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  • Multi-speaker GlowTTS model for Talrómur 2 (prerelease) (22.10)

    This release includes a partially trained multi-speaker model using the GlowTTS architecture in the Coqui TTS library [1]. The model is trained on all of the speakers in the Talrómur 2 [2] corpus. The release includes the model, training log, model configuration file and the recipe used to train the model. The model included here is the best model available during the training at the time of publishing. At run time it is possible to choose any of the voices to produce a similar sounding synthesized voice. Þessi útgáfa inniheldur módel þjálfað á mörgum röddum með notkun GlowTTS nálgunarinnar í Coqui TTS verkfærakistunni [1]. Módelið er þjálfað á öllum röddum í Talrómur 2 [2] gagnasafninu. Innifalið í pakkanum er módelið, þjálfunarsaga, skjal með stillingum fyrir módelið og forskriftin sem var notuð til að þjálfa módelið. Módelið sem er hér inni er besta módelið í þjálfunarferlinu á þeim tíma sem þetta er gefið út. Þegar módelið er keyrt er hægt að velja hvaða rödd sem er úr Talrómur 2 gagnasafninu til að búa til upptöku með sambærilegri rödd. [1] https://github.com/cadia-lvl/coqui-ai-TTS/releases/tag/M9 [2] http://hdl.handle.net/20.500.12537/167

  • RÚV-DI Speaker Diarization v5 models (21.05)

    English This archive contains files generated from the recipe in kaldi-speaker-diarization/v5/. Its contents should be placed in a similar directory type, with symbolic links to diarization/, sid/, steps/, etc. It was created when Kaldi's master branch was at git commit 321d3959dabf667ea73cc98881400614308ccbbb. v5 These models are trained on the Althingi Parliamentary Speech corpus available on malfong.is. It uses MFCCS, x-vectors, PLDA and AHC. The recipe uses the Icelandic Rúv-di corpus as two hold out sets for tuning parameters. The Icelandic Rúv-di corpus is currently not publicly available. Íslenska Þetta skjalasafn inniheldur skrár frá kaldi-speaker-diarization v5. Innihaldi skjalasafnsins ætti að setja í eins möppu, með hlekki (symlinks) á diarization, sid, steps, o.s.frv. Notast var við Kaldi af master grein og Git commit 321d3959dabf667ea73cc98881400614308ccbbb. v5 Þessi líkön eru þjálfuð á gagnasafninu Alþingisræður til talgreiningar sem er aðgengilegt á malfong.is. Þau nota MFCC, x-vigra, PLDA, og AHC. Uppskriftin notar RÚV-di gagnasafnið sem hold-out gagnasöfn til að stilla forsendur. Eins og er þá er RÚV-di gagnasafnið ekki aðgengilegt almenningi.

  • Universal Dependencies 2.4 Models for UDPipe (2019-05-31)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 90 treebanks of 60 languages of Universal Depenencies 2.4 Treebanks, created solely using UD 2.4 data (http://hdl.handle.net/11234/1-2988). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_24_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

  • TimeAssign

    TimeAssign is a program which recognizes temporal expressions and assigns TimeML labels to words in Polish text using a Bi-LSTM based neural net and wordform embeddings.

  • The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian 1.1

    The model for lemmatisation of standard Serbian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~97.9. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.

  • The CLASSLA-Stanza model for lemmatisation of standard Serbian 2.1

    The model for lemmatisation of standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) combined with the Serbian non-standard training corpus ReLDI-NormTagNER-sr (http://hdl.handle.net/11356/1794) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on a combination of the standard (SETimes.SR) and non-standard (ReLDI-NormTagNER-sr) Serbian training corpora.

  • Universal Dependencies 2.5 Models for UDPipe (2019-12-06)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 94 treebanks of 61 languages of Universal Depenencies 2.5 Treebanks, created solely using UD 2.5 data (http://hdl.handle.net/11234/1-3105). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_25_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

  • Slovenian RoBERTa contextual embeddings model: SloBERTa 2.0

    The monolingual Slovene RoBERTa (A Robustly Optimized Bidirectional Encoder Representations from Transformers) model is a state-of-the-art model representing words/tokens as contextually dependent word embeddings, used for various NLP tasks. Word embeddings can be extracted for every word occurrence and then used in training a model for an end task, but typically the whole RoBERTa model is fine-tuned end-to-end. SloBERTa model is closely related to French Camembert model https://camembert-model.fr/. The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on https://github.com/clarinsi/Slovene-BERT-Tool Compared with the previous version (1.0), this version was trained for further 61 epochs (v1.0 37 epochs, v2.0 98 epochs), for a total of 200,000 iterations/updates. The released model here is a pytorch neural network model, intended for usage with the transformers library https://github.com/huggingface/transformers (sloberta.2.0.transformers.tar.gz) or fairseq library https://github.com/pytorch/fairseq (sloberta.2.0.fairseq.tar.gz)

  • The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.1

    The model for lemmatisation of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and using the Sloleks inflectional lexicon (http://hdl.handle.net/11356/1230). The estimated F1 of the lemma annotations is ~99.0. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.