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wav2vec2-large-xlsr-korean-demo-test2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
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wav2vec2-large-xlsr-korean-demo-test2

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0566
  • Wer: 0.5224

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
31.2541 0.3 400 5.4002 1.0
4.9419 0.59 800 5.3336 1.0
4.8926 0.89 1200 5.0531 1.0
4.7218 1.19 1600 4.5172 1.0
4.0218 1.49 2000 3.1418 0.9518
3.0654 1.78 2400 2.4376 0.9041
2.6226 2.08 2800 2.0151 0.8643
2.2944 2.38 3200 1.8025 0.8290
2.1872 2.67 3600 1.6469 0.7962
2.0747 2.97 4000 1.5165 0.7714
1.8479 3.27 4400 1.4281 0.7694
1.8288 3.57 4800 1.3791 0.7326
1.801 3.86 5200 1.3328 0.7177
1.6723 4.16 5600 1.2954 0.7192
1.5925 4.46 6000 1.3137 0.6953
1.5709 4.75 6400 1.2086 0.6973
1.5294 5.05 6800 1.1811 0.6730
1.3844 5.35 7200 1.2053 0.6769
1.3906 5.65 7600 1.1287 0.6556
1.4088 5.94 8000 1.1251 0.6466
1.2989 6.24 8400 1.1577 0.6546
1.2523 6.54 8800 1.0643 0.6377
1.2651 6.84 9200 1.0865 0.6417
1.2209 7.13 9600 1.0981 0.6272
1.1435 7.43 10000 1.1195 0.6317
1.1616 7.73 10400 1.0672 0.6327
1.1272 8.02 10800 1.0413 0.6248
1.043 8.32 11200 1.0555 0.6233
1.0523 8.62 11600 1.0372 0.6178
1.0208 8.92 12000 1.0170 0.6128
0.9895 9.21 12400 1.0354 0.5934
0.95 9.51 12800 1.1019 0.6039
0.9705 9.81 13200 1.0229 0.5855
0.9202 10.1 13600 1.0364 0.5919
0.8644 10.4 14000 1.0721 0.5984
0.8641 10.7 14400 1.0383 0.5905
0.8924 11.0 14800 0.9947 0.5760
0.7914 11.29 15200 1.0270 0.5885
0.7882 11.59 15600 1.0271 0.5741
0.8116 11.89 16000 0.9937 0.5741
0.7584 12.18 16400 0.9924 0.5626
0.7051 12.48 16800 1.0023 0.5572
0.7232 12.78 17200 1.0479 0.5512
0.7149 13.08 17600 1.0475 0.5765
0.6579 13.37 18000 1.0218 0.5552
0.6615 13.67 18400 1.0339 0.5631
0.6629 13.97 18800 1.0239 0.5621
0.6221 14.26 19200 1.0331 0.5537
0.6159 14.56 19600 1.0640 0.5532
0.6032 14.86 20000 1.0192 0.5567
0.5748 15.16 20400 1.0093 0.5507
0.5614 15.45 20800 1.0458 0.5472
0.5626 15.75 21200 1.0318 0.5398
0.5429 16.05 21600 1.0112 0.5278
0.5407 16.34 22000 1.0120 0.5278
0.511 16.64 22400 1.0335 0.5249
0.5316 16.94 22800 1.0146 0.5348
0.4949 17.24 23200 1.0287 0.5388
0.496 17.53 23600 1.0229 0.5348
0.4986 17.83 24000 1.0094 0.5313
0.4787 18.13 24400 1.0620 0.5234
0.4508 18.42 24800 1.0401 0.5323
0.4754 18.72 25200 1.0543 0.5303
0.4584 19.02 25600 1.0433 0.5194
0.4431 19.32 26000 1.0597 0.5249
0.4448 19.61 26400 1.0548 0.5229
0.4475 19.91 26800 1.0566 0.5224

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Evaluation results

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