danieleV9H's picture
Update README.md
b3aadc1
metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: hubert-base-timit-demo-google-colab-ft30ep_v5
    results: []

hubert-base-timit-demo-google-colab-ft30ep_v5

This model is a fine-tuned version of facebook/hubert-base-ls960 on the timit-asr dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4763
  • Wer: 0.3322

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
3.9596 0.87 500 3.1237 1.0
2.5388 1.73 1000 1.1689 0.9184
1.0448 2.6 1500 0.6106 0.5878
0.6793 3.46 2000 0.4912 0.5200
0.5234 4.33 2500 0.4529 0.4798
0.4368 5.19 3000 0.4239 0.4543
0.3839 6.06 3500 0.4326 0.4339
0.3315 6.92 4000 0.4265 0.4173
0.2878 7.79 4500 0.4304 0.4068
0.25 8.65 5000 0.4130 0.3940
0.242 9.52 5500 0.4310 0.3938
0.2182 10.38 6000 0.4204 0.3843
0.2063 11.25 6500 0.4449 0.3816
0.2099 12.11 7000 0.4016 0.3681
0.1795 12.98 7500 0.4027 0.3647
0.1604 13.84 8000 0.4294 0.3664
0.1683 14.71 8500 0.4412 0.3661
0.1452 15.57 9000 0.4484 0.3588
0.1491 16.44 9500 0.4508 0.3515
0.1388 17.3 10000 0.4240 0.3518
0.1399 18.17 10500 0.4605 0.3513
0.1265 19.03 11000 0.4412 0.3485
0.1137 19.9 11500 0.4520 0.3467
0.106 20.76 12000 0.4873 0.3426
0.1243 21.63 12500 0.4456 0.3396
0.1055 22.49 13000 0.4819 0.3406
0.1124 23.36 13500 0.4613 0.3391
0.1064 24.22 14000 0.4842 0.3430
0.0875 25.09 14500 0.4661 0.3348
0.086 25.95 15000 0.4724 0.3371
0.0842 26.82 15500 0.4982 0.3381
0.0834 27.68 16000 0.4856 0.3337
0.0918 28.55 16500 0.4783 0.3344
0.0773 29.41 17000 0.4763 0.3322

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu113
  • Datasets 1.18.3
  • Tokenizers 0.12.1