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from collections import defaultdict
import os
import json
import csv
csv.field_size_limit(100000000)
import datasets
_NAME="cv17_es_other_automatically_verified"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".mp3"
_DESCRIPTION = """
Split called -other- of the Spanish Common Voice v17.0 that was automatically verified
using various ASR system.
"""
_CITATION = """
@misc{carlosmena2024cv17autoveri,
title={Spanish Common Voice v17.0 Split Other Automatically Verified},
author={Mena, Carlos},
publisher={Barcelona Supercomputing Center}
year={2024},
url={https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified},
}
"""
_HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified"
_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
_BASE_DATA_DIR = "corpus/"
_METADATA_OTHER = os.path.join(_BASE_DATA_DIR,"files","other.tsv")
_TARS_REPO = os.path.join(_BASE_DATA_DIR,"files","tars_repo.paths")
class CV17EsOtherAutomaticallyVerifiedConfig(datasets.BuilderConfig):
"""BuilderConfig for The Spanish Common Voice v17.0 Split Other Automatically Verified"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class CV17EsOtherAutomaticallyVerified(datasets.GeneratorBasedBuilder):
"""Spanish Common Voice v17.0 Split Other Automatically Verified"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
CV17EsOtherAutomaticallyVerifiedConfig(
name=_NAME,
version=datasets.Version(_VERSION),
)
]
def _info(self):
features = datasets.Features(
{
"audio": datasets.Audio(sampling_rate=16000),
"client_id": datasets.Value("string"),
"path": datasets.Value("string"),
"sentence_id": datasets.Value("string"),
"sentence": datasets.Value("string"),
"sentence_domain": datasets.Value("string"),
"up_votes": datasets.Value("int32"),
"down_votes": datasets.Value("int32"),
"age": datasets.Value("string"),
"gender": datasets.Value("string"),
"accents": datasets.Value("string"),
"variant": datasets.Value("string"),
"locale": datasets.Value("string"),
"segment": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
metadata_other=dl_manager.download_and_extract(_METADATA_OTHER)
tars_repo=dl_manager.download_and_extract(_TARS_REPO)
hash_tar_files=defaultdict(dict)
with open(tars_repo,'r') as f:
hash_tar_files['other']=[path.replace('\n','') for path in f]
hash_meta_paths={"other":metadata_other}
audio_paths = dl_manager.download(hash_tar_files)
splits=["other"]
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="other",
gen_kwargs={
"audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["other"]],
"local_extracted_archives_paths": local_extracted_audio_paths["other"],
"metadata_paths": hash_meta_paths["other"],
}
),
]
def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
features = ["client_id","sentence_id","sentence","sentence_domain","up_votes",
"down_votes","age","gender", "accents","variant","locale","segment"]
with open(metadata_paths) as f:
metadata = {x["path"]: 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]
audio_id=audio_id+_AUDIO_EXTENSIONS
path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
try:
yield audio_id, {
"path": audio_id,
**{feature: metadata[audio_id][feature] for feature in features},
"audio": {"path": path, "bytes": audio_file.read()},
}
except:
continue
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