sew-d-small-100k-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: 1.7541
- Wer: 0.8061
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 |
| 2.7718 | 4.14 | 600 | 2.7787 | 1.0 |
| 2.6711 | 4.83 | 700 | 2.6401 | 0.9786 |
| 2.6403 | 5.52 | 800 | 2.5435 | 1.0392 |
| 2.4052 | 6.21 | 900 | 2.4580 | 1.0706 |
| 2.1708 | 6.9 | 1000 | 2.2800 | 1.0090 |
| 2.2555 | 7.59 | 1100 | 2.1493 | 0.9579 |
| 2.3673 | 8.28 | 1200 | 2.0709 | 0.9051 |
| 2.091 | 8.97 | 1300 | 2.0258 | 0.8926 |
| 1.8433 | 9.66 | 1400 | 1.9645 | 0.8243 |
| 1.6824 | 10.34 | 1500 | 1.9211 | 0.8707 |
| 2.2282 | 11.03 | 1600 | 1.8914 | 0.8695 |
| 1.9027 | 11.72 | 1700 | 1.8718 | 0.8343 |
| 1.6303 | 12.41 | 1800 | 1.8646 | 0.8232 |
| 1.648 | 13.1 | 1900 | 1.8297 | 0.8177 |
| 2.0429 | 13.79 | 2000 | 1.8127 | 0.8642 |
| 1.8833 | 14.48 | 2100 | 1.8005 | 0.8307 |
| 1.5996 | 15.17 | 2200 | 1.7926 | 0.8467 |
| 1.4876 | 15.86 | 2300 | 1.7795 | 0.8341 |
| 1.8925 | 16.55 | 2400 | 1.7716 | 0.8199 |
| 1.814 | 17.24 | 2500 | 1.7846 | 0.8086 |
| 1.536 | 17.93 | 2600 | 1.7655 | 0.8019 |
| 1.4476 | 18.62 | 2700 | 1.7599 | 0.8070 |
| 1.7629 | 19.31 | 2800 | 1.7589 | 0.8119 |
| 1.7646 | 20.0 | 2900 | 1.7541 | 0.8061 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3