readme: ---
  language:
  - sv-SE
  license: apache-2.0
  tags:
  - robust-speech-event
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_8_0
  model-index:
  - name: ''
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 8.0
        type: mozilla-foundation/common_voice_8_0
        args: sv-SE
      metrics:
      - name: Test WER
        type: wer
        value: 102.43
  ---
  
  <!-- 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. -->
  
  # 
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3347
  - Wer: 1.0286
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 20
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 10.7838       | 0.01  | 5    | 14.5035         | 1.0    |
  | 13.0582       | 0.03  | 10   | 13.6658         | 1.0    |
  | 7.3034        | 0.04  | 15   | 9.7898          | 1.0    |
  | 6.1847        | 0.05  | 20   | 6.9148          | 1.0    |
  | 5.3371        | 0.07  | 25   | 5.3661          | 1.0    |
  | 4.4274        | 0.08  | 30   | 4.6945          | 1.0    |
  | 4.0918        | 0.1   | 35   | 4.3172          | 1.0    |
  | 4.1734        | 0.11  | 40   | 4.0759          | 1.0    |
  | 3.7332        | 0.12  | 45   | 3.9039          | 1.0    |
  | 3.6871        | 0.14  | 50   | 3.7777          | 1.0    |
  | 3.4428        | 0.15  | 55   | 3.6718          | 1.0    |
  | 3.5514        | 0.16  | 60   | 3.5947          | 1.0    |
  | 3.4307        | 0.18  | 65   | 3.5144          | 1.0    |
  | 3.4102        | 0.19  | 70   | 3.4432          | 1.0    |
  | 3.4964        | 0.21  | 75   | 3.3890          | 1.0    |
  | 3.3936        | 0.22  | 80   | 3.3467          | 1.0    |
  | 3.3051        | 0.23  | 85   | 3.3102          | 1.0    |
  | 3.278         | 0.25  | 90   | 3.2801          | 1.0    |
  | 3.2223        | 0.26  | 95   | 3.2440          | 1.0    |
  | 3.1888        | 0.27  | 100  | 3.2900          | 1.0    |
  | 3.218         | 0.29  | 105  | 3.2627          | 1.0    |
  | 3.1308        | 0.3   | 110  | 3.2152          | 1.0    |
  | 3.109         | 0.31  | 115  | 3.1686          | 1.0    |
  | 3.1188        | 0.33  | 120  | 3.1734          | 1.0    |
  | 3.1132        | 0.34  | 125  | 3.1431          | 1.0    |
  | 3.0667        | 0.36  | 130  | 3.1686          | 1.0    |
  | 3.1167        | 0.37  | 135  | 3.1885          | 1.0    |
  | 3.0592        | 0.38  | 140  | 3.1100          | 1.0    |
  | 3.0531        | 0.4   | 145  | 3.1149          | 1.0    |
  | 3.1224        | 0.41  | 150  | 3.1205          | 1.0    |
  | 3.0651        | 0.42  | 155  | 3.1101          | 1.0    |
  | 3.0077        | 0.44  | 160  | 3.0980          | 1.0    |
  | 3.0027        | 0.45  | 165  | 3.1132          | 1.0    |
  | 3.0423        | 0.47  | 170  | 3.0886          | 1.0    |
  | 3.0462        | 0.48  | 175  | 3.0865          | 1.0    |
  | 3.0701        | 0.49  | 180  | 3.0863          | 1.0    |
  | 3.0871        | 0.51  | 185  | 3.0825          | 1.0    |
  | 3.0585        | 0.52  | 190  | 3.0720          | 1.0    |
  | 3.0274        | 0.53  | 195  | 3.0736          | 1.0    |
  | 3.0983        | 0.55  | 200  | 3.0658          | 1.0    |
  | 3.0538        | 0.56  | 205  | 3.1241          | 1.0    |
  | 3.0862        | 0.57  | 210  | 3.0573          | 1.0    |
  | 3.0041        | 0.59  | 215  | 3.0608          | 1.0    |
  | 3.027         | 0.6   | 220  | 3.0614          | 1.0    |
  | 2.9916        | 0.62  | 225  | 3.0527          | 1.0    |
  | 3.0157        | 0.63  | 230  | 3.0514          | 1.0    |
  | 3.0429        | 0.64  | 235  | 3.0391          | 1.0    |
  | 2.999         | 0.66  | 240  | 3.0462          | 1.0    |
  | 3.0053        | 0.67  | 245  | 3.0438          | 1.0    |
  | 2.9812        | 0.68  | 250  | 3.0447          | 1.0    |
  | 3.0062        | 0.7   | 255  | 3.0660          | 1.0    |
  | 3.0045        | 0.71  | 260  | 3.0103          | 1.0    |
  | 2.9684        | 0.73  | 265  | 3.0106          | 1.0    |
  | 2.9885        | 0.74  | 270  | 3.0014          | 1.0    |
  | 3.0062        | 0.75  