readme: ---
  language:
  - nb-NO
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - false
  - nb-NO
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
  datasets:
  - NbAiLab/NPSC
  model-index:
  - name: XLS-R-300M-LM - Norwegian
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: NPSC
        type: NbAiLab/NPSC
      metrics:
      - name: Eval WER
        type: wer
        value: 15.4
      - name: Eval CER
        type: cer
        value: 5.48
  ---
  
  
  # XLS-R-300M-LM - Norwegian
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Norwegian [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) dataset.
   
  ### Scores without Language Model
  Without using a language model, it achieves the following scores on the NPSC Eval set
  It achieves the following results on the evaluation set without a language model:
  - WER: 0.2110
  - CER: 0.0622
  
  ### Scores with Language Model
  A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.
  
  - WER: 0.1540
  - CER: 0.0548
  
  ## Team
  The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.
  
  ## Model description
  This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH).
  
  ## Intended uses & limitations
  Demo version only. The model will be updated later this week.
  
  ## Training and evaluation data
  The model is trained and evaluated on [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC). Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..
  
  ## Training procedure
  ### Training hyperparameters
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
  - mixed_precision_training: Native AMP
  
  
  