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sew-d-small-100k-ft-timit
This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 1.7482
- Wer: 0.7987
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 |
|---|---|---|---|---|
| 4.2068 | 0.69 | 100 | 4.0802 | 1.0 |
| 2.9805 | 1.38 | 200 | 2.9792 | 1.0 |
| 2.9781 | 2.07 | 300 | 2.9408 | 1.0 |
| 2.9655 | 2.76 | 400 | 2.9143 | 1.0 |
| 2.8953 | 3.45 | 500 | 2.8775 | 1.0 |
| 2.7719 | 4.14 | 600 | 2.7815 | 0.9999 |
| 2.6531 | 4.83 | 700 | 2.6375 | 1.0065 |
| 2.6425 | 5.52 | 800 | 2.5602 | 1.0210 |
| 2.3963 | 6.21 | 900 | 2.4665 | 1.0591 |
| 2.1447 | 6.9 | 1000 | 2.2792 | 0.9848 |
| 2.2719 | 7.59 | 1100 | 2.2237 | 0.9465 |
| 2.3629 | 8.28 | 1200 | 2.1058 | 0.8907 |
| 2.0913 | 8.97 | 1300 | 2.0113 | 0.9070 |
| 1.8334 | 9.66 | 1400 | 1.9466 | 0.8177 |
| 1.6608 | 10.34 | 1500 | 1.9217 | 0.8698 |
| 2.2194 | 11.03 | 1600 | 1.9091 | 0.8727 |
| 1.9002 | 11.72 | 1700 | 1.8746 | 0.8332 |
| 1.6268 | 12.41 | 1800 | 1.8782 | 0.7951 |
| 1.6455 | 13.1 | 1900 | 1.8230 | 0.8225 |
| 2.0308 | 13.79 | 2000 | 1.8067 | 0.8560 |
| 1.855 | 14.48 | 2100 | 1.8129 | 0.8177 |
| 1.5901 | 15.17 | 2200 | 1.7891 | 0.8367 |
| 1.4848 | 15.86 | 2300 | 1.7821 | 0.8201 |
| 1.8754 | 16.55 | 2400 | 1.7700 | 0.8137 |
| 1.7975 | 17.24 | 2500 | 1.7795 | 0.8171 |
| 1.5194 | 17.93 | 2600 | 1.7605 | 0.7977 |
| 1.4374 | 18.62 | 2700 | 1.7529 | 0.7978 |
| 1.7498 | 19.31 | 2800 | 1.7522 | 0.8023 |
| 1.7452 | 20.0 | 2900 | 1.7482 | 0.7987 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
