readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - NbAiLab/NCC_S
  metrics:
  - wer
  model-index:
  - name: "Whisper Tiny Norwegian Bokm\xE5l"
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: NbAiLab/NCC_S
        type: NbAiLab/NCC_S
        config: 'no'
        split: validation
        args: 'no'
      metrics:
      - name: Wer
        type: wer
        value: 24.878197320341048
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Whisper Tiny Norwegian Bokmål
  
  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the NbAiLab/NCC_S dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5100
  - Wer: 24.8782
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-06
  - train_batch_size: 256
  - eval_batch_size: 64
  - 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: 100000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer     |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|
  | 1.8819        | 0.01  | 1000   | 1.1869          | 61.9671 |
  | 1.6425        | 0.02  | 2000   | 0.9991          | 53.6541 |
  | 1.548         | 0.03  | 3000   | 0.9147          | 50.2132 |
  | 1.4636        | 0.04  | 4000   | 0.8605          | 47.0767 |
  | 1.4113        | 0.05  | 5000   | 0.8253          | 45.7369 |
  | 1.3484        | 0.01  | 6000   | 0.7946          | 43.4531 |
  | 1.3127        | 0.02  | 7000   | 0.7740          | 42.2655 |
  | 1.2994        | 0.03  | 8000   | 0.7551          | 40.8952 |
  | 1.265         | 0.04  | 9000   | 0.7378          | 39.8599 |
  | 1.2458        | 0.05  | 10000  | 0.7257          | 39.8904 |
  | 1.2257        | 0.06  | 11000  | 0.7114          | 39.7990 |
  | 1.2126        | 0.07  | 12000  | 0.6972          | 37.8806 |
  | 1.1971        | 0.08  | 13000  | 0.6871          | 37.3021 |
  | 1.1786        | 1.01  | 14000  | 0.6786          | 37.4239 |
  | 1.1486        | 1.02  | 15000  | 0.6703          | 36.9976 |
  | 1.1505        | 1.03  | 16000  | 0.6647          | 36.3581 |
  | 1.1238        | 1.04  | 17000  | 0.6559          | 36.3886 |
  | 1.1184        | 1.05  | 18000  | 0.6509          | 36.5104 |
  | 1.115         | 1.06  | 19000  | 0.6452          | 35.9927 |
  | 1.1013        | 1.07  | 20000  | 0.6382          | 34.5006 |
  | 1.0969        | 1.08  | 21000  | 0.6331          | 34.3484 |
  | 1.0784        | 2.0   | 22000  | 0.6304          | 34.2875 |
  | 1.0774        | 2.01  | 23000  | 0.6249          | 34.1048 |
  | 1.0719        | 2.02  | 24000  | 0.6194          | 33.8307 |
  | 1.0638        | 2.03  | 25000  | 0.6158          | 32.9781 |
  | 1.0592        | 2.04  | 26000  | 0.6105          | 32.6431 |
  | 1.0493        | 2.05  | 27000  | 0.6041          | 32.7345 |
  | 1.047         | 2.06  | 28000  | 0.6040          | 32.7649 |
  | 1.0323        | 2.07  | 29000  | 0.5984          | 31.6078 |
  | 1.0189        | 3.0   | 30000  | 0.5957          | 31.3033 |
  | 1.0078        | 3.01  | 31000  | 0.5924          | 31.4251 |
  | 1.0146        | 3.02  | 32000  | 0.5940          | 31.3033 |
  | 1.0128        | 3.03  | 33000  | 0.5892          | 31.0292 |
  | 1.0025        | 3.04  | 34000  | 0.5873          | 31.1815 |
  | 0.999         | 3.05  | 35000  | 0.5838          | 30.6334 |
  | 1.0045        | 3.06  | 36000  | 0.5799          | 30.4202 |
  | 1.0005        | 3.07  | 37000  | 0.5770          | 30.1766 |
  | 1.0017        | 3.08  | 38000  | 0.5733          | 29.6590 |
  | 0.9878        | 4.01  | 39000  | 0.5745          | 30.2680 |
  | 0.9854        | 4.02  | 40000  | 0.5720          | 30.0548 |
  | 0.9624        | 4.03  | 41000  | 0.5703          | 29.5981 |
  | 0.9639        | 4.04  | 42000  | 0.5681          | 29.5067 |
  | 0.9569        | 4.05  | 43000  | 0.5679          | 29.6285 |
  | 0.9682        | 4.06  | 44000  | 0.5643          | 29.5676 |
  | 0.9539        | 4.07  | 45000  | 0.5601          | 29.5676 |
  | 0.946         | 4.08  | 46000  | 0.5562          | 29.7199 |
  | 0.9429        | 5.01  | 47000  | 0.5592          | 29.2935 |
  | 0.9462        | 5.02  | 48000  | 0.5540          | 29.0804 |
  | 0.9312        | 5.03  | 49000  | 0.5535          | 29.2935 |
  | 0.9462        | 5.04  | 50000  | 0.5536          | 28.6845 |
  | 0.922         | 5.05  | 51000  | 0.5539          | 28.7150 |
  | 0.9253        | 5.06  | 52000  | 0.5510          | 28.8368 |
  | 0.9065        | 0.01  | 53000  | 0.5493          | 28.5932 |
  | 0.9096        | 0.02  | 54000  | 0.549