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wav2vec2-large-xls-r-300m-urdu
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m. It achieves the following results on the evaluation set:
- Loss: 0.5285
- Wer: 0.1702
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 35
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 16.9618 | 0.74 | 32 | 15.0745 | 1.0 |
| 9.1928 | 1.49 | 64 | 5.9361 | 1.0 |
| 4.9307 | 2.23 | 96 | 4.2924 | 1.0 |
| 3.8917 | 2.98 | 128 | 3.5873 | 1.0 |
| 3.3867 | 3.72 | 160 | 3.2594 | 1.0 |
| 3.2107 | 4.47 | 192 | 3.1718 | 1.0 |
| 3.1395 | 5.21 | 224 | 3.1281 | 1.0 |
| 3.115 | 5.95 | 256 | 3.1238 | 1.0 |
| 3.0801 | 6.7 | 288 | 3.0674 | 1.0 |
| 2.9725 | 7.44 | 320 | 2.8277 | 1.0 |
| 2.4159 | 8.19 | 352 | 1.7186 | 0.9036 |
| 1.3377 | 8.93 | 384 | 1.0271 | 0.6433 |
| 0.8591 | 9.67 | 416 | 0.8087 | 0.5441 |
| 0.726 | 10.42 | 448 | 0.7263 | 0.4634 |
| 0.6242 | 11.16 | 480 | 0.6783 | 0.4156 |
| 0.5417 | 11.91 | 512 | 0.6611 | 0.4305 |
| 0.4784 | 12.65 | 544 | 0.6300 | 0.3926 |
| 0.4198 | 13.4 | 576 | 0.5646 | 0.3499 |
| 0.3798 | 14.14 | 608 | 0.5919 | 0.3229 |
| 0.3356 | 14.88 | 640 | 0.5715 | 0.3369 |
| 0.2954 | 15.63 | 672 | 0.5325 | 0.2728 |
| 0.264 | 16.37 | 704 | 0.5535 | 0.2689 |
| 0.2535 | 17.12 | 736 | 0.5467 | 0.2366 |
| 0.2277 | 17.86 | 768 | 0.5219 | 0.2345 |
| 0.2141 | 18.6 | 800 | 0.5314 | 0.2487 |
| 0.2036 | 19.35 | 832 | 0.5382 | 0.2236 |
| 0.2021 | 20.09 | 864 | 0.5038 | 0.1922 |
| 0.1676 | 20.84 | 896 | 0.5238 | 0.2033 |
| 0.1544 | 21.58 | 928 | 0.5069 | 0.1866 |
| 0.1512 | 22.33 | 960 | 0.5045 | 0.1965 |
| 0.1512 | 23.07 | 992 | 0.5167 | 0.1862 |
| 0.1399 | 23.81 | 1024 | 0.5236 | 0.1840 |
| 0.1291 | 24.56 | 1056 | 0.5234 | 0.1957 |
| 0.1274 | 25.3 | 1088 | 0.5348 | 0.1943 |
| 0.127 | 26.05 | 1120 | 0.4978 | 0.1719 |
| 0.1105 | 26.79 | 1152 | 0.5067 | 0.1767 |
| 0.1069 | 27.53 | 1184 | 0.5150 | 0.1758 |
| 0.1058 | 28.28 | 1216 | 0.5218 | 0.1844 |
| 0.0999 | 29.02 | 1248 | 0.5375 | 0.1852 |
| 0.0964 | 29.77 | 1280 | 0.5373 | 0.1843 |
| 0.0971 | 30.51 | 1312 | 0.5190 | 0.1776 |
| 0.0906 | 31.26 | 1344 | 0.5217 | 0.1747 |
| 0.0909 | 32.0 | 1376 | 0.5204 | 0.1778 |
| 0.0784 | 32.74 | 1408 | 0.5336 | 0.1756 |
| 0.0823 | 33.49 | 1440 | 0.5281 | 0.1699 |
| 0.0834 | 34.23 | 1472 | 0.5292 | 0.1700 |
| 0.0827 | 34.98 | 1504 | 0.5285 | 0.1702 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
