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.

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}, 
}

---

Project Los Angeles

Tegridy Code 2025