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data-augmentation-whitenoise-timit-1155
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: 0.5458
- Wer: 0.3324
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
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
3.5204 | 0.8 | 500 | 1.6948 | 0.9531 |
0.8435 | 1.6 | 1000 | 0.5367 | 0.5113 |
0.4449 | 2.4 | 1500 | 0.4612 | 0.4528 |
0.3182 | 3.21 | 2000 | 0.4314 | 0.4156 |
0.2328 | 4.01 | 2500 | 0.4250 | 0.4031 |
0.1897 | 4.81 | 3000 | 0.4630 | 0.4023 |
0.1628 | 5.61 | 3500 | 0.4445 | 0.3922 |
0.1472 | 6.41 | 4000 | 0.4452 | 0.3793 |
0.1293 | 7.21 | 4500 | 0.4715 | 0.3847 |
0.1176 | 8.01 | 5000 | 0.4267 | 0.3757 |
0.1023 | 8.81 | 5500 | 0.4494 | 0.3821 |
0.092 | 9.62 | 6000 | 0.4501 | 0.3704 |
0.0926 | 10.42 | 6500 | 0.4722 | 0.3643 |
0.0784 | 11.22 | 7000 | 0.5033 | 0.3765 |
0.077 | 12.02 | 7500 | 0.5165 | 0.3684 |
0.0704 | 12.82 | 8000 | 0.5138 | 0.3646 |
0.0599 | 13.62 | 8500 | 0.5664 | 0.3674 |
0.0582 | 14.42 | 9000 | 0.5188 | 0.3575 |
0.0526 | 15.22 | 9500 | 0.5605 | 0.3621 |
0.0512 | 16.03 | 10000 | 0.5400 | 0.3585 |
0.0468 | 16.83 | 10500 | 0.5471 | 0.3603 |
0.0445 | 17.63 | 11000 | 0.5168 | 0.3555 |
0.0411 | 18.43 | 11500 | 0.5772 | 0.3542 |
0.0394 | 19.23 | 12000 | 0.5079 | 0.3567 |
0.0354 | 20.03 | 12500 | 0.5427 | 0.3613 |
0.0325 | 20.83 | 13000 | 0.5532 | 0.3572 |
0.0318 | 21.63 | 13500 | 0.5223 | 0.3514 |
0.0269 | 22.44 | 14000 | 0.6002 | 0.3460 |
0.028 | 23.24 | 14500 | 0.5591 | 0.3432 |
0.0254 | 24.04 | 15000 | 0.5837 | 0.3432 |
0.0235 | 24.84 | 15500 | 0.5571 | 0.3397 |
0.0223 | 25.64 | 16000 | 0.5470 | 0.3383 |
0.0193 | 26.44 | 16500 | 0.5611 | 0.3367 |
0.0227 | 27.24 | 17000 | 0.5405 | 0.3342 |
0.0183 | 28.04 | 17500 | 0.5205 | 0.3330 |
0.017 | 28.85 | 18000 | 0.5512 | 0.3330 |
0.0167 | 29.65 | 18500 | 0.5458 | 0.3324 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1