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import os |
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import datasets |
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import pandas as pd |
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from datasets.tasks import AudioClassification |
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_NAMES = [ |
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"gao_hu", |
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"er_hu", |
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"zhong_hu", |
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"ge_hu", |
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"di_yin_ge_hu", |
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"jing_hu", |
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"ban_hu", |
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"bang_di", |
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"qu_di", |
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"xin_di", |
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"da_di", |
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"gao_yin_sheng", |
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"zhong_yin_sheng", |
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"di_yin_sheng", |
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"gao_yin_suo_na", |
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"zhong_yin_suo_na", |
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"ci_zhong_yin_suo_na", |
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"di_yin_suo_na", |
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"gao_yin_guan", |
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"zhong_yin_guan", |
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"di_yin_guan", |
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"bei_di_yin_guan", |
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"ba_wu", |
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"xun", |
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"xiao", |
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"liu_qin", |
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"xiao_ruan", |
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"pi_pa", |
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"yang_qin", |
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"zhong_ruan", |
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"da_ruan", |
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"gu_zheng", |
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"gu_qin", |
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"kong_hou", |
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"san_xian", |
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"yun_luo", |
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"bian_zhong", |
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"violin", |
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"viola", |
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"cello", |
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"double_bass", |
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"piccolo", |
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"flute", |
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"oboe", |
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"clarinet", |
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"bassoon", |
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"saxophone", |
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"trumpet", |
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"trombone", |
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"horn", |
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"tuba", |
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"harp", |
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"tubular_bells", |
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"bells", |
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"xylophone", |
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"vibraphone", |
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"marimba", |
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"piano", |
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"clavichord", |
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"accordion", |
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"organ", |
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] |
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_HOMEPAGE = f"https://www.modelscope.cn/datasets/ccmusic-database/{os.path.basename(__file__)[:-3]}" |
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_DOMAIN = f"{_HOMEPAGE}/resolve/master/data" |
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_URLS = { |
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"audio": f"{_DOMAIN}/audio.zip", |
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"mel": f"{_DOMAIN}/mel.zip", |
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"Chinese": f"{_DOMAIN}/Chinese.csv", |
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"Western": f"{_DOMAIN}/Western.csv", |
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} |
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class instrument_timbre(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"audio": datasets.Audio(sampling_rate=44100), |
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"mel": datasets.Image(), |
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"instrument": datasets.features.ClassLabel(names=_NAMES), |
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"slim": datasets.Value("float32"), |
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"bright": datasets.Value("float32"), |
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"dim": datasets.Value("float32"), |
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"sharp": datasets.Value("float32"), |
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"thick": datasets.Value("float32"), |
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"thin": datasets.Value("float32"), |
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"solid": datasets.Value("float32"), |
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"clear": datasets.Value("float32"), |
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"dry": datasets.Value("float32"), |
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"plump": datasets.Value("float32"), |
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"rough": datasets.Value("float32"), |
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"pure": datasets.Value("float32"), |
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"hoarse": datasets.Value("float32"), |
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"harmonious": datasets.Value("float32"), |
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"soft": datasets.Value("float32"), |
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"turbid": datasets.Value("float32"), |
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} |
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), |
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supervised_keys=("audio", "instrument"), |
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homepage=_HOMEPAGE, |
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license="CC-BY-NC-ND", |
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version="1.2.0", |
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task_templates=[ |
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AudioClassification( |
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task="audio-classification", |
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audio_column="audio", |
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label_column="instrument", |
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) |
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], |
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) |
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def _split_generators(self, dl_manager): |
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audio_files = dl_manager.download_and_extract(_URLS["audio"]) |
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mel_files = dl_manager.download_and_extract(_URLS["mel"]) |
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cn_ins_eval = dl_manager.download(_URLS["Chinese"]) |
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en_ins_eval = dl_manager.download(_URLS["Western"]) |
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cn_labels = pd.read_csv(cn_ins_eval, index_col="instrument_id") |
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en_labels = pd.read_csv(en_ins_eval, index_col="instrument_id") |
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cn_dataset, en_dataset = {}, {} |
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for path in dl_manager.iter_files([audio_files]): |
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fname: str = os.path.basename(path) |
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i = int(fname.split(".wa")[0]) - 1 |
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if fname.endswith(".wav"): |
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region = os.path.basename(os.path.dirname(path)) |
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labels = cn_labels if region == "Chinese" else en_labels |
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data = { |
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"audio": path, |
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"mel": "", |
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"instrument": labels.iloc[i]["instrument_name"], |
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"slim": labels.iloc[i]["slim"], |
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"bright": labels.iloc[i]["bright"], |
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"dim": labels.iloc[i]["dim"], |
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"sharp": labels.iloc[i]["sharp"], |
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"thick": labels.iloc[i]["thick"], |
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"thin": labels.iloc[i]["thin"], |
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"solid": labels.iloc[i]["solid"], |
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"clear": labels.iloc[i]["clear"], |
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"dry": labels.iloc[i]["dry"], |
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"plump": labels.iloc[i]["plump"], |
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"rough": labels.iloc[i]["rough"], |
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"pure": labels.iloc[i]["pure"], |
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"hoarse": labels.iloc[i]["hoarse"], |
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"harmonious": labels.iloc[i]["harmonious"], |
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"soft": labels.iloc[i]["soft"], |
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"turbid": labels.iloc[i]["turbid"], |
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} |
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if region == "Chinese": |
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cn_dataset[i] = data |
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else: |
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en_dataset[i] = data |
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for path in dl_manager.iter_files([mel_files]): |
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fname = os.path.basename(path) |
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i = int(fname.split(".jp")[0]) - 1 |
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if fname.endswith(".jpg"): |
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if os.path.basename(os.path.dirname(path)) == "Chinese": |
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cn_dataset[i]["mel"] = path |
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else: |
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en_dataset[i]["mel"] = path |
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return [ |
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datasets.SplitGenerator( |
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name="Chinese", |
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gen_kwargs={ |
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"files": [cn_dataset[k] for k in sorted(cn_dataset)], |
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}, |
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), |
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datasets.SplitGenerator( |
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name="Western", |
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gen_kwargs={ |
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"files": [en_dataset[k] for k in sorted(en_dataset)], |
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}, |
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), |
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] |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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yield i, path |
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