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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: 0.5155
- Wer: 0.3388
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 |
|---|---|---|---|---|
| 3.5822 | 1.0 | 500 | 2.4127 | 1.0 |
| 0.9838 | 2.01 | 1000 | 0.5401 | 0.5363 |
| 0.4308 | 3.01 | 1500 | 0.4380 | 0.4592 |
| 0.3086 | 4.02 | 2000 | 0.4409 | 0.4503 |
| 0.2324 | 5.02 | 2500 | 0.4148 | 0.4041 |
| 0.202 | 6.02 | 3000 | 0.4214 | 0.3882 |
| 0.1595 | 7.03 | 3500 | 0.4489 | 0.3875 |
| 0.1383 | 8.03 | 4000 | 0.4225 | 0.3858 |
| 0.1246 | 9.04 | 4500 | 0.4512 | 0.3846 |
| 0.104 | 10.04 | 5000 | 0.4676 | 0.3875 |
| 0.0949 | 11.04 | 5500 | 0.4389 | 0.3683 |
| 0.0899 | 12.05 | 6000 | 0.4964 | 0.3803 |
| 0.0854 | 13.05 | 6500 | 0.5397 | 0.3798 |
| 0.0728 | 14.06 | 7000 | 0.4823 | 0.3666 |
| 0.065 | 15.06 | 7500 | 0.5187 | 0.3648 |
| 0.0573 | 16.06 | 8000 | 0.5378 | 0.3715 |
| 0.0546 | 17.07 | 8500 | 0.5239 | 0.3705 |
| 0.0573 | 18.07 | 9000 | 0.5094 | 0.3554 |
| 0.0478 | 19.08 | 9500 | 0.5334 | 0.3657 |
| 0.0673 | 20.08 | 10000 | 0.5300 | 0.3528 |
| 0.0434 | 21.08 | 10500 | 0.5314 | 0.3528 |
| 0.0363 | 22.09 | 11000 | 0.5540 | 0.3512 |
| 0.0326 | 23.09 | 11500 | 0.5514 | 0.3510 |
| 0.0332 | 24.1 | 12000 | 0.5439 | 0.3492 |
| 0.0275 | 25.1 | 12500 | 0.5273 | 0.3432 |
| 0.0267 | 26.1 | 13000 | 0.5068 | 0.3430 |
| 0.0243 | 27.11 | 13500 | 0.5131 | 0.3388 |
| 0.0228 | 28.11 | 14000 | 0.5247 | 0.3406 |
| 0.0227 | 29.12 | 14500 | 0.5155 | 0.3388 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
