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  • Slovene Conformer CTC BPE E2E Automated Speech Recognition model RSDO-DS2-ASR-E2E 2.0

    This Conformer CTC BPE E2E Automated Speech Recognition model was trained following the NVIDIA NeMo Conformer-CTC recipe (for details see the official NVIDIA NeMo NMT documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/intro.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for transcribing Slovene speech to text. The training, development and test datasets were based on the Artur dataset and consisted of 630.38, 16.48 and 15.12 hours of transcribed speech in standardised form, respectively. The model was trained for 200 epochs and reached WER 0.0429 on the development and WER 0.0558 on the test dataset.

  • Polish Speech Services

    This archive contains the source code and configuration of the speech tools web service available at http://mowa.clarin-pl.eu/mowa. The services provided include: + speech to text alignemnt + speaker diarization + speech transcription + speech activity detection and noise classification + keyword spotting

  • Yfirlestur Word 22.10

    Yfirlestur Word is the source code for a spelling and grammar correction add-on for Icelandic, for use with Microsoft Word. The plugin provides error annotation and replacement, based on user interaction. The source code is intended for third party development and can be installed and tested locally using Node.js. The plugin requires third party correction software for its functionality. For development and testing, the open-access Yfirlestur.is API produced by Miðeind was used (see: https://github.com/icelandic-lt/Yfirlestur)) but is not intended for production use. This software is licensed under the MIT License. More information at https://github.com/icelandic-lt/Yfirlestur-Word.

  • Prep for Adventure: A game for the acquisition of English prepositions

    The presented game is designed to teach the six most frequent English prepositions (to, of, in, for, on, and with) at the A1 to A2 levels of proficiency. Prep for Adventure is a single-player game comprised of five separate tasks – jumping puzzle, cooking, town maze, lighting the goblets, and a banter with a classmate. Their mechanics are then combined in the final task (The Final Fight) to elicit the correct responses of the subject. The language used in the game is adjusted for the subjects’ level of proficiency, the game is fully voiced and offers a degree of customization. All tasks are based on the gap-filling type of exercise where subjects have to complete a sentence with a missing word, either by typing it in or via different kinds of multiple-choice formats. The game is designed to advance the subjects’ performance in prepositional structures by exposing players to as much input as possible. The length of one average playthrough is approximately 30-45 minutes. The game was created in the RPG Maker MV engine where RPG stands for role-playing game, which is a genre of a game in which the player adopts a role/roles of a fictional character/characters in a (partly or fully) invented setting. The game story: The Grammar School of Witchcraft has been taken over by the Evil Preposition Magician and the player is trying to win their school back alongside with a young witch named Morphologina (the player’s guide).

  • Slovene Text Denormalizator RSDO-DS2-DENORM 1.0

    This Text Denormalisator converts Slovene spoken-form text into written-form text. Typically it is used as a post-processing step in Automatic Speech Recognition, which traditionally outputs spoken-form text. As input it accepts text in either string form, list of tokens, or a list of dictionaries with a mandatory "text" field. The output is a dictionary. Example of use: denormalize("Danes, osmega sedmega dva tisoč dvaindvajset, je lep sončen dan, saj je zunaj prijetnih petindvajset stopinj Celzija.") {'denormalized_content': [{'text': 'Danes', 'index': [0]}, {'text': ',', 'index': [1]}, {'text': '8.', 'index': [2]}, {'text': '7.', 'index': [3]}, {'text': '2022', 'index': [4, 5, 6]}, {'text': ',', 'index': [7]}, {'text': 'je', 'index': [8]}, {'text': 'lep', 'index': [9]}, {'text': 'sončen', 'index': [10]}, {'text': 'dan', 'index': [11]}, {'text': ',', 'index': [12]}, {'text': 'saj', 'index': [13]}, {'text': 'je', 'index': [14]}, {'text': 'zunaj', 'index': [15]}, {'text': 'prijetnih', 'index': [16]}, {'text': '25', 'index': [17]}, {'text': '°C', 'index': [18, 19]}, {'text': '.', 'index': [20]}], 'denormalized_string': 'Danes, 8. 7. 2022, je lep sončen dan, saj je zunaj prijetnih 25 °C.'}

  • WordnetLoom 2.0

    WordneLoom 2.0 executable files for plWordnet 4.0. Source code available at https://github.com/CLARIN-PL/WordnetLoom WordnetLoom – is an wordnet editor application built for the needs of the construction of a the largest Polish wordnet called plWordNet. WordnetLoom provides two means of interaction: a form-based, implemented initially, and a visual, graph-based introduced recently. The visual, graph-based interactive presentation of the wordnet structure enables browsing and its direct editing on the structure of lexico-semantic relations and synsets. WordnetLooms works in a distributed environment, i.e. several linguists can work simulanuously from different sites on the same central database.

  • Dependency tree extraction tool STARK 1.0

    STARK is a python-based command-line tool for extraction of dependency trees from parsed corpora, aimed at corpus-driven linguistic investigations of syntactic phenomena of various kinds. It supports the CONLL-U format (https://universaldependencies.org/format.html) as input and returns a list of all relevant dependency trees, frequencies, and other associated information in the form of a tab-separated .tsv file. For installation, execution and the description of various user-defined parameter settings, see the official project page at: https://gitea.cjvt.si/lkrsnik/STARK. This entry corresponds to commit 421f12cac6 in the Git repository.

  • Colloc -- A Tool for Automatic Identification of Multiword Expressions

    Colloc -- a tool for automatic identification of multiword expressions (MWE) is freely available for online use at http://resursai.mwe.lt/atpazintuvas. As material for training DELFI.lt corpus (http://tekstynas.mwe.lt/) was used. For identification combination of 2 trained models (RNN bi-LSTM and CRF) is used. Automatically identified MWE can be retrieved in 2 formats -- list of MWE or / and text with annotated MWE.

  • Rule-based g2p for Icelandic

    Manually developed grapheme-to-phoneme (g2p) transcription rules for Icelandic, written in Thrax grammar syntax. The rules are for the standard Icelandic pronunciation, the northern variation, the north-eastern variation and the south pronunciation variation. The package also contains a command line tool in C++. Handskrifaðar hljóðritunarreglur fyrir íslensku, skrifaðar í Thrax. Reglurnar eru skrifaðar fyrir hefðbundinn íslenskan framburð, fyrir harðmæli, raddaðan framburð og hv-framburð. Skipanalínutól skrifað í C++ fylgir.