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wav2vec2-xls-r-300m-asr_af-run2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5239
- Wer: 0.3726
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: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 11.7451 | 0.58 | 50 | 7.2710 | 1.0 |
| 4.7931 | 1.17 | 100 | 4.0381 | 1.0 |
| 3.4944 | 1.75 | 150 | 3.1782 | 1.0 |
| 3.06 | 2.34 | 200 | 2.9951 | 1.0 |
| 3.0031 | 2.92 | 250 | 2.9964 | 1.0 |
| 2.9814 | 3.51 | 300 | 2.9652 | 1.0 |
| 2.9524 | 4.09 | 350 | 2.9419 | 0.9998 |
| 2.9014 | 4.68 | 400 | 2.8213 | 1.0 |
| 2.2569 | 5.26 | 450 | 1.6105 | 0.9433 |
| 1.3008 | 5.85 | 500 | 1.0090 | 0.8021 |
| 0.8965 | 6.43 | 550 | 0.7727 | 0.6551 |
| 0.7134 | 7.02 | 600 | 0.6579 | 0.6102 |
| 0.5275 | 7.6 | 650 | 0.5956 | 0.6005 |
| 0.4413 | 8.19 | 700 | 0.5558 | 0.5079 |
| 0.3582 | 8.77 | 750 | 0.5719 | 0.5459 |
| 0.296 | 9.36 | 800 | 0.5389 | 0.4822 |
| 0.2557 | 9.94 | 850 | 0.4608 | 0.4541 |
| 0.2051 | 10.53 | 900 | 0.4822 | 0.4290 |
| 0.1911 | 11.11 | 950 | 0.5035 | 0.4209 |
| 0.1635 | 11.7 | 1000 | 0.5319 | 0.4263 |
| 0.1582 | 12.28 | 1050 | 0.5075 | 0.4124 |
| 0.1387 | 12.87 | 1100 | 0.4759 | 0.4055 |
| 0.1251 | 13.45 | 1150 | 0.4925 | 0.3970 |
| 0.1164 | 14.04 | 1200 | 0.4933 | 0.3998 |
| 0.1052 | 14.62 | 1250 | 0.4587 | 0.3995 |
| 0.1023 | 15.2 | 1300 | 0.4863 | 0.3950 |
| 0.0918 | 15.79 | 1350 | 0.5114 | 0.3858 |
| 0.09 | 16.37 | 1400 | 0.5444 | 0.3940 |
| 0.086 | 16.96 | 1450 | 0.5071 | 0.3806 |
| 0.0798 | 17.54 | 1500 | 0.4914 | 0.3809 |
| 0.0728 | 18.13 | 1550 | 0.5425 | 0.3807 |
| 0.0678 | 18.71 | 1600 | 0.5221 | 0.3731 |
| 0.0667 | 19.3 | 1650 | 0.5239 | 0.3726 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
