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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: 0.5480
- Wer: 0.3437
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 |
|---|---|---|---|---|
| 3.5237 | 1.0 | 500 | 1.7277 | 0.9752 |
| 0.8339 | 2.01 | 1000 | 0.5413 | 0.5316 |
| 0.4277 | 3.01 | 1500 | 0.4732 | 0.4754 |
| 0.2907 | 4.02 | 2000 | 0.4571 | 0.4476 |
| 0.2254 | 5.02 | 2500 | 0.4611 | 0.4105 |
| 0.1911 | 6.02 | 3000 | 0.4448 | 0.4072 |
| 0.1595 | 7.03 | 3500 | 0.4517 | 0.3843 |
| 0.1377 | 8.03 | 4000 | 0.4551 | 0.3881 |
| 0.1197 | 9.04 | 4500 | 0.4853 | 0.3772 |
| 0.1049 | 10.04 | 5000 | 0.4617 | 0.3707 |
| 0.097 | 11.04 | 5500 | 0.4633 | 0.3622 |
| 0.0872 | 12.05 | 6000 | 0.4635 | 0.3690 |
| 0.0797 | 13.05 | 6500 | 0.5196 | 0.3749 |
| 0.0731 | 14.06 | 7000 | 0.5029 | 0.3639 |
| 0.0667 | 15.06 | 7500 | 0.5053 | 0.3614 |
| 0.0618 | 16.06 | 8000 | 0.5627 | 0.3638 |
| 0.0562 | 17.07 | 8500 | 0.5484 | 0.3577 |
| 0.0567 | 18.07 | 9000 | 0.5163 | 0.3560 |
| 0.0452 | 19.08 | 9500 | 0.5012 | 0.3538 |
| 0.044 | 20.08 | 10000 | 0.4931 | 0.3534 |
| 0.0424 | 21.08 | 10500 | 0.5147 | 0.3519 |
| 0.0356 | 22.09 | 11000 | 0.5540 | 0.3521 |
| 0.0322 | 23.09 | 11500 | 0.5565 | 0.3509 |
| 0.0333 | 24.1 | 12000 | 0.5315 | 0.3428 |
| 0.0281 | 25.1 | 12500 | 0.5284 | 0.3425 |
| 0.0261 | 26.1 | 13000 | 0.5101 | 0.3446 |
| 0.0256 | 27.11 | 13500 | 0.5432 | 0.3415 |
| 0.0229 | 28.11 | 14000 | 0.5484 | 0.3446 |
| 0.0212 | 29.12 | 14500 | 0.5480 | 0.3437 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
