Datasets:
File size: 5,589 Bytes
f76bd87 98f62d0 f76bd87 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
from collections import defaultdict
import os
import json
import csv
import datasets
_NAME="ciempiess_complementary"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
The CIEMPIESS COMPLEMENTARY Corpus was created with the voices of 10 male and 10 female volunteers reading isolated words. The words were chosen to assure users to get, at least, twenty instances of every single phoneme and allophone of the Mexican phonetic alphabet called Mexbet.
"""
_CITATION = """
@misc{carlosmenaciempiesscomplementary2019,
title={CIEMPIESS COMPLEMENTARY CORPUS: Audio and Transcripts of Spanish Isolated Words.},
ldc_catalog_no={LDC2019S07},
DOI={https://doi.org/10.35111/xdx5-n815},
author={Hernandez Mena, Carlos Daniel and Jiménez Sandoval, Susana Alejandra},
journal={Linguistic Data Consortium, Philadelphia},
year={2019},
url={https://catalog.ldc.upenn.edu/LDC2019S07},
}
"""
_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2019S07"
_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 CiempiessComplementaryConfig(datasets.BuilderConfig):
"""BuilderConfig for CIEMPIESS COMPLEMENTARY Corpus"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class CiempiessComplementary(datasets.GeneratorBasedBuilder):
"""CIEMPIESS COMPLEMENTARY Corpus"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
CiempiessComplementaryConfig(
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"),
"utt_type": datasets.Value("string"),
"age": datasets.Value("int32"),
"education": datasets.Value("string"),
"birthplace": datasets.Value("string"),
"residence": datasets.Value("string"),
"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","utt_type","age","education","birthplace","residence","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()},
}
|