readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-j-roman-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-j-roman-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: 1.2233
  - Wer: 0.1437
  
  ## 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: 2
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.3479        | 1.22  | 400   | 1.6349          | 0.3868 |
  | 0.8621        | 2.45  | 800   | 1.0185          | 0.2346 |
  | 0.5421        | 3.67  | 1200  | 0.7549          | 0.1868 |
  | 0.3932        | 4.89  | 1600  | 0.7893          | 0.1811 |
  | 0.332         | 6.12  | 2000  | 0.9318          | 0.1919 |
  | 0.2902        | 7.34  | 2400  | 0.8263          | 0.1839 |
  | 0.2542        | 8.56  | 2800  | 0.8491          | 0.1829 |
  | 0.2355        | 9.79  | 3200  | 0.8820          | 0.1805 |
  | 0.2206        | 11.01 | 3600  | 0.9183          | 0.1748 |
  | 0.2041        | 12.23 | 4000  | 0.9131          | 0.1725 |
  | 0.1878        | 13.46 | 4400  | 0.9075          | 0.1699 |
  | 0.1733        | 14.68 | 4800  | 0.8456          | 0.1665 |
  | 0.1746        | 15.9  | 5200  | 0.9353          | 0.1745 |
  | 0.1671        | 17.13 | 5600  | 0.9318          | 0.1713 |
  | 0.1641        | 18.35 | 6000  | 0.8804          | 0.1661 |
  | 0.1578        | 19.57 | 6400  | 0.9849          | 0.1795 |
  | 0.1534        | 20.8  | 6800  | 1.0036          | 0.1637 |
  | 0.1484        | 22.02 | 7200  | 0.9618          | 0.1722 |
  | 0.1431        | 23.24 | 7600  | 0.9947          | 0.1680 |
  | 0.139         | 24.46 | 8000  | 0.9923          | 0.1729 |
  | 0.134         | 25.69 | 8400  | 1.0015          | 0.1641 |
  | 0.1298        | 26.91 | 8800  | 0.9930          | 0.1704 |
  | 0.1253        | 28.13 | 9200  | 0.9977          | 0.1605 |
  | 0.1178        | 29.36 | 9600  | 0.9756          | 0.1653 |
  | 0.1178        | 30.58 | 10000 | 1.1122          | 0.1784 |
  | 0.1165        | 31.8  | 10400 | 0.9883          | 0.1655 |
  | 0.1073        | 33.03 | 10800 | 1.1286          | 0.1677 |
  | 0.1121        | 34.25 | 11200 | 1.0406          | 0.1660 |
  | 0.1081        | 35.47 | 11600 | 1.0976          | 0.1678 |
  | 0.109         | 36.7  | 12000 | 1.0915          | 0.1722 |
  | 0.1027        | 37.92 | 12400 | 1.1167          | 0.1712 |
  | 0.0925        | 39.14 | 12800 | 1.1598          | 0.1693 |
  | 0.0913        | 40.37 | 13200 | 1.0712          | 0.1640 |
  | 0.0895        | 41.59 | 13600 | 1.1692          | 0.1745 |
  | 0.0908        | 42.81 | 14000 | 1.1248          | 0.1641 |
  | 0.0905        | 44.04 | 14400 | 1.0523          | 0.1678 |
  | 0.0864        | 45.26 | 14800 | 1.0261          | 0.1626 |
  | 0.0843        | 46.48 | 15200 | 1.0746          | 0.1676 |
  | 0.0759        | 47.71 | 15600 | 1.1035          | 0.1596 |
  | 0.0758        | 48.93 | 16000 | 1.0977          | 0.1622 |
  | 0.0743        | 50.15 | 16400 | 1.1203          | 0.1677 |
  | 0.0826        | 51.38 | 16800 | 1.0983          | 0.1651 |
  | 0.0743        | 52.6  | 17200 | 1.1452          | 0.1622 |
  | 0.0713        | 53.82 | 17600 | 1.0882          | 0.1623 |
  | 0.0651        | 55.05 | 18000 | 1.0588          | 0.1608 |
  | 0.0669        | 56.27 | 18400 | 1.1332          | 0.1600 |
  | 0.0626        | 57.49 | 18800 | 1.0747          | 0.1562 |
  | 0.0646        | 58.72 | 19200 | 1.0585          | 0.1599 |
  | 0.0639        | 59.94 | 19600 | 1.0106          | 0.1543 |
  | 0.0603        | 61.16 | 20000 | 1.0875          | 0.1585 |
  | 0.0551        | 62.39 | 20400 | 1.1273          | 0.1537 |
  | 0.0553        | 63.61 | 20800 | 1.1376          | 0.1577 |
  | 0.052         | 64.83 | 21200 | 1.1429          | 0.1553 |
  | 0.0506        | 66.06 | 21600 | 1.0872          | 0.1577 |
  | 0.0495        | 67.28 | 22000 | 1.0954          | 0.1488 |
  | 0.0483        | 68.5  | 22400 | 1.1397          | 0.1524 |
  | 0.0421        | 69.72 | 22800 | 1.2144          | 0.1581 |
  | 0.0457        | 70.95 | 23200 | 1.1581          | 0.1532 |
  | 0.0405        | 72.17 | 23600 | 1.2150          | 0.1566 |
  | 0.0409        | 73.39 | 24000 | 1.117