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from collections import defaultdict |
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import os |
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import json |
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import csv |
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import datasets |
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_NAME="tedx_spanish" |
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_VERSION="1.0.0" |
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_DESCRIPTION = """ |
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The TEDX SPANISH CORPUS is a dataset created from TEDx talks in Spanish and it |
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aims to be used in the Automatic Speech Recognition (ASR) Task. |
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""" |
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_CITATION = """ |
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@misc{carlosmenatedxspanish2019, |
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title={TEDX SPANISH CORPUS: Audio and Transcripts in Spanish in a CIEMPIESS Corpus style, taken from the TEDx Talks.}, |
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author={Hernandez Mena, Carlos Daniel}, |
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year={2019}, |
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url={https://huggingface.co/ciempiess/tedx_spanish}, |
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} |
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""" |
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_HOMEPAGE = "https://huggingface.co/ciempiess/tedx_spanish" |
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_LICENSE = "CC-BY-NC-ND-4.0, See https://creativecommons.org/licenses/by-nc-nd/4.0/" |
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_BASE_DATA_DIR = "corpus/" |
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_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv") |
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_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths") |
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class TedxSpanishConfig(datasets.BuilderConfig): |
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"""BuilderConfig for TEDX SPANISH CORPUS""" |
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def __init__(self, name, **kwargs): |
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name=_NAME |
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super().__init__(name=name, **kwargs) |
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class TedxSpanish(datasets.GeneratorBasedBuilder): |
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"""TEDX SPANISH CORPUS""" |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIGS = [ |
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TedxSpanishConfig( |
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name=_NAME, |
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version=datasets.Version(_VERSION), |
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) |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio_id": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16000), |
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"speaker_id": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"duration": datasets.Value("float32"), |
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"normalized_text": 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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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) |
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tars_train=dl_manager.download_and_extract(_TARS_TRAIN) |
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hash_tar_files=defaultdict(dict) |
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with open(tars_train,'r') as f: |
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hash_tar_files['train']=[path.replace('\n','') for path in f] |
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hash_meta_paths={"train":metadata_train} |
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audio_paths = dl_manager.download(hash_tar_files) |
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splits=["train"] |
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local_extracted_audio_paths = ( |
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dl_manager.extract(audio_paths) if not dl_manager.is_streaming else |
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{ |
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split:[None] * len(audio_paths[split]) for split in splits |
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} |
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) |
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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_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["train"], |
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"metadata_paths": hash_meta_paths["train"], |
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} |
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), |
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] |
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
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features = ["speaker_id","gender","duration","normalized_text"] |
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with open(metadata_paths) as f: |
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metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")} |
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for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths): |
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for audio_filename, audio_file in audio_archive: |
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audio_id =os.path.splitext(os.path.basename(audio_filename))[0] |
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path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename |
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yield audio_id, { |
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"audio_id": audio_id, |
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**{feature: metadata[audio_id][feature] for feature in features}, |
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"audio": {"path": path, "bytes": audio_file.read()}, |
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} |
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