readme: ---
  language:
  - sv
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_11_0
  - babelbox/babelbox_voice
  - google/fleurs
  model-index:
  - name: Whisper Medium Swedish
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: sv-SE
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 9.89
  ---
  
  
  # Whisper Medium Swedish
  
  This model is a fine-tuned version of [Whisper Medium Nordic](https://huggingface.co/marinone94/whisper-medium-nordic) on the [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation), the [babelbox/babelbox_voice](https://huggingface.co/datasets/babelbox/babelbox_voice) (NST SV - train split) and the [google/fleurs](https://huggingface.co/datasets/google/fleurs) (sv_se - train+validation+test) datasets.
  It achieves the following results on the evaluation set:
  - eval_loss: 0.2483
  - eval_wer: 9.8914
  - eval_runtime: 2924.8709
  - eval_samples_per_second: 1.733
  - eval_steps_per_second: 0.108
  
  ## 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
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 250
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  
  ### WandB run
  https://wandb.ai/pn-aa/whisper/runs/z2lzjx4x?workspace=user-emilio_marinone
  