readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-300m-th-cv11_0
    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-xls-r-300m-th-cv11_0
  
  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: 0.3391
  - Wer: 0.2915
  - Cer: 0.0651
  - Clean Cer: 0.0508
  - Learning Rate: 0.0000
  
  ## 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: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    | Clean Cer | Rate   |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
  | 7.5397        | 0.37  | 500   | 3.5716          | 1.0    | 0.9811 | 0.9774    | 0.0001 |
  | 1.7478        | 0.75  | 1000  | 0.7702          | 0.8097 | 0.2296 | 0.1746    | 0.0001 |
  | 0.7687        | 1.12  | 1500  | 0.4997          | 0.5392 | 0.1415 | 0.1182    | 0.0001 |
  | 0.6064        | 1.5   | 2000  | 0.4270          | 0.4956 | 0.1238 | 0.1001    | 0.0001 |
  | 0.5473        | 1.87  | 2500  | 0.3809          | 0.4489 | 0.1105 | 0.0898    | 0.0001 |
  | 0.454         | 2.24  | 3000  | 0.3585          | 0.4256 | 0.1021 | 0.0813    | 0.0001 |
  | 0.4219        | 2.62  | 3500  | 0.3375          | 0.4063 | 0.0974 | 0.0777    | 0.0001 |
  | 0.4075        | 2.99  | 4000  | 0.3274          | 0.4036 | 0.0948 | 0.0746    | 0.0001 |
  | 0.3355        | 3.37  | 4500  | 0.3257          | 0.3782 | 0.0898 | 0.0729    | 0.0001 |
  | 0.3203        | 3.74  | 5000  | 0.3024          | 0.3561 | 0.0830 | 0.0659    | 0.0001 |
  | 0.3151        | 4.11  | 5500  | 0.3038          | 0.3606 | 0.0830 | 0.0653    | 0.0001 |
  | 0.2713        | 4.49  | 6000  | 0.3052          | 0.3595 | 0.0832 | 0.0655    | 0.0001 |
  | 0.2685        | 4.86  | 6500  | 0.2933          | 0.3436 | 0.0796 | 0.0628    | 0.0001 |
  | 0.2379        | 5.24  | 7000  | 0.3020          | 0.3362 | 0.0763 | 0.0608    | 0.0000 |
  | 0.224         | 5.61  | 7500  | 0.2874          | 0.3265 | 0.0745 | 0.0589    | 0.0000 |
  | 0.2204        | 5.98  | 8000  | 0.2922          | 0.3191 | 0.0724 | 0.0576    | 0.0000 |
  | 0.1927        | 6.36  | 8500  | 0.3107          | 0.3163 | 0.0719 | 0.0568    | 0.0000 |
  | 0.1875        | 6.73  | 9000  | 0.3034          | 0.3084 | 0.0703 | 0.0554    | 0.0000 |
  | 0.1786        | 7.11  | 9500  | 0.3210          | 0.3107 | 0.0702 | 0.0553    | 0.0000 |
  | 0.1606        | 7.48  | 10000 | 0.3231          | 0.3062 | 0.0688 | 0.0541    | 0.0000 |
  | 0.1594        | 7.85  | 10500 | 0.3234          | 0.3033 | 0.0680 | 0.0535    | 0.0000 |
  | 0.1498        | 8.23  | 11000 | 0.3276          | 0.3035 | 0.0680 | 0.0530    | 0.0000 |
  | 0.1396        | 8.6   | 11500 | 0.3265          | 0.2975 | 0.0668 | 0.0520    | 0.0000 |
  | 0.142         | 8.98  | 12000 | 0.3236          | 0.2930 | 0.0659 | 0.0515    | 0.0000 |
  | 0.1242        | 9.35  | 12500 | 0.3403          | 0.2921 | 0.0655 | 0.0511    | 0.0000 |
  | 0.1225        | 9.72  | 13000 | 0.3391          | 0.2915 | 0.0651 | 0.0508    | 0.0000 |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  