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• Samrómur-Adolescents Kaldi Recipe 22.06

  2 resources

  The "Samrómur-Adolescents Kaldi Recipe 22.06" is a code recipe intended to show how to integrate the adolescent portion of the corpus "Samrómur Children's Icelandic Speech Data 21.09" [1] and the "Icelandic Language Models with Pronunciations 22.01" [2] to create automatic speech recognition systems using the Kaldi toolkit [3].

  Use "Samrómur-Adolescents Kaldi Recipe 22.06"

• Samrómur DeepSpeech Recipe 22.06

  2 resources

  The "Samrómur DeepSpeech Recipe 22.06" is a code recipe intended to show how to integrate the corpus "Samromur 21.05" [1] and the "DeepSpeech Scorer for Icelandic 22.06" [2] to create automatic speech recognition systems using the Mozilla's DeepSpeech recognizer [3].

  Use "Samrómur DeepSpeech Recipe 22.06"

• GlowTTS models for Talrómur1 (22.10)

  6 resources

  This release contains GlowTTS models for four different voices from the Talrómur 1 [1] corpus. The models were trained using the Coqui TTS library after it was adapted for Icelandic. Included is the model, model configuration, log file for the training and the recipe used for each model. Þessi útgáfa inniheldur þjálfuð GlowTTS módel fyrir fjórar mismunandi raddir úr Talrómur 1 [1] gagnasafninu. Módelin voru þjálfuð með Coqui TTS verkfærakistunni sem búið var að aðlaga fyrir íslensku. Innifalið fyrir hverja rödd er módelið, skjal með stillingum á módelinu, þjálfunarsaga og forskriftin sem var notuð. [1] http://hdl.handle.net/20.500.12537/104

  Use "GlowTTS models for Talrómur1 (22.10)"

• Samrómur NeMo Recipe 22.06

  2 resources

  The "Samrómur NeMo Recipe 22.06" is a code recipe intended to show how to integrate the corpus "Samromur 21.05" [1] and the "6-GRAM Language Model in Icelandic for NeMo (Binary Format) 22.06" [2] to create automatic speech recognition systems using the NVIDIA-NeMo framework [3].

  Use "Samrómur NeMo Recipe 22.06"

• IceEval - Icelandic Natural Language Processing Benchmark 22.09

  1 resources

  IceEval is a benchmark for evaluating and comparing the quality of pre-trained language models. The models are evaluated on a selection of four NLP tasks for Icelandic: part-of-speech tagging (using the MIM-GOLD corpus), named entity recognition (using the MIM-GOLD-NER corpus), dependency parsing (using the IcePaHC-UD corpus) and automatic text summarization (using the IceSum corpus). IceEval includes scripts for downloading the datasets, splitting them into training, validation and test splits and training and evaluating models for each task. The benchmark uses the Transformers, DiaParser and TransformerSum libraries for fine-tuning and evaluation. IceEval er tól til að meta og bera saman forþjálfuð mállíkön. Líkönin eru metin á fjórum máltækniverkefnum fyrir íslensku: mörkun (með MIM-GOLD málheildinni), nafnakennslum (með MIM-GOLD-NER málheildinni), þáttun (með IcePaHC-UD málheildinni) og sjálfvirkri samantekt (með IceSum málheildinni). IceEval inniheldur skriftur til að sækja gagnasöfnin, skipta þeim í þjálfunar- og prófunargögn og að fínstilla og meta líkön fyrir hvert verkefni. Transformers, DiaParser og TransformerSum forritasöfnin eru notuð til að fínstilla líkönin.

  Use "IceEval - Icelandic Natural Language Processing Benchmark 22.09"