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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: 0.4770
- Wer: 0.3360
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 |
|---|---|---|---|---|
| 3.6401 | 1.0 | 500 | 2.4138 | 1.0 |
| 0.9717 | 2.01 | 1000 | 0.6175 | 0.5531 |
| 0.4393 | 3.01 | 1500 | 0.4309 | 0.4414 |
| 0.2976 | 4.02 | 2000 | 0.4167 | 0.4162 |
| 0.2345 | 5.02 | 2500 | 0.4273 | 0.3927 |
| 0.1919 | 6.02 | 3000 | 0.3983 | 0.3886 |
| 0.1565 | 7.03 | 3500 | 0.5581 | 0.3928 |
| 0.1439 | 8.03 | 4000 | 0.4509 | 0.3821 |
| 0.1266 | 9.04 | 4500 | 0.4733 | 0.3774 |
| 0.1091 | 10.04 | 5000 | 0.4755 | 0.3808 |
| 0.1001 | 11.04 | 5500 | 0.4435 | 0.3689 |
| 0.0911 | 12.05 | 6000 | 0.4962 | 0.3897 |
| 0.0813 | 13.05 | 6500 | 0.5031 | 0.3622 |
| 0.0729 | 14.06 | 7000 | 0.4853 | 0.3597 |
| 0.0651 | 15.06 | 7500 | 0.5180 | 0.3577 |
| 0.0608 | 16.06 | 8000 | 0.5251 | 0.3630 |
| 0.0592 | 17.07 | 8500 | 0.4915 | 0.3591 |
| 0.0577 | 18.07 | 9000 | 0.4724 | 0.3656 |
| 0.0463 | 19.08 | 9500 | 0.4536 | 0.3546 |
| 0.0475 | 20.08 | 10000 | 0.5107 | 0.3546 |
| 0.0464 | 21.08 | 10500 | 0.4829 | 0.3464 |
| 0.0369 | 22.09 | 11000 | 0.4844 | 0.3448 |
| 0.0327 | 23.09 | 11500 | 0.4865 | 0.3437 |
| 0.0337 | 24.1 | 12000 | 0.4825 | 0.3488 |
| 0.0271 | 25.1 | 12500 | 0.4824 | 0.3445 |
| 0.0236 | 26.1 | 13000 | 0.4747 | 0.3397 |
| 0.0243 | 27.11 | 13500 | 0.4840 | 0.3397 |
| 0.0226 | 28.11 | 14000 | 0.4716 | 0.3354 |
| 0.0235 | 29.12 | 14500 | 0.4770 | 0.3360 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
