| from collections import defaultdict
|
| import os
|
| import json
|
| import csv
|
|
|
| import datasets
|
|
|
| _NAME="spanish_trans_uq"
|
| _VERSION="1.0.0"
|
| _AUDIO_EXTENSIONS=".wav"
|
|
|
| _DESCRIPTION = """
|
| A custom dataset to evaluate UQ methods
|
|
|
| """
|
|
|
| _CITATION = """
|
| TODO
|
| """
|
|
|
| _HOMEPAGE = "todo"
|
|
|
| _LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
|
|
|
| _BASE_DATA_DIR = "corpus/"
|
| _METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
|
|
|
| _TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
|
|
|
| class SpanishTransUQTestConfig(datasets.BuilderConfig):
|
| """BuilderConfig for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
|
|
|
| def __init__(self, name, **kwargs):
|
| name=_NAME
|
| super().__init__(name=name, **kwargs)
|
|
|
| class SpanishTransUQTestConfig(datasets.GeneratorBasedBuilder):
|
| """for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
|
|
|
| VERSION = datasets.Version(_VERSION)
|
| BUILDER_CONFIGS = [
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| SpanishTransUQTestConfig(
|
| name=_NAME,
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| version=datasets.Version(_VERSION),
|
| )
|
| ]
|
|
|
| def _info(self):
|
| features = datasets.Features(
|
| {
|
| "audio_id": datasets.Value("string"),
|
| "audio": datasets.Audio(sampling_rate=16000),
|
| "normalized_text": datasets.Value("string"),
|
| }
|
| )
|
| 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,
|
| )
|
|
|
| def _split_generators(self, dl_manager):
|
|
|
| metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
|
|
|
| tars_test=dl_manager.download_and_extract(_TARS_TEST)
|
|
|
| hash_tar_files=defaultdict(dict)
|
|
|
| with open(tars_test,'r') as f:
|
| 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
|
| {
|
| split:[None] * len(audio_paths[split]) for split in splits
|
| }
|
| )
|
|
|
| return [
|
| datasets.SplitGenerator(
|
| name=datasets.Split.TEST,
|
| gen_kwargs={
|
| "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
|
| "local_extracted_archives_paths": local_extracted_audio_paths["test"],
|
| "metadata_paths": hash_meta_paths["test"],
|
| }
|
| ),
|
| ]
|
|
|
| def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
|
|
|
| features = ["speaker_id","gender","duration","normalized_text"]
|
|
|
| with open(metadata_paths) as f:
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| metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
|
|
|
| for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
|
| for audio_filename, audio_file in audio_archive:
|
|
|
| audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
|
| path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
|
|
|
| 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()},
|
| }
|
|
|
