<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
wav2vec2-xls-r-300m-ar-4
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.7888
- Wer: 0.3697
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: 0.001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.8069 | 1.18 | 400 | 1.7793 | 0.9883 |
| 1.1949 | 2.35 | 800 | 0.9662 | 0.7908 |
| 0.8996 | 3.53 | 1200 | 0.8404 | 0.7154 |
| 0.7652 | 4.71 | 1600 | 0.7478 | 0.6379 |
| 0.6611 | 5.88 | 2000 | 0.7687 | 0.6229 |
| 0.6015 | 7.06 | 2400 | 0.7153 | 0.5948 |
| 0.5444 | 8.24 | 2800 | 0.7062 | 0.5826 |
| 0.4872 | 9.41 | 3200 | 0.6568 | 0.5414 |
| 0.4729 | 10.59 | 3600 | 0.6817 | 0.5599 |
| 0.4238 | 11.76 | 4000 | 0.6406 | 0.5262 |
| 0.4022 | 12.94 | 4400 | 0.6797 | 0.5184 |
| 0.3945 | 14.12 | 4800 | 0.6744 | 0.5147 |
| 0.3711 | 15.29 | 5200 | 0.6807 | 0.5090 |
| 0.3318 | 16.47 | 5600 | 0.6286 | 0.5011 |
| 0.3132 | 17.65 | 6000 | 0.6481 | 0.4814 |
| 0.2992 | 18.82 | 6400 | 0.6454 | 0.4958 |
| 0.2734 | 20.0 | 6800 | 0.6465 | 0.4825 |
| 0.2534 | 21.18 | 7200 | 0.6559 | 0.4658 |
| 0.2505 | 22.35 | 7600 | 0.6601 | 0.4618 |
| 0.2495 | 23.53 | 8000 | 0.7080 | 0.4813 |
| 0.2387 | 24.71 | 8400 | 0.6635 | 0.4508 |
| 0.2154 | 25.88 | 8800 | 0.6442 | 0.4538 |
| 0.2096 | 27.06 | 9200 | 0.7399 | 0.4579 |
| 0.2007 | 28.24 | 9600 | 0.6957 | 0.4512 |
| 0.1942 | 29.41 | 10000 | 0.6642 | 0.4267 |
| 0.1854 | 30.59 | 10400 | 0.6842 | 0.4393 |
| 0.1782 | 31.76 | 10800 | 0.7007 | 0.4393 |
| 0.1751 | 32.94 | 11200 | 0.7063 | 0.4321 |
| 0.1695 | 34.12 | 11600 | 0.7057 | 0.4330 |
| 0.1638 | 35.29 | 12000 | 0.7416 | 0.4266 |
| 0.1531 | 36.47 | 12400 | 0.7420 | 0.4273 |
| 0.1475 | 37.65 | 12800 | 0.7334 | 0.4218 |
| 0.1388 | 38.82 | 13200 | 0.7420 | 0.4227 |
| 0.1372 | 40.0 | 13600 | 0.7492 | 0.4238 |
| 0.1341 | 41.18 | 14000 | 0.7803 | 0.4193 |
| 0.133 | 42.35 | 14400 | 0.7396 | 0.4105 |
| 0.1238 | 43.53 | 14800 | 0.7561 | 0.4098 |
| 0.1163 | 44.71 | 15200 | 0.7987 | 0.4049 |
| 0.116 | 45.88 | 15600 | 0.7769 | 0.4093 |
| 0.1079 | 47.06 | 16000 | 0.7780 | 0.3986 |
| 0.1043 | 48.24 | 16400 | 0.7674 | 0.3905 |
| 0.1004 | 49.41 | 16800 | 0.7931 | 0.3949 |
| 0.0987 | 50.59 | 17200 | 0.7605 | 0.3938 |
| 0.0963 | 51.76 | 17600 | 0.7735 | 0.3858 |
| 0.0905 | 52.94 | 18000 | 0.7504 | 0.3802 |
| 0.086 | 54.12 | 18400 | 0.8038 | 0.3867 |
| 0.0839 | 55.29 | 18800 | 0.7887 | 0.3797 |
| 0.0798 | 56.47 | 19200 | 0.7832 | 0.3705 |
| 0.0785 | 57.65 | 19600 | 0.7771 | 0.3706 |
| 0.0765 | 58.82 | 20000 | 0.7858 | 0.3703 |
| 0.0739 | 60.0 | 20400 | 0.7888 | 0.3697 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.11.0
- Datasets 1.18.3
- Tokenizers 0.10.3
