://huggingface.co/datasets/BreadAi/Sourdough-midi-dataset}}, |
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doi = { 10.57967/hf/4743 }, |
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publisher = { Hugging Face } |
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} |
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``` |
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```bibtex |
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@inproceedings{bradshawaria, |
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title={Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling}, |
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author={Bradshaw, Louis and Colton, Simon}, |
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booktitle={International Conference on Learning Representations}, |
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year={2025}, |
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url={https://openreview.net/forum?id=X5hrhgndxW}, |
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} |
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``` |
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```bibtex |
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@misc{TegridyMIDIDataset2025, |
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title = {Tegridy MIDI Dataset: Ultimate Multi-Instrumental MIDI Dataset for MIR and Music AI purposes}, |
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author = {Alex Lev}, |
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publisher = {Project Los Angeles / Tegridy Code}, |
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year = {2025}, |
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url = {https://github.com/asigalov61/Tegridy-MIDI-Dataset} |
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``` |
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*** |
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### Project Los Angeles |
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### Tegridy Code 2025 |
