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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: 0.5452
- Wer: 0.3296
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 |
|---|---|---|---|---|
| 3.5557 | 1.0 | 500 | 1.9362 | 1.0072 |
| 0.867 | 2.01 | 1000 | 0.5197 | 0.5173 |
| 0.4281 | 3.01 | 1500 | 0.4609 | 0.4552 |
| 0.3002 | 4.02 | 2000 | 0.4066 | 0.4129 |
| 0.2252 | 5.02 | 2500 | 0.4122 | 0.3952 |
| 0.1857 | 6.02 | 3000 | 0.4650 | 0.3990 |
| 0.1541 | 7.03 | 3500 | 0.4784 | 0.3834 |
| 0.1372 | 8.03 | 4000 | 0.3875 | 0.3805 |
| 0.1213 | 9.04 | 4500 | 0.5606 | 0.4002 |
| 0.1043 | 10.04 | 5000 | 0.4713 | 0.3762 |
| 0.0972 | 11.04 | 5500 | 0.4770 | 0.3692 |
| 0.0876 | 12.05 | 6000 | 0.4755 | 0.3671 |
| 0.0812 | 13.05 | 6500 | 0.4854 | 0.3616 |
| 0.0705 | 14.06 | 7000 | 0.4380 | 0.3659 |
| 0.0759 | 15.06 | 7500 | 0.5025 | 0.3516 |
| 0.0709 | 16.06 | 8000 | 0.5310 | 0.3577 |
| 0.0572 | 17.07 | 8500 | 0.5097 | 0.3561 |
| 0.0572 | 18.07 | 9000 | 0.5150 | 0.3510 |
| 0.0482 | 19.08 | 9500 | 0.4954 | 0.3488 |
| 0.0703 | 20.08 | 10000 | 0.5279 | 0.3512 |
| 0.0457 | 21.08 | 10500 | 0.5336 | 0.3459 |
| 0.036 | 22.09 | 11000 | 0.5471 | 0.3440 |
| 0.0368 | 23.09 | 11500 | 0.5109 | 0.3417 |
| 0.0342 | 24.1 | 12000 | 0.5506 | 0.3415 |
| 0.0318 | 25.1 | 12500 | 0.5291 | 0.3357 |
| 0.03 | 26.1 | 13000 | 0.5347 | 0.3363 |
| 0.026 | 27.11 | 13500 | 0.5475 | 0.3318 |
| 0.0232 | 28.11 | 14000 | 0.5628 | 0.3332 |
| 0.0246 | 29.12 | 14500 | 0.5452 | 0.3296 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
