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sew-small-100k-timit
This model is a fine-tuned version of asapp/sew-small-100k on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.4926
- Wer: 0.2988
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 |
|---|---|---|---|---|
| 3.071 | 0.69 | 100 | 3.0262 | 1.0 |
| 2.9304 | 1.38 | 200 | 2.9297 | 1.0 |
| 2.8823 | 2.07 | 300 | 2.8367 | 1.0 |
| 1.5668 | 2.76 | 400 | 1.2310 | 0.8807 |
| 0.7422 | 3.45 | 500 | 0.7080 | 0.5957 |
| 0.4121 | 4.14 | 600 | 0.5829 | 0.5073 |
| 0.3981 | 4.83 | 700 | 0.5153 | 0.4461 |
| 0.5038 | 5.52 | 800 | 0.4908 | 0.4151 |
| 0.2899 | 6.21 | 900 | 0.5122 | 0.4111 |
| 0.2198 | 6.9 | 1000 | 0.4908 | 0.3803 |
| 0.2129 | 7.59 | 1100 | 0.4668 | 0.3789 |
| 0.3007 | 8.28 | 1200 | 0.4788 | 0.3562 |
| 0.2264 | 8.97 | 1300 | 0.5113 | 0.3635 |
| 0.1536 | 9.66 | 1400 | 0.4950 | 0.3441 |
| 0.1206 | 10.34 | 1500 | 0.5062 | 0.3421 |
| 0.2021 | 11.03 | 1600 | 0.4900 | 0.3283 |
| 0.1458 | 11.72 | 1700 | 0.5019 | 0.3307 |
| 0.1151 | 12.41 | 1800 | 0.4989 | 0.3270 |
| 0.0985 | 13.1 | 1900 | 0.4925 | 0.3173 |
| 0.1412 | 13.79 | 2000 | 0.4868 | 0.3125 |
| 0.1579 | 14.48 | 2100 | 0.4983 | 0.3147 |
| 0.1043 | 15.17 | 2200 | 0.4914 | 0.3091 |
| 0.0773 | 15.86 | 2300 | 0.4858 | 0.3102 |
| 0.1327 | 16.55 | 2400 | 0.5084 | 0.3064 |
| 0.1281 | 17.24 | 2500 | 0.5017 | 0.3025 |
| 0.0845 | 17.93 | 2600 | 0.5001 | 0.3012 |
| 0.0717 | 18.62 | 2700 | 0.4894 | 0.3004 |
| 0.0835 | 19.31 | 2800 | 0.4963 | 0.2998 |
| 0.1181 | 20.0 | 2900 | 0.4926 | 0.2988 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
