readme: ---
  language:
  - pt
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: output
    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. -->
  
  # output
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1505
  - Wer: 0.1352
  
  ## 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: 7.5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - 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: 2000
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.1367        | 0.64  | 500   | 3.8825          | 1.0    |
  | 2.9677        | 1.29  | 1000  | 2.9498          | 1.0    |
  | 1.5884        | 1.93  | 1500  | 0.6722          | 0.6493 |
  | 1.2292        | 2.57  | 2000  | 0.3635          | 0.3202 |
  | 1.1314        | 3.22  | 2500  | 0.2970          | 0.2680 |
  | 1.0879        | 3.86  | 3000  | 0.2671          | 0.2486 |
  | 1.0344        | 4.5   | 3500  | 0.2625          | 0.2239 |
  | 1.0109        | 5.15  | 4000  | 0.2520          | 0.2230 |
  | 0.9966        | 5.79  | 4500  | 0.2280          | 0.2105 |
  | 0.9815        | 6.43  | 5000  | 0.2254          | 0.2179 |
  | 0.9744        | 7.08  | 5500  | 0.2301          | 0.2137 |
  | 0.9487        | 7.72  | 6000  | 0.2224          | 0.2051 |
  | 0.9431        | 8.37  | 6500  | 0.2105          | 0.1992 |
  | 0.9365        | 9.01  | 7000  | 0.2114          | 0.2019 |
  | 0.9268        | 9.65  | 7500  | 0.2097          | 0.1988 |
  | 0.9292        | 10.3  | 8000  | 0.2120          | 0.1986 |
  | 0.929         | 10.94 | 8500  | 0.2048          | 0.1998 |
  | 0.9017        | 11.58 | 9000  | 0.2035          | 0.1999 |
  | 0.8898        | 12.23 | 9500  | 0.1961          | 0.1908 |
  | 0.8799        | 12.87 | 10000 | 0.1945          | 0.1817 |
  | 0.869         | 13.51 | 10500 | 0.1929          | 0.1844 |
  | 0.8572        | 14.16 | 11000 | 0.1941          | 0.1888 |
  | 0.8691        | 14.8  | 11500 | 0.1912          | 0.1804 |
  | 0.8645        | 15.44 | 12000 | 0.1950          | 0.1851 |
  | 0.8468        | 16.09 | 12500 | 0.1879          | 0.1770 |
  | 0.8405        | 16.73 | 13000 | 0.1881          | 0.1759 |
  | 0.8647        | 17.37 | 13500 | 0.1861          | 0.1740 |
  | 0.8477        | 18.02 | 14000 | 0.1782          | 0.1702 |
  | 0.811         | 18.66 | 14500 | 0.1915          | 0.1757 |
  | 0.8165        | 19.3  | 15000 | 0.1820          | 0.1724 |
  | 0.8166        | 19.95 | 15500 | 0.1798          | 0.1697 |
  | 0.8167        | 20.59 | 16000 | 0.1805          | 0.1752 |
  | 0.7908        | 21.24 | 16500 | 0.1761          | 0.1699 |
  | 0.7925        | 21.88 | 17000 | 0.1740          | 0.1709 |
  | 0.7803        | 22.52 | 17500 | 0.1815          | 0.1727 |
  | 0.7839        | 23.17 | 18000 | 0.1737          | 0.1694 |
  | 0.7815        | 23.81 | 18500 | 0.1732          | 0.1630 |
  | 0.767         | 24.45 | 19000 | 0.1724          | 0.1648 |
  | 0.7672        | 25.1  | 19500 | 0.1706          | 0.1596 |
  | 0.7691        | 25.74 | 20000 | 0.1718          | 0.1618 |
  | 0.7547        | 26.38 | 20500 | 0.1694          | 0.1565 |
  | 0.7498        | 27.03 | 21000 | 0.1706          | 0.1582 |
  | 0.7459        | 27.67 | 21500 | 0.1663          | 0.1586 |
  | 0.7374        | 28.31 | 22000 | 0.1651          | 0.1567 |
  | 0.7499        | 28.96 | 22500 | 0.1668          | 0.1549 |
  | 0.7471        | 29.6  | 23000 | 0.1667          | 0.1553 |
  | 0.7369        | 30.24 | 23500 | 0.1659          | 0.1556 |
  | 0.7389        | 30.89 | 24000 | 0.1668          | 0.1538 |
  | 0.7197        | 31.53 | 24500 | 0.1687          | 0.1561 |
  | 0.71          | 32.17 | 25000 | 0.1666          | 0.1516 |
  | 0.7199        | 32.82 | 25500 | 0.1640          | 0.1523 |
  | 0.7194        | 33.46 | 26000 | 0.1659          | 0.1528 |
  | 0.6923        | 34.11 | 26500 | 0.1662          | 0.1507 |
  | 0.7054        | 34.75 | 27000 | 0.1641          | 0.1486 |
  | 0.6955        | 35.39 | 27500 | 0.1634          | 0.1497 |
  | 0.7084        | 36.04 | 28000 | 0.1618          | 0.1478 |
  | 0.6917        | 36.68 | 28500 | 0.1589          | 0.1471 |
  | 0.687         | 37.32 | 29000 | 0.1589          | 0.1450 |
  | 0.6914        | 37.97 | 29500 | 0.1588          