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"""ALFFAAmharic automatic speech recognition dataset.""" |
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import os |
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from pathlib import Path |
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import datasets |
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_CITATION = """\ |
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@inproceedings{ |
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title={ALFFAAmharic Acoustic-Phonetic Continuous Speech Corpus}, |
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author={Samuael et al}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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The ALFFAAmharic corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies |
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and for the evaluation of automatic speech recognition systems. |
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""" |
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class ALFFAAmharicASRConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ALFFAAmharicASR.""" |
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def __init__(self, **kwargs): |
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""" |
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Args: |
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data_dir: `string`, the path to the folder containing the files in the |
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downloaded .tar |
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citation: `string`, citation for the data set |
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url: `string`, url for information about the data set |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ALFFAAmharicASRConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs) |
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class ALFFAAmharic(datasets.GeneratorBasedBuilder): |
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"""ALFFAAmharicASR dataset.""" |
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BUILDER_CONFIGS = [ALFFAAmharicASRConfig(name="clean", description="'Clean' speech.")] |
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@property |
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def manual_download_instructions(self): |
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return ( |
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"To use ALFFAAmharic you have to download it manually. " |
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"`datasets.load_dataset('ALFFAAmharic_asr', data_dir='path/to/folder/folder_name')`" |
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) |
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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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{ |
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"file": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("file", "text"), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
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if not os.path.exists(data_dir): |
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raise FileNotFoundError( |
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f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('ALFFAAmharic_asr', data_dir=...)` that includes files unzipped from the ALFFAAmharic zip. Manual download instructions: {self.manual_download_instructions}" |
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) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}), |
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] |
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def _generate_examples(self, split, data_dir): |
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"""Generate examples from ALFFAAmharic archive_path based on the test/train csv information.""" |
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file = open(f"{data_dir}/{split}/text.txt", "r", encoding="utf-8") |
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lines = file.readlines() |
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file.close() |
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for i in range(len(lines)): |
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splited = lines[i].strip("\n").split(" ") |
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if len(splited)==0: |
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continue |
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wav_path = f"{data_dir}/{split}/wav/{splited[0]}.wav" |
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transcript = " ".join(splited[1:]) |
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yield i, { |
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"file": str(wav_path), |
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"audio": str(wav_path), |
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"text": transcript, |
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} |
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def with_case_insensitive_suffix(path: Path, suffix: str): |
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path = path.with_suffix(suffix.lower()) |
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path = path if path.exists() else path.with_suffix(suffix.upper()) |
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return path |
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