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sew-d-small-100k-ft-timit-2
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: 1.7357
- Wer: 0.7935
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 |
|---|---|---|---|---|
| 4.1554 | 0.69 | 100 | 4.0531 | 1.0 |
| 2.9584 | 1.38 | 200 | 2.9775 | 1.0 |
| 2.9355 | 2.07 | 300 | 2.9412 | 1.0 |
| 2.9048 | 2.76 | 400 | 2.9143 | 1.0 |
| 2.8568 | 3.45 | 500 | 2.8786 | 1.0 |
| 2.7248 | 4.14 | 600 | 2.7553 | 0.9833 |
| 2.6124 | 4.83 | 700 | 2.5874 | 1.0511 |
| 2.5463 | 5.52 | 800 | 2.4630 | 1.0883 |
| 2.3302 | 6.21 | 900 | 2.3948 | 1.0651 |
| 2.0669 | 6.9 | 1000 | 2.2228 | 0.9920 |
| 2.1991 | 7.59 | 1100 | 2.0815 | 0.9185 |
| 2.293 | 8.28 | 1200 | 2.0229 | 0.8674 |
| 2.0366 | 8.97 | 1300 | 1.9590 | 0.9165 |
| 1.767 | 9.66 | 1400 | 1.9129 | 0.8125 |
| 1.6222 | 10.34 | 1500 | 1.8868 | 0.8259 |
| 2.173 | 11.03 | 1600 | 1.8691 | 0.8661 |
| 1.8614 | 11.72 | 1700 | 1.8388 | 0.8250 |
| 1.5928 | 12.41 | 1800 | 1.8528 | 0.7772 |
| 1.5978 | 13.1 | 1900 | 1.8002 | 0.7892 |
| 1.9886 | 13.79 | 2000 | 1.7848 | 0.8448 |
| 1.8042 | 14.48 | 2100 | 1.7819 | 0.8156 |
| 1.5488 | 15.17 | 2200 | 1.7615 | 0.8228 |
| 1.4468 | 15.86 | 2300 | 1.7565 | 0.7946 |
| 1.8153 | 16.55 | 2400 | 1.7537 | 0.8341 |
| 1.77 | 17.24 | 2500 | 1.7527 | 0.7958 |
| 1.4742 | 17.93 | 2600 | 1.7592 | 0.7850 |
| 1.4088 | 18.62 | 2700 | 1.7421 | 0.8149 |
| 1.7066 | 19.31 | 2800 | 1.7382 | 0.7977 |
| 1.7068 | 20.0 | 2900 | 1.7357 | 0.7935 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
