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# 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 os
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 = "https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download"
_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."""
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
my_urls = _URL
language_pair = self.config.language_pair
year = self.config.year
data_dir = dl_manager.download_and_extract(my_urls)
zip_file_pair0 = os.path.join(data_dir, _YEAR_FOLDER[year] + "/ted_" + language_pair[0] + _YEAR[year] + ".zip")
zip_file_pair1 = os.path.join(data_dir, _YEAR_FOLDER[year] + "/ted_" + language_pair[1] + _YEAR[year] + ".zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": [zip_file_pair0, zip_file_pair1],
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
# The key is not important, it's more here for legacy reason (legacy from tfds)
source = filepath[0]
target = filepath[1]
language_pair = self.config.name.split("_")
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
if os.path.exists(source) and os.path.exists(target):
with zipfile.ZipFile(source) as zf_source:
with zipfile.ZipFile(target) as zf_target:
try:
tree_source = ET.parse(zf_source.open(source.split("/")[-1][:-3] + "xml"))
root_source = tree_source.getroot()
source_talks = et_to_dict(root_source).get("xml").get("file")
tree_target = ET.parse(zf_target.open(target.split("/")[-1][:-3] + "xml"))
root_target = tree_target.getroot()
target_talks = et_to_dict(root_target).get("xml").get("file")
source_ids = [talk.get("head")[0].get("talkid") for talk in source_talks]
target_ids = [talk.get("head")[0].get("talkid") for talk in target_talks]
except Exception as pe:
logger.warning(f"ERROR: {pe}")
logger.warning(
f"This likely means that you have a malformed XML file!\nEither {source} or {target}\n"
)
source_ids = list()
target_ids = list()
else:
logger.warning(f"File doesn't exist {source} or {target}")
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
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