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import os
import random
import hashlib
import datasets
from datasets.tasks import ImageClassification
_NAMES = {
"all": ["m_bel", "f_bel", "m_folk", "f_folk"],
"gender": ["female", "male"],
"singing_method": ["Folk_Singing", "Bel_Canto"],
}
_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}"
_DOMAIN = f"{_HOMEPAGE}/resolve/master/data"
_URLS = {
"audio": f"{_DOMAIN}/audio.zip",
"mel": f"{_DOMAIN}/mel.zip",
"eval": f"{_DOMAIN}/eval.zip",
}
class bel_canto(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=(
datasets.Features(
{
"audio": datasets.Audio(sampling_rate=22050),
"mel": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES["all"]),
"gender": datasets.features.ClassLabel(names=_NAMES["gender"]),
"singing_method": datasets.features.ClassLabel(
names=_NAMES["singing_method"]
),
}
)
if self.config.name == "default"
else (
datasets.Features(
{
"mel": datasets.Image(),
"cqt": datasets.Image(),
"chroma": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES["all"]),
"gender": datasets.features.ClassLabel(
names=_NAMES["gender"]
),
"singing_method": datasets.features.ClassLabel(
names=_NAMES["singing_method"]
),
}
)
)
),
supervised_keys=("mel", "label"),
homepage=_HOMEPAGE,
license="CC-BY-NC-ND",
version="1.2.0",
task_templates=[
ImageClassification(
task="image-classification",
image_column="mel",
label_column="label",
)
],
)
def _str2md5(self, original_string: str):
md5_obj = hashlib.md5()
md5_obj.update(original_string.encode("utf-8"))
return md5_obj.hexdigest()
def _split_generators(self, dl_manager):
dataset = []
if self.config.name == "default":
files = {}
audio_files = dl_manager.download_and_extract(_URLS["audio"])
mel_files = dl_manager.download_and_extract(_URLS["mel"])
for fpath in dl_manager.iter_files([audio_files]):
fname: str = os.path.basename(fpath)
if fname.endswith(".wav"):
cls = os.path.basename(os.path.dirname(fpath)) + "/"
item_id = self._str2md5(cls + fname.split(".wa")[0])
files[item_id] = {"audio": fpath}
for fpath in dl_manager.iter_files([mel_files]):
fname = os.path.basename(fpath)
if fname.endswith(".jpg"):
cls = os.path.basename(os.path.dirname(fpath)) + "/"
item_id = self._str2md5(cls + fname.split(".jp")[0])
files[item_id]["mel"] = fpath
dataset = list(files.values())
else:
data_files = dl_manager.download_and_extract(_URLS["eval"])
for fpath in dl_manager.iter_files([data_files]):
fname = os.path.basename(fpath)
if "mel" in fpath and fname.endswith(".jpg"):
dataset.append(fpath)
categories = {}
for name in _NAMES["all"]:
categories[name] = []
for data in dataset:
fpath = data["audio"] if self.config.name == "default" else data
label = os.path.basename(os.path.dirname(fpath))
categories[label].append(data)
testset, validset, trainset = [], [], []
for cls in categories:
random.shuffle(categories[cls])
count = len(categories[cls])
p80 = int(count * 0.8)
p90 = int(count * 0.9)
trainset += categories[cls][:p80]
validset += categories[cls][p80:p90]
testset += categories[cls][p90:]
random.shuffle(trainset)
random.shuffle(validset)
random.shuffle(testset)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"files": trainset}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"files": validset}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"files": testset}
),
]
def _generate_examples(self, files):
if self.config.name == "default":
for i, item in enumerate(files):
label: str = os.path.basename(os.path.dirname(item["audio"]))
yield i, {
"audio": item["audio"],
"mel": item["mel"],
"label": label,
"gender": ("male" if label.split("_")[0] == "m" else "female"),
"singing_method": (
"Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
),
}
else:
for i, fpath in enumerate(files):
label = os.path.basename(os.path.dirname(fpath))
yield i, {
"mel": fpath,
"cqt": fpath.replace("mel", "cqt"),
"chroma": fpath.replace("mel", "chroma"),
"label": label,
"gender": ("male" if label.split("_")[0] == "m" else "female"),
"singing_method": (
"Bel_Canto" if label.split("_")[1] == "bel" else "Folk_Singing"
),
}
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