Datasets:
File size: 6,705 Bytes
34246d4 f4b16ad 34246d4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
from collections import defaultdict
import os
import json
import csv
import datasets
_NAME="althingi_asr"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"
_DESCRIPTION = """
Althingi Parliamentary Speech consists of approximately 542 hours of recorded speech from Althingi, the Icelandic Parliament. Speeches date from 2005-2016.
"""
_CITATION = """
@misc{helgadottiralthingi2021,
title={Althingi Parliamentary Speech},
ldc_catalog_no={LDC2021S01},
DOI={https://doi.org/10.35111/695b-6697},
author={Helgadóttir, Inga Rún and Kjaran, Róbert and Nikulásdóttir, Anna Björk and Guðnason, Jón},
publisher={Reykjavík University}
journal={Linguistic Data Consortium, Philadelphia},
year={2021},
url={https://catalog.ldc.upenn.edu/LDC2021S01},
}
"""
_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC2021S01"
_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"
_BASE_DATA_DIR = "corpus/"
_METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv")
_METADATA_TEST = os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
_METADATA_DEV = os.path.join(_BASE_DATA_DIR,"files", "metadata_dev.tsv")
_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
_TARS_TEST = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
_TARS_DEV = os.path.join(_BASE_DATA_DIR,"files", "tars_dev.paths")
class AlthingiAsrConfig(datasets.BuilderConfig):
"""BuilderConfig for Althingi Corpus"""
def __init__(self, name, **kwargs):
name=_NAME
super().__init__(name=name, **kwargs)
class AlthingiAsr(datasets.GeneratorBasedBuilder):
"""Althingi Parliamentary Speech"""
VERSION = datasets.Version(_VERSION)
BUILDER_CONFIGS = [
AlthingiAsrConfig(
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"),
"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)
metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
metadata_dev=dl_manager.download_and_extract(_METADATA_DEV)
tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
tars_test=dl_manager.download_and_extract(_TARS_TEST)
tars_dev=dl_manager.download_and_extract(_TARS_DEV)
hash_tar_files=defaultdict(dict)
with open(tars_train,'r') as f:
hash_tar_files['train']=[path.replace('\n','') for path in f]
with open(tars_test,'r') as f:
hash_tar_files['test']=[path.replace('\n','') for path in f]
with open(tars_dev,'r') as f:
hash_tar_files['dev']=[path.replace('\n','') for path in f]
hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev}
audio_paths = dl_manager.download(hash_tar_files)
splits=["train","dev","test"]
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"],
}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]],
"local_extracted_archives_paths": local_extracted_audio_paths["dev"],
"metadata_paths": hash_meta_paths["dev"],
}
),
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","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]
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()},
} |