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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: 0.5093
- Wer: 0.3413
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 |
|---|---|---|---|---|
| 3.5009 | 1.0 | 500 | 1.6207 | 0.9471 |
| 0.8414 | 2.01 | 1000 | 0.5128 | 0.5033 |
| 0.4366 | 3.01 | 1500 | 0.4449 | 0.4450 |
| 0.3015 | 4.02 | 2000 | 0.3835 | 0.4108 |
| 0.2281 | 5.02 | 2500 | 0.3989 | 0.4109 |
| 0.1914 | 6.02 | 3000 | 0.4286 | 0.3982 |
| 0.1555 | 7.03 | 3500 | 0.4547 | 0.3889 |
| 0.1349 | 8.03 | 4000 | 0.3876 | 0.3779 |
| 0.1252 | 9.04 | 4500 | 0.4460 | 0.3810 |
| 0.1066 | 10.04 | 5000 | 0.3905 | 0.3772 |
| 0.0979 | 11.04 | 5500 | 0.4469 | 0.3646 |
| 0.0883 | 12.05 | 6000 | 0.4547 | 0.3612 |
| 0.0801 | 13.05 | 6500 | 0.4741 | 0.3645 |
| 0.0709 | 14.06 | 7000 | 0.4682 | 0.3592 |
| 0.0665 | 15.06 | 7500 | 0.4689 | 0.3647 |
| 0.0579 | 16.06 | 8000 | 0.5330 | 0.3622 |
| 0.0556 | 17.07 | 8500 | 0.4885 | 0.3575 |
| 0.0547 | 18.07 | 9000 | 0.4936 | 0.3543 |
| 0.0462 | 19.08 | 9500 | 0.4928 | 0.3524 |
| 0.0475 | 20.08 | 10000 | 0.5286 | 0.3525 |
| 0.0426 | 21.08 | 10500 | 0.5100 | 0.3550 |
| 0.0364 | 22.09 | 11000 | 0.5372 | 0.3493 |
| 0.0306 | 23.09 | 11500 | 0.5049 | 0.3443 |
| 0.0314 | 24.1 | 12000 | 0.5223 | 0.3519 |
| 0.0261 | 25.1 | 12500 | 0.5380 | 0.3486 |
| 0.0257 | 26.1 | 13000 | 0.5326 | 0.3484 |
| 0.0252 | 27.11 | 13500 | 0.5299 | 0.3446 |
| 0.0226 | 28.11 | 14000 | 0.5174 | 0.3424 |
| 0.0232 | 29.12 | 14500 | 0.5093 | 0.3413 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
