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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: 0.5351
- Wer: 0.3384
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 |
|---|---|---|---|---|
| 3.6311 | 1.0 | 500 | 2.6700 | 1.0 |
| 1.0104 | 2.01 | 1000 | 0.5289 | 0.5277 |
| 0.4483 | 3.01 | 1500 | 0.4576 | 0.4623 |
| 0.3089 | 4.02 | 2000 | 0.4483 | 0.4255 |
| 0.2278 | 5.02 | 2500 | 0.4463 | 0.4022 |
| 0.1886 | 6.02 | 3000 | 0.4653 | 0.3938 |
| 0.1578 | 7.03 | 3500 | 0.4624 | 0.3855 |
| 0.1429 | 8.03 | 4000 | 0.4420 | 0.3854 |
| 0.1244 | 9.04 | 4500 | 0.4980 | 0.3787 |
| 0.1126 | 10.04 | 5000 | 0.4311 | 0.3785 |
| 0.1082 | 11.04 | 5500 | 0.5114 | 0.3782 |
| 0.0888 | 12.05 | 6000 | 0.5392 | 0.3725 |
| 0.0835 | 13.05 | 6500 | 0.6011 | 0.3941 |
| 0.074 | 14.06 | 7000 | 0.5030 | 0.3652 |
| 0.0667 | 15.06 | 7500 | 0.5041 | 0.3583 |
| 0.0595 | 16.06 | 8000 | 0.5125 | 0.3605 |
| 0.0578 | 17.07 | 8500 | 0.5206 | 0.3592 |
| 0.0573 | 18.07 | 9000 | 0.5208 | 0.3643 |
| 0.0469 | 19.08 | 9500 | 0.4670 | 0.3537 |
| 0.0442 | 20.08 | 10000 | 0.5388 | 0.3497 |
| 0.0417 | 21.08 | 10500 | 0.5213 | 0.3581 |
| 0.0361 | 22.09 | 11000 | 0.5096 | 0.3465 |
| 0.0338 | 23.09 | 11500 | 0.5178 | 0.3459 |
| 0.0333 | 24.1 | 12000 | 0.5240 | 0.3490 |
| 0.0256 | 25.1 | 12500 | 0.5438 | 0.3464 |
| 0.0248 | 26.1 | 13000 | 0.5182 | 0.3412 |
| 0.0231 | 27.11 | 13500 | 0.5628 | 0.3423 |
| 0.0228 | 28.11 | 14000 | 0.5416 | 0.3419 |
| 0.0223 | 29.12 | 14500 | 0.5351 | 0.3384 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
