asigalov61/Godzilla-Piano
Godzilla Piano 1.1M+ select normalized solo piano scores representations from 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.… See the full description on the dataset page: https://huggingface.co/datasets/asigalov61/Godzilla-Piano.
2220
Godzilla Piano
1.1M+ select normalized solo piano scores representations from 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
#===================================================================
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
#===================================================================
# !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
@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}@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 }
}@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},
}