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wav2vec2-base-timit-demo-google-colab

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: 0.6348
  • Wer: 0.3204

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: 4
  • 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
4.2767 0.5 500 2.9921 1.0
1.509 1.01 1000 0.8223 0.6031
0.7226 1.51 1500 0.6185 0.4935
0.5777 2.01 2000 0.5600 0.4569
0.4306 2.51 2500 0.4985 0.4229
0.3854 3.02 3000 0.5113 0.4200
0.3161 3.52 3500 0.5197 0.4042
0.2904 4.02 4000 0.4900 0.3936
0.2404 4.52 4500 0.5209 0.3797
0.2546 5.03 5000 0.4836 0.3855
0.2278 5.53 5500 0.5194 0.3676
0.2049 6.03 6000 0.5647 0.4042
0.199 6.53 6500 0.5699 0.3932
0.1932 7.04 7000 0.5498 0.3694
0.1633 7.54 7500 0.5918 0.3686
0.1674 8.04 8000 0.5298 0.3716
0.1496 8.54 8500 0.5788 0.3726
0.1488 9.05 9000 0.5603 0.3664
0.1286 9.55 9500 0.5427 0.3550
0.1364 10.05 10000 0.5794 0.3621
0.1177 10.55 10500 0.5587 0.3606
0.1126 11.06 11000 0.5788 0.3519
0.1272 11.56 11500 0.5859 0.3595
0.1414 12.06 12000 0.5852 0.3586
0.1081 12.56 12500 0.5653 0.3727
0.1073 13.07 13000 0.5653 0.3526
0.0922 13.57 13500 0.5758 0.3583
0.09 14.07 14000 0.5990 0.3599
0.0987 14.57 14500 0.5837 0.3516
0.0823 15.08 15000 0.5639 0.3454
0.0752 15.58 15500 0.5663 0.3542
0.0714 16.08 16000 0.6273 0.3419
0.0693 16.58 16500 0.6389 0.3441
0.0634 17.09 17000 0.6006 0.3409
0.063 17.59 17500 0.6456 0.3444
0.0627 18.09 18000 0.6706 0.3458
0.0519 18.59 18500 0.6370 0.3396
0.059 19.1 19000 0.6602 0.3390
0.0495 19.6 19500 0.6642 0.3364
0.0601 20.1 20000 0.6495 0.3408
0.07 20.6 20500 0.6526 0.3476
0.0517 21.11 21000 0.6265 0.3401
0.0434 21.61 21500 0.6364 0.3372
0.0383 22.11 22000 0.6742 0.3377
0.0372 22.61 22500 0.6499 0.3330
0.0329 23.12 23000 0.6877 0.3307
0.0366 23.62 23500 0.6351 0.3303
0.0372 24.12 24000 0.6547 0.3286
0.031 24.62 24500 0.6757 0.3304
0.0367 25.13 25000 0.6507 0.3312
0.0309 25.63 25500 0.6645 0.3298
0.03 26.13 26000 0.6342 0.3325
0.0274 26.63 26500 0.6614 0.3255
0.0236 27.14 27000 0.6614 0.3222
0.0263 27.64 27500 0.6560 0.3242
0.0264 28.14 28000 0.6337 0.3237
0.0234 28.64 28500 0.6322 0.3208
0.0249 29.15 29000 0.6367 0.3218
0.0252 29.65 29500 0.6348 0.3204

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.8.2+cu111
  • Datasets 1.17.0
  • Tokenizers 0.11.6
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