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This model is a fine-tuned version of ./checkpoint-10500 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7540
  • Wer: 0.4647
  • Cer: 0.1318

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: 0.0002
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 120.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss Wer
1.0779 4.59 500 0.2354 0.8260 0.7395
0.7573 9.17 1000 0.2100 0.7544 0.6960
0.8225 13.76 1500 0.2021 0.6867 0.6672
0.621 18.35 2000 0.1874 0.6824 0.6209
0.6362 22.94 2500 0.1904 0.6712 0.6286
0.624 27.52 3000 0.1820 0.6940 0.6116
0.4781 32.11 3500 0.1735 0.6966 0.5989
0.5685 36.7 4000 0.1769 0.6742 0.5971
0.4384 41.28 4500 0.1767 0.6904 0.5999
0.5509 45.87 5000 0.1692 0.6734 0.5641
0.3665 50.46 5500 0.1680 0.7018 0.5662
0.3914 55.05 6000 0.1631 0.7121 0.5552
0.2467 59.63 6500 0.1563 0.6657 0.5374
0.2576 64.22 7000 0.1554 0.6920 0.5316
0.2711 68.81 7500 0.1495 0.6900 0.5176
0.2626 73.39 8000 0.1454 0.6843 0.5043
0.1377 77.98 8500 0.1470 0.7383 0.5101
0.2005 82.57 9000 0.1430 0.7228 0.5045
0.1355 87.16 9500 0.1375 0.7231 0.4869
0.0431 91.74 10000 0.1350 0.7397 0.4749
0.0586 96.33 10500 0.1339 0.7360 0.4754
0.0896 100.92 11000 0.7187 0.4885 0.1398
0.183 105.5 11500 0.7310 0.4838 0.1392
0.0963 110.09 12000 0.7643 0.4759 0.1362
0.0437 114.68 12500 0.7525 0.4641 0.1328
0.1122 119.27 13000 0.7535 0.4651 0.1317

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0
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Dataset used to train Baybars/wav2vec2-xls-r-1b-turkish

Evaluation results

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