readme: ---
  license: apache-2.0
  language: br
  tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-xls-r-300m-Br-small
    results:
    - task:
        name: Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice br
        type: common_voice
        args: br
      metrics:
      - name: Test WER
        type: wer
        value: 66.75
  ---
  
  
  # wav2vec2-xls-r-300m-Br-small
  
  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: 1.0573
  - Wer: 0.6675
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 5.7464        | 2.79  | 400  | 1.7474          | 1.1018 |
  | 1.1117        | 5.59  | 800  | 0.9434          | 0.8697 |
  | 0.6481        | 8.39  | 1200 | 0.9251          | 0.7910 |
  | 0.4754        | 11.19 | 1600 | 0.9208          | 0.7412 |
  | 0.3602        | 13.98 | 2000 | 0.9284          | 0.7232 |
  | 0.2873        | 16.78 | 2400 | 0.9299          | 0.6940 |
  | 0.2386        | 19.58 | 2800 | 1.0182          | 0.6927 |
  | 0.1971        | 22.38 | 3200 | 1.0456          | 0.6898 |
  | 0.1749        | 25.17 | 3600 | 1.0208          | 0.6769 |
  | 0.1487        | 27.97 | 4000 | 1.0573          | 0.6675 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  