Datasets:
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from collections import defaultdict
import os
import json
import csv
import datasets
_NAME="ciempiess_light"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
The CIEMPIESS LIGHT is a corpus in Mexican Spanish destined to train acoustic models for the speech recognition task. The corpus was manually transcribed and it contains audio recordings from male and female speakers taken from radio shows.
"""
_CITATION = """
@misc{carlosmenaciempiesslight2017,
title={CIEMPIESS LIGHT CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.},
ldc_catalog_no={LDC2017S23},
DOI={https://doi.org/10.35111/64rg-yk97},
author={Hernandez Mena, Carlos Daniel and Herrera, Abel},
journal={Linguistic Data Consortium, Philadelphia},
year={2017},
url={https://catalog.ldc.upenn.edu/LDC2017S23},
}
"""
_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2017S23"
_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
_BASE_DATA_DIR = "corpus/"
_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv")
_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths")
class CiempiessLightConfig(datasets.BuilderConfig):
"""BuilderConfig for CIEMPIESS LIGHT Corpus"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class CiempiessLight(datasets.GeneratorBasedBuilder):
"""CIEMPIESS LIGHT Corpus"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
CiempiessLightConfig(
name=_NAME,
version=datasets.Version(_VERSION),
)
]
def _info(self):
features = datasets.Features(
{
"audio_id": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16000),
"speaker_id": datasets.Value("string"),
"gender": datasets.Value("string"),
"duration": datasets.Value("float32"),
"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_train=dl_manager.download_and_extract(_METADATA_TRAIN)
tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
hash_tar_files=defaultdict(dict)
with open(tars_train,'r') as f:
hash_tar_files['train']=[path.replace('\n','') for path in f]
hash_meta_paths={"train":metadata_train}
audio_paths = dl_manager.download(hash_tar_files)
splits=["train"]
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.TRAIN,
gen_kwargs={
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
"local_extracted_archives_paths": local_extracted_audio_paths["train"],
"metadata_paths": hash_meta_paths["train"],
}
),
]
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 = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[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()},
}
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