Datasets:
License:
| import datasets | |
| import csv | |
| import os | |
| # For a future citation perhaps? | |
| # _CITATION = """\ | |
| # @inproceedings{luong-vu-2016-non, | |
| # title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System", | |
| # author = "Luong, Hieu-Thi and | |
| # Vu, Hai-Quan", | |
| # booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)", | |
| # month = dec, | |
| # year = "2016", | |
| # address = "Osaka, Japan", | |
| # publisher = "The COLING 2016 Organizing Committee", | |
| # url = "https://aclanthology.org/W16-5207", | |
| # pages = "51--55", | |
| # } | |
| # """ | |
| _DESCRIPTION = """\ | |
| Dataset consisting of isolated beatbox samples , | |
| reimplementation of the dataset from the following | |
| paper: BaDumTss: Multi-task Learning for Beatbox Transcription | |
| """ | |
| _HOMEPAGE = "https://doi.org/10.1007/978-3-031-05981-0_14" | |
| _LICENSE = "MIT" | |
| _DATA_URL = "https://huggingface.co/datasets/maxardito/beatbox/resolve/main/dataset" | |
| class BeatboxDataset(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| "path": | |
| datasets.Value("string"), | |
| "class": | |
| datasets.Value("string"), | |
| "audio": | |
| datasets.Audio(sampling_rate=16_000), | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| # citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| dl_manager.download_config.ignore_url_params = True | |
| audio_path = dl_manager.download(_DATA_URL) | |
| local_extracted_archive = dl_manager.extract( | |
| audio_path) if not dl_manager.is_streaming else None | |
| path_to_clips = "dataset" | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "local_extracted_archive": | |
| local_extracted_archive, | |
| "audio_files": | |
| dl_manager.iter_archive(audio_path), | |
| "metadata_path": | |
| dl_manager.download_and_extract( | |
| "dataset/metadata_train.csv.gz"), | |
| "path_to_clips": | |
| path_to_clips, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "local_extracted_archive": | |
| local_extracted_archive, | |
| "audio_files": | |
| dl_manager.iter_archive(audio_path), | |
| "metadata_path": | |
| dl_manager.download_and_extract( | |
| "dataset/metadata_test.csv.gz"), | |
| "path_to_clips": | |
| path_to_clips, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples( | |
| self, | |
| local_extracted_archive, | |
| audio_files, | |
| metadata_path, | |
| path_to_clips, | |
| ): | |
| """Yields examples.""" | |
| data_fields = list(self._info().features.keys()) | |
| metadata = {} | |
| with open(metadata_path, "r", encoding="utf-8") as f: | |
| reader = csv.DictReader(f) | |
| for row in reader: | |
| row["path"] = os.path.join(path_to_clips, row["path"]) | |
| # if data is incomplete, fill with empty values | |
| for field in data_fields: | |
| if field not in row: | |
| row[field] = "" | |
| metadata[row["path"]] = row | |
| id_ = 0 | |
| for path, f in audio_files: | |
| if path in metadata: | |
| result = dict(metadata[path]) | |
| # set the audio feature and the path to the extracted file | |
| path = os.path.join(local_extracted_archive, | |
| path) if local_extracted_archive else path | |
| result["audio"] = {"path": path, "bytes": f.read()} | |
| result["path"] = path | |
| yield id_, result | |
| id_ += 1 | |