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import os
import datasets
import pandas as pd
from datasets.tasks import AudioClassification
_NAMES = [
# Chinese 0-36
"gao_hu",
"er_hu",
"zhong_hu",
"ge_hu",
"di_yin_ge_hu",
"jing_hu",
"ban_hu",
"bang_di",
"qu_di",
"xin_di",
"da_di",
"gao_yin_sheng",
"zhong_yin_sheng",
"di_yin_sheng",
"gao_yin_suo_na",
"zhong_yin_suo_na",
"ci_zhong_yin_suo_na",
"di_yin_suo_na",
"gao_yin_guan",
"zhong_yin_guan",
"di_yin_guan",
"bei_di_yin_guan",
"ba_wu",
"xun",
"xiao",
"liu_qin",
"xiao_ruan",
"pi_pa",
"yang_qin",
"zhong_ruan",
"da_ruan",
"gu_zheng",
"gu_qin",
"kong_hou",
"san_xian",
"yun_luo",
"bian_zhong",
# Western 37-60
"violin",
"viola",
"cello",
"double_bass",
"piccolo",
"flute",
"oboe",
"clarinet",
"bassoon",
"saxophone",
"trumpet",
"trombone",
"horn",
"tuba",
"harp",
"tubular_bells",
"bells",
"xylophone",
"vibraphone",
"marimba",
"piano",
"clavichord",
"accordion",
"organ",
]
_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",
"Chinese": f"{_DOMAIN}/Chinese.csv",
"Western": f"{_DOMAIN}/Western.csv",
}
class instrument_timbre(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"audio": datasets.Audio(sampling_rate=44100),
"mel": datasets.Image(),
"instrument": datasets.features.ClassLabel(names=_NAMES),
"slim": datasets.Value("float32"),
"bright": datasets.Value("float32"),
"dark": datasets.Value("float32"),
"sharp": datasets.Value("float32"),
"thick": datasets.Value("float32"),
"thin": datasets.Value("float32"),
"vigorous": datasets.Value("float32"),
"silvery": datasets.Value("float32"),
"raspy": datasets.Value("float32"),
"full": datasets.Value("float32"),
"coarse": datasets.Value("float32"),
"pure": datasets.Value("float32"),
"hoarse": datasets.Value("float32"),
"consonant": datasets.Value("float32"),
"mellow": datasets.Value("float32"),
"muddy": datasets.Value("float32"),
}
),
supervised_keys=("audio", "instrument"),
homepage=_HOMEPAGE,
license="CC-BY-NC-ND",
version="1.2.0",
task_templates=[
AudioClassification(
task="audio-classification",
audio_column="audio",
label_column="instrument",
)
],
)
def _split_generators(self, dl_manager):
audio_files = dl_manager.download_and_extract(_URLS["audio"])
mel_files = dl_manager.download_and_extract(_URLS["mel"])
cn_ins_eval = dl_manager.download(_URLS["Chinese"])
en_ins_eval = dl_manager.download(_URLS["Western"])
cn_labels = pd.read_csv(cn_ins_eval, index_col="instrument_id")
en_labels = pd.read_csv(en_ins_eval, index_col="instrument_id")
cn_dataset, en_dataset = {}, {}
for path in dl_manager.iter_files([audio_files]):
fname: str = os.path.basename(path)
i = int(fname.split(".wa")[0]) - 1
if fname.endswith(".wav"):
region = os.path.basename(os.path.dirname(path))
labels = cn_labels if region == "Chinese" else en_labels
data = {
"audio": path,
"mel": "",
"instrument": labels.iloc[i]["instrument_name"],
"slim": labels.iloc[i]["slim"],
"bright": labels.iloc[i]["bright"],
"dark": labels.iloc[i]["dim"],
"sharp": labels.iloc[i]["sharp"],
"thick": labels.iloc[i]["thick"],
"thin": labels.iloc[i]["thin"],
"vigorous": labels.iloc[i]["solid"],
"silvery": labels.iloc[i]["clear"],
"raspy": labels.iloc[i]["dry"],
"full": labels.iloc[i]["plump"],
"coarse": labels.iloc[i]["rough"],
"pure": labels.iloc[i]["pure"],
"hoarse": labels.iloc[i]["hoarse"],
"consonant": labels.iloc[i]["harmonious"],
"mellow": labels.iloc[i]["soft"],
"muddy": labels.iloc[i]["turbid"],
}
if region == "Chinese":
cn_dataset[i] = data
else:
en_dataset[i] = data
for path in dl_manager.iter_files([mel_files]):
fname = os.path.basename(path)
i = int(fname.split(".jp")[0]) - 1
if fname.endswith(".jpg"):
if os.path.basename(os.path.dirname(path)) == "Chinese":
cn_dataset[i]["mel"] = path
else:
en_dataset[i]["mel"] = path
return [
datasets.SplitGenerator(
name="Chinese",
gen_kwargs={
"files": [cn_dataset[k] for k in sorted(cn_dataset)],
},
),
datasets.SplitGenerator(
name="Western",
gen_kwargs={
"files": [en_dataset[k] for k in sorted(en_dataset)],
},
),
]
def _generate_examples(self, files):
for i, path in enumerate(files):
yield i, path
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