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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="ciempiess_test" |
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_VERSION="1.0.0" |
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_AUDIO_EXTENSIONS=".flac" |
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_DESCRIPTION = """ |
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The CIEMPIESS TEST Corpus is a gender balanced corpus destined to test acoustic models for the speech recognition task. The corpus was manually transcribed and it contains audio recordings from 10 male and 10 female speakers. The CIEMPIESS TEST is one of the three corpora included at the LDC's \"CIEMPIESS Experimentation\" (LDC2019S07). |
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""" |
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_CITATION = """ |
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@misc{carlosmenaciempiesstest2022, |
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title={CIEMPIESS TEST CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.}, |
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ldc_catalog_no={LDC2019S07}, |
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DOI={https://doi.org/10.35111/xdx5-n815}, |
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author={Hernandez Mena, Carlos Daniel}, |
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journal={Linguistic Data Consortium, Philadelphia}, |
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year={2019}, |
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url={https://catalog.ldc.upenn.edu/LDC2019S07}, |
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} |
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""" |
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_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2019S07" |
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_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/" |
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_BASE_DATA_DIR = "corpus/" |
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_METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv") |
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_TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths") |
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class CiempiessTestConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CIEMPIESS TEST 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 CiempiessTest(datasets.GeneratorBasedBuilder): |
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"""CIEMPIESS TEST Corpus""" |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIGS = [ |
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CiempiessTestConfig( |
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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_test=dl_manager.download_and_extract(_METADATA_TEST) |
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tars_test=dl_manager.download_and_extract(_TARS_TEST) |
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hash_tar_files=defaultdict(dict) |
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with open(tars_test,'r') as f: |
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hash_tar_files['test']=[path.replace('\n','') for path in f] |
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hash_meta_paths={"test":metadata_test} |
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audio_paths = dl_manager.download(hash_tar_files) |
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splits=["test"] |
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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.TEST, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["test"], |
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"metadata_paths": hash_meta_paths["test"], |
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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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