readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - dataset/riksdagen
  metrics:
  - wer
  model-index:
  - name: whisper-small-sv
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: dataset/riksdagen audiofolder
        type: dataset/riksdagen
        config: test
        split: test
        args: audiofolder
      metrics:
      - name: WER
        type: wer
        value: 0.22405586116204554
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: sv-SE
        split: test
        args:
          language: sv-SE
      metrics:
        - name: WER
          type: wer
          value: 26.69
  ---
  
  <!-- 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
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the dataset/riksdagen audiofolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2917
  - Wer: 0.2241
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - training_steps: 20000
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.5023        | 0.04  | 250   | 0.5072          | 0.2949 |
  | 0.4678        | 0.08  | 500   | 0.4632          | 0.2780 |
  | 0.4233        | 0.12  | 750   | 0.4384          | 0.2749 |
  | 0.4113        | 0.17  | 1000  | 0.4205          | 0.2673 |
  | 0.3994        | 0.21  | 1250  | 0.4079          | 0.2649 |
  | 0.3841        | 0.25  | 1500  | 0.3947          | 0.2609 |
  | 0.3775        | 0.29  | 1750  | 0.3854          | 0.2564 |
  | 0.383         | 0.33  | 2000  | 0.3781          | 0.2540 |
  | 0.3651        | 0.37  | 2250  | 0.3721          | 0.2532 |
  | 0.3456        | 0.42  | 2500  | 0.3651          | 0.2517 |
  | 0.3719        | 0.46  | 2750  | 0.3612          | 0.2481 |
  | 0.3399        | 0.5   | 3000  | 0.3561          | 0.2437 |
  | 0.3428        | 0.54  | 3250  | 0.3522          | 0.2465 |
  | 0.3442        | 0.58  | 3500  | 0.3451          | 0.2399 |
  | 0.3315        | 0.62  | 3750  | 0.3431          | 0.2417 |
  | 0.3299        | 0.66  | 4000  | 0.3404          | 0.2428 |
  | 0.3417        | 0.71  | 4250  | 0.3373          | 0.2395 |
  | 0.3399        | 0.75  | 4500  | 0.3332          | 0.2390 |
  | 0.3222        | 0.79  | 4750  | 0.3310          | 0.2385 |
  | 0.3319        | 0.83  | 5000  | 0.3291          | 0.2372 |
  | 0.3188        | 0.87  | 5250  | 0.3265          | 0.2359 |
  | 0.3197        | 0.91  | 5500  | 0.3240          | 0.2378 |
  | 0.3099        | 0.96  | 5750  | 0.3215          | 0.2342 |
  | 0.3132        | 1.0   | 6000  | 0.3195          | 0.2374 |
  | 0.286         | 1.04  | 6250  | 0.3179          | 0.2348 |
  | 0.2765        | 1.08  | 6500  | 0.3166          | 0.2354 |
  | 0.2795        | 1.12  | 6750  | 0.3153          | 0.2324 |
  | 0.2825        | 1.16  | 7000  | 0.3145          | 0.2316 |
  | 0.2865        | 1.21  | 7250  | 0.3144          | 0.2329 |
  | 0.2703        | 1.25  | 7500  | 0.3126          | 0.2326 |
  | 0.2792        | 1.29  | 7750  | 0.3121          | 0.2324 |
  | 0.2749        | 1.33  | 8000  | 0.3106          | 0.2325 |
  | 0.2762        | 1.37  | 8250  | 0.3093          | 0.2315 |
  | 0.2813        | 1.41  | 8500  | 0.3080          | 0.2302 |
  | 0.2755        | 1.45  | 8750  | 0.3078          | 0.2321 |
  | 0.2779        | 1.5   | 9000  | 0.3062          | 0.2305 |
  | 0.2764        | 1.54  | 9250  | 0.3059          | 0.2336 |
  | 0.2763        | 1.58  | 9500  | 0.3041          | 0.2310 |
  | 0.2723        | 1.62  | 9750  | 0.3027          | 0.2292 |
  | 0.2756        | 1.66  | 10000 | 0.3026          | 0.2301 |
  | 0.2663        | 1.7   | 10250 | 0.3008          | 0.2262 |
  | 0.269         | 1.75  | 10500 | 0.3006          | 0.2280 |
  | 0.2682        | 1.79  | 10750 | 0.3002          | 0.2291 |
  | 0.2721        | 1.83  | 11000 | 0.2994          | 0.2267 |
  | 0.2681        | 1.87  | 11250 | 0.2987          | 0.2288 |
  | 0.278         | 1.91  | 11500 | 0.2978          | 0.2296 |
  | 0.2625        | 1.95  | 11750 | 0.2978          | 0.2278 |
  | 0.2583        | 1.99  | 12000 | 0.2967          | 0.2259 |
  | 0.2403        | 2.04  | 12250 | 0.2976          | 0.2276 |
  | 0.2414        | 2.08  | 12500 | 0.2972          | 0.2264 |
  | 0.251         | 2.12  | 12750 | 0.2969          | 0.2256 |
  | 0.