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wav2vec2-base-timit-fine-tuned
This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.3457
- Wer: 0.2151
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: 32
- eval_batch_size: 1
- 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: 20.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.1621 | 0.69 | 100 | 3.1102 | 1.0 |
| 2.9592 | 1.38 | 200 | 2.9603 | 1.0 |
| 2.9116 | 2.07 | 300 | 2.8921 | 1.0 |
| 2.1332 | 2.76 | 400 | 1.9718 | 0.9958 |
| 0.8477 | 3.45 | 500 | 0.7813 | 0.5237 |
| 0.4251 | 4.14 | 600 | 0.5166 | 0.3982 |
| 0.3743 | 4.83 | 700 | 0.4400 | 0.3578 |
| 0.4194 | 5.52 | 800 | 0.4077 | 0.3370 |
| 0.23 | 6.21 | 900 | 0.4018 | 0.3142 |
| 0.1554 | 6.9 | 1000 | 0.3623 | 0.2995 |
| 0.1511 | 7.59 | 1100 | 0.3433 | 0.2697 |
| 0.1983 | 8.28 | 1200 | 0.3539 | 0.2715 |
| 0.1443 | 8.97 | 1300 | 0.3622 | 0.2551 |
| 0.0971 | 9.66 | 1400 | 0.3580 | 0.2519 |
| 0.0764 | 10.34 | 1500 | 0.3529 | 0.2437 |
| 0.1203 | 11.03 | 1600 | 0.3455 | 0.2431 |
| 0.0881 | 11.72 | 1700 | 0.3648 | 0.2415 |
| 0.0521 | 12.41 | 1800 | 0.3564 | 0.2320 |
| 0.0434 | 13.1 | 1900 | 0.3485 | 0.2270 |
| 0.0864 | 13.79 | 2000 | 0.3517 | 0.2228 |
| 0.0651 | 14.48 | 2100 | 0.3506 | 0.2285 |
| 0.0423 | 15.17 | 2200 | 0.3428 | 0.2247 |
| 0.0302 | 15.86 | 2300 | 0.3372 | 0.2198 |
| 0.0548 | 16.55 | 2400 | 0.3496 | 0.2196 |
| 0.0674 | 17.24 | 2500 | 0.3407 | 0.2166 |
| 0.0291 | 17.93 | 2600 | 0.3512 | 0.2171 |
| 0.0298 | 18.62 | 2700 | 0.3363 | 0.2158 |
| 0.0419 | 19.31 | 2800 | 0.3493 | 0.2145 |
| 0.046 | 20.0 | 2900 | 0.3457 | 0.2151 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
