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wav2vec2-large-xls-r-300m-urdu-CV_8_0-and-PRUS_v2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3541
- Wer: 0.6532
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: 25
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 14.8521 | 0.52 | 32 | 20.0617 | 1.0 |
| 9.2152 | 1.05 | 64 | 7.8943 | 1.0 |
| 4.8598 | 1.57 | 96 | 5.1558 | 1.0 |
| 3.866 | 2.1 | 128 | 3.9680 | 1.0 |
| 3.3517 | 2.62 | 160 | 3.4201 | 1.0 |
| 3.2029 | 3.15 | 192 | 3.2355 | 1.0 |
| 3.1509 | 3.67 | 224 | 3.2337 | 1.0 |
| 3.1399 | 4.2 | 256 | 3.1627 | 1.0 |
| 3.0848 | 4.72 | 288 | 3.0550 | 1.0 |
| 2.9806 | 5.25 | 320 | 2.8343 | 0.9996 |
| 2.3814 | 5.77 | 352 | 2.0685 | 0.9523 |
| 1.2936 | 6.3 | 384 | 1.5907 | 0.8657 |
| 0.8656 | 6.82 | 416 | 1.3810 | 0.8235 |
| 0.7014 | 7.34 | 448 | 1.3838 | 0.7920 |
| 0.6015 | 7.87 | 480 | 1.3479 | 0.8046 |
| 0.5341 | 8.39 | 512 | 1.2613 | 0.7757 |
| 0.5031 | 8.92 | 544 | 1.2818 | 0.7890 |
| 0.4349 | 9.44 | 576 | 1.3171 | 0.7739 |
| 0.4198 | 9.97 | 608 | 1.2420 | 0.7750 |
| 0.3593 | 10.49 | 640 | 1.2991 | 0.7587 |
| 0.3252 | 11.02 | 672 | 1.2653 | 0.7228 |
| 0.2715 | 11.54 | 704 | 1.2488 | 0.7350 |
| 0.2733 | 12.07 | 736 | 1.2639 | 0.7110 |
| 0.2338 | 12.59 | 768 | 1.3733 | 0.7454 |
| 0.2403 | 13.11 | 800 | 1.3908 | 0.7228 |
| 0.2106 | 13.64 | 832 | 1.3384 | 0.7224 |
| 0.2041 | 14.16 | 864 | 1.3770 | 0.7050 |
| 0.1814 | 14.69 | 896 | 1.3526 | 0.6932 |
| 0.1742 | 15.21 | 928 | 1.3486 | 0.6895 |
| 0.1658 | 15.74 | 960 | 1.3210 | 0.6936 |
| 0.1455 | 16.26 | 992 | 1.3292 | 0.6858 |
| 0.1399 | 16.79 | 1024 | 1.3521 | 0.6828 |
| 0.1325 | 17.31 | 1056 | 1.3339 | 0.6876 |
| 0.1256 | 17.84 | 1088 | 1.3389 | 0.6836 |
| 0.1219 | 18.36 | 1120 | 1.3496 | 0.6769 |
| 0.1212 | 18.89 | 1152 | 1.3277 | 0.6776 |
| 0.1097 | 19.41 | 1184 | 1.3594 | 0.6762 |
| 0.1129 | 19.93 | 1216 | 1.3448 | 0.6688 |
| 0.1036 | 20.46 | 1248 | 1.3295 | 0.6710 |
| 0.1035 | 20.98 | 1280 | 1.3243 | 0.6577 |
| 0.094 | 21.51 | 1312 | 1.3832 | 0.6591 |
| 0.0912 | 22.03 | 1344 | 1.3857 | 0.6584 |
| 0.0815 | 22.56 | 1376 | 1.3739 | 0.6547 |
| 0.0864 | 23.08 | 1408 | 1.3649 | 0.6554 |
| 0.0772 | 23.61 | 1440 | 1.3791 | 0.6458 |
| 0.0894 | 24.13 | 1472 | 1.3630 | 0.6488 |
| 0.0776 | 24.66 | 1504 | 1.3541 | 0.6532 |
Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
