readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-300m-teste4
    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. -->
  
  # wav2vec2-300m-teste4
  
  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: 0.3276
  - Wer: 0.3489
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 10.0237       | 0.49  | 100  | 4.2075          | 0.9792 |
  | 3.313         | 0.98  | 200  | 3.0232          | 0.9792 |
  | 2.9469        | 1.47  | 300  | 2.7591          | 0.9792 |
  | 1.4217        | 1.96  | 400  | 0.8397          | 0.6219 |
  | 0.5598        | 2.45  | 500  | 0.6085          | 0.5087 |
  | 0.4507        | 2.94  | 600  | 0.4512          | 0.4317 |
  | 0.2775        | 3.43  | 700  | 0.3839          | 0.3751 |
  | 0.2047        | 3.92  | 800  | 0.3276          | 0.3489 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu102
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  