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Millad_Customer_RN
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.5635
- Wer: 0.8113
- Cer: 0.4817
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: 4000
- num_epochs: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.9257 | 13.33 | 2000 | 2.0606 | 0.9767 | 0.5500 |
| 1.4828 | 26.67 | 4000 | 2.1161 | 0.9019 | 0.4932 |
| 1.2582 | 40.0 | 6000 | 2.0589 | 0.8504 | 0.4942 |
| 0.9804 | 53.33 | 8000 | 2.4633 | 0.8745 | 0.4763 |
| 0.7862 | 66.67 | 10000 | 2.4794 | 0.8861 | 0.4944 |
| 0.6492 | 80.0 | 12000 | 2.8693 | 0.8554 | 0.4928 |
| 0.5375 | 93.33 | 14000 | 2.6125 | 0.8296 | 0.4802 |
| 0.4462 | 106.67 | 16000 | 2.7591 | 0.8770 | 0.4974 |
| 0.3873 | 120.0 | 18000 | 3.0325 | 0.8379 | 0.4800 |
| 0.3445 | 133.33 | 20000 | 2.9965 | 0.8761 | 0.4986 |
| 0.3087 | 146.67 | 22000 | 3.3437 | 0.8221 | 0.4923 |
| 0.2755 | 160.0 | 24000 | 3.3022 | 0.8803 | 0.5211 |
| 0.2467 | 173.33 | 26000 | 3.2348 | 0.8479 | 0.4933 |
| 0.2281 | 186.67 | 28000 | 3.8010 | 0.8695 | 0.5081 |
| 0.2119 | 200.0 | 30000 | 3.0446 | 0.8545 | 0.4902 |
| 0.194 | 213.33 | 32000 | 3.0873 | 0.8454 | 0.4840 |
| 0.1677 | 226.67 | 34000 | 3.6184 | 0.8645 | 0.5019 |
| 0.1642 | 240.0 | 36000 | 3.2480 | 0.8412 | 0.4903 |
| 0.1656 | 253.33 | 38000 | 3.4379 | 0.8362 | 0.4816 |
| 0.1371 | 266.67 | 40000 | 3.5117 | 0.8479 | 0.5040 |
| 0.1301 | 280.0 | 42000 | 3.4360 | 0.8404 | 0.4870 |
| 0.128 | 293.33 | 44000 | 3.6589 | 0.8537 | 0.4977 |
| 0.1152 | 306.67 | 46000 | 4.2359 | 0.8545 | 0.5051 |
| 0.1119 | 320.0 | 48000 | 3.5818 | 0.7980 | 0.4882 |
| 0.1026 | 333.33 | 50000 | 3.7618 | 0.8013 | 0.4865 |
| 0.0945 | 346.67 | 52000 | 4.2197 | 0.8404 | 0.5028 |
| 0.0962 | 360.0 | 54000 | 3.9231 | 0.8653 | 0.5030 |
| 0.088 | 373.33 | 56000 | 3.8400 | 0.8354 | 0.4914 |
| 0.0743 | 386.67 | 58000 | 3.4924 | 0.8088 | 0.4824 |
| 0.0811 | 400.0 | 60000 | 3.8370 | 0.8396 | 0.4861 |
| 0.0696 | 413.33 | 62000 | 4.2808 | 0.8412 | 0.5065 |
| 0.0692 | 426.67 | 64000 | 4.0161 | 0.8088 | 0.4744 |
| 0.0622 | 440.0 | 66000 | 3.9080 | 0.8163 | 0.4910 |
| 0.0591 | 453.33 | 68000 | 3.9838 | 0.8113 | 0.4823 |
| 0.0527 | 466.67 | 70000 | 3.8067 | 0.8329 | 0.4914 |
| 0.056 | 480.0 | 72000 | 4.1415 | 0.8096 | 0.4782 |
| 0.0535 | 493.33 | 74000 | 4.3350 | 0.8229 | 0.4828 |
| 0.0531 | 506.67 | 76000 | 3.9808 | 0.8071 | 0.4807 |
| 0.0451 | 520.0 | 78000 | 4.0301 | 0.7988 | 0.4816 |
| 0.044 | 533.33 | 80000 | 4.4680 | 0.8371 | 0.4921 |
| 0.0389 | 546.67 | 82000 | 4.1380 | 0.8121 | 0.4819 |
| 0.0392 | 560.0 | 84000 | 4.3910 | 0.7930 | 0.4763 |
| 0.0389 | 573.33 | 86000 | 4.5086 | 0.8055 | 0.4802 |
| 0.0355 | 586.67 | 88000 | 4.6259 | 0.8113 | 0.4821 |
| 0.0307 | 600.0 | 90000 | 4.5635 | 0.8113 | 0.4817 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
