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  • Slovene Conformer CTC BPE E2E Automated Speech Recognition model PROTOVERB-ASR-E2E 1.0

    This Conformer CTC BPE E2E Automated Speech Recognition model was trained following the NVIDIA NeMo Conformer-CTC fine-tuning 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 starting point was the Conformer CTC BPE E2E Automated Speech Recognition model RSDO-DS2-ASR-E2E 2.0, which was fine-tuned on the Protoverb closed dataset. The model was fine-tuned for 20 epochs, which improved the performance on the Protoverb test dataset for 9.8% relative WER, and for 3.3% relative WER on the Slobench dataset.

  • 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.

  • 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.

  • Neural Machine Translation model for Slovene-English language pair RSDO-DS4-NMT 1.2.6

    This Neural Machine Translation model for Slovene-English language pair was trained following the NVIDIA NeMo NMT AAYN recipe (for details see the official NVIDIA NeMo NMT documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/machine_translation/machine_translation.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for translating text written in Slovene language to English and vice versa. The training corpus was built from publicly available datasets, including Parallel corpus EN-SL RSDO4 1.0 (https://www.clarin.si/repository/xmlui/handle/11356/1457), as well as a small portion of proprietary data. In total the training corpus consisted of 32.638.758 translation pairs and the validation corpus consisted of 8.163 translation pairs. The model was trained on 64GPUs and on the validation corpus reached a SacreBleu score of 48.3191 (at epoch 37) for translation from Slovene to English and a SacreBleu score of 53.8191 (at epoch 47) for translation from English to Slovene.

  • 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.

  • 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.

  • 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.