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exp12-reducedTorgoOnly-predComparison
This model is a fine-tuned version of yongjian/wav2vec2-large-a on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2016
- Wer: 1.0412
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: 4
- eval_batch_size: 8
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 35.9644 | 0.92 | 500 | 3.6081 | 1.0084 |
| 3.2094 | 1.83 | 1000 | 2.7519 | 1.0 |
| 2.8848 | 2.75 | 1500 | 2.7494 | 1.0014 |
| 2.7505 | 3.66 | 2000 | 2.5622 | 1.2840 |
| 2.6354 | 4.58 | 2500 | 2.3878 | 1.2819 |
| 2.3473 | 5.49 | 3000 | 2.0214 | 1.2666 |
| 2.0339 | 6.41 | 3500 | 1.8040 | 1.2394 |
| 1.7779 | 7.33 | 4000 | 1.5898 | 1.2289 |
| 1.5254 | 8.24 | 4500 | 1.7275 | 1.2080 |
| 1.4553 | 9.16 | 5000 | 1.3815 | 1.1786 |
| 1.3222 | 10.07 | 5500 | 1.3647 | 1.1835 |
| 1.1964 | 10.99 | 6000 | 1.2442 | 1.1528 |
| 1.1169 | 11.9 | 6500 | 1.5896 | 1.2059 |
| 1.0342 | 12.82 | 7000 | 1.3880 | 1.1766 |
| 0.989 | 13.74 | 7500 | 1.2111 | 1.1396 |
| 0.9109 | 14.65 | 8000 | 1.3362 | 1.1137 |
| 0.8875 | 15.57 | 8500 | 1.2594 | 1.1326 |
| 0.8053 | 16.48 | 9000 | 1.1858 | 1.1242 |
| 0.7566 | 17.4 | 9500 | 1.1987 | 1.1117 |
| 0.7284 | 18.32 | 10000 | 1.2963 | 1.0998 |
| 0.7345 | 19.23 | 10500 | 1.1835 | 1.0865 |
| 0.6424 | 20.15 | 11000 | 1.1564 | 1.0907 |
| 0.6323 | 21.06 | 11500 | 1.2123 | 1.0851 |
| 0.5871 | 21.98 | 12000 | 1.2736 | 1.0691 |
| 0.5788 | 22.89 | 12500 | 1.2094 | 1.0768 |
| 0.5368 | 23.81 | 13000 | 1.1626 | 1.0398 |
| 0.5357 | 24.73 | 13500 | 1.1960 | 1.0607 |
| 0.5407 | 25.64 | 14000 | 1.1724 | 1.0586 |
| 0.491 | 26.56 | 14500 | 1.1877 | 1.0426 |
| 0.4866 | 27.47 | 15000 | 1.2227 | 1.0593 |
| 0.5011 | 28.39 | 15500 | 1.2033 | 1.0440 |
| 0.4634 | 29.3 | 16000 | 1.2016 | 1.0412 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
