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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: 0.5108
- Wer: 0.3342
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 |
|---|---|---|---|---|
| 3.6383 | 1.0 | 500 | 2.3747 | 1.0 |
| 0.9624 | 2.01 | 1000 | 0.5724 | 0.5213 |
| 0.4521 | 3.01 | 1500 | 0.4892 | 0.4794 |
| 0.3126 | 4.02 | 2000 | 0.4250 | 0.3991 |
| 0.2299 | 5.02 | 2500 | 0.4288 | 0.3929 |
| 0.195 | 6.02 | 3000 | 0.4707 | 0.3974 |
| 0.1602 | 7.03 | 3500 | 0.4731 | 0.4034 |
| 0.1477 | 8.03 | 4000 | 0.4405 | 0.3896 |
| 0.1284 | 9.04 | 4500 | 0.4663 | 0.3850 |
| 0.1114 | 10.04 | 5000 | 0.4814 | 0.3759 |
| 0.1024 | 11.04 | 5500 | 0.4821 | 0.3701 |
| 0.0973 | 12.05 | 6000 | 0.4718 | 0.3709 |
| 0.0832 | 13.05 | 6500 | 0.5257 | 0.3678 |
| 0.0741 | 14.06 | 7000 | 0.4741 | 0.3621 |
| 0.0696 | 15.06 | 7500 | 0.5073 | 0.3710 |
| 0.0664 | 16.06 | 8000 | 0.4886 | 0.3651 |
| 0.0613 | 17.07 | 8500 | 0.5300 | 0.3588 |
| 0.0612 | 18.07 | 9000 | 0.4983 | 0.3543 |
| 0.049 | 19.08 | 9500 | 0.5158 | 0.3592 |
| 0.0455 | 20.08 | 10000 | 0.5213 | 0.3525 |
| 0.042 | 21.08 | 10500 | 0.4979 | 0.3474 |
| 0.0376 | 22.09 | 11000 | 0.5335 | 0.3493 |
| 0.0331 | 23.09 | 11500 | 0.5276 | 0.3451 |
| 0.0346 | 24.1 | 12000 | 0.5106 | 0.3428 |
| 0.0294 | 25.1 | 12500 | 0.5414 | 0.3426 |
| 0.0265 | 26.1 | 13000 | 0.5234 | 0.3363 |
| 0.0273 | 27.11 | 13500 | 0.5207 | 0.3356 |
| 0.0255 | 28.11 | 14000 | 0.5092 | 0.3354 |
| 0.0248 | 29.12 | 14500 | 0.5108 | 0.3342 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
