readme: ---
  language:
  - no
  license: apache-2.0
  tags:
  - whisper-event
  - norwegian
  datasets:
  - NbAiLab/NCC_S
  - NbAiLab/NPSC
  - NbAiLab/NST
  metrics:
  - wer
  model-index:
  - name: Whisper Small Norwegian Bokmål
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: FLEURS
        type: google/fleurs
        config: nb_no
        split: validation
        args: nb_no
      metrics:
      - name: Wer
        type: wer
        value: 15.56
  ---
  
  # Whisper Small Norwegian Bokmål
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) trained on several datasets.
  
  It is currently in the middle of a large training. Currently it achieves the following results on the evaluation set:
  - Loss: 0.3230
  - Wer: 15.56
  
  ## Model description
  
  The model is trained on a large corpus of roughly 5.000 hours of voice. The sources are subtitles from the Norwegian broadcaster NRK, transcribed speeches from the Norwegian parliament and voice recordings from Norsk Språkteknologi. 
  
  ## Intended uses & limitations
  
  The model will be free for everyone to use when it is finished.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-06
  - train_batch_size: 128
  - gradient_accumulation_steps: 2
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant with warmup
  - lr_scheduler_warmup_steps: 1000
  - training_steps: 50.000 (currently @1.000)
  - mixed_precision_training: fp16
  - deepspeed: true
  
  ### Live Training results
  See [Tensorboad Metrics](https://huggingface.co/NbAiLab/whisper-small-nob/tensorboard)
  
  
  
  
  