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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: 0.5090
- Wer: 0.3435
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 |
|---|---|---|---|---|
| 3.5501 | 1.0 | 500 | 1.9752 | 0.9950 |
| 0.8608 | 2.01 | 1000 | 0.5051 | 0.5035 |
| 0.43 | 3.01 | 1500 | 0.4485 | 0.4525 |
| 0.2921 | 4.02 | 2000 | 0.4658 | 0.4332 |
| 0.2248 | 5.02 | 2500 | 0.4262 | 0.4268 |
| 0.1863 | 6.02 | 3000 | 0.4126 | 0.3977 |
| 0.1542 | 7.03 | 3500 | 0.4795 | 0.3987 |
| 0.1374 | 8.03 | 4000 | 0.4882 | 0.3982 |
| 0.1231 | 9.04 | 4500 | 0.4312 | 0.3790 |
| 0.1082 | 10.04 | 5000 | 0.4344 | 0.3679 |
| 0.0949 | 11.04 | 5500 | 0.4720 | 0.3769 |
| 0.0897 | 12.05 | 6000 | 0.5382 | 0.3706 |
| 0.0816 | 13.05 | 6500 | 0.4946 | 0.3618 |
| 0.0726 | 14.06 | 7000 | 0.5383 | 0.3630 |
| 0.0656 | 15.06 | 7500 | 0.4944 | 0.3693 |
| 0.059 | 16.06 | 8000 | 0.5096 | 0.3639 |
| 0.0572 | 17.07 | 8500 | 0.5066 | 0.3572 |
| 0.0559 | 18.07 | 9000 | 0.5366 | 0.3610 |
| 0.0468 | 19.08 | 9500 | 0.5103 | 0.3604 |
| 0.0413 | 20.08 | 10000 | 0.5126 | 0.3496 |
| 0.044 | 21.08 | 10500 | 0.5055 | 0.3524 |
| 0.0351 | 22.09 | 11000 | 0.5526 | 0.3515 |
| 0.0328 | 23.09 | 11500 | 0.4884 | 0.3512 |
| 0.032 | 24.1 | 12000 | 0.5167 | 0.3474 |
| 0.0271 | 25.1 | 12500 | 0.5027 | 0.3495 |
| 0.0229 | 26.1 | 13000 | 0.5076 | 0.3444 |
| 0.0252 | 27.11 | 13500 | 0.5122 | 0.3464 |
| 0.0224 | 28.11 | 14000 | 0.5133 | 0.3447 |
| 0.0236 | 29.12 | 14500 | 0.5090 | 0.3435 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
