readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-vios-commonvoice-1
    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-base-vios-commonvoice-1
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8913
  - Wer: 0.3621
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.4706        | 0.55  | 500   | 3.4725          | 1.0    |
  | 3.202         | 1.1   | 1000  | 2.7555          | 1.0008 |
  | 1.0507        | 1.66  | 1500  | 1.0481          | 0.6196 |
  | 0.7325        | 2.21  | 2000  | 0.8120          | 0.4958 |
  | 0.599         | 2.76  | 2500  | 0.7035          | 0.4447 |
  | 0.5224        | 3.31  | 3000  | 0.6761          | 0.4078 |
  | 0.4844        | 3.86  | 3500  | 0.6688          | 0.4011 |
  | 0.4234        | 4.42  | 4000  | 0.6080          | 0.3729 |
  | 0.4237        | 4.97  | 4500  | 0.5953          | 0.3556 |
  | 0.3986        | 5.52  | 5000  | 0.6054          | 0.3478 |
  | 0.3554        | 6.07  | 5500  | 0.6193          | 0.3479 |
  | 0.3446        | 6.62  | 6000  | 0.5809          | 0.3302 |
  | 0.3104        | 7.17  | 6500  | 0.5713          | 0.3283 |
  | 0.3166        | 7.73  | 7000  | 0.5593          | 0.3133 |
  | 0.2938        | 8.28  | 7500  | 0.5645          | 0.3081 |
  | 0.3061        | 8.83  | 8000  | 0.5508          | 0.3020 |
  | 0.2986        | 9.38  | 8500  | 0.5462          | 0.3024 |
  | 0.2939        | 9.93  | 9000  | 0.5544          | 0.3028 |
  | 0.2633        | 10.49 | 9500  | 0.5496          | 0.3024 |
  | 0.2683        | 11.04 | 10000 | 0.5439          | 0.2946 |
  | 0.2714        | 11.59 | 10500 | 0.5524          | 0.2947 |
  | 0.2354        | 12.14 | 11000 | 0.5267          | 0.2918 |
  | 0.2488        | 12.69 | 11500 | 0.5728          | 0.2938 |
  | 0.2479        | 13.25 | 12000 | 0.5802          | 0.2951 |
  | 0.245         | 13.8  | 12500 | 0.5571          | 0.2890 |
  | 0.2422        | 14.35 | 13000 | 0.5531          | 0.2871 |
  | 0.2369        | 14.9  | 13500 | 0.5453          | 0.2860 |
  | 0.2345        | 15.45 | 14000 | 0.5452          | 0.2847 |
  | 0.2507        | 16.0  | 14500 | 0.5536          | 0.2884 |
  | 0.2454        | 16.56 | 15000 | 0.5577          | 0.2871 |
  | 0.2729        | 17.11 | 15500 | 0.6019          | 0.2931 |
  | 0.2743        | 17.66 | 16000 | 0.5619          | 0.2905 |
  | 0.3031        | 18.21 | 16500 | 0.6401          | 0.3006 |
  | 0.315         | 18.76 | 17000 | 0.6044          | 0.2990 |
  | 0.4025        | 19.32 | 17500 | 0.6739          | 0.3304 |
  | 0.4915        | 19.87 | 18000 | 0.7267          | 0.3472 |
  | 0.5539        | 20.42 | 18500 | 0.8078          | 0.3483 |
  | 0.7138        | 20.97 | 19000 | 0.9362          | 0.3765 |
  | 0.5766        | 21.52 | 19500 | 0.7921          | 0.3392 |
  | 0.688         | 22.08 | 20000 | 0.8833          | 0.3693 |
  | 0.6964        | 22.63 | 20500 | 0.9137          | 0.3469 |
  | 0.7389        | 23.18 | 21000 | 0.9379          | 0.3460 |
  | 0.7851        | 23.73 | 21500 | 1.0438          | 0.3653 |
  | 0.7619        | 24.28 | 22000 | 0.9313          | 0.3873 |
  | 0.7175        | 24.83 | 22500 | 0.8668          | 0.3789 |
  | 0.6842        | 25.39 | 23000 | 0.8243          | 0.3761 |
  | 0.6941        | 25.94 | 23500 | 0.8557          | 0.3804 |
  | 0.7167        | 26.49 | 24000 | 0.8618          | 0.3875 |
  | 0.721         | 27.04 | 24500 | 0.8686          | 0.3764 |
  | 0.6949        | 27.59 | 25000 | 0.8773          | 0.3690 |
  | 0.727         | 28.15 | 25500 | 0.8769          | 0.3666 |
  | 0.7363        | 28.7  | 26000 | 0.8867          | 0.3634 |
  | 0.7157        | 29.25 | 26500 | 0.8895          | 0.3626 |
  | 0.7385        | 29.8  | 27000 | 0.8913          | 0.3621 |
  
  
  ### Framework versions
  
  - Transformers 4.19.3
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  