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wav2vec2-xls-r-300m-npsc-bokmaal

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metadata
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-300m-npsc-bokmaal
    results: []

wav2vec2-xls-r-300m-npsc-bokmaal

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1663
  • Wer: 0.0932

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0969 0.32 500 0.1773 0.1054
0.0929 0.64 1000 0.1672 0.1061
0.1018 0.97 1500 0.1770 0.1067
0.0871 1.29 2000 0.1832 0.1087
0.0908 1.61 2500 0.1830 0.1101
0.0975 1.93 3000 0.1848 0.1100
0.0936 2.26 3500 0.1853 0.1113
0.1025 2.58 4000 0.1958 0.1149
0.0989 2.9 4500 0.1776 0.1123
0.0946 3.22 5000 0.1825 0.1097
0.0859 3.55 5500 0.1864 0.1072
0.0867 3.87 6000 0.1886 0.1081
0.0783 4.19 6500 0.1883 0.1063
0.0804 4.51 7000 0.1831 0.1063
0.0797 4.84 7500 0.1884 0.1058
0.0705 5.16 8000 0.1802 0.1057
0.0795 5.48 8500 0.1854 0.1038
0.0711 5.8 9000 0.1766 0.1032
0.0973 6.13 9500 0.1663 0.1014
0.087 6.45 10000 0.1664 0.1014
0.0962 6.77 10500 0.1631 0.1009
0.0857 7.09 11000 0.1659 0.1002
0.0882 7.41 11500 0.1668 0.1007
0.0784 7.74 12000 0.1688 0.0996
0.0838 8.06 12500 0.1675 0.0984
0.0863 8.38 13000 0.1639 0.0979
0.0763 8.7 13500 0.1638 0.0980
0.0822 9.03 14000 0.1709 0.0972
0.0769 9.35 14500 0.1700 0.0965
0.0838 9.67 15000 0.1703 0.0974
0.0799 9.99 15500 0.1667 0.0957
0.0712 10.32 16000 0.1754 0.0960
0.0737 10.64 16500 0.1725 0.0968
0.0851 10.96 17000 0.1733 0.0958
0.076 11.28 17500 0.1682 0.0954
0.0712 11.61 18000 0.1713 0.0943
0.0745 11.93 18500 0.1662 0.0951
0.0864 12.25 19000 0.1692 0.0947
0.0937 12.57 19500 0.1624 0.0943
0.0915 12.89 20000 0.1678 0.0942
0.0926 13.22 20500 0.1641 0.0945
0.0912 13.54 21000 0.1665 0.0937
0.0917 13.86 21500 0.1648 0.0936
0.094 14.18 22000 0.1635 0.0935
0.0864 14.51 22500 0.1678 0.0934
0.0899 14.83 23000 0.1663 0.0932

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.0