Datasets:

ccmusic-database
/

pianos

	import os
	import random
	import datasets
	from datasets.tasks import ImageClassification, AudioClassification


	_NAMES = [
	"PearlRiver",
	"YoungChang",
	"Steinway-T",
	"Hsinghai",
	"Kawai",
	"Steinway",
	"Kawai-G",
	"Yamaha",
	]

	_DBNAME = os.path.basename(__file__).split(".")[0]

	_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic/{_DBNAME}"

	_DOMAIN = f"https://www.modelscope.cn/api/v1/datasets/ccmusic/{_DBNAME}/repo?Revision=master&FilePath=data"

	_CITATION = """\
	@dataset{zhaorui_liu_2021_5676893,
	author = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Zijin Li},
	title = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
	month = {mar},
	year = {2024},
	publisher = {HuggingFace},
	version = {1.2},
	url = {https://huggingface.co/ccmusic-database}
	}
	"""

	_DESCRIPTION = """\
	Piano-Sound-Quality is a dataset of piano sound. It consists of 8 kinds of pianos including PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G, Yamaha(recorded by Shaohua Ji with SONY PCM-D100). Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing and collected by Monan Zhou.
	"""

	_PITCHES = {
	"009": "A2",
	"010": "A2#/B2b",
	"011": "B2",
	"100": "C1",
	"101": "C1#/D1b",
	"102": "D1",
	"103": "D1#/E1b",
	"104": "E1",
	"105": "F1",
	"106": "F1#/G1b",
	"107": "G1",
	"108": "G1#/A1b",
	"109": "A1",
	"110": "A1#/B1b",
	"111": "B1",
	"200": "C",
	"201": "C#/Db",
	"202": "D",
	"203": "D#/Eb",
	"204": "E",
	"205": "F",
	"206": "F#/Gb",
	"207": "G",
	"208": "G#/Ab",
	"209": "A",
	"210": "A#/Bb",
	"211": "B",
	"300": "c",
	"301": "c#/db",
	"302": "d",
	"303": "d#/eb",
	"304": "e",
	"305": "f",
	"306": "f#/gb",
	"307": "g",
	"308": "g#/ab",
	"309": "a",
	"310": "a#/bb",
	"311": "b",
	"400": "c1",
	"401": "c1#/d1b",
	"402": "d1",
	"403": "d1#/e1b",
	"404": "e1",
	"405": "f1",
	"406": "f1#/g1b",
	"407": "g1",
	"408": "g1#/a1b",
	"409": "a1",
	"410": "a1#/b1b",
	"411": "b1",
	"500": "c2",
	"501": "c2#/d2b",
	"502": "d2",
	"503": "d2#/e2b",
	"504": "e2",
	"505": "f2",
	"506": "f2#/g2b",
	"507": "g2",
	"508": "g2#/a2b",
	"509": "a2",
	"510": "a2#/b2b",
	"511": "b2",
	"600": "c3",
	"601": "c3#/d3b",
	"602": "d3",
	"603": "d3#/e3b",
	"604": "e3",
	"605": "f3",
	"606": "f3#/g3b",
	"607": "g3",
	"608": "g3#/a3b",
	"609": "a3",
	"610": "a3#/b3b",
	"611": "b3",
	"700": "c4",
	"701": "c4#/d4b",
	"702": "d4",
	"703": "d4#/e4b",
	"704": "e4",
	"705": "f4",
	"706": "f4#/g4b",
	"707": "g4",
	"708": "g4#/a4b",
	"709": "a4",
	"710": "a4#/b4b",
	"711": "b4",
	"800": "c5",
	}

	_URLS = {
	"audio": f"{_DOMAIN}/audio.zip",
	"mel": f"{_DOMAIN}/mel.zip",
	"eval": f"{_DOMAIN}/eval.zip",
	}


	class pianos_Config(datasets.BuilderConfig):
	def __init__(self, features, supervised_keys, task_templates, **kwargs):
	super(pianos_Config, self).__init__(version=datasets.Version("0.1.2"), **kwargs)
	self.features = features
	self.supervised_keys = supervised_keys
	self.task_templates = task_templates


	class pianos(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("0.1.2")
	BUILDER_CONFIGS = [
	pianos_Config(
	name="eval",
	features=datasets.Features(
	{
	"mel": datasets.Image(),
	"label": datasets.features.ClassLabel(names=_NAMES),
	"pitch": datasets.features.ClassLabel(
	names=list(_PITCHES.values())
	),
	}
	),
	supervised_keys=("mel", "label"),
	task_templates=[
	ImageClassification(
	task="image-classification",
	image_column="mel",
	label_column="label",
	)
	],
	),
	pianos_Config(
	name="default",
	features=datasets.Features(
	{
	"audio": datasets.Audio(sampling_rate=22050),
	"mel": datasets.Image(),
	"label": datasets.features.ClassLabel(names=_NAMES),
	"pitch": datasets.features.ClassLabel(
	names=list(_PITCHES.values())
	),
	}
	),
	supervised_keys=("audio", "label"),
	task_templates=[
	AudioClassification(
	task="audio-classification",
	audio_column="audio",
	label_column="label",
	)
	],
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=self.config.features,
	homepage=_HOMEPAGE,
	license="mit",
	citation=_CITATION,
	supervised_keys=self.config.supervised_keys,
	task_templates=self.config.task_templates,
	)

	def _split_generators(self, dl_manager):
	dataset = []
	if self.config.name == "eval":
	data_files = dl_manager.download_and_extract(_URLS["eval"])
	for path in dl_manager.iter_files([data_files]):
	fname = os.path.basename(path)
	if fname.endswith(".jpg"):
	dataset.append(
	{
	"mel": path,
	"label": os.path.basename(os.path.dirname(path)),
	"pitch": _PITCHES[fname.split("_")[0]],
	}
	)
	else:
	subset = {}
	audio_files = dl_manager.download_and_extract(_URLS["audio"])
	for path in dl_manager.iter_files([audio_files]):
	fname = os.path.basename(path)
	if fname.endswith(".wav"):
	subset[fname.split(".")[0]] = {
	"audio": path,
	"label": os.path.basename(os.path.dirname(path)),
	"pitch": _PITCHES[fname[1:4]],
	}

	mel_files = dl_manager.download_and_extract(_URLS["mel"])
	for path in dl_manager.iter_files([mel_files]):
	fname = os.path.basename(path)
	if fname.endswith(".jpg"):
	subset[fname.split(".")[0]]["mel"] = path

	dataset = list(subset.values())

	random.shuffle(dataset)
	count = len(dataset)
	p80 = int(0.8 * count)
	p90 = int(0.9 * count)

	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 _generate_examples(self, files):
	if self.config.name == "eval":
	for i, path in enumerate(files):
	yield i, {
	"mel": path["mel"],
	"label": path["label"],
	"pitch": path["pitch"],
	}

	else:
	for i, path in enumerate(files):
	yield i, {
	"audio": path["audio"],
	"mel": path["mel"],
	"label": path["label"],
	"pitch": path["pitch"],
	}