Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.zstandard filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+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
+licenses:
+- cc-by-nc-4-0
+multilinguality:
+- translation
+size_categories:
+- 100K< n<500k
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- machine-translation
+---
+
+# Dataset Card for Web Inventory of Transcribed & Translated(WIT) Ted Talks
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://wit3.fbk.eu/home
+- **Repository:** https://drive.google.com/file/d/1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z/view?usp=sharing
+- **Paper:** https://www.aclweb.org/anthology/2012.eamt-1.60.pdf
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Mauro Cettolo](mailto:cettolo@fbk.eu)
+[Roldano Cattoni](mailto:cattoni@fbk.eu)
+
+
+### Dataset Summary
+
+The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform. 
+
+To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
+E.g.
+
+`dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")`
+
+The full list of languages is: 'af', 'am', 'ar', 'arq', 'art-x-bork', 'as', 'ast', 'az', 'be', 'bg', 'bi', 'bn', 'bo', 'bs', 'ca', 'ceb', 'cnh', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fil', 'fr', 'fr-ca', 'ga', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hup', 'hy', 'id', 'ig', 'inh', 'is', 'it', 'ja', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lb', 'lo', 'lt', 'ltg', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'my', 'nb', 'ne', 'nl', 'nn', 'oc', 'pa', 'pl', 'ps', 'pt', 'pt-br', 'ro', 'ru', 'rup', 'sh', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'srp', 'sv', 'sw', 'szl', 'ta', 'te', 'tg', 'th', 'tl', 'tlh', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'zh', 'zh-cn', 'zh-tw'.
+
+The full list of years is: '2014', '2015', '2016'.
+
+### Supported Tasks and Leaderboards
+
+machine learning task, language modeling and generation
+
+### Languages
+
+Ted talks are mostly held in English (`en`). Almost all of the talks have been translated, by volunteers, into Arabic, Bulgarian, Chinese (simplified), French, Italian, Korean, Portuguese (Brazil) and Spanish. For about 70 other languages, the number of translated talks ranges from several hundreds (e.g. such as other Dutch, German, Hebrew, Romanian) to one (e.g. Hausa, Hupa, Bislama, Ingush, Maltese)
+
+## Dataset Structure
+
+### Data Instances
+
+One example from the dataset is:
+
+```
+{'translation': {'hi': 'जब मार्च २०१४ में इबोला का प्रकोप छाया, पर्डिस सबेटी और उनकी टीम को वाइरस के जीनोम का अनुक्रमण करना था, सीखना था कि यह कैसे परवतिर्त होते हैं और फैलते हैं। सबेटी ने तुरंत ही अपने अनुसंधान को वेब में जारी किया, ताकि दुनिया भर के वाइरस ट्रैकर्स और वैज्ञानिक इस तत्काल लड़ाई में शामिल हो सकें। इस बातचीत में, वह दिखाती हैं कि सबका सहयोग ही कुंजी है वाइरस को रोकने के लिए--और लड़ने के लिए आगे आने वाले हमलों से। सबेटी ने कहा,"हमने खुले तौर पर काम किया, साझा किया और साथ काम किया"। "हमे दुनिया को एक वाइरस के विनाश से नहीं, पर अरबों दिलों और दिमागों की एकता से परिभाषित करना है"।',
+  'nl': 'Toen Ebola in maart 2014 uitbrak, zijn Pardis Sabeti en haar team aan het werk gegaan om het genoom in kaart te brengen. Zo ontdekten ze hoe het virus zich verspreidde en muteerde. Sabeti zette direct haar onderzoek op het internet, zodat wereldwijd virus-jagers en wetenschappers mee konden werken aan de strijd. In deze talk laat ze zien hoe die openheid geholpen heeft bij het stoppen van het virus en hoe het kan helpen bij de strijd tegen het volgende virus. "We moesten transparant werken, delen en samenwerken". Sabeti zegt:"Laat de wereld niet ten onder gaan aan een virus, maar verlicht worden door miljoenen harten en geesten die samenwerken."'}}
+```
+
+The original XML files are formatted like this example:
+
+
+```
+<file id="1">
+  <head>
+    <url>http://www.ted.com/talks/ryan_holladay_to_hear_this_music_you_have_to_be_there_literally.html</url>
+    <pagesize>66634</pagesize>
+    <dtime>Sun Jan 12 15:17:32 CET 2014</dtime>
+    <content-type>text/html; charset=utf-8</content-type>
+    <encoding>utf-8</encoding>
+    <videourl>http://download.ted.com/talks/RyanHolladay_2013S.mp4</videourl>
+    <videopath>talks/RyanHolladay_2013S.mp4</videopath>
+    <transcription>
+      <seekvideo id="2939">(Music)</seekvideo>
+      <seekvideo id="7555">For any of you who have visited or lived in New York City,</seekvideo>
+      <seekvideo id="11221">these shots might start to look familiar.</seekvideo>
+      <seekvideo id="16116">This is Central Park,</seekvideo>
+      .
+      .
+      .
+      <seekvideo id="361992">for people to interact with</seekvideo>
+      <seekvideo id="363709">and experience music.</seekvideo>
