readme: ---
  language:
    - sv
  license: apache-2.0
  tags:
  - hf-asr-leaderboard
  - generated_from_trainer
  - whisper-event
  datasets:
  - common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Medium Sv
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 sv-SE
        type: mozilla-foundation/common_voice_11_0
        config: sv-SE
        split: test
        args: sv-SE
      metrics:
      - name: Wer
        type: wer
        value: 10.712174146734748
  ---
  
  <!-- 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. -->
  
  # openai/whisper-medium
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on 
  NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set:
  - Loss: 0.2636
  - Wer: 10.7122
  
  ## 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: 32
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0746        | 0.2   | 1000 | 0.2904          | 13.4695 |
  | 0.0564        | 0.4   | 2000 | 0.3121          | 13.2384 |
  | 0.0532        | 0.6   | 3000 | 0.2862          | 11.9726 |
  | 0.0387        | 0.8   | 4000 | 0.2629          | 11.6931 |
  | 0.0279        | 1.14  | 5000 | 0.2636          | 10.7122 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  