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"""Albayzin automatic speech recognition dataset. |
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""" |
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import os |
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from pathlib import Path |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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_CITATION = """\ |
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@inproceedings{conf/interspeech/MorenoPBLLMN93, |
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title = {Albayzin speech database: design of the phonetic corpus.}, |
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author = {Moreno, Asunción and Poch, Dolors and Bonafonte, Antonio and Lleida, Eduardo and Llisterri, Joaquim and Mariño, José B. and Nadeu, Climent}, |
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booktitle = {EUROSPEECH}, |
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crossref = {conf/interspeech/1993}, |
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ee = {http://www.isca-speech.org/archive/eurospeech_1993/e93_0175.html}, |
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publisher = {ISCA}, |
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url = {https://dblp.uni-trier.de/rec/conf/interspeech/MorenoPBLLMN93.html}, |
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year = 1993 |
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} |
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""" |
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_DESCRIPTION = """\ |
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Albayzín, an Spanish Speech Recognition Database. |
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Albayzin is a spoken database for Spanish designed for speech recognition purposes. |
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It is composed by utterances from a set of phonetically balanced sentences, containing |
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a balanced set of males and females of ages from 18 to 55. |
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""" |
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_HOMEPAGE = "http://catalogue.elra.info/en-us/repository/browse/ELRA-S0089/" |
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class AlbayzinConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Albayzin.""" |
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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(AlbayzinConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class Albayzin(datasets.GeneratorBasedBuilder): |
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"""Albayzin dataset.""" |
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BUILDER_CONFIGS = [AlbayzinConfig(name="default", description="'Default' configuration.")] |
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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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"id": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("file", "text"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], |
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) |
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def _split_generators(self, dl_manager): |
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if dl_manager.manual_dir == None : |
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raise ValueError( |
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f"Make sure you insert a manual dir via `datasets.load_dataset('Albayzin', data_dir=...)` that points to the Albayzin dataset directory." |
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) |
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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('Albayzin', data_dir=...)` that points to the Albayzin dataseti directory." |
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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 _load_transcripts(self, split, data_dir): |
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replacements = { |
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"'a":"á" , "'e":"é" , "'i":"í" , "'o":"ó" , "'u":"ú" , |
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"'A":"Á" , "'E":"É" , "'I":"Í" , "'O":"Ó" , "'U":"Ú" , |
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"~n":"ñ" , "'~N":"Ñ" , "~u":"ü" |
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} |
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split2dir = {'train':'FA.TXT' , 'test':'FT.TXT'} |
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path = Path(data_dir,'texto',split2dir[split]) |
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with path.open(encoding="ascii") as f: |
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text = {} |
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for line in f: |
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for k,v in replacements.items(): |
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line = line.replace(k,v) |
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words = line.split() |
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if len(words) < 3 : continue |
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text[int(words[0])] = ' '.join(words[2:]) |
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return text |
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def _generate_examples(self, split, data_dir): |
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"""Generate examples from Albayzin archive_path.""" |
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split2dir = {'train':'SUB_APRE' , 'test':'SUB_PRUE'} |
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wav_paths = sorted(Path(data_dir,'wav/CF',split2dir[split]).glob(f"**/*.wav")) |
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transcripts = self._load_transcripts(split, data_dir) |
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for key,wav_path in enumerate(wav_paths): |
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id_ = wav_path.stem |
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transcript = transcripts[int(id_[-4:])] |
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example = { |
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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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"id": id_, |
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} |
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yield key, example |
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