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  • Project: Development of Slovene in a Digital Environment
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  • NeMo Punctuation and Capitalisation service RSDO-DS2-P&C-API 1.0

    Punctuation and Capitalisation service for NeMo models. For more details about building such models, see the official NVIDIA NeMo documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/punctuation_and_capitalization.html) and NVIDIA NeMo GitHub (https://github.com/NVIDIA/NeMo). A model for punctuation and capitalisation restoration in lowercased non-punctuated Slovene text can be downloaded from http://hdl.handle.net/11356/1735. The service accepts as input either a single string or list of strings for which punctuation and capitalisation should be restored. The result will be in the same format as the request, either a single string or list of strings. The maximal accepted text length is 5000c. Note that punctuation and capitalization of one 5000c text block on cpu will take advantage of all available cores and may take ~30s (on a system with 24 vCPU). See the service README.md for further details.

  • Q-CAT Corpus Annotation Tool 1.5

    The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system. Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations) Version 1.5 supports listening to audio recordings (provided in the # sound_url comment line in CONLL-U)

  • Face-domain-specific automatic speech recognition models

    This entry contains all the files required to implement face-domain-specific automatic speech recognition (ASR) applications using the Kaldi ASR toolkit (https://github.com/kaldi-asr/kaldi), including the acoustic model, language model, and other relevant files. It also includes all the scripts and configuration files needed to use these models for implementing face-domain-specific automatic speech recognition. The acoustic model was trained using the relevant Kaldi ASR tools (https://github.com/kaldi-asr/kaldi) and the Artur speech corpus (http://hdl.handle.net/11356/1776; http://hdl.handle.net/11356/1772). The language model was trained using the domain-specific text data involving face descriptions obtained by translating the Face2Text English dataset (https://github.com/mtanti/face2text-dataset) into the Slovenian language. These models, combined with other necessary files like the HCLG.fst and decoding scripts, enable the implementation of face-domain-specific ASR applications. Two speech corpora ("test" and "obrazi") and two Kaldi ASR models ("graph_splosni" and "graph_obrazi") can be selected for conducting speech recognition tests by setting the variable "graph" and "test_sets" in the "local/test_recognition.sh" script. Acoustic speech features can be extracted and speech recognition tests can be conducted using the "local/test_recognition.sh" script. Speech recognition test results can be obtained using the "results.sh" script. The KALDI_ROOT environment variable also needs to be set in the script "path.sh" to set the path to the Kaldi ASR toolkit installation folder.

  • Q-CAT Corpus Annotation Tool 1.4

    The Q-CAT (Querying-Supported Corpus Annotation Tool) is a tool for manual linguistic annotation of corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system. Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI). Version 1.4 introduces new features in command line mode (filtering by sentence ID, multiple link type visualizations)

  • NeMo Neural Machine Translation service RSDO-DS4-NMT-API 1.0

    Neural Machine Translation service for NeMo AAYN Base models. For more details about building such models, see the official NVIDIA NeMo documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/machine_translation/machine_translation.html) and NVIDIA NeMo GitHub (https://github.com/NVIDIA/NeMo). A model for language pair SL-EN can be downloaded from http://hdl.handle.net/11356/1736. The service accepts the source language and target language, and either a single string or list of strings to be translated. The result will be in the same format as the request, either as a single string or list of strings. The maximal accepted text length is 5000c. Note that transcription of one 5000c text block on cpu will take advantage of all available cores, consume up to 3GB RAM and may take ~200s (on a system with 24 vCPU). See the service README.md for further details.

  • Slovenian commonsense reasoning model SloMET-ATOMIC 2020

    The SloMET-ATOMIC 2020 is a Slovene commonsense reasoning model that is able to predict commonsense descriptions in a natural language for a given input sentence. The model is an adaptation of the Slovene GPT-2 model (https://huggingface.co/cjvt/gpt-sl-base) that has been finetuned using the SloATOMIC 2020 corpus (http://hdl.handle.net/11356/1724), consisting of 1.33M everyday interence knowledge tuples about entities and events. The released model is a pytorch neural network model, intended for usage with the transformers library (https://github.com/huggingface/transformers).

  • NeMo Conformer CTC BPE E2E Automated Speech Recognition service RSDO-DS2-ASR-E2E-API 1.1

    Automated Speech Recognition service for NeMo Conformer CTC BPE E2E models. For more details about building such models, see the official NVIDIA NeMo documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/intro.html) and NVIDIA NeMo GitHub (https://github.com/NVIDIA/NeMo). A model for automated speech recognition of Slovene speech can be downloaded from http://hdl.handle.net/11356/1740. The service accepts as input audio files in WAV 16kHz, 16bit PCM, mono format. The maximal accepted audio duration is 300s. Note that transcription of one 300s audio file on cpu will take advantage of all available cores, consume up to 16GB RAM and may take ~180s (on a system with 24 vCPU). See the service README.md for further details.

  • Q-CAT Corpus Annotation Tool 1.3

    The Q-CAT (Querying-Supported Corpus Annotation Tool) is a computational tool for manual annotation of language corpora, which also enables advanced queries on top of these annotations. The tool has been used in various annotation campaigns related to the ssj500k reference training corpus of Slovenian (http://hdl.handle.net/11356/1210), such as named entities, dependency syntax, semantic roles and multi-word expressions, but it can also be used for adding new annotation layers of various types to this or other language corpora. Q-CAT is a .NET application, which runs on Windows operating system. Version 1.1 enables the automatic attribution of token IDs and personalized font adjustments. Version 1.2 supports the CONLL-U format and working with UD POS tags. Version 1.3 supports adding new layers of annotation on top of CONLL-U (and then saving the corpus as XML TEI).

  • Slovene Punctuation and Capitalisation model RSDO-DS2-P&C 3.6

    This Punctuation and Capitalisation model was trained following the NVIDIA NeMo Punctuation and Capitalisation recipe (for details see the official NVIDIA NeMo P&C documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/punctuation_and_capitalization.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for restoring punctuation (,.!?) and capital letters in lowercased non-punctuated Slovene text. The training corpus was built from publicly available datasets, as well as a small portion of proprietary data. In total the training corpus consisted of 38.829.529 sentences and the validation corpus consisted of 2.092.497 sentences.

  • Slovene Text Normalizator RSDO-DS2-NORM 1.0

    This Text Normalisator converts Slovene text from written-form into its spoken-form. Traditionally it is an essential preprocessing step before text-to-speech (TTS). As input it accepts text as a string, and returns a dictionary with fields "input_text", "normalised_text", "status" and "logs". Example: normalize_text("Sodobna definicija Celzijeve temperaturne lestvice, ki velja od leta 1954, je, da je temperatura trojne točke vode enaka 0,01 °C.") {'input_text': 'Sodobna definicija Celzijeve temperaturne lestvice, ki velja od leta 1954, je, da je temperatura trojne točke vode enaka 0,01 °C.', 'normalized_text': 'Sodobna definicija Celzijeve temperaturne lestvice, ki velja od leta tisoč devetsto štiriinpetdeset, je, da je temperatura trojne točke vode enaka nič celih nič ena stopinje Celzija.', 'status': 1, 'logs': [('1954', 'tisoč devetsto štiriinpetdeset'), ('0,01', 'nič celih nič ena'), ('°C', 'stopinje Celzija')]} For further details see README.md.