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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: 0.5406
- Wer: 0.3393
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 |
|---|---|---|---|---|
| 3.5789 | 1.0 | 500 | 2.1382 | 1.0004 |
| 0.9156 | 2.01 | 1000 | 0.5722 | 0.5177 |
| 0.446 | 3.01 | 1500 | 0.4804 | 0.4662 |
| 0.3014 | 4.02 | 2000 | 0.4326 | 0.4278 |
| 0.2326 | 5.02 | 2500 | 0.4359 | 0.4074 |
| 0.1928 | 6.02 | 3000 | 0.4353 | 0.4004 |
| 0.16 | 7.03 | 3500 | 0.4414 | 0.3838 |
| 0.1421 | 8.03 | 4000 | 0.4251 | 0.3865 |
| 0.1251 | 9.04 | 4500 | 0.5128 | 0.3786 |
| 0.1085 | 10.04 | 5000 | 0.4985 | 0.3841 |
| 0.1005 | 11.04 | 5500 | 0.5047 | 0.3750 |
| 0.0876 | 12.05 | 6000 | 0.4934 | 0.3651 |
| 0.0806 | 13.05 | 6500 | 0.5299 | 0.3684 |
| 0.0723 | 14.06 | 7000 | 0.5206 | 0.3639 |
| 0.0682 | 15.06 | 7500 | 0.5409 | 0.3623 |
| 0.0637 | 16.06 | 8000 | 0.5135 | 0.3657 |
| 0.0573 | 17.07 | 8500 | 0.5416 | 0.3564 |
| 0.0563 | 18.07 | 9000 | 0.5378 | 0.3610 |
| 0.046 | 19.08 | 9500 | 0.5530 | 0.3523 |
| 0.0475 | 20.08 | 10000 | 0.5603 | 0.3544 |
| 0.0421 | 21.08 | 10500 | 0.5027 | 0.3532 |
| 0.0354 | 22.09 | 11000 | 0.5579 | 0.3511 |
| 0.0347 | 23.09 | 11500 | 0.5536 | 0.3590 |
| 0.0326 | 24.1 | 12000 | 0.5594 | 0.3504 |
| 0.0277 | 25.1 | 12500 | 0.5058 | 0.3470 |
| 0.0261 | 26.1 | 13000 | 0.5304 | 0.3488 |
| 0.0255 | 27.11 | 13500 | 0.5499 | 0.3407 |
| 0.0243 | 28.11 | 14000 | 0.5489 | 0.3415 |
| 0.0238 | 29.12 | 14500 | 0.5406 | 0.3393 |
Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
