File size: 13,981 Bytes
c53e100 dee3130 c53e100 1ffc506 c53e100 713fa3b c53e100 dee3130 c53e100 6daa6a8 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 e38e23b c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 6daa6a8 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 dee3130 c53e100 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TED TALKS IWSLT: Web Inventory of Transcribed and Translated Ted Talks in 109 languages."""
import io
import xml.etree.ElementTree as ET
import zipfile
from collections import defaultdict
import datasets
logger = datasets.logging.get_logger(__name__)
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{cettolo-etal-2012-wit3,
title = "{WIT}3: Web Inventory of Transcribed and Translated Talks",
author = "Cettolo, Mauro and
Girardi, Christian and
Federico, Marcello",
booktitle = "Proceedings of the 16th Annual conference of the European Association for Machine Translation",
month = may # " 28{--}30",
year = "2012",
address = "Trento, Italy",
publisher = "European Association for Machine Translation",
url = "https://www.aclweb.org/anthology/2012.eamt-1.60",
pages = "261--268",
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007,
the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English
and their translations in more than 80 languages. Aside from its cultural and social relevance, this content, which is published under the Creative Commons BYNC-ND license, also represents a precious
language resource for the machine translation research community, thanks to its size, variety of topics, and covered languages.
This effort repurposes the original content in a way which is more convenient for machine translation researchers.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://wit3.fbk.eu/"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC-BY-NC-4.0"
# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "data/XML_releases.tgz"
_LANGUAGES = (
"mr",
"eu",
"hr",
"rup",
"szl",
"lo",
"ms",
"ht",
"hy",
"mg",
"arq",
"uk",
"ku",
"ig",
"sr",
"ug",
"ne",
"pt-br",
"sq",
"af",
"km",
"en",
"tt",
"ja",
"inh",
"mn",
"eo",
"ka",
"nb",
"fil",
"uz",
"fi",
"tl",
"el",
"tg",
"bn",
"si",
"gu",
"sk",
"kn",
"ar",
"hup",
"zh-tw",
"sl",
"be",
"bo",
"fr",
"ps",
"tr",
"ltg",
"la",
"ko",
"lv",
"nl",
"fa",
"ru",
"et",
"vi",
"pa",
"my",
"sw",
"az",
"sv",
"ga",
"sh",
"it",
"da",
"lt",
"kk",
"mk",
"tlh",
"he",
"ceb",
"bg",
"fr-ca",
"ha",
"ml",
"mt",
"as",
"pt",
"zh-cn",
"cnh",
"ro",
"hi",
"es",
"id",
"bs",
"so",
"cs",
"te",
"ky",
"hu",
"th",
"pl",
"nn",
"ca",
"is",
"ta",
"de",
"srp",
"ast",
"bi",
"lb",
"art-x-bork",
"am",
"oc",
"zh",
"ur",
"gl",
)
# Please note that only few pairs are shown here. You can use config to generate data for all language pairs
_LANGUAGE_PAIRS = [
("eu", "ca"),
("nl", "en"),
("nl", "hi"),
("de", "ja"),
("fr-ca", "hi"),
]
# Year subscripts for the specific folder
_YEAR = {"2014": "-20140120", "2015": "-20150530", "2016": "-20160408"}
_YEAR_FOLDER = {
"2014": "XML_releases/xml-20140120",
"2015": "XML_releases/xml-20150616",
"2016": "XML_releases/xml",
}
class TedTalksIWSLTConfig(datasets.BuilderConfig):
""" "Builder Config for the TedTalks IWSLT dataset"""
def __init__(self, language_pair=(None, None), year=None, **kwargs):
"""BuilderConfig for TedTalks IWSLT dataset.
Args:
for the `datasets.features.text.TextEncoder` used for the features feature.
language_pair: pair of languages that will be used for translation. Should
contain 2-letter coded strings. First will be used at source and second
as target in supervised mode. For example: ("pl", "en").
**kwargs: keyword arguments forwarded to super.
"""
# Validate language pair.
name = "%s_%s_%s" % (language_pair[0], language_pair[1], year)
source, target = language_pair
assert source in _LANGUAGES, f"Invalid source code in language pair: {source}"
assert target in _LANGUAGES, f"Invalid target code in language pair: {target}"
assert (
source != target
), f"Source::{source} and Target::{target} language pairs cannot be the same!"
