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wav2vec2-base-timit-eng
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: 0.5047
- Wer: 0.2233
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 |
|---|---|---|---|---|
| 3.5485 | 1.0 | 500 | 1.9954 | 1.0042 |
| 0.9068 | 2.01 | 1000 | 0.6418 | 0.4572 |
| 0.4398 | 3.01 | 1500 | 0.4586 | 0.3629 |
| 0.3023 | 4.02 | 2000 | 0.4464 | 0.3248 |
| 0.2328 | 5.02 | 2500 | 0.4019 | 0.2969 |
| 0.1899 | 6.02 | 3000 | 0.4363 | 0.2961 |
| 0.163 | 7.03 | 3500 | 0.4832 | 0.2872 |
| 0.1442 | 8.03 | 4000 | 0.4421 | 0.2801 |
| 0.1246 | 9.04 | 4500 | 0.4757 | 0.2659 |
| 0.1122 | 10.04 | 5000 | 0.4693 | 0.2648 |
| 0.102 | 11.04 | 5500 | 0.4834 | 0.2549 |
| 0.0919 | 12.05 | 6000 | 0.4558 | 0.2633 |
| 0.0866 | 13.05 | 6500 | 0.4527 | 0.2641 |
| 0.0762 | 14.06 | 7000 | 0.4394 | 0.2565 |
| 0.0705 | 15.06 | 7500 | 0.5240 | 0.2609 |
| 0.0647 | 16.06 | 8000 | 0.4980 | 0.2522 |
| 0.0608 | 17.07 | 8500 | 0.5163 | 0.2589 |
| 0.0576 | 18.07 | 9000 | 0.4991 | 0.2565 |
| 0.0499 | 19.08 | 9500 | 0.4750 | 0.2457 |
| 0.047 | 20.08 | 10000 | 0.5162 | 0.2447 |
| 0.0418 | 21.08 | 10500 | 0.4801 | 0.2413 |
| 0.0383 | 22.09 | 11000 | 0.4961 | 0.2394 |
| 0.0342 | 23.09 | 11500 | 0.5209 | 0.2386 |
| 0.032 | 24.1 | 12000 | 0.4970 | 0.2364 |
| 0.0293 | 25.1 | 12500 | 0.4789 | 0.2309 |
| 0.0265 | 26.1 | 13000 | 0.4948 | 0.2302 |
| 0.0269 | 27.11 | 13500 | 0.4917 | 0.2249 |
| 0.0237 | 28.11 | 14000 | 0.4991 | 0.2238 |
| 0.022 | 29.12 | 14500 | 0.5047 | 0.2233 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
