Datasets:
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import os
import random
import hashlib
import datasets
_NAMES = {
"4_classes": [
"trill",
"staccato",
"slide",
"others",
],
"7_classes": [
"trill_short_up",
"trill_long",
"staccato",
"slide_up",
"slide_legato",
"slide_down",
"others",
],
"11_classes": [
"vibrato",
"trill",
"tremolo",
"staccato",
"ricochet",
"pizzicato",
"percussive",
"legato_slide_glissando",
"harmonic",
"diangong",
"detache",
],
}
_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 erhu_playing_tech(datasets.GeneratorBasedBuilder):
def _info(self):
if self.config.name == "default":
self.config.name = "11_classes"
return datasets.DatasetInfo(
features=(
datasets.Features(
{
"audio": datasets.Audio(sampling_rate=44100),
"mel": datasets.Image(),
"label": datasets.features.ClassLabel(
names=_NAMES[self.config.name]
),
}
)
if self.config.name != "eval"
else datasets.Features(
{
"mel": datasets.Image(),
"cqt": datasets.Image(),
"chroma": datasets.Image(),
"label": datasets.features.ClassLabel(
names=_NAMES["11_classes"]
),
}
)
),
homepage=_HOMEPAGE,
license="CC-BY-NC-ND",
version="1.2.0",
)
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):
if self.config.name != "eval":
audio_files = dl_manager.download_and_extract(_URLS["audio"])
mel_files = dl_manager.download_and_extract(_URLS["mel"])
files = {}
for fpath in dl_manager.iter_files([audio_files]):
fname = os.path.basename(fpath)
dirname = os.path.dirname(fpath)
subset = os.path.basename(os.path.dirname(dirname))
if self.config.name == subset and fname.endswith(".wav"):
cls = f"{subset}/{os.path.basename(dirname)}/"
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)
dirname = os.path.dirname(fpath)
subset = os.path.basename(os.path.dirname(dirname))
if self.config.name == subset and fname.endswith(".jpg"):
cls = f"{subset}/{os.path.basename(dirname)}/"
item_id = self._str2md5(cls + fname.split(".jp")[0])
files[item_id]["mel"] = fpath
dataset = list(files.values())
else:
eval_files = dl_manager.download_and_extract(_URLS["eval"])
dataset = []
for fpath in dl_manager.iter_files([eval_files]):
fname: str = os.path.basename(fpath)
if "_mel" in fname and fname.endswith(".jpg"):
dataset.append({"mel": fpath, "label": fname.split("__")[0]})
categories = {}
names = _NAMES["11_classes" if "eval" in self.config.name else self.config.name]
for name in names:
categories[name] = []
for data in dataset:
if self.config.name != "eval":
data["label"] = os.path.basename(os.path.dirname(data["audio"]))
categories[data["label"]].append(data)
testset, validset, trainset = [], [], []
for cls in categories:
random.shuffle(categories[cls])
count = len(categories[cls])
p60 = int(count * 0.6)
p80 = int(count * 0.8)
trainset += categories[cls][:p60]
validset += categories[cls][p60:p80]
testset += categories[cls][p80:]
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 != "eval":
for i, item in enumerate(files):
yield i, item
else:
for i, item in enumerate(files):
yield i, {
"mel": item["mel"],
"cqt": item["mel"].replace("_mel", "_cqt"),
"chroma": item["mel"].replace("_mel", "_chroma"),
"label": item["label"],
}
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