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torgo_xlsr_finetune-M05-2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5128
- Wer: 1.1148
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: 8
- 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 |
|---|---|---|---|---|
| 22.2047 | 0.86 | 500 | 3.3446 | 0.9930 |
| 3.3679 | 1.72 | 1000 | 2.9591 | 0.9930 |
| 2.8813 | 2.58 | 1500 | 2.7978 | 0.9930 |
| 2.7207 | 3.44 | 2000 | 2.5604 | 0.9930 |
| 2.3274 | 4.3 | 2500 | 2.0135 | 1.4468 |
| 1.5821 | 5.16 | 3000 | 1.6148 | 1.5686 |
| 1.1549 | 6.02 | 3500 | 1.3447 | 1.5014 |
| 0.8908 | 6.88 | 4000 | 1.3315 | 1.4524 |
| 0.7204 | 7.75 | 4500 | 1.3250 | 1.3894 |
| 0.6209 | 8.61 | 5000 | 1.2566 | 1.3697 |
| 0.5507 | 9.47 | 5500 | 1.2300 | 1.3221 |
| 0.4622 | 10.33 | 6000 | 1.3826 | 1.3165 |
| 0.4503 | 11.19 | 6500 | 1.2769 | 1.2717 |
| 0.4026 | 12.05 | 7000 | 1.3531 | 1.2955 |
| 0.3617 | 12.91 | 7500 | 1.2806 | 1.2521 |
| 0.3239 | 13.77 | 8000 | 1.5507 | 1.2437 |
| 0.3051 | 14.63 | 8500 | 1.6217 | 1.2563 |
| 0.2983 | 15.49 | 9000 | 1.5210 | 1.2185 |
| 0.2766 | 16.35 | 9500 | 1.4787 | 1.2143 |
| 0.2642 | 17.21 | 10000 | 1.6284 | 1.2311 |
| 0.2358 | 18.07 | 10500 | 1.3203 | 1.1891 |
| 0.2303 | 18.93 | 11000 | 1.5233 | 1.2185 |
| 0.2166 | 19.79 | 11500 | 1.5111 | 1.2129 |
| 0.2162 | 20.65 | 12000 | 1.5128 | 1.1919 |
| 0.1981 | 21.51 | 12500 | 1.4668 | 1.1877 |
| 0.1736 | 22.38 | 13000 | 1.5041 | 1.1485 |
| 0.1725 | 23.24 | 13500 | 1.5306 | 1.1639 |
| 0.1632 | 24.1 | 14000 | 1.3756 | 1.1373 |
| 0.1597 | 24.96 | 14500 | 1.5404 | 1.1345 |
| 0.1571 | 25.82 | 15000 | 1.4863 | 1.1359 |
| 0.1569 | 26.68 | 15500 | 1.4775 | 1.1401 |
| 0.1431 | 27.54 | 16000 | 1.5410 | 1.1218 |
| 0.1373 | 28.4 | 16500 | 1.5212 | 1.1246 |
| 0.1461 | 29.26 | 17000 | 1.5128 | 1.1148 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
