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