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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: 0.5195
- Wer: 0.3386
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 |
|---|---|---|---|---|
| 3.5345 | 1.0 | 500 | 2.1466 | 1.0010 |
| 0.949 | 2.01 | 1000 | 0.5687 | 0.5492 |
| 0.445 | 3.01 | 1500 | 0.4562 | 0.4717 |
| 0.2998 | 4.02 | 2000 | 0.4154 | 0.4401 |
| 0.2242 | 5.02 | 2500 | 0.3887 | 0.4034 |
| 0.1834 | 6.02 | 3000 | 0.4262 | 0.3905 |
| 0.1573 | 7.03 | 3500 | 0.4200 | 0.3927 |
| 0.1431 | 8.03 | 4000 | 0.4194 | 0.3869 |
| 0.1205 | 9.04 | 4500 | 0.4600 | 0.3912 |
| 0.1082 | 10.04 | 5000 | 0.4613 | 0.3776 |
| 0.0984 | 11.04 | 5500 | 0.4926 | 0.3860 |
| 0.0872 | 12.05 | 6000 | 0.4869 | 0.3780 |
| 0.0826 | 13.05 | 6500 | 0.5033 | 0.3690 |
| 0.0717 | 14.06 | 7000 | 0.4827 | 0.3791 |
| 0.0658 | 15.06 | 7500 | 0.4816 | 0.3650 |
| 0.0579 | 16.06 | 8000 | 0.5433 | 0.3689 |
| 0.056 | 17.07 | 8500 | 0.5513 | 0.3672 |
| 0.0579 | 18.07 | 9000 | 0.4813 | 0.3632 |
| 0.0461 | 19.08 | 9500 | 0.4846 | 0.3501 |
| 0.0431 | 20.08 | 10000 | 0.5449 | 0.3637 |
| 0.043 | 21.08 | 10500 | 0.4906 | 0.3538 |
| 0.0334 | 22.09 | 11000 | 0.5081 | 0.3477 |
| 0.0322 | 23.09 | 11500 | 0.5184 | 0.3439 |
| 0.0316 | 24.1 | 12000 | 0.5412 | 0.3450 |
| 0.0262 | 25.1 | 12500 | 0.5113 | 0.3425 |
| 0.0267 | 26.1 | 13000 | 0.4888 | 0.3414 |
| 0.0258 | 27.11 | 13500 | 0.5071 | 0.3371 |
| 0.0226 | 28.11 | 14000 | 0.5311 | 0.3380 |
| 0.0233 | 29.12 | 14500 | 0.5195 | 0.3386 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
