readme: ---
  datasets:
  - coscan-speech2
  license: cc0-1.0
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: wav2vec2-large-voxrex-swedish-coscan-no-region
    results:
    - dataset:
        name: Coscan Speech
        type: NbAiLab/coscan-speech2
      metrics:
      - name: Test Accuracy on Coscan Speech
        type: accuracy
        value: 0.6155107552811807
      - name: Validation Accuracy on Coscan Speech
        type: accuracy
        value: 0.8773432861141742
      - name: Test F1 (micro) on Coscan Speech
        type: f1
        value: 0.6155107552811807
      - name: Validation F1 (micro) on Coscan Speech
        type: f1
        value: 0.8773432861141742
      task:
        name: Audio Classification
        type: audio-classification
  tags:
  - generated_from_trainer
  ---
  
  <!-- 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. -->
  
  # wav2vec2-large-voxrex-swedish-coscan-no-region
  
  This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex-swedish](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) on the coscan-speech2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0151
  - Accuracy: 0.8773
  - F1: 0.8773
  - Precision: 0.8773
  - Recall: 0.8773
  
  ## 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-05
  - train_batch_size: 16
  - 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_ratio: 0.1
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.1651        | 1.0   | 6468  | 0.5657          | 0.8650   | 0.8650 | 0.8650    | 0.8650 |
  | 0.1217        | 2.0   | 12936 | 0.9411          | 0.8487   | 0.8487 | 0.8487    | 0.8487 |
  | 0.0013        | 3.0   | 19404 | 0.9991          | 0.8617   | 0.8617 | 0.8617    | 0.8617 |
  | 0.0652        | 4.0   | 25872 | 1.0151          | 0.8773   | 0.8773 | 0.8773    | 0.8773 |
  | 0.0001        | 5.0   | 32340 | 1.1031          | 0.8700   | 0.8700 | 0.8700    | 0.8700 |
  
  
  ### Classification report on Coscan Speech (test set)
  
  ```
                           precision    recall  f1-score   support
  
  Bergen og Ytre Vestland       0.65      0.97      0.78      1809
       Hedmark og Oppland       0.12      0.06      0.08      2302
                 Nordland       0.97      0.47      0.63      2195
             Oslo-området       0.78      0.42      0.55      6957
                 Sunnmøre       0.94      0.71      0.81      2636
           Sør-Vestlandet       0.96      0.46      0.62      2860
                Sørlandet       0.62      0.81      0.70      2490
                    Troms       0.67      1.00      0.80      2867
                Trøndelag       0.52      0.94      0.67      2666
           Voss og omland       0.70      0.71      0.71      2641
           Ytre Oslofjord       0.20      0.49      0.29      1678
  
                 accuracy                           0.62     31101
                macro avg       0.65      0.64      0.60     31101
             weighted avg       0.68      0.62      0.61     31101
  
  ```
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 2.4.1.dev0
  - Tokenizers 0.12.1