Datasets:
Size:
10K<n<100K
License:
import os | |
import socket | |
import random | |
import datasets | |
from datasets.tasks import ImageClassification | |
_NAMES_1 = { | |
1: "Classic", | |
2: "Non_classic" | |
} | |
_NAMES_2 = { | |
3: "Symphony", | |
4: "Opera", | |
5: "Solo", | |
6: "Chamber", | |
7: "Pop", | |
8: "Dance_and_house", | |
9: "Indie", | |
10: "Soul_or_r_and_b", | |
11: "Rock" | |
} | |
_NAMES_3 = { | |
3: "Symphony", | |
4: "Opera", | |
5: "Solo", | |
6: "Chamber", | |
12: "Pop_vocal_ballad", | |
13: "Adult_contemporary", | |
14: "Teen_pop", | |
15: "Contemporary_dance_pop", | |
16: "Dance_pop", | |
17: "Classic_indie_pop", | |
18: "Chamber_cabaret_and_art_pop", | |
10: "Soul_or_r_and_b", | |
19: "Adult_alternative_rock", | |
20: "Uplifting_anthemic_rock", | |
21: "Soft_rock", | |
22: "Acoustic_pop" | |
} | |
_DBNAME = os.path.basename(__file__).split('.')[0] | |
_HOMEPAGE = f"https://huggingface.co/datasets/ccmusic-database/{_DBNAME}" | |
_CITATION = """\ | |
@dataset{zhaorui_liu_2021_5676893, | |
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li}, | |
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research}, | |
month = {nov}, | |
year = {2021}, | |
publisher = {Zenodo}, | |
version = {1.1}, | |
doi = {10.5281/zenodo.5676893}, | |
url = {https://doi.org/10.5281/zenodo.5676893} | |
} | |
""" | |
_DESCRIPTION = """\ | |
This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total. | |
""" | |
class music_genre(datasets.GeneratorBasedBuilder): | |
def _info(self): | |
return datasets.DatasetInfo( | |
features=datasets.Features( | |
{ | |
"mel": datasets.Image(), | |
"cqt": datasets.Image(), | |
"chroma": datasets.Image(), | |
"fst_level_label": datasets.features.ClassLabel(names=list(_NAMES_1.values())), | |
"sec_level_label": datasets.features.ClassLabel(names=list(_NAMES_2.values())), | |
"thr_level_label": datasets.features.ClassLabel(names=list(_NAMES_3.values())) | |
} | |
), | |
supervised_keys=("mel", "sec_level_label"), | |
homepage=_HOMEPAGE, | |
license="mit", | |
citation=_CITATION, | |
description=_DESCRIPTION, | |
task_templates=[ | |
ImageClassification( | |
task="image-classification", | |
image_column="mel", | |
label_column="sec_level_label", | |
) | |
] | |
) | |
def _cdn_url(self, ip='127.0.0.1', port=80): | |
try: | |
# easy for local test | |
with socket.create_connection((ip, port), timeout=5): | |
return f'http://{ip}/{_DBNAME}/data/genre_data.zip' | |
except (socket.timeout, socket.error): | |
return f"{_HOMEPAGE}/resolve/main/data/genre_data.zip" | |
def _split_generators(self, dl_manager): | |
data_files = dl_manager.download_and_extract(self._cdn_url()) | |
files = dl_manager.iter_files([data_files]) | |
dataset = [] | |
for path in files: | |
if os.path.basename(path).endswith(".jpg") and 'mel' in path: | |
dataset.append({ | |
'mel': path, | |
'cqt': path.replace('\\mel\\', '\\cqt\\').replace('/mel/', '/cqt/'), | |
'chroma': path.replace('\\mel\\', '\\chroma\\').replace('/mel/', '/chroma/') | |
}) | |
random.shuffle(dataset) | |
data_count = len(dataset) | |
p80 = int(data_count * 0.8) | |
p90 = int(data_count * 0.9) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"files": dataset[:p80] | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"files": dataset[p80:p90] | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"files": dataset[p90:] | |
}, | |
), | |
] | |
def _calc_label(self, path, depth, substr='/mel/'): | |
spect = substr | |
dirpath = os.path.dirname(path) | |
substr_index = dirpath.find(spect) | |
if substr_index < 0: | |
spect = spect.replace('/', '\\') | |
substr_index = dirpath.find(spect) | |
labstr = dirpath[substr_index + len(spect):] | |
labs = labstr.split('/') | |
if len(labs) < 2: | |
labs = labstr.split('\\') | |
if depth <= len(labs): | |
return int(labs[depth - 1].split('_')[0]) | |
else: | |
return int(labs[-1].split('_')[0]) | |
def _generate_examples(self, files): | |
for i, path in enumerate(files): | |
yield i, { | |
"mel": path['mel'], | |
"cqt": path['cqt'], | |
"chroma": path['chroma'], | |
"fst_level_label": _NAMES_1[self._calc_label(path['mel'], 1)], | |
"sec_level_label": _NAMES_2[self._calc_label(path['mel'], 2)], | |
"thr_level_label": _NAMES_3[self._calc_label(path['mel'], 3)] | |
} | |