assert year in _YEAR.keys()
description = (
f"Translation Ted Talks dataset (WIT3) between {source} and {target}"
)
super(TedTalksIWSLTConfig, self).__init__(
name=name,
description=description,
**kwargs,
)
self.language_pair = language_pair
self.year = year
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class TedTalksIWSLT(datasets.GeneratorBasedBuilder):
"""TED TALKS IWSLT: Web Inventory of Transcribed and Translated Ted Talks in 109 languages."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIG_CLASS = TedTalksIWSLTConfig
BUILDER_CONFIGS = [
TedTalksIWSLTConfig(
language_pair=language_pair, year=year, version=datasets.Version("1.1.0")
)
for language_pair in _LANGUAGE_PAIRS
for year in _YEAR.keys()
]
def _info(self):
features = datasets.Features(
{
"translation": datasets.features.Translation(
languages=self.config.language_pair
),
},
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_archive(data_dir),
},
),
]
def _generate_examples(self, files):
"""Yields examples."""
def parse_zip_file(path, file):
def et_to_dict(tree):
"""This is used to convert the xml to a list of dicts"""
dct = {tree.tag: {} if tree.attrib else None}
children = list(tree)
if children:
dd = defaultdict(list)
for dc in map(et_to_dict, children):
for k, v in dc.items():
dd[k].append(v)
dct = {tree.tag: dd}
if tree.attrib:
dct[tree.tag].update((k, v) for k, v in tree.attrib.items())
if tree.text:
text = tree.text.strip()
if children or tree.attrib:
if text:
dct[tree.tag]["text"] = text
else:
dct[tree.tag] = text
return dct
with zipfile.ZipFile(io.BytesIO(file)) as zf:
try:
tree = ET.parse(zf.open(path.split("/")[-1][:-3] + "xml"))
root = tree.getroot()
talks = et_to_dict(root).get("xml").get("file")
ids = [talk.get("head")[0].get("talkid") for talk in talks]
except Exception as pe:
logger.warning(f"ERROR: {pe}")
logger.warning(
"This likely means that you have a malformed XML file!"
)
ids = []
return talks, ids
language_pair = self.config.language_pair
year = self.config.year
source_file_path = (
_YEAR_FOLDER[year] + "/ted_" + language_pair[0] + _YEAR[year] + ".zip"
)
target_file_path = (
_YEAR_FOLDER[year] + "/ted_" + language_pair[1] + _YEAR[year] + ".zip"
)
source_talks, source_ids = None, None
target_talks, target_ids = None, None
for path, file in files:
if source_ids is not None and target_ids is not None:
break
if source_ids is None and path.endswith(source_file_path):
source_talks, source_ids = parse_zip_file(path, file.read())
elif target_ids is None and path.endswith(target_file_path):
target_talks, target_ids = parse_zip_file(path, file.read())
if source_ids is None or target_ids is None:
source_ids = list()
target_ids = list()
comm_talkids = [talkid for talkid in target_ids if talkid in source_ids]
translation = list()
for talkid in comm_talkids:
source = list(
filter(
lambda talk: talk.get("head")[0].get("talkid") == talkid,
source_talks,
)
)
target = list(
filter(
lambda talk: talk.get("head")[0].get("talkid") == talkid,
target_talks,
)
)
if len(source) == 0 or len(target) == 0:
pass
else:
source = source[0]
target = target[0]
if source.get("head")[0].get("description") and target.get("head")[0].get(
"description"
):
if (
source.get("head")[0].get("description")[0]
and target.get("head")[0].get("description")[0]
):
temp_dict = dict()
temp_dict["id"] = source.get("head")[0].get("talkid")[0] + "_1"
temp_dict[language_pair[0]] = (
source.get("head")[0]
.get("description")[0]
.replace("TED Talk Subtitles and Transcript: ", "")
)
temp_dict[language_pair[1]] = (
target.get("head")[0]
.get("description")[0]
.replace("TED Talk Subtitles and Transcript: ", "")
)
translation.append(temp_dict)
if source.get("head")[0].get("title") and target.get("head")[0].get(
"title"
):
if (
source.get("head")[0].get("title")[0]
and target.get("head")[0].get("title")[0]
):
temp_dict = dict()
temp_dict["id"] = source.get("head")[0].get("talkid")[0] + "_2"
temp_dict[language_pair[0]] = source.get("head")[0].get("title")[0]
temp_dict[language_pair[1]] = target.get("head")[0].get("title")[0]
translation.append(temp_dict)
if source.get("head")[0].get("seekvideo") and target.get("head")[0].get(
"seekvideo"
):
source_transc = (
source.get("head")[0].get("transcription")[0].get("seekvideo")
)
target_transc = (
target.get("head")[0].get("transcription")[0].get("seekvideo")
)
transc = zip(source_transc, target_transc)
transcriptions = [
{
"id": s.get("id"),
language_pair[0]: s.get("text"),
language_pair[1]: t.get("text"),
}
for s, t in transc
]
translation.extend(transcriptions)
for talk_segment in translation:
result = {
"translation": {
language_pair[0]: talk_segment[language_pair[0]],
language_pair[1]: talk_segment[language_pair[1]],
}
}
yield talk_segment["id"], result
|