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asigalov61/Godzilla-Mono-Melodies

Godzilla Mono Melodies 654k+ select monophonic melodies with accompaniment and drums from Godzilla MIDI dataset Installation and use Load dataset #=================================================================== from datasets import load_dataset #=================================================================== godzilla_mono_melodies = load_dataset('asigalov61/Godzilla-Mono-Melodies') dataset_split = 'train'… See the full description on the dataset page: https://huggingface.co/datasets/asigalov61/Godzilla-Mono-Melodies.

sourceHugging Facecc-by-nc-sa-4.0updated 1y agoView on Hugging Face
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Godzilla Mono Melodies

654k+ select monophonic melodies with accompaniment and drums from Godzilla MIDI dataset

Godzilla-MIDI-Dataset-Illustration (5).png


Installation and use


Load dataset

python
#===================================================================

from datasets import load_dataset

#===================================================================

godzilla_mono_melodies = load_dataset('asigalov61/Godzilla-Mono-Melodies')

dataset_split = 'train'
dataset_entry_index = 0

dataset_entry = godzilla_mono_melodies[dataset_split][dataset_entry_index]

midi_hash = dataset_entry['md5']
midi_melody = dataset_entry['melody']
midi_melody_accompaniment = dataset_entry['melody_accompaniment']

print(midi_hash)
print(midi_melody[:15])
print(midi_melody_accompaniment[: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
    dur = 1
    vel = 90
    pitch = 60
    channel = 0
    patch = 0

    channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15]
    patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0]
    velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80]

    for m in midi_score:

        if 0 <= m < 256:
            time += m * 16

        elif 256 < m < 512:
            dur = (m-256) * 16

        elif 512 < m < 2048:
            cha = (m-512) // 128
            pitch = (m-512) % 128

            channel = channels_map[cha]
            patch = patches_map[channel]
            vel = velocities_map[channel]

            score.append(['note', time, dur, channel, pitch, vel, patch])

    return score
    
#===================================================================

ms_MIDI_score = decode_to_ms_MIDI_score(midi_melody_accompaniment)

#===================================================================

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 }
}

Project Los Angeles

Tegridy Code 2025