| import os | |
| import datasets | |
| import pandas as pd | |
| from datasets.tasks import AudioClassification | |
| _NAMES = { | |
| "songs": [f"song{i}" for i in range(1, 7)], | |
| "singers": [f"singer{i}" for i in range(1, 23)], | |
| } | |
| _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", | |
| } | |
| class acapella(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| features=datasets.Features( | |
| { | |
| "audio": datasets.Audio(sampling_rate=48000), | |
| "mel": datasets.Image(), | |
| "singer_id": datasets.features.ClassLabel(names=_NAMES["singers"]), | |
| "pitch": datasets.Value("float32"), | |
| "rhythm": datasets.Value("float32"), | |
| "vocal_range": datasets.Value("float32"), | |
| "timbre": datasets.Value("float32"), | |
| "pronunciation": datasets.Value("float32"), | |
| "vibrato": datasets.Value("float32"), | |
| "dynamic": datasets.Value("float32"), | |
| "breath_control": datasets.Value("float32"), | |
| "overall_performance": datasets.Value("float32"), | |
| } | |
| ), | |
| supervised_keys=("audio", "singer_id"), | |
| homepage=_HOMEPAGE, | |
| license="CC-BY-NC-ND", | |
| version="1.2.0", | |
| task_templates=[ | |
| AudioClassification( | |
| task="audio-classification", | |
| audio_column="audio", | |
| label_column="singer_id", | |
| ) | |
| ], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| songs = {} | |
| for index in _NAMES["songs"]: | |
| csv_files = dl_manager.download(f"{_DOMAIN}/{index}.csv") | |
| song_eval = pd.read_csv(csv_files, index_col="singer_id") | |
| scores = [] | |
| for i in range(len(_NAMES["singers"])): | |
| scores.append( | |
| { | |
| "pitch": song_eval.iloc[i]["pitch"], | |
| "rhythm": song_eval.iloc[i]["rhythm"], | |
| "vocal_range": song_eval.iloc[i]["vocal_range"], | |
| "timbre": song_eval.iloc[i]["timbre"], | |
| "pronunciation": song_eval.iloc[i]["pronunciation"], | |
| "vibrato": song_eval.iloc[i]["vibrato"], | |
| "dynamic": song_eval.iloc[i]["dynamic"], | |
| "breath_control": song_eval.iloc[i]["breath_control"], | |
| "overall_performance": song_eval.iloc[i]["overall_performance"], | |
| } | |
| ) | |
| songs[index] = scores | |
| audio_files = dl_manager.download_and_extract(_URLS["audio"]) | |
| for fpath in dl_manager.iter_files([audio_files]): | |
| fname: str = os.path.basename(fpath) | |
| if fname.endswith(".wav"): | |
| song_id = os.path.basename(os.path.dirname(fpath)) | |
| singer_id = int(fname.split("(")[1].split(")")[0]) - 1 | |
| songs[song_id][singer_id]["audio"] = fpath | |
| mel_files = dl_manager.download_and_extract(_URLS["mel"]) | |
| for fpath in dl_manager.iter_files([mel_files]): | |
| fname = os.path.basename(fpath) | |
| if fname.endswith(".jpg"): | |
| song_id = os.path.basename(os.path.dirname(fpath)) | |
| singer_id = int(fname.split("(")[1].split(")")[0]) - 1 | |
| songs[song_id][singer_id]["mel"] = fpath | |
| split_generator = [] | |
| for key in songs.keys(): | |
| split_generator.append( | |
| datasets.SplitGenerator( | |
| name=key, | |
| gen_kwargs={"files": songs[key]}, | |
| ) | |
| ) | |
| return split_generator | |
| def _generate_examples(self, files): | |
| for i, item in enumerate(files): | |
| yield i, { | |
| "audio": item["audio"], | |
| "mel": item["mel"], | |
| "singer_id": i, | |
| "pitch": item["pitch"], | |
| "rhythm": item["rhythm"], | |
| "vocal_range": item["vocal_range"], | |
| "timbre": item["timbre"], | |
| "pronunciation": item["pronunciation"], | |
| "vibrato": item["vibrato"], | |
| "dynamic": item["dynamic"], | |
| "breath_control": item["breath_control"], | |
| "overall_performance": item["overall_performance"], | |
| } | |