readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-indonesia-colab
    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-indonesia-colab
  
  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: 0.3278
  - Wer: 0.2831
  
  ## 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: 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.0256        | 3.23  | 400  | 0.8938          | 0.8095 |
  | 0.4608        | 6.45  | 800  | 0.3986          | 0.4415 |
  | 0.2037        | 9.68  | 1200 | 0.3712          | 0.3881 |
  | 0.1423        | 12.9  | 1600 | 0.3362          | 0.3547 |
  | 0.1125        | 16.13 | 2000 | 0.3612          | 0.3452 |
  | 0.0879        | 19.35 | 2400 | 0.3589          | 0.3201 |
  | 0.0706        | 22.58 | 2800 | 0.3449          | 0.2989 |
  | 0.0558        | 25.81 | 3200 | 0.3371          | 0.2941 |
  | 0.0459        | 29.03 | 3600 | 0.3278          | 0.2831 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  