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wav2vec2-base-cynthia-timit

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

  • Loss: 0.4888
  • Wer: 0.3315

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.7674 1.0 500 2.8994 1.0
1.3538 2.01 1000 0.5623 0.5630
0.5416 3.01 1500 0.4595 0.4765
0.3563 4.02 2000 0.4435 0.4328
0.2869 5.02 2500 0.4035 0.4145
0.2536 6.02 3000 0.4090 0.3945
0.2072 7.03 3500 0.4188 0.3809
0.1825 8.03 4000 0.4139 0.3865
0.1754 9.04 4500 0.4320 0.3763
0.1477 10.04 5000 0.4668 0.3699
0.1418 11.04 5500 0.4439 0.3683
0.1207 12.05 6000 0.4419 0.3678
0.115 13.05 6500 0.4606 0.3786
0.1022 14.06 7000 0.4403 0.3610
0.1019 15.06 7500 0.4966 0.3609
0.0898 16.06 8000 0.4675 0.3586
0.0824 17.07 8500 0.4844 0.3583
0.0737 18.07 9000 0.4801 0.3534
0.076 19.08 9500 0.4945 0.3529
0.0627 20.08 10000 0.4700 0.3417
0.0723 21.08 10500 0.4630 0.3449
0.0597 22.09 11000 0.5164 0.3456
0.0566 23.09 11500 0.4957 0.3401
0.0453 24.1 12000 0.5032 0.3419
0.0492 25.1 12500 0.5391 0.3387
0.0524 26.1 13000 0.5057 0.3348
0.0381 27.11 13500 0.5098 0.3331
0.0402 28.11 14000 0.5087 0.3353
0.0358 29.12 14500 0.4888 0.3315

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.10.0+cu111
  • Datasets 1.13.3
  • Tokenizers 0.10.3
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