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@@ -43,7 +43,7 @@ This is one of several Wav2Vec-models our team created during the 🤗 hosted [R
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  | NbAiLab/nb-wav2vec2-300m-bokmaal (this model) | 7.03 | |
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  | [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) | 12.22 | |
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  ## Dataset
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- In parallell with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://huggingface.co/datasets/NbAiLab/NPSC) to the 🤗 Dataset format and used that as the main source for training.
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  ## Code
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  We do release all code developed during the event so that the Norwegian NLP community can build upon this to develop even better Norwegian ASR models. The finetuning of these models are not very compute demanding. You should after following the instructions here, be able to train your own automatic speech recognition system in less than a day with an average GPU.
 
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  | NbAiLab/nb-wav2vec2-300m-bokmaal (this model) | 7.03 | |
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  | [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) | 12.22 | |
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  ## Dataset
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+ In parallell with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
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  ## Code
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  We do release all code developed during the event so that the Norwegian NLP community can build upon this to develop even better Norwegian ASR models. The finetuning of these models are not very compute demanding. You should after following the instructions here, be able to train your own automatic speech recognition system in less than a day with an average GPU.