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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: 0.5436
- Wer: 0.3401
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 |
|---|---|---|---|---|
| 3.5276 | 1.0 | 500 | 1.9983 | 1.0066 |
| 0.8606 | 2.01 | 1000 | 0.5323 | 0.5220 |
| 0.4339 | 3.01 | 1500 | 0.4697 | 0.4512 |
| 0.3026 | 4.02 | 2000 | 0.4342 | 0.4266 |
| 0.2297 | 5.02 | 2500 | 0.5001 | 0.4135 |
| 0.1939 | 6.02 | 3000 | 0.4350 | 0.3897 |
| 0.1613 | 7.03 | 3500 | 0.4740 | 0.3883 |
| 0.1452 | 8.03 | 4000 | 0.4289 | 0.3825 |
| 0.1362 | 9.04 | 4500 | 0.4721 | 0.3927 |
| 0.1146 | 10.04 | 5000 | 0.4707 | 0.3730 |
| 0.1061 | 11.04 | 5500 | 0.4470 | 0.3701 |
| 0.0947 | 12.05 | 6000 | 0.4694 | 0.3722 |
| 0.0852 | 13.05 | 6500 | 0.5222 | 0.3733 |
| 0.0741 | 14.06 | 7000 | 0.4881 | 0.3657 |
| 0.069 | 15.06 | 7500 | 0.4957 | 0.3677 |
| 0.0679 | 16.06 | 8000 | 0.5241 | 0.3634 |
| 0.0618 | 17.07 | 8500 | 0.5091 | 0.3564 |
| 0.0576 | 18.07 | 9000 | 0.5055 | 0.3557 |
| 0.0493 | 19.08 | 9500 | 0.5013 | 0.3515 |
| 0.0469 | 20.08 | 10000 | 0.5506 | 0.3530 |
| 0.044 | 21.08 | 10500 | 0.5564 | 0.3528 |
| 0.0368 | 22.09 | 11000 | 0.5213 | 0.3509 |
| 0.0355 | 23.09 | 11500 | 0.5707 | 0.3495 |
| 0.0357 | 24.1 | 12000 | 0.5558 | 0.3483 |
| 0.0285 | 25.1 | 12500 | 0.5613 | 0.3455 |
| 0.0285 | 26.1 | 13000 | 0.5533 | 0.3480 |
| 0.0266 | 27.11 | 13500 | 0.5526 | 0.3462 |
| 0.0249 | 28.11 | 14000 | 0.5488 | 0.3429 |
| 0.0237 | 29.12 | 14500 | 0.5436 | 0.3401 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu115
- Datasets 1.18.3
- Tokenizers 0.12.1
