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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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import torchaudio |
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import warnings |
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_NAME="samromur_children" |
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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 Samrómur Children corpus contains more than 137000 validated speech-recordings uttered by Icelandic children. |
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""" |
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_CITATION = """ |
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@misc{menasamromurchildren2022, |
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title={Samrómur Children Icelandic Speech 1.0}, |
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ldc_catalog_no={LDC2022S11}, |
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DOI={https://doi.org/10.35111/frrj-qd60}, |
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author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsson, Ragnar and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Magnúsdóttir, Eydís Huld and Þórhallsdóttir, Ragnheiður and Guðnason, Jón}, |
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publisher={Reykjavík University} |
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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/LDC2022S11}, |
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} |
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""" |
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_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2022S11" |
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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_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv") |
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_METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv") |
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_METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv") |
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_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths") |
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_TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths") |
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_TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths") |
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class SamromurChildrenConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Samromur Children""" |
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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 SamromurChildren(datasets.GeneratorBasedBuilder): |
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"""Samrómur Children Icelandic Speech 1.0""" |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIGS = [ |
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SamromurChildrenConfig( |
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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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"age": 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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metadata_test=dl_manager.download_and_extract(_METADATA_TEST) |
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metadata_dev=dl_manager.download_and_extract(_METADATA_DEV) |
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tars_train=dl_manager.download_and_extract(_TARS_TRAIN) |
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tars_test=dl_manager.download_and_extract(_TARS_TEST) |
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tars_dev=dl_manager.download_and_extract(_TARS_DEV) |
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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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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_dev,'r') as f: |
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hash_tar_files['dev']=[path.replace('\n','') for path in f] |
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hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev} |
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audio_paths = dl_manager.download(hash_tar_files) |
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splits=["train","dev","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.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=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]], |
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"local_extracted_archives_paths": local_extracted_audio_paths["dev"], |
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"metadata_paths": hash_meta_paths["dev"], |
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} |
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), |
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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","age","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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waveform, sample_rate = torchaudio.load(path) |
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if waveform.numel() == 0: |
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warnings.warn(f"Empty audio file: {str(audio_id)}") |
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continue |
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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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