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wav2vec2-15epochs-3e4
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2620
- Wer: 0.2097
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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0349 | 0.36 | 100 | 0.2561 | 0.2127 |
| 0.0248 | 0.72 | 200 | 0.2822 | 0.2133 |
| 0.0298 | 1.08 | 300 | 0.2751 | 0.2317 |
| 0.0459 | 1.44 | 400 | 0.3767 | 0.2538 |
| 0.0693 | 1.8 | 500 | 0.4385 | 0.3031 |
| 0.06 | 2.16 | 600 | 0.4180 | 0.2919 |
| 0.0663 | 2.52 | 700 | 0.3613 | 0.2707 |
| 0.0638 | 2.88 | 800 | 0.3703 | 0.2887 |
| 0.0823 | 3.24 | 900 | 0.3058 | 0.2815 |
| 0.0673 | 3.6 | 1000 | 0.3425 | 0.2793 |
| 0.049 | 3.96 | 1100 | 0.3508 | 0.2597 |
| 0.0588 | 4.32 | 1200 | 0.3257 | 0.2542 |
| 0.0527 | 4.68 | 1300 | 0.3560 | 0.2660 |
| 0.0799 | 5.04 | 1400 | 0.3480 | 0.2544 |
| 0.0526 | 5.4 | 1500 | 0.3653 | 0.2634 |
| 0.0688 | 5.76 | 1600 | 0.3146 | 0.2499 |
| 0.0651 | 6.12 | 1700 | 0.3179 | 0.2479 |
| 0.0752 | 6.47 | 1800 | 0.3011 | 0.2359 |
| 0.0833 | 6.83 | 1900 | 0.3444 | 0.2469 |
| 0.055 | 7.19 | 2000 | 0.3242 | 0.2438 |
| 0.0626 | 7.55 | 2100 | 0.3166 | 0.2343 |
| 0.0558 | 7.91 | 2200 | 0.3307 | 0.2385 |
| 0.061 | 8.27 | 2300 | 0.3255 | 0.2304 |
| 0.0529 | 8.63 | 2400 | 0.2864 | 0.2365 |
| 0.0431 | 8.99 | 2500 | 0.3058 | 0.2276 |
| 0.0737 | 9.35 | 2600 | 0.2943 | 0.2284 |
| 0.0514 | 9.71 | 2700 | 0.3015 | 0.2277 |
| 0.0893 | 10.07 | 2800 | 0.2709 | 0.2287 |
| 0.0752 | 10.43 | 2900 | 0.2897 | 0.2264 |
| 0.0604 | 10.79 | 3000 | 0.2772 | 0.2238 |
| 0.0433 | 11.15 | 3100 | 0.2577 | 0.2188 |
| 0.0325 | 11.51 | 3200 | 0.2671 | 0.2162 |
| 0.0389 | 11.87 | 3300 | 0.2694 | 0.2177 |
| 0.0487 | 12.23 | 3400 | 0.2835 | 0.2210 |
| 0.0318 | 12.59 | 3500 | 0.2754 | 0.2153 |
| 0.0268 | 12.95 | 3600 | 0.2668 | 0.2153 |
| 0.0494 | 13.31 | 3700 | 0.2734 | 0.2149 |
| 0.0645 | 13.67 | 3800 | 0.2450 | 0.2111 |
| 0.0318 | 14.03 | 3900 | 0.2580 | 0.2099 |
| 0.0454 | 14.39 | 4000 | 0.2656 | 0.2092 |
| 0.0187 | 14.75 | 4100 | 0.2620 | 0.2097 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 2.9.0
- Tokenizers 0.10.3
