readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - dataset/riksdagen
  metrics:
  - wer
  model-index:
  - name: whisper-tiny-sv
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: dataset/riksdagen audiofolder
        type: dataset/riksdagen
        config: audiofolder
        split: train
        args: audiofolder
      metrics:
      - name: Wer
        type: wer
        value: 0.3700987201570632
  ---
  
  <!-- 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-sv
  
  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the dataset/riksdagen audiofolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6435
  - Wer: 0.3701
  
  ## 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: 32
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - total_train_batch_size: 128
  - total_eval_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - training_steps: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 1.0032        | 0.08  | 250  | 1.0075          | 0.5063 |
  | 0.8983        | 0.17  | 500  | 0.8945          | 0.4649 |
  | 0.8227        | 0.25  | 750  | 0.8336          | 0.4491 |
  | 0.777         | 0.33  | 1000 | 0.7931          | 0.4314 |
  | 0.7728        | 0.42  | 1250 | 0.7640          | 0.4217 |
  | 0.7141        | 0.5   | 1500 | 0.7407          | 0.4134 |
  | 0.7208        | 0.58  | 1750 | 0.7225          | 0.4023 |
  | 0.6911        | 0.66  | 2000 | 0.7083          | 0.3942 |
  | 0.6924        | 0.75  | 2250 | 0.6948          | 0.3911 |
  | 0.6702        | 0.83  | 2500 | 0.6849          | 0.3884 |
  | 0.663         | 0.91  | 2750 | 0.6766          | 0.3769 |
  | 0.6548        | 1.0   | 3000 | 0.6686          | 0.3759 |
  | 0.638         | 1.08  | 3250 | 0.6627          | 0.3728 |
  | 0.6222        | 1.16  | 3500 | 0.6574          | 0.3733 |
  | 0.6323        | 1.25  | 3750 | 0.6528          | 0.3691 |
  | 0.6192        | 1.33  | 4000 | 0.6498          | 0.3688 |
  | 0.633         | 1.41  | 4250 | 0.6469          | 0.3677 |
  | 0.6229        | 1.5   | 4500 | 0.6451          | 0.3681 |
  | 0.6246        | 1.58  | 4750 | 0.6439          | 0.3706 |
  | 0.6214        | 1.66  | 5000 | 0.6435          | 0.3701 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.12.0a0+8a1a93a
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  