NbAiLab/nb-wav2vec2-1b-nynorsk
automatic-speech-recognition model by NbAiLab
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Model card & documentation
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This model is finetuned on top of feature extractor XLS-R from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model: This is one of several Wav2Vec-models our team created during the 🤗 hosted Robust Speech Event. This is the complete list of our models and their final scores: In parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in 🤗 Dataset format and used that as the main source for training. We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition…
Read the full model card ↗ · Preview checked 2026-10-30T07:08:27.030Z
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- Norwegian Wav2Vec2 Model - 1B Nynorsk
- Model description
- Dataset
- Code
- Team
- Training procedure
- Language Model
- Parameters
- Citation
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Model overview
NbAiLab/nb-wav2vec2-1b-nynorsk is a automatic-speech-recognition model repository published by NbAiLab on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- automatic-speech-recognition
- Library
- transformers
- Recent downloads (30 days)
- 870,696
- Cumulative likes
- 4
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 962,540,962
- Architecture
- Wav2Vec2ForCTC
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-06-09T14:37:24.000Z
- Last modified
- 2024-12-27T15:19:56.000Z
Compatibility and lineage
Reported languages: nn, no
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:32.899Z. Daily imports are snapshots, not real-time monitoring.
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