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Project_NLP
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: 0.5324
- Wer: 0.3355
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 |
|---|---|---|---|---|
| 3.5697 | 1.0 | 500 | 2.1035 | 0.9979 |
| 0.8932 | 2.01 | 1000 | 0.5649 | 0.5621 |
| 0.4363 | 3.01 | 1500 | 0.4326 | 0.4612 |
| 0.3035 | 4.02 | 2000 | 0.4120 | 0.4191 |
| 0.2343 | 5.02 | 2500 | 0.4199 | 0.3985 |
| 0.1921 | 6.02 | 3000 | 0.4380 | 0.4043 |
| 0.1549 | 7.03 | 3500 | 0.4456 | 0.3925 |
| 0.1385 | 8.03 | 4000 | 0.4264 | 0.3871 |
| 0.1217 | 9.04 | 4500 | 0.4744 | 0.3774 |
| 0.1041 | 10.04 | 5000 | 0.4498 | 0.3745 |
| 0.0968 | 11.04 | 5500 | 0.4716 | 0.3628 |
| 0.0893 | 12.05 | 6000 | 0.4680 | 0.3764 |
| 0.078 | 13.05 | 6500 | 0.5100 | 0.3623 |
| 0.0704 | 14.06 | 7000 | 0.4893 | 0.3552 |
| 0.0659 | 15.06 | 7500 | 0.4956 | 0.3565 |
| 0.0578 | 16.06 | 8000 | 0.5450 | 0.3595 |
| 0.0563 | 17.07 | 8500 | 0.4891 | 0.3614 |
| 0.0557 | 18.07 | 9000 | 0.5307 | 0.3548 |
| 0.0447 | 19.08 | 9500 | 0.4923 | 0.3493 |
| 0.0456 | 20.08 | 10000 | 0.5156 | 0.3479 |
| 0.0407 | 21.08 | 10500 | 0.4979 | 0.3389 |
| 0.0354 | 22.09 | 11000 | 0.5549 | 0.3462 |
| 0.0322 | 23.09 | 11500 | 0.5601 | 0.3439 |
| 0.0342 | 24.1 | 12000 | 0.5131 | 0.3451 |
| 0.0276 | 25.1 | 12500 | 0.5206 | 0.3392 |
| 0.0245 | 26.1 | 13000 | 0.5337 | 0.3373 |
| 0.0226 | 27.11 | 13500 | 0.5311 | 0.3353 |
| 0.0229 | 28.11 | 14000 | 0.5375 | 0.3373 |
| 0.0225 | 29.12 | 14500 | 0.5324 | 0.3355 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
