readme: ---
  language:
  - nl
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - nl
  - robust-speech-event
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-nl
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice
        type: common_voice
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 17.17
      - name: Test CER
        type: cer
        value: 5.13
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Dev Data
        type: speech-recognition-community-v2/dev_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 35.76
      - name: Test CER
        type: cer
        value: 13.99
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Robust Speech Event - Test Data
        type: speech-recognition-community-v2/eval_data
        args: nl
      metrics:
      - name: Test WER
        type: wer
        value: 37.19
  ---
  
  <!-- 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-xls-r-300m-nl
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the test set:
  - Loss: 0.3923
  - Wer: 0.1748
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 500
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.5787        | 0.89  | 400   | 0.6354          | 0.5643 |
  | 0.3036        | 1.78  | 800   | 0.3690          | 0.3552 |
  | 0.188         | 2.67  | 1200  | 0.3239          | 0.2958 |
  | 0.1434        | 3.56  | 1600  | 0.3093          | 0.2515 |
  | 0.1245        | 4.44  | 2000  | 0.3024          | 0.2433 |
  | 0.1095        | 5.33  | 2400  | 0.3249          | 0.2643 |
  | 0.0979        | 6.22  | 2800  | 0.3191          | 0.2281 |
  | 0.0915        | 7.11  | 3200  | 0.3152          | 0.2216 |
  | 0.0829        | 8.0   | 3600  | 0.3419          | 0.2218 |
  | 0.0777        | 8.89  | 4000  | 0.3432          | 0.2132 |
  | 0.073         | 9.78  | 4400  | 0.3223          | 0.2131 |
  | 0.0688        | 10.67 | 4800  | 0.3094          | 0.2152 |
  | 0.0647        | 11.56 | 5200  | 0.3411          | 0.2152 |
  | 0.0639        | 12.44 | 5600  | 0.3762          | 0.2135 |
  | 0.0599        | 13.33 | 6000  | 0.3790          | 0.2137 |
  | 0.0572        | 14.22 | 6400  | 0.3693          | 0.2118 |
  | 0.0563        | 15.11 | 6800  | 0.3495          | 0.2139 |
  | 0.0521        | 16.0  | 7200  | 0.3800          | 0.2023 |
  | 0.0508        | 16.89 | 7600  | 0.3678          | 0.2033 |
  | 0.0513        | 17.78 | 8000  | 0.3845          | 0.1987 |
  | 0.0476        | 18.67 | 8400  | 0.3511          | 0.2037 |
  | 0.045         | 19.56 | 8800  | 0.3794          | 0.1994 |
  | 0.044         | 20.44 | 9200  | 0.3525          | 0.2050 |
  | 0.043         | 21.33 | 9600  | 0.4082          | 0.2007 |
  | 0.0409        | 22.22 | 10000 | 0.3866          | 0.2004 |
  | 0.0393        | 23.11 | 10400 | 0.3899          | 0.2008 |
  | 0.0382        | 24.0  | 10800 | 0.3626          | 0.1951 |
  | 0.039         | 24.89 | 11200 | 0.3936          | 0.1953 |
  | 0.0361        | 25.78 | 11600 | 0.4262          | 0.1928 |
  | 0.0362        | 26.67 | 12000 | 0.3796          | 0.1934 |
  | 0.033         | 27.56 | 12400 | 0.3616          | 0.1934 |
  | 0.0321        | 28.44 | 12800 | 0.3742          | 0.1933 |
  | 0.0325        | 29.33 | 13200 | 0.3582          | 0.1869 |
  | 0.0309        | 30.22 | 13600 | 0.3717          | 0.1874 |
  | 0.029         | 31.11 | 14000 | 0.3814          | 0.1894 |
  | 0.0296        | 32.0  | 14400 | 0.3698          | 0.1877 |
  | 0.0281        | 32.89 | 14800 | 0.3976          | 0.1899 |
  | 0.0275        | 33.78 | 15200 | 0.3854          | 0.1858 |
  | 0.0264        | 34.67 | 15600 | 0.4021          | 0.1889 |
  | 0.0261        | 35.56 | 16000 | 0.3850          | 0.1830 |
  | 0.0242        | 36.44 | 16400 | 0.4091          | 0.1878 |
  | 0.0245        | 37.33 | 16800 | 0.4012          | 0.1846 |
  | 0.0243        | 38.22 | 17200 | 0.3996          | 0.183