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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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""" BizzBuddy AI Dataset""" |
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_CITATION = """\ |
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@article{gerz2021multilingual, |
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title={Wake word data for Voice assistant trigger in English from spoken data}, |
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author={Ahmed, Nicholas}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Wake is training and evaluation resource for wake word |
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detection task with spoken data. It covers the wake and not wake |
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intents collected from a multiple participants who agreed to contribute to the development |
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of the system on the wake word and the not wake words is a subset of the common voice and speech commands dataset. |
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""" |
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_ALL_CONFIGS = sorted([ |
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"en-US" |
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]) |
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_DESCRIPTION = "Wake is a dataset for the wake word detection task with spoken data." |
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_DATA_URL = 'https://huggingface.co/datasets/Ahmed-ibn-Harun/wake-w/resolve/main/data.tar.gz' |
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class WakeConfig(datasets.BuilderConfig): |
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"""BuilderConfig for xtreme-s""" |
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def __init__( |
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self, name, description, data_url |
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): |
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super(WakeConfig, self).__init__( |
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name=self.name, |
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version=datasets.Version("1.0.0", ""), |
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description=self.description, |
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) |
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self.name = name |
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self.description = description |
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self.data_url = data_url |
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def _build_config(name): |
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return WakeConfig( |
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name=name, |
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description=_DESCRIPTION, |
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data_url=_DATA_URL, |
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) |
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class Wake(datasets.GeneratorBasedBuilder): |
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DEFAULT_WRITER_BATCH_SIZE = 1000 |
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BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS + ["all"]] |
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def _info(self): |
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task_templates = None |
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langs = _ALL_CONFIGS |
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features = datasets.Features( |
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{ |
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"path": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=8_000), |
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"wake": datasets.ClassLabel( |
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names=[ |
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0, |
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1, |
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] |
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), |
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"lang_id": datasets.ClassLabel(names=langs), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=("audio", "transcription"), |
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citation=_CITATION, |
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task_templates=task_templates, |
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) |
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def _split_generators(self, dl_manager): |
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langs = ( |
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_ALL_CONFIGS |
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if self.config.name == "all" |
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else [self.config.name] |
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) |
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archive_path = dl_manager.download_and_extract(self.config.data_url) |
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audio_path = dl_manager.extract( |
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os.path.join(archive_path, "audio.tar.gz") |
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) |
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text_path = dl_manager.extract( |
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os.path.join(archive_path, "text.tar.gz") |
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) |
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text_path = {l: os.path.join(text_path, f"{l}.csv") for l in langs} |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"audio_path": audio_path, |
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"text_paths": text_path, |
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}, |
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) |
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] |
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def _generate_examples(self, audio_path, text_paths): |
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key = 0 |
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for lang in text_paths.keys(): |
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text_path = text_paths[lang] |
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with open(text_path, encoding="utf-8") as csv_file: |
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csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True) |
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next(csv_reader) |
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for row in csv_reader: |
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file_path, intent_class = row |
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file_path = os.path.join(audio_path, *file_path.split("/")) |
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yield key, { |
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"path": file_path, |
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"audio": file_path, |
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"wake": intent_class, |
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"lang_id": _ALL_CONFIGS.index(lang), |
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} |
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key += 1 |
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