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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: 0.4732
- Wer: 0.3300
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 |
|---|---|---|---|---|
| 3.2982 | 1.0 | 500 | 1.3852 | 0.9990 |
| 0.8067 | 2.01 | 1000 | 0.5318 | 0.5140 |
| 0.4393 | 3.01 | 1500 | 0.4500 | 0.4570 |
| 0.3007 | 4.02 | 2000 | 0.4259 | 0.4091 |
| 0.2306 | 5.02 | 2500 | 0.4092 | 0.3962 |
| 0.1845 | 6.02 | 3000 | 0.3949 | 0.3834 |
| 0.1516 | 7.03 | 3500 | 0.4144 | 0.3759 |
| 0.1347 | 8.03 | 4000 | 0.3958 | 0.3689 |
| 0.1217 | 9.04 | 4500 | 0.4455 | 0.3754 |
| 0.1039 | 10.04 | 5000 | 0.4228 | 0.3684 |
| 0.0921 | 11.04 | 5500 | 0.4310 | 0.3566 |
| 0.082 | 12.05 | 6000 | 0.4549 | 0.3617 |
| 0.078 | 13.05 | 6500 | 0.4535 | 0.3661 |
| 0.0668 | 14.06 | 7000 | 0.4726 | 0.3557 |
| 0.0648 | 15.06 | 7500 | 0.4414 | 0.3512 |
| 0.0581 | 16.06 | 8000 | 0.4781 | 0.3548 |
| 0.057 | 17.07 | 8500 | 0.4626 | 0.3588 |
| 0.0532 | 18.07 | 9000 | 0.5065 | 0.3495 |
| 0.0442 | 19.08 | 9500 | 0.4645 | 0.3390 |
| 0.0432 | 20.08 | 10000 | 0.4786 | 0.3466 |
| 0.0416 | 21.08 | 10500 | 0.4487 | 0.3425 |
| 0.0337 | 22.09 | 11000 | 0.4878 | 0.3416 |
| 0.0305 | 23.09 | 11500 | 0.4787 | 0.3413 |
| 0.0319 | 24.1 | 12000 | 0.4707 | 0.3395 |
| 0.0262 | 25.1 | 12500 | 0.4875 | 0.3345 |
| 0.0266 | 26.1 | 13000 | 0.4801 | 0.3343 |
| 0.025 | 27.11 | 13500 | 0.4926 | 0.3320 |
| 0.022 | 28.11 | 14000 | 0.4894 | 0.3313 |
| 0.0227 | 29.12 | 14500 | 0.4732 | 0.3300 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
