readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-dutch-baseline
    results: []
  ---
  
  <!-- 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-dutch-baseline
  
  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 evaluation set:
  - Loss: 0.5107
  - Wer: 0.2674
  - Cer: 0.0863
  
  ## 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: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 32
  - total_eval_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 3.655         | 1.31  | 400  | 0.9337          | 0.7332 | 0.2534 |
  | 0.42          | 2.61  | 800  | 0.5018          | 0.4115 | 0.1374 |
  | 0.2267        | 3.92  | 1200 | 0.4776          | 0.3791 | 0.1259 |
  | 0.1624        | 5.23  | 1600 | 0.4807          | 0.3590 | 0.1208 |
  | 0.135         | 6.54  | 2000 | 0.4899          | 0.3417 | 0.1121 |
  | 0.1179        | 7.84  | 2400 | 0.5096          | 0.3445 | 0.1133 |
  | 0.1035        | 9.15  | 2800 | 0.4563          | 0.3455 | 0.1129 |
  | 0.092         | 10.46 | 3200 | 0.5061          | 0.3382 | 0.1127 |
  | 0.0804        | 11.76 | 3600 | 0.4969          | 0.3285 | 0.1088 |
  | 0.0748        | 13.07 | 4000 | 0.5274          | 0.3380 | 0.1114 |
  | 0.0669        | 14.38 | 4400 | 0.5201          | 0.3115 | 0.1028 |
  | 0.0588        | 15.69 | 4800 | 0.5238          | 0.3212 | 0.1054 |
  | 0.0561        | 16.99 | 5200 | 0.5273          | 0.3185 | 0.1044 |
  | 0.0513        | 18.3  | 5600 | 0.5577          | 0.3032 | 0.1010 |
  | 0.0476        | 19.61 | 6000 | 0.5298          | 0.3050 | 0.1008 |
  | 0.0408        | 20.91 | 6400 | 0.5725          | 0.2982 | 0.0984 |
  | 0.0376        | 22.22 | 6800 | 0.5605          | 0.2953 | 0.0966 |
  | 0.0339        | 23.53 | 7200 | 0.5419          | 0.2865 | 0.0938 |
  | 0.0315        | 24.84 | 7600 | 0.5530          | 0.2782 | 0.0915 |
  | 0.0286        | 26.14 | 8000 | 0.5354          | 0.2788 | 0.0917 |
  | 0.0259        | 27.45 | 8400 | 0.5245          | 0.2715 | 0.0878 |
  | 0.0231        | 28.76 | 8800 | 0.5107          | 0.2674 | 0.0863 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu102
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  