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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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_DESCRIPTION = "Custom dataset for extracting audio files and matching sentences." |
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_DATA_URL = "https://huggingface.co/datasets/ugshanyu/dataset/resolve/main" |
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class CustomDataset(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio": datasets.Audio(sampling_rate=48_000), |
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"sentence": datasets.Value("string"), |
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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", "sentence"), |
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homepage=None, |
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citation=None, |
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) |
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def _split_generators(self, dl_manager): |
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audio_path = dl_manager.download_and_extract(_DATA_URL+"/audo.zip") |
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csv_path = dl_manager.download_and_extract(_DATA_URL+"/comma.csv") |
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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={"audio_path": audio_path, "csv_path": csv_path}, |
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) |
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] |
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def _generate_examples(self, audio_path, csv_path): |
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print(audio_path) |
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print(csv_path) |
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key = 0 |
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print(os.listdir(audio_path)) |
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with open(csv_path, encoding="utf-8") as csv_file: |
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csv_reader = csv.DictReader(csv_file) |
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for row in csv_reader: |
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original_sentence_id, sentence, locale = row.values() |
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audio_file = f"{original_sentence_id}.mp3" |
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audio_file_path = os.path.join(audio_path, audio_file) |
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yield key, { |
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"audio": audio_file_path, |
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"sentence": sentence, |
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} |
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key += 1 |
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