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---
license: cc-by-nc-sa-4.0
task_categories:
- audio-classification
language:
- en
tags:
- MIDI
- music
- score
- representations
- tokenized
- music AI
pretty_name: godzillapiano
size_categories:
- 1M<n<10M
dataset_info:
features:
- name: dataset
dtype: string
- name: md5
dtype: string
- name: score
sequence: int64
splits:
- name: train
num_bytes: 56796114600
num_examples: 1138048
download_size: 7136701973
dataset_size: 56796114600
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Godzilla Piano
## 1.1M+ select normalized solo piano scores representations from [Godzilla MIDI dataset](https://huggingface.co/datasets/projectlosangeles/Godzilla-MIDI-Dataset)
```
Godzilla's Musical Transformation by Microsoft Copilot
In the neon glow of a midnight throng,
A beast with headphones hums along.
Where thunderous roars once led the fray,
Now delicate keystrokes steal the day.
Godzilla, master of storm and lore,
Swaps terror for tunes, a musical score.
Each note a spark on the ebony keys,
Transforming chaos into rhythmic ease.
Under moonlit skies and electric sound,
The once-feared monster becomes beautifully profound.
A gentle reminder in each playful beat,
Even legends can find joy in something sweet.
So let the bass and melody entwine,
In a magical dance, uniquely divine.
For in each unexpected twist and gleam,
Lies the heart of a dreamer and a dreamer's dream.
```
***
## Installation and use
***
### Load dataset
```python
#===================================================================
from datasets import load_dataset
#===================================================================
godzilla_piano = load_dataset('asigalov61/Godzilla-Piano')
dataset_split = 'train'
dataset_entry_index = 0
dataset_entry = godzilla_piano[dataset_split][dataset_entry_index]
midi_dataset = dataset_entry['dataset']
midi_hash = dataset_entry['md5']
midi_score = dataset_entry['score']
print(midi_dataset)
print(midi_hash)
print(midi_score[:15])
```
***
### Decode score to MIDI
```python
#===================================================================
# !git clone --depth 1 https://github.com/asigalov61/tegridy-tools
#===================================================================
import TMIDIX
#===================================================================
def decode_to_ms_MIDI_score(midi_score):
score = []
time = 0
for m in midi_score:
if 0 <= m < 128:
time += m * 32
elif 128 < m < 256:
dur = (m-128) * 32
elif 256 < m < 384:
pitch = (m-256)
elif 384 < m < 512:
vel = (m-384)
score.append(['note', time, dur, 0, pitch, vel, 0])
return score
#===================================================================
ms_MIDI_score = decode_to_ms_MIDI_score(midi_score)
#===================================================================
detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(ms_MIDI_score,
output_signature = midi_hash,
output_file_name = midi_hash,
track_name='Project Los Angeles'
)
```
***
## 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},
}
```
***
### Project Los Angeles
### Tegridy Code 2025 |