wav2vec2-base-timit-fine-tuned

This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3457
  • Wer: 0.2151

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: 32
  • eval_batch_size: 1
  • 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: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.1621 0.69 100 3.1102 1.0
2.9592 1.38 200 2.9603 1.0
2.9116 2.07 300 2.8921 1.0
2.1332 2.76 400 1.9718 0.9958
0.8477 3.45 500 0.7813 0.5237
0.4251 4.14 600 0.5166 0.3982
0.3743 4.83 700 0.4400 0.3578
0.4194 5.52 800 0.4077 0.3370
0.23 6.21 900 0.4018 0.3142
0.1554 6.9 1000 0.3623 0.2995
0.1511 7.59 1100 0.3433 0.2697
0.1983 8.28 1200 0.3539 0.2715
0.1443 8.97 1300 0.3622 0.2551
0.0971 9.66 1400 0.3580 0.2519
0.0764 10.34 1500 0.3529 0.2437
0.1203 11.03 1600 0.3455 0.2431
0.0881 11.72 1700 0.3648 0.2415
0.0521 12.41 1800 0.3564 0.2320
0.0434 13.1 1900 0.3485 0.2270
0.0864 13.79 2000 0.3517 0.2228
0.0651 14.48 2100 0.3506 0.2285
0.0423 15.17 2200 0.3428 0.2247
0.0302 15.86 2300 0.3372 0.2198
0.0548 16.55 2400 0.3496 0.2196
0.0674 17.24 2500 0.3407 0.2166
0.0291 17.93 2600 0.3512 0.2171
0.0298 18.62 2700 0.3363 0.2158
0.0419 19.31 2800 0.3493 0.2145
0.046 20.0 2900 0.3457 0.2151

Framework versions

  • Transformers 4.12.0.dev0
  • Pytorch 1.8.1
  • Datasets 1.14.1.dev0
  • Tokenizers 0.10.3
Downloads last month
11
Hosted inference API
or
This model can be loaded on the Inference API on-demand.