+      <seekvideo id="365451">Thank you.</seekvideo>
+      <seekvideo id="367495">(Applause)</seekvideo>
+    </transcription>
+    <talkid>1903</talkid>
+    <title>Ryan Holladay: To hear this music you have to be there. Literally</title>
+    <description>The music industry ......segments of sounds that only play when a listener is physically nearby. (Filmed at TED@BCG.)</description>
+    <keywords>entertainment,music,technology</keywords>
+    <image>http://images.ted.com/images/ted/d98c17773da6f84e9f915895c270c7ffd2de3778_389x292.jpg</image>
+    <date>2014/01/12</date>
+    <wordnum>885</wordnum>
+    <charnum>5051</charnum>
+  </head>
+  <content>(Music) For any of you who have visited or lived in New York City, these shots might start to look familiar. This is Central Park, ............new ways for people to interact with and experience music. Thank you. (Applause)</content>
+</file>
+```
+
+### Data Fields
+The fields of the dataset are:
+- translation:
+  - <lang1>: text in <lang1>
+  - <lang2>L translated text in <lang2> 
+
+Information about the original data files:
+
+For each language, a single XML file is generated which includes all talks subtitled in 
+that language. Each talk is enclosed in tags `<file id="int">` and `</file>` and includes, among other tags: 
+
+| Tags | Description |
+|---|:---|
+| `<url>`| the address of the original HTML document of the talk |
+| `<speaker>` | the name of the talk speaker |
+| `<talkid>` | the numeric talk identifier |
+| `<transcript>` | talk subtitles split in captions |
+| `<date>` | the issue date of the talk |
+| `<content>` |  talk subtitles |
+
+
+### Data Splits
+
+The paper doesn't provide any specific train-test-dev splits. However data can be split by available years (2014, 2015, 2016)
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+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.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The talks were collected from the [Ted Conference website](http://www.ted.com/)
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+Translation has been contributed by volunteers
+
+### Personal and Sensitive Information
+
+No personal and sensitive information is provided in the dataset. All talks are publicly available
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+In statistical machine translation, large amount of in-domain parallel data are usually required to properly train translation and reordering models. With more than 900+ Ted talks (as of 2011) and translation in more than 90+ languages. This dataset provides a useful resource for the MT research community. 
+
+In turn, this enables easy access to a vast treasure trove of human knowledge. 
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+The original dataset was curated by:
+[Mauro Cettolo](mailto:cettolo@fbk.eu)
+[Roldano Cattoni](mailto:cattoni@fbk.eu)
+
+Author: 
+Christian Girardi
+
+For issues with the HuggingFace Dataset implementation, reach out: [Aakash Gupta](mailto:aakashg80@gmail.com)
+
+### Licensing Information
+
+cc-by-nc-4.0
+
+### Citation Information
+```
+@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",
+}
+
+```
+
+### Contributions
+
+Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.

dataset_infos.json

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ted_talks_iwslt.py

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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."""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+import xml.etree.ElementTree as ET
+import zipfile
+from collections import defaultdict
+
+import datasets
+
+
+# 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,
+            version=datasets.Version("1.1.0", ""),
+            **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)
+        for language_pair in _LANGUAGE_PAIRS
+        for year in _YEAR.keys()
+    ]
+
+    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self, language_pair=(None, None), year=None, **kwargs):
+
+        print(self.config.language_pair)
+        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]
+
+        # print(filepath)
+        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:
+                        logging.warning(f"ERROR: {pe}")
+                        logging.warning(
+                            f"This likely means that you have a malformed XML file!\nEither {source} or {target}\n"
+                        )
+                        source_ids = list()
+                        target_ids = list()
+        else:
+            print(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)
+        # print(translation)
+        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