# coding=utf-8 # Copyright 2023 The HuggingFace Datasets Authors. # # Licensed under the Creative Commons version 4.0 and Mozilla Public License version 2.0, # (the "Licenses"); you may not use this file except in compliance with the Licenses. # You may obtain a copies of the Licenses at # # https://creativecommons.org/licenses/by/4.0/ # and https://www.mozilla.org/en-US/MPL/2.0/ # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the Licenses for the specific language governing permissions and # limitations under the Licenses. # Lint as: python3 import csv import os import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{pudo23_interspeech, author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki}, title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset}, year={2023}, booktitle={Proc. Interspeech 2023}, } """ _DESCRIPTION = """\ Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models. """ _BASE_URL = "https://huggingface.co/datasets/voiceintelligenceresearch/MOCKS/tree/main" _DL_URLS_TEMPLATE = { "data": "%s/%s/test/%s/data.tar.gz", "transcription" : "%s/%s/test/data_%s_transcription.tsv", "positive" : "%s/%s/test/%s/all.pair.positive.tsv", "similar" : "%s/%s/test/%s/all.pair.similar.tsv", "different" : "%s/%s/test/%s/all.pair.different.tsv", "positive_subset" : "%s/%s/test/%s/subset.pair.positive.tsv", "similar_subset" : "%s/%s/test/%s/subset.pair.similar.tsv", "different_subset" : "%s/%s/test/%s/subset.pair.different.tsv", } _MOCKS_SETS = [ "en.LS-clean", "en.LS-other", "en.MCV", "de.MCV", "es.MCV", "fr.MCV", "it.MCV"] _MOCKS_SUFFIXES = [ "", ".positive", ".similar", ".different", ".subset", ".positive_subset", ".similar_subset", ".different_subset"] class Mocks(datasets.GeneratorBasedBuilder): """Mocks Dataset.""" DEFAULT_CONFIG_NAME = "en.LS-clean" BUILDER_CONFIGS = [datasets.BuilderConfig(name=subset+suffix, description=subset+suffix) for subset in _MOCKS_SETS for suffix in _MOCKS_SUFFIXES] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features({ "keyword_id": datasets.Value("string"), "keyword_transcription": datasets.Value("string"), "test_id": datasets.Value("string"), "test_transcription": datasets.Value("string"), "test_audio": datasets.Audio(sampling_rate=16000), "label": datasets.Value("bool"), } ), homepage=_BASE_URL, citation=_CITATION ) def _split_generators(self, dl_manager): logger.info("split_generator") name_split = self.config.name.split(".") subset_lang = name_split[0] subset_name = name_split[1] if len(name_split) == 2: pairs_types = ["positive", "similar", "different"] elif name_split[2] == "subset": pairs_types = ["positive_subset", "similar_subset", "different_subset"] else: pairs_types = [name_split[2]] offline_archive_path = dl_manager.download({ k: v%(subset_lang, subset_name, "offline") for k, v in _DL_URLS_TEMPLATE.items() }) online_archive_path = dl_manager.download({ k: v%(subset_lang, subset_name, "online") for k, v in _DL_URLS_TEMPLATE.items() }) split_offline = [datasets.SplitGenerator( name="offline", gen_kwargs={ "audio_files": dl_manager.iter_archive(offline_archive_path["data"]), "transcription_keyword": offline_archive_path["transcription"], "transcription_test": offline_archive_path["transcription"], "pairs": [offline_archive_path[pair_type] for pair_type in pairs_types], } ) ] split_online = [datasets.SplitGenerator( name="online", gen_kwargs={ "audio_files": dl_manager.iter_archive(online_archive_path["data"]), "transcription_keyword": offline_archive_path["transcription"], "transcription_test": online_archive_path["transcription"], "pairs": [online_archive_path[pair_type] for pair_type in pairs_types], } ) ] return split_offline + split_online def _read_transcription(self, transcription_path): transcription_metadata = {} with open(transcription_path, encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t") next(reader, None) for row in reader: _, audio_id = os.path.split(row[0]) transcription = row[1] transcription_metadata[audio_id] = { "audio_id": audio_id, "transcription": transcription} return transcription_metadata def _generate_examples(self, audio_files, transcription_keyword, transcription_test, pairs): transcription_keyword_metadata = self._read_transcription(transcription_keyword) transcription_test_metadata = self._read_transcription(transcription_test) pair_metadata = {} for pair in pairs: with open(pair, encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t") next(reader, None) for row in reader: _, keyword_id = os.path.split(row[0]) _, test_id = os.path.split(row[1]) if keyword_id not in transcription_keyword_metadata: logger.error("No transcription and audio for keyword %s"%(keyword_id)) continue if test_id not in transcription_test_metadata: logger.error("No transcription and audio for test case %s"%(test_id)) continue if test_id not in pair_metadata: pair_metadata[test_id] = [] pair_metadata[test_id].append([keyword_id, int(row[-1])]) id_ = 0 for test_path, test_f in audio_files: _, test_id = os.path.split(test_path) if test_id in pair_metadata: test_audio = {"bytes": test_f.read()} for keyword_id, label in pair_metadata[test_id]: yield id_, { "keyword_id": keyword_id, "keyword_transcription": transcription_keyword_metadata[keyword_id]["transcription"], "test_id": test_id, "test_transcription": transcription_test_metadata[test_id]["transcription"], "test_audio": test_audio, "label": label} id_ += 1