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|
| 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 |
|
|
|
|
