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import csv |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{pudo23_interspeech, |
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author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki}, |
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title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset}, |
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year={2023}, |
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booktitle={Proc. Interspeech 2023}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive |
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audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models. |
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""" |
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_BASE_URL = "https://huggingface.co/datasets/voiceintelligenceresearch/MOCKS/tree/main" |
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_DL_URLS_TEMPLATE = { |
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"data": "%s/%s/test/%s/data.tar.gz", |
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"transcription" : "%s/%s/test/data_%s_transcription.tsv", |
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"positive" : "%s/%s/test/%s/all.pair.positive.tsv", |
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"similar" : "%s/%s/test/%s/all.pair.similar.tsv", |
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"different" : "%s/%s/test/%s/all.pair.different.tsv", |
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"positive_subset" : "%s/%s/test/%s/subset.pair.positive.tsv", |
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"similar_subset" : "%s/%s/test/%s/subset.pair.similar.tsv", |
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"different_subset" : "%s/%s/test/%s/subset.pair.different.tsv", |
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} |
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_MOCKS_SETS = [ |
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"en.LS-clean", |
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"en.LS-other", |
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"en.MCV", |
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"de.MCV", |
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"es.MCV", |
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"fr.MCV", |
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"it.MCV"] |
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_MOCKS_SUFFIXES = [ |
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"", |
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".positive", |
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".similar", |
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".different", |
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".subset", |
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".positive_subset", |
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".similar_subset", |
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".different_subset"] |
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class Mocks(datasets.GeneratorBasedBuilder): |
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"""Mocks Dataset.""" |
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DEFAULT_CONFIG_NAME = "en.LS-clean" |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name=subset+suffix, description=subset+suffix) |
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for subset in _MOCKS_SETS for suffix in _MOCKS_SUFFIXES] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"keyword_id": datasets.Value("string"), |
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"keyword_transcription": datasets.Value("string"), |
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"test_id": datasets.Value("string"), |
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"test_transcription": datasets.Value("string"), |
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"test_audio": datasets.Audio(sampling_rate=16000), |
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"label": datasets.Value("bool"), |
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} |
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), |
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homepage=_BASE_URL, |
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citation=_CITATION |
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) |
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def _split_generators(self, dl_manager): |
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logger.info("split_generator") |
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name_split = self.config.name.split(".") |
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subset_lang = name_split[0] |
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subset_name = name_split[1] |
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if len(name_split) == 2: |
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pairs_types = ["positive", "similar", "different"] |
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elif name_split[2] == "subset": |
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pairs_types = ["positive_subset", "similar_subset", "different_subset"] |
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else: |
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pairs_types = [name_split[2]] |
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offline_archive_path = dl_manager.download({ |
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k: v%(subset_lang, subset_name, "offline") |
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for k, v in _DL_URLS_TEMPLATE.items() |
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}) |
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online_archive_path = dl_manager.download({ |
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k: v%(subset_lang, subset_name, "online") |
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for k, v in _DL_URLS_TEMPLATE.items() |
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}) |
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split_offline = [datasets.SplitGenerator( |
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name="offline", |
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gen_kwargs={ |
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"audio_files": dl_manager.iter_archive(offline_archive_path["data"]), |
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"transcription_keyword": offline_archive_path["transcription"], |
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"transcription_test": offline_archive_path["transcription"], |
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"pairs": [offline_archive_path[pair_type] for pair_type in pairs_types], |
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} |
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) |
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] |
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split_online = [datasets.SplitGenerator( |
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name="online", |
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gen_kwargs={ |
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"audio_files": dl_manager.iter_archive(online_archive_path["data"]), |
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"transcription_keyword": offline_archive_path["transcription"], |
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"transcription_test": online_archive_path["transcription"], |
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"pairs": [online_archive_path[pair_type] for pair_type in pairs_types], |
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} |
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) |
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] |
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return split_offline + split_online |
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def _read_transcription(self, transcription_path): |
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transcription_metadata = {} |
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with open(transcription_path, encoding="utf-8") as f: |
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reader = csv.reader(f, delimiter="\t") |
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next(reader, None) |
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for row in reader: |
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_, audio_id = os.path.split(row[0]) |
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transcription = row[1] |
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transcription_metadata[audio_id] = { |
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"audio_id": audio_id, |
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"transcription": transcription} |
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return transcription_metadata |
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def _generate_examples(self, audio_files, transcription_keyword, transcription_test, pairs): |
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transcription_keyword_metadata = self._read_transcription(transcription_keyword) |
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transcription_test_metadata = self._read_transcription(transcription_test) |
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pair_metadata = {} |
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for pair in pairs: |
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with open(pair, encoding="utf-8") as f: |
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reader = csv.reader(f, delimiter="\t") |
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next(reader, None) |
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for row in reader: |
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_, keyword_id = os.path.split(row[0]) |
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_, test_id = os.path.split(row[1]) |
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if keyword_id not in transcription_keyword_metadata: |
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logger.error("No transcription and audio for keyword %s"%(keyword_id)) |
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continue |
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if test_id not in transcription_test_metadata: |
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logger.error("No transcription and audio for test case %s"%(test_id)) |
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continue |
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if test_id not in pair_metadata: |
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pair_metadata[test_id] = [] |
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pair_metadata[test_id].append([keyword_id, int(row[-1])]) |
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id_ = 0 |
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for test_path, test_f in audio_files: |
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_, test_id = os.path.split(test_path) |
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if test_id in pair_metadata: |
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test_audio = {"bytes": test_f.read()} |
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for keyword_id, label in pair_metadata[test_id]: |
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yield id_, { |
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"keyword_id": keyword_id, |
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"keyword_transcription": transcription_keyword_metadata[keyword_id]["transcription"], |
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"test_id": test_id, |
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"test_transcription": transcription_test_metadata[test_id]["transcription"], |
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"test_audio": test_audio, |
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"label": label} |
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id_ += 1 |
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