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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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csv.field_size_limit(100000000) |
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import datasets |
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_NAME="annotated_catalan_common_voice_v17" |
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_VERSION="1.0.0" |
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_AUDIO_EXTENSIONS=".mp3" |
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_DESCRIPTION = """ |
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This version of the Catalan sentences of the Common Voice corpus v17 |
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includes metadata (gender and accent) for 263 speakers annotated by a team of experts. |
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""" |
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_CITATION = """ |
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@misc{armentanoannotated2024, |
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title={Annotated Catalan Common Voice v17}, |
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author={Armentano, Carme}, |
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publisher={Barcelona Supercomputing Center} |
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year={2024}, |
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url={https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17}, |
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} |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/annotated_catalan_common_voice_v17" |
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_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/" |
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_BASE_DATA_DIR = "corpus/" |
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_METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.tsv") |
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_METADATA_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.tsv") |
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_METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.tsv") |
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_METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.tsv") |
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_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.tsv") |
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_METADATA_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.tsv") |
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_TARS_DEV = os.path.join(_BASE_DATA_DIR,"files","annotated_dev.paths") |
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_TARS_INVALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_invalidated.paths") |
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_TARS_OTHER = os.path.join(_BASE_DATA_DIR,"files","annotated_other.paths") |
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_TARS_TEST = os.path.join(_BASE_DATA_DIR,"files","annotated_test.paths") |
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_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","annotated_train.paths") |
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_TARS_VALIDATED = os.path.join(_BASE_DATA_DIR,"files","annotated_validated.paths") |
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class AnnotatedCatalanCommonVoicev17Config(datasets.BuilderConfig): |
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"""BuilderConfig for The Annotated Catalan Common Voice v17""" |
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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 AnnotatedCatalanCommonVoicev17(datasets.GeneratorBasedBuilder): |
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"""Annotated Catalan Common Voice v17""" |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIGS = [ |
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AnnotatedCatalanCommonVoicev17Config( |
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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": datasets.Audio(sampling_rate=16000), |
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"client_id": datasets.Value("string"), |
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"path": datasets.Value("string"), |
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"sentence_id": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"sentence_domain": datasets.Value("string"), |
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"up_votes": datasets.Value("int32"), |
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"down_votes": datasets.Value("int32"), |
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"age": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"accents": datasets.Value("string"), |
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"variant": datasets.Value("string"), |
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"locale": datasets.Value("string"), |
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"segment": datasets.Value("string"), |
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"mean quality": datasets.Value("string"), |
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"stdev quality": datasets.Value("string"), |
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"annotated_accent": datasets.Value("string"), |
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"annotated_accent_agreement": datasets.Value("string"), |
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"annotated_gender": datasets.Value("string"), |
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"annotated_gender_agreement": datasets.Value("string"), |
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"propagated_gender": datasets.Value("string"), |
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"propagated_accents": datasets.Value("string"), |
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"propagated_accents_norm": datasets.Value("string"), |
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"variant_norm": datasets.Value("string"), |
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"assigned_accent": datasets.Value("string"), |
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"assigned_gender": 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_dev=dl_manager.download_and_extract(_METADATA_DEV) |
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metadata_invalidated=dl_manager.download_and_extract(_METADATA_INVALIDATED) |
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metadata_other=dl_manager.download_and_extract(_METADATA_OTHER) |
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metadata_test=dl_manager.download_and_extract(_METADATA_TEST) |
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metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN) |
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metadata_validated=dl_manager.download_and_extract(_METADATA_VALIDATED) |
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tars_dev=dl_manager.download_and_extract(_TARS_DEV) |
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tars_invalidated=dl_manager.download_and_extract(_TARS_INVALIDATED) |
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tars_other=dl_manager.download_and_extract(_TARS_OTHER) |
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tars_test=dl_manager.download_and_extract(_TARS_TEST) |
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tars_train=dl_manager.download_and_extract(_TARS_TRAIN) |
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tars_validated=dl_manager.download_and_extract(_TARS_VALIDATED) |
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hash_tar_files=defaultdict(dict) |
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with open(tars_dev,'r') as f: |
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hash_tar_files['validation']=[path.replace('\n','') for path in f] |
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with open(tars_invalidated,'r') as f: |
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hash_tar_files['invalidated']=[path.replace('\n','') for path in f] |
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with open(tars_other,'r') as f: |
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hash_tar_files['other']=[path.replace('\n','') for path in f] |
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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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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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with open(tars_validated,'r') as f: |
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hash_tar_files['validated']=[path.replace('\n','') for path in f] |
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hash_meta_paths={"validation":metadata_dev, |
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"invalidated":metadata_invalidated, |
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"other":metadata_other, |
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"test":metadata_test, |
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"train":metadata_train, |
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"validated":metadata_validated} |
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audio_paths = dl_manager.download(hash_tar_files) |
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splits=["validation","invalidated","other","test","train","validated"] |
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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="validation", |
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gen_kwargs={ |
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"audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["validation"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["validation"], |
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"metadata_paths": hash_meta_paths["validation"], |
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} |
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), |
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datasets.SplitGenerator( |
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name="invalidated", |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["invalidated"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["invalidated"], |
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"metadata_paths": hash_meta_paths["invalidated"], |
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} |
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), |
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datasets.SplitGenerator( |
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name="other", |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["other"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["other"], |
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"metadata_paths": hash_meta_paths["other"], |
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} |
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), |
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datasets.SplitGenerator( |
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name="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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datasets.SplitGenerator( |
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name="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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datasets.SplitGenerator( |
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name="validated", |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["validated"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["validated"], |
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"metadata_paths": hash_meta_paths["validated"], |
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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 = ["client_id","path","sentence_id","sentence","sentence_domain","up_votes", |
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"down_votes","age","gender","accents","variant","locale","segment", |
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"mean quality","stdev quality","annotated_accent","annotated_accent_agreement", |
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"annotated_gender","annotated_gender_agreement","propagated_gender", |
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"propagated_accents","propagated_accents_norm","variant_norm","assigned_accent", |
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"assigned_gender"] |
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with open(metadata_paths) as f: |
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metadata = {x["path"]: 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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audio_id=audio_id+_AUDIO_EXTENSIONS |
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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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try: |
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yield audio_id, { |
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"path": 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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except: |
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continue |
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