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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: 0.5035
- Wer: 0.3346
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 |
|---|---|---|---|---|
| 1.1411 | 1.0 | 500 | 0.6675 | 0.6001 |
| 0.5668 | 2.01 | 1000 | 0.4699 | 0.4973 |
| 0.3773 | 3.01 | 1500 | 0.4475 | 0.4403 |
| 0.2696 | 4.02 | 2000 | 0.4162 | 0.4166 |
| 0.2165 | 5.02 | 2500 | 0.3809 | 0.4011 |
| 0.1849 | 6.02 | 3000 | 0.4120 | 0.3849 |
| 0.1542 | 7.03 | 3500 | 0.4436 | 0.3770 |
| 0.1385 | 8.03 | 4000 | 0.3977 | 0.3732 |
| 0.1224 | 9.04 | 4500 | 0.4530 | 0.3672 |
| 0.1233 | 10.04 | 5000 | 0.3949 | 0.3596 |
| 0.1078 | 11.04 | 5500 | 0.4616 | 0.3539 |
| 0.097 | 12.05 | 6000 | 0.4354 | 0.3697 |
| 0.0821 | 13.05 | 6500 | 0.4370 | 0.3643 |
| 0.0724 | 14.06 | 7000 | 0.4729 | 0.3587 |
| 0.0678 | 15.06 | 7500 | 0.5822 | 0.3742 |
| 0.0632 | 16.06 | 8000 | 0.4460 | 0.3513 |
| 0.0627 | 17.07 | 8500 | 0.5531 | 0.3537 |
| 0.0574 | 18.07 | 9000 | 0.5262 | 0.3575 |
| 0.0515 | 19.08 | 9500 | 0.4794 | 0.3488 |
| 0.0475 | 20.08 | 10000 | 0.4941 | 0.3458 |
| 0.0463 | 21.08 | 10500 | 0.4741 | 0.3377 |
| 0.0392 | 22.09 | 11000 | 0.5390 | 0.3381 |
| 0.0401 | 23.09 | 11500 | 0.4984 | 0.3413 |
| 0.0371 | 24.1 | 12000 | 0.5112 | 0.3460 |
| 0.0305 | 25.1 | 12500 | 0.5255 | 0.3418 |
| 0.0278 | 26.1 | 13000 | 0.5045 | 0.3389 |
| 0.0265 | 27.11 | 13500 | 0.4990 | 0.3371 |
| 0.0248 | 28.11 | 14000 | 0.5242 | 0.3362 |
| 0.0249 | 29.12 | 14500 | 0.5035 | 0.3346 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
