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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: 0.5185
- Wer: 0.3370
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 |
|---|---|---|---|---|
| 3.5137 | 1.0 | 500 | 1.6719 | 0.9580 |
| 0.8324 | 2.01 | 1000 | 0.5546 | 0.5341 |
| 0.4365 | 3.01 | 1500 | 0.4567 | 0.4635 |
| 0.3058 | 4.02 | 2000 | 0.4429 | 0.4454 |
| 0.2284 | 5.02 | 2500 | 0.4734 | 0.4186 |
| 0.1892 | 6.02 | 3000 | 0.4191 | 0.4030 |
| 0.1542 | 7.03 | 3500 | 0.4522 | 0.3985 |
| 0.1364 | 8.03 | 4000 | 0.4749 | 0.3922 |
| 0.1239 | 9.04 | 4500 | 0.4950 | 0.3977 |
| 0.1092 | 10.04 | 5000 | 0.4468 | 0.3779 |
| 0.0956 | 11.04 | 5500 | 0.4897 | 0.3789 |
| 0.0897 | 12.05 | 6000 | 0.4927 | 0.3718 |
| 0.0792 | 13.05 | 6500 | 0.5242 | 0.3699 |
| 0.0731 | 14.06 | 7000 | 0.5202 | 0.3772 |
| 0.0681 | 15.06 | 7500 | 0.5046 | 0.3637 |
| 0.062 | 16.06 | 8000 | 0.5336 | 0.3664 |
| 0.0556 | 17.07 | 8500 | 0.5017 | 0.3633 |
| 0.0556 | 18.07 | 9000 | 0.5466 | 0.3736 |
| 0.0461 | 19.08 | 9500 | 0.5489 | 0.3566 |
| 0.0439 | 20.08 | 10000 | 0.5399 | 0.3559 |
| 0.0397 | 21.08 | 10500 | 0.5154 | 0.3539 |
| 0.0346 | 22.09 | 11000 | 0.5170 | 0.3513 |
| 0.0338 | 23.09 | 11500 | 0.5236 | 0.3492 |
| 0.0342 | 24.1 | 12000 | 0.5288 | 0.3493 |
| 0.0282 | 25.1 | 12500 | 0.5147 | 0.3449 |
| 0.0251 | 26.1 | 13000 | 0.5092 | 0.3442 |
| 0.0268 | 27.11 | 13500 | 0.5093 | 0.3413 |
| 0.021 | 28.11 | 14000 | 0.5310 | 0.3399 |
| 0.022 | 29.12 | 14500 | 0.5185 | 0.3370 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
