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wav2vec2-xls-r-1b-dv
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.1702
- Wer: 0.2123
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: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|---|---|---|---|---|
| 3.8412 | 0.66 | 400 | 0.7160 | 0.7913 |
| 0.6832 | 1.33 | 800 | 0.3401 | 0.5268 |
| 0.4624 | 1.99 | 1200 | 0.2671 | 0.4683 |
| 0.3832 | 2.65 | 1600 | 0.2395 | 0.4410 |
| 0.3443 | 3.32 | 2000 | 0.2410 | 0.4296 |
| 0.324 | 3.98 | 2400 | 0.2302 | 0.4143 |
| 0.2934 | 4.64 | 2800 | 0.2402 | 0.4136 |
| 0.2773 | 5.31 | 3200 | 0.2134 | 0.4088 |
| 0.2638 | 5.97 | 3600 | 0.2072 | 0.4037 |
| 0.2479 | 6.63 | 4000 | 0.2036 | 0.3876 |
| 0.2424 | 7.3 | 4400 | 0.2037 | 0.3767 |
| 0.2249 | 7.96 | 4800 | 0.1959 | 0.3802 |
| 0.2169 | 8.62 | 5200 | 0.1943 | 0.3813 |
| 0.2109 | 9.29 | 5600 | 0.1944 | 0.3691 |
| 0.1991 | 9.95 | 6000 | 0.1870 | 0.3589 |
| 0.1917 | 10.61 | 6400 | 0.1834 | 0.3485 |
| 0.1862 | 11.28 | 6800 | 0.1857 | 0.3486 |
| 0.1744 | 11.94 | 7200 | 0.1812 | 0.3330 |
| 0.171 | 12.6 | 7600 | 0.1797 | 0.3436 |
| 0.1599 | 13.27 | 8000 | 0.1839 | 0.3319 |
| 0.1597 | 13.93 | 8400 | 0.1737 | 0.3385 |
| 0.1494 | 14.59 | 8800 | 0.1807 | 0.3239 |
| 0.1444 | 15.26 | 9200 | 0.1750 | 0.3155 |
| 0.1382 | 15.92 | 9600 | 0.1705 | 0.3084 |
| 0.1299 | 16.58 | 10000 | 0.1777 | 0.2999 |
| 0.1306 | 17.25 | 10400 | 0.1765 | 0.3056 |
| 0.1239 | 17.91 | 10800 | 0.1676 | 0.2864 |
| 0.1149 | 18.57 | 11200 | 0.1774 | 0.2861 |
| 0.1134 | 19.24 | 11600 | 0.1654 | 0.2699 |
| 0.1101 | 19.9 | 12000 | 0.1621 | 0.2651 |
| 0.1038 | 20.56 | 12400 | 0.1686 | 0.2610 |
| 0.1038 | 21.23 | 12800 | 0.1722 | 0.2559 |
| 0.0988 | 21.89 | 13200 | 0.1708 | 0.2486 |
| 0.0949 | 22.55 | 13600 | 0.1696 | 0.2453 |
| 0.0913 | 23.22 | 14000 | 0.1677 | 0.2424 |
| 0.0879 | 23.88 | 14400 | 0.1640 | 0.2359 |
| 0.0888 | 24.54 | 14800 | 0.1697 | 0.2347 |
| 0.0826 | 25.21 | 15200 | 0.1709 | 0.2314 |
| 0.0819 | 25.87 | 15600 | 0.1679 | 0.2256 |
| 0.0793 | 26.53 | 16000 | 0.1701 | 0.2214 |
| 0.0773 | 27.2 | 16400 | 0.1682 | 0.2176 |
| 0.0783 | 27.86 | 16800 | 0.1685 | 0.2165 |
| 0.074 | 28.52 | 17200 | 0.1688 | 0.2155 |
| 0.0753 | 29.19 | 17600 | 0.1695 | 0.2110 |
| 0.0699 | 29.85 | 18000 | 0.1702 | 0.2123 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
