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MilladRN
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: 3.4355
- Wer: 0.4907
- Cer: 0.2802
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 |
|---|---|---|---|---|---|
| 3.3347 | 33.9 | 2000 | 2.2561 | 0.9888 | 0.6087 |
| 1.3337 | 67.8 | 4000 | 1.8137 | 0.6877 | 0.3407 |
| 0.6504 | 101.69 | 6000 | 2.0718 | 0.6245 | 0.3229 |
| 0.404 | 135.59 | 8000 | 2.2246 | 0.6004 | 0.3221 |
| 0.2877 | 169.49 | 10000 | 2.2624 | 0.5836 | 0.3107 |
| 0.2149 | 203.39 | 12000 | 2.3788 | 0.5279 | 0.2802 |
| 0.1693 | 237.29 | 14000 | 1.8928 | 0.5502 | 0.2937 |
| 0.1383 | 271.19 | 16000 | 2.7520 | 0.5725 | 0.3103 |
| 0.1169 | 305.08 | 18000 | 2.2552 | 0.5446 | 0.2968 |
| 0.1011 | 338.98 | 20000 | 2.6794 | 0.5725 | 0.3119 |
| 0.0996 | 372.88 | 22000 | 2.4704 | 0.5595 | 0.3142 |
| 0.0665 | 406.78 | 24000 | 2.9073 | 0.5836 | 0.3194 |
| 0.0538 | 440.68 | 26000 | 3.1357 | 0.5632 | 0.3213 |
| 0.0538 | 474.58 | 28000 | 2.5639 | 0.5613 | 0.3091 |
| 0.0493 | 508.47 | 30000 | 3.3801 | 0.5613 | 0.3119 |
| 0.0451 | 542.37 | 32000 | 3.5469 | 0.5428 | 0.3158 |
| 0.0307 | 576.27 | 34000 | 4.2243 | 0.5390 | 0.3126 |
| 0.0301 | 610.17 | 36000 | 3.6666 | 0.5297 | 0.2929 |
| 0.0269 | 644.07 | 38000 | 3.2164 | 0.5 | 0.2838 |
| 0.0182 | 677.97 | 40000 | 3.0557 | 0.4963 | 0.2779 |
| 0.0191 | 711.86 | 42000 | 3.5190 | 0.5130 | 0.2921 |
| 0.0133 | 745.76 | 44000 | 3.4355 | 0.4907 | 0.2802 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
