<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
wav2vec2-base-STTTest
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: 0.5198
- Wer: 0.3393
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 |
|---|---|---|---|---|
| 0.231 | 1.0 | 500 | 0.4337 | 0.4100 |
| 0.1845 | 2.01 | 1000 | 0.4296 | 0.3931 |
| 0.1551 | 3.01 | 1500 | 0.4397 | 0.3770 |
| 0.1479 | 4.02 | 2000 | 0.4524 | 0.3827 |
| 0.1186 | 5.02 | 2500 | 0.5182 | 0.3795 |
| 0.1079 | 6.02 | 3000 | 0.4799 | 0.3737 |
| 0.0974 | 7.03 | 3500 | 0.4966 | 0.3860 |
| 0.0878 | 8.03 | 4000 | 0.4993 | 0.3699 |
| 0.0788 | 9.04 | 4500 | 0.5183 | 0.3678 |
| 0.0732 | 10.04 | 5000 | 0.5064 | 0.3635 |
| 0.0664 | 11.04 | 5500 | 0.5330 | 0.3663 |
| 0.0596 | 12.05 | 6000 | 0.5147 | 0.3516 |
| 0.0538 | 13.05 | 6500 | 0.5254 | 0.3581 |
| 0.0535 | 14.06 | 7000 | 0.4902 | 0.3534 |
| 0.0492 | 15.06 | 7500 | 0.5115 | 0.3488 |
| 0.0455 | 16.06 | 8000 | 0.5250 | 0.3472 |
| 0.0434 | 17.07 | 8500 | 0.5338 | 0.3515 |
| 0.0351 | 18.07 | 9000 | 0.5365 | 0.3444 |
| 0.0341 | 19.08 | 9500 | 0.4886 | 0.3439 |
| 0.0332 | 20.08 | 10000 | 0.5234 | 0.3475 |
| 0.0289 | 21.08 | 10500 | 0.5375 | 0.3464 |
| 0.028 | 22.09 | 11000 | 0.5395 | 0.3478 |
| 0.0225 | 23.09 | 11500 | 0.5236 | 0.3428 |
| 0.0244 | 24.1 | 12000 | 0.5122 | 0.3402 |
| 0.0246 | 25.1 | 12500 | 0.5212 | 0.3390 |
| 0.0214 | 26.1 | 13000 | 0.5198 | 0.3393 |
| 0.0179 | 27.11 | 13500 | 0.5198 | 0.3393 |
| 0.0194 | 28.11 | 14000 | 0.5198 | 0.3393 |
| 0.0193 | 29.12 | 14500 | 0.5198 | 0.3393 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1+cu111
- Datasets 1.18.3
- Tokenizers 0.12.1
