The segment of Československý zvukový týdeník Aktualita (Czechoslovak Aktualita Sound Newsreel), 1938, issue no. 52 promotes a collection of toys and books for the children of Sudetenland refugees. The footage shows parts of the collection organised by Pilsen soldiers under the leadership of Jan Klouda, an engineer and doctor of technical sciences.
The representative full-text digitalized HetWiK corpus is composed of 140 manually annotated texts of the German Resistance between 1933 and 1945. This includes both well-known and relatively unknown documents, public writings, like pamphlets or memoranda, as well as private texts, e.g. letters, journal or prison entries and biographies. Thus the corpus represents the diverse groups as well as the heterogeneity of verbal resistance and allows the study of resistance in relation to the language usage.
The HetWiK corpus can be used free of charge. A detailed register of the individual texts and further information about the tagset can be found on the project-homepage (german). In addition to the CATMA5 XML-format we provide a standoff-JSON format and CEC6-Files (CorpusExplorer) - so you can export the HetWiK corpus in different formats.
This text corpus contains a carefully optimized set of sentences that could be used in the process of preparing a speech corpus for the development of personalized text-to-speech system. It was designed primarily for the voice conservation procedure that must be performed in a relatively short period before a person loses his/her own voice, typically because of the total laryngectomy.
Total laryngectomy is a radical treatment procedure which is often unavoidable to save life of patients who were diagnosed with severe laryngeal cancer. In spite of being very effective with respect to the primary treatment, it significantly handicaps the patients due to the permanent loss of their ability to use voice and produce speech. Luckily, the modern methods of computer text-to-speech (TTS) synthesis offer a possibility for "digital conservation" of patient's original voice for his/her future speech communication -- a procedure called voice banking or voice conservation. Moreover, the banking procedure can be undertaken by any person facing voice degradation or loss in farther future, or who is simply is willing to keep his/her voice-print.
HindEnCorp parallel texts (sentence-aligned) come from the following sources:
Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).
Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi.
EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages.
Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.

For the current release, we are extending the parallel corpus using these sources:
Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi.
TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available.
The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus.
Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files.
Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary. and LM2010013,
Data
----
Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
}
Data
----
Hindi Visual Genome 1.1 is an updated version of Hindi Visual Genome 1.0. The update concerns primarily the text part of Hindi Visual Genome, fixing translation issues reported during WAT 2019 multimodal task. In the image part, only one segment and thus one image were removed from the dataset.
Hindi Visual Genome 1.1 serves in "WAT 2020 Multi-Modal Machine Translation Task".
Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' consists of 1.4K segments and it was released for WAT2019 multi-modal task. The challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple
tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28930 143164 145448
Dev 998 4922 4978
Test 1595 7853 7852
Challenge Test 1400 8186 8639
------- --------- ---------------- -------------
Total 32923 164125 166917
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
volume={23},
number={4},
pages={1499--1505},
year={2019}
}
A Hindi corpus of texts downloaded mostly from news sites. Contains both the original raw texts and an extensively cleaned-up and tokenized version suitable for language modeling. 18M sentences, 308M tokens and FP7-ICT-2007-3-231720 (EuroMatrix Plus), 7E09003 (Czech part of EM+)
HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India
Languages
This is a collection of folksongs for 26 languages that form a dialect continuum in North India and nearby regions.
Namely Angika, Awadhi, Baiga, Bengali, Bhadrawahi, Bhili, Bhojpuri, Braj, Bundeli, Chhattisgarhi, Garhwali, Gujarati, Haryanvi, Himachali, Hindi, Kanauji, Khadi Boli, Korku, Kumaoni, Magahi, Malvi, Marathi, Nimadi, Panjabi, Rajasthani, Sanskrit.
This data is originally collected by the Kavita Kosh Project at http://www.kavitakosh.org/ . Here are the main characteristics of the languages in this collection:
- They are all Indic languages except for Korku.
- The majority of them are closely related to the standard Hindi dialect genealogically (such as Hariyanvi and Bhojpuri), although the collection also contains languages such as Bengali and Gujarati which are more distant relatives.
- They are all primarily spoken in (North) India (Bengali is also spoken in Bangladesh)
- All except Sanksrit are alive languages
Data
Categorising them by pre-existing available NLP resources, we have:
* Band 1 languages : Hindi, Panjabi, Gujarati, Bengali, Nepali. These languages already have other large standard datasets available. Kavita Kosh may have very little data for these languages.
* Band 2 languages: Bhojpuri, Magahi, Awadhi, Braj. These languages have growing interest and some datasets of a relatively small size as compared to Band 1 language resources.
* Band 3 languages: All other languages in the collection are previously zero-resource languages. These are the languages for which this dataset is the most relevant.
Script
This dataset is entirely in Devanagari. Content in the case of languages not written in Devanagari (such as Bengali and Gujarati) has been transliterated by the Kavita Kosh Project.
Format
The dataset contains a single text file containing folksongs per language. Folksongs are separated from each other by an empty line. The first line of a new piece is the title of the folksong, and line separation within folksongs is preserved.