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  • Project: Development of Slovene in a Digital Environment
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  • 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.'}

  • 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.

  • Q-CAT Corpus Annotation Tool 1.2

    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.