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Millad
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: 3.2265
- Wer: 0.5465
- Cer: 0.3162
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: 750
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.2911 | 33.9 | 2000 | 2.2097 | 0.9963 | 0.6047 |
| 1.3419 | 67.8 | 4000 | 1.9042 | 0.7007 | 0.3565 |
| 0.6542 | 101.69 | 6000 | 1.7195 | 0.5985 | 0.3194 |
| 0.373 | 135.59 | 8000 | 2.2219 | 0.6078 | 0.3241 |
| 0.2805 | 169.49 | 10000 | 2.3114 | 0.6320 | 0.3304 |
| 0.2014 | 203.39 | 12000 | 2.6898 | 0.6338 | 0.3597 |
| 0.1611 | 237.29 | 14000 | 2.7808 | 0.6041 | 0.3379 |
| 0.1265 | 271.19 | 16000 | 2.8304 | 0.5632 | 0.3289 |
| 0.1082 | 305.08 | 18000 | 2.8373 | 0.5874 | 0.3344 |
| 0.103 | 338.98 | 20000 | 2.8580 | 0.5743 | 0.3292 |
| 0.0854 | 372.88 | 22000 | 2.5413 | 0.5539 | 0.3186 |
| 0.0675 | 406.78 | 24000 | 2.5523 | 0.5502 | 0.3229 |
| 0.0531 | 440.68 | 26000 | 2.9369 | 0.5483 | 0.3142 |
| 0.0504 | 474.58 | 28000 | 3.1416 | 0.5595 | 0.3225 |
| 0.0388 | 508.47 | 30000 | 2.5655 | 0.5390 | 0.3111 |
| 0.0396 | 542.37 | 32000 | 3.1923 | 0.5558 | 0.3178 |
| 0.0274 | 576.27 | 34000 | 2.9235 | 0.5520 | 0.3257 |
| 0.0361 | 610.17 | 36000 | 3.3828 | 0.5762 | 0.3312 |
| 0.02 | 644.07 | 38000 | 3.3822 | 0.5874 | 0.3466 |
| 0.0176 | 677.97 | 40000 | 3.1191 | 0.5539 | 0.3209 |
| 0.0181 | 711.86 | 42000 | 3.2022 | 0.5576 | 0.3237 |
| 0.0124 | 745.76 | 44000 | 3.2265 | 0.5465 | 0.3162 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
