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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: 0.5320
- Wer: 0.3362
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 |
|---|---|---|---|---|
| 3.5752 | 1.0 | 500 | 2.2162 | 1.0392 |
| 0.9682 | 2.01 | 1000 | 0.5208 | 0.5191 |
| 0.4414 | 3.01 | 1500 | 0.4623 | 0.4528 |
| 0.2971 | 4.02 | 2000 | 0.4553 | 0.4421 |
| 0.2328 | 5.02 | 2500 | 0.4960 | 0.4208 |
| 0.1891 | 6.02 | 3000 | 0.4213 | 0.3812 |
| 0.1571 | 7.03 | 3500 | 0.4863 | 0.3865 |
| 0.1544 | 8.03 | 4000 | 0.4677 | 0.3910 |
| 0.1302 | 9.04 | 4500 | 0.6305 | 0.4042 |
| 0.1111 | 10.04 | 5000 | 0.5104 | 0.3830 |
| 0.0986 | 11.04 | 5500 | 0.5332 | 0.3808 |
| 0.088 | 12.05 | 6000 | 0.4494 | 0.3674 |
| 0.0827 | 13.05 | 6500 | 0.4779 | 0.3748 |
| 0.074 | 14.06 | 7000 | 0.5315 | 0.3738 |
| 0.0664 | 15.06 | 7500 | 0.5367 | 0.3661 |
| 0.0568 | 16.06 | 8000 | 0.5707 | 0.3817 |
| 0.057 | 17.07 | 8500 | 0.5381 | 0.3719 |
| 0.0561 | 18.07 | 9000 | 0.5353 | 0.3705 |
| 0.0487 | 19.08 | 9500 | 0.5087 | 0.3579 |
| 0.0444 | 20.08 | 10000 | 0.4910 | 0.3596 |
| 0.0433 | 21.08 | 10500 | 0.4931 | 0.3497 |
| 0.0363 | 22.09 | 11000 | 0.5414 | 0.3488 |
| 0.0318 | 23.09 | 11500 | 0.5405 | 0.3472 |
| 0.033 | 24.1 | 12000 | 0.5476 | 0.3449 |
| 0.0262 | 25.1 | 12500 | 0.5529 | 0.3443 |
| 0.0255 | 26.1 | 13000 | 0.5299 | 0.3417 |
| 0.0252 | 27.11 | 13500 | 0.5092 | 0.3363 |
| 0.0221 | 28.11 | 14000 | 0.5309 | 0.3357 |
| 0.0223 | 29.12 | 14500 | 0.5320 | 0.3362 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
