readme: ---
  language:
  - sv
  license: apache-2.0
  tags:
  - i-dont-know-what-im-doing
  - generated_from_trainer
  datasets:
  - fimster/NST_small_whisper
  metrics:
  - wer
  model-index:
  - name: Whisper Small sv-SE NST - Lab 2
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: NST Swedish ASR
        type: fimster/NST_small_whisper
        config: speech
        split: None
        args: 'config: speech, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 10.167794316644112
  ---
  
  <!-- 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 Small sv-SE NST - Lab 2
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NST Swedish ASR dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1305
  - Wer: 10.1678
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.1635        | 0.67  | 1000 | 0.1694          | 13.4993 |
  | 0.07          | 1.33  | 2000 | 0.1431          | 11.3802 |
  | 0.0597        | 2.0   | 3000 | 0.1302          | 10.4682 |
  | 0.0193        | 2.67  | 4000 | 0.1305          | 10.1678 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  