metadata
license: cc-by-sa-4.0
language:
- en
tags:
- MIDI
- Monster
- Piano
- Representations
pretty_name: monsterpiano
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: midi_hash
dtype: string
- name: midi_score
sequence: int64
splits:
- name: train
num_bytes: 29760009360
num_examples: 580204
download_size: 3984483820
dataset_size: 29760009360
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Monster Piano
580204 solo Piano MIDI scores representations from Monster MIDI dataset
Installation and use
Load dataset
from datasets import load_dataset
monster_piano = load_dataset('asigalov61/Monster-Piano')
dataset_split = 'train'
dataset_entry_index = 0
dataset_entry = monster_piano[dataset_split][dataset_entry_index]
midi_hash = dataset_entry['midi_hash']
midi_score = dataset_entry['midi_score']
print(midi_hash)
print(midi_score[:15])
Decode to MIDI
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'
)