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: 0.5079
- Wer: 0.3365
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 |
|---|---|---|---|---|
| 3.4933 | 1.0 | 500 | 1.7711 | 0.9978 |
| 0.8658 | 2.01 | 1000 | 0.6262 | 0.5295 |
| 0.4405 | 3.01 | 1500 | 0.4841 | 0.4845 |
| 0.3062 | 4.02 | 2000 | 0.4897 | 0.4215 |
| 0.233 | 5.02 | 2500 | 0.4326 | 0.4101 |
| 0.1896 | 6.02 | 3000 | 0.4924 | 0.4078 |
| 0.1589 | 7.03 | 3500 | 0.4430 | 0.3896 |
| 0.1391 | 8.03 | 4000 | 0.4334 | 0.3889 |
| 0.1216 | 9.04 | 4500 | 0.4691 | 0.3828 |
| 0.1063 | 10.04 | 5000 | 0.4726 | 0.3705 |
| 0.0992 | 11.04 | 5500 | 0.4333 | 0.3690 |
| 0.0872 | 12.05 | 6000 | 0.4986 | 0.3771 |
| 0.0829 | 13.05 | 6500 | 0.4903 | 0.3685 |
| 0.0713 | 14.06 | 7000 | 0.5293 | 0.3655 |
| 0.068 | 15.06 | 7500 | 0.5039 | 0.3612 |
| 0.0621 | 16.06 | 8000 | 0.5314 | 0.3665 |
| 0.0571 | 17.07 | 8500 | 0.5038 | 0.3572 |
| 0.0585 | 18.07 | 9000 | 0.4718 | 0.3550 |
| 0.0487 | 19.08 | 9500 | 0.5482 | 0.3626 |
| 0.0459 | 20.08 | 10000 | 0.5239 | 0.3545 |
| 0.0419 | 21.08 | 10500 | 0.5096 | 0.3473 |
| 0.0362 | 22.09 | 11000 | 0.5222 | 0.3500 |
| 0.0331 | 23.09 | 11500 | 0.5062 | 0.3489 |
| 0.0352 | 24.1 | 12000 | 0.4913 | 0.3459 |
| 0.0315 | 25.1 | 12500 | 0.4701 | 0.3412 |
| 0.028 | 26.1 | 13000 | 0.5178 | 0.3402 |
| 0.0255 | 27.11 | 13500 | 0.5168 | 0.3405 |
| 0.0228 | 28.11 | 14000 | 0.5154 | 0.3368 |
| 0.0232 | 29.12 | 14500 | 0.5079 | 0.3365 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1