|
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_fine_tune = os.path.join(_BASE_DATA_DIR,"files", "metadata_fine_tune.tsv")
|
|
|
|
_TARS_fine_tune = os.path.join(_BASE_DATA_DIR,"files", "tars_fine_tune.paths")
|
|
|
|
class SpanishTransUQfine_tuneConfig(datasets.BuilderConfig):
|
|
"""BuilderConfig for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
|
|
|
|
def __init__(self, name, **kwargs):
|
|
name=_NAME
|
|
super().__init__(name=name, **kwargs)
|
|
|
|
class SpanishTransUQfine_tuneConfig(datasets.GeneratorBasedBuilder):
|
|
"""for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
|
|
|
|
VERSION = datasets.Version(_VERSION)
|
|
BUILDER_CONFIGS = [
|
|
SpanishTransUQfine_tuneConfig(
|
|
name=_NAME,
|
|
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(
|
|
description=_DESCRIPTION,
|
|
features=features,
|
|
homepage=_HOMEPAGE,
|
|
license=_LICENSE,
|
|
citation=_CITATION,
|
|
)
|
|
|
|
def _split_generators(self, dl_manager):
|
|
|
|
metadata_fine_tune=dl_manager.download_and_extract(_METADATA_fine_tune)
|
|
|
|
tars_fine_tune=dl_manager.download_and_extract(_TARS_fine_tune)
|
|
|
|
hash_tar_files=defaultdict(dict)
|
|
|
|
with open(tars_fine_tune,'r') as f:
|
|
hash_tar_files['fine_tune']=[path.replace('\n','') for path in f]
|
|
|
|
hash_meta_paths={"fine_tune":metadata_fine_tune}
|
|
audio_paths = dl_manager.download(hash_tar_files)
|
|
|
|
splits=["fine_tune"]
|
|
local_extracted_audio_paths = (
|
|
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.fine_tune,
|
|
gen_kwargs={
|
|
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fine_tune"]],
|
|
"local_extracted_archives_paths": local_extracted_audio_paths["fine_tune"],
|
|
"metadata_paths": hash_meta_paths["fine_tune"],
|
|
}
|
|
),
|
|
]
|
|
|
|
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:
|
|
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, {
|
|
"audio_id": audio_id,
|
|
**{feature: metadata[audio_id][feature] for feature in features},
|
|
"audio": {"path": path, "bytes": audio_file.read()},
|
|
}
|
|
|
