metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-sinhala-original-split-part4-epoch30-final
results: []
wav2vec2-large-xls-r-300m-sinhala-original-split-part4-epoch30-final
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: 0.1837
- Wer: 0.1334
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.0113 | 0.47 | 400 | 3.4040 | 1.0 |
| 1.3642 | 0.93 | 800 | 0.5373 | 0.6817 |
| 0.4891 | 1.4 | 1200 | 0.2962 | 0.4354 |
| 0.402 | 1.87 | 1600 | 0.2717 | 0.3688 |
| 0.3209 | 2.34 | 2000 | 0.2088 | 0.3262 |
| 0.2993 | 2.8 | 2400 | 0.1909 | 0.2587 |
| 0.2613 | 3.27 | 2800 | 0.1873 | 0.2502 |
| 0.2381 | 3.74 | 3200 | 0.1763 | 0.2310 |
| 0.2224 | 4.21 | 3600 | 0.1812 | 0.2142 |
| 0.2047 | 4.67 | 4000 | 0.1693 | 0.2089 |
| 0.1937 | 5.14 | 4400 | 0.1753 | 0.2085 |
| 0.1808 | 5.61 | 4800 | 0.1718 | 0.2153 |
| 0.1711 | 6.07 | 5200 | 0.1887 | 0.2206 |
| 0.1568 | 6.54 | 5600 | 0.1769 | 0.2111 |
| 0.161 | 7.01 | 6000 | 0.1701 | 0.2132 |
| 0.1391 | 7.48 | 6400 | 0.2001 | 0.2196 |
| 0.1447 | 7.94 | 6800 | 0.1749 | 0.2047 |
| 0.1237 | 8.41 | 7200 | 0.1833 | 0.2081 |
| 0.129 | 8.88 | 7600 | 0.1789 | 0.1993 |
| 0.1155 | 9.35 | 8000 | 0.1756 | 0.1838 |
| 0.1168 | 9.81 | 8400 | 0.1744 | 0.1913 |
| 0.1089 | 10.28 | 8800 | 0.1689 | 0.1793 |
| 0.1109 | 10.75 | 9200 | 0.1747 | 0.1785 |
| 0.0987 | 11.21 | 9600 | 0.1667 | 0.1769 |
| 0.0998 | 11.68 | 10000 | 0.1603 | 0.1715 |
| 0.094 | 12.15 | 10400 | 0.1649 | 0.1668 |
| 0.0942 | 12.62 | 10800 | 0.1654 | 0.1719 |
| 0.0912 | 13.08 | 11200 | 0.1840 | 0.1719 |
| 0.085 | 13.55 | 11600 | 0.1812 | 0.1778 |
| 0.0798 | 14.02 | 12000 | 0.1744 | 0.1704 |
| 0.0762 | 14.49 | 12400 | 0.1968 | 0.1702 |
| 0.078 | 14.95 | 12800 | 0.1897 | 0.1726 |
| 0.0717 | 15.42 | 13200 | 0.1795 | 0.1769 |
| 0.0753 | 15.89 | 13600 | 0.1940 | 0.1704 |
| 0.0718 | 16.36 | 14000 | 0.1944 | 0.1632 |
| 0.0671 | 16.82 | 14400 | 0.1731 | 0.1588 |
| 0.0656 | 17.29 | 14800 | 0.1999 | 0.1713 |
| 0.0626 | 17.76 | 15200 | 0.1844 | 0.1655 |
| 0.0617 | 18.22 | 15600 | 0.1920 | 0.1621 |
| 0.0613 | 18.69 | 16000 | 0.1856 | 0.1611 |
| 0.0576 | 19.16 | 16400 | 0.1794 | 0.1573 |
| 0.0592 | 19.63 | 16800 | 0.1949 | 0.1558 |
| 0.0551 | 20.09 | 17200 | 0.1850 | 0.1551 |
| 0.0526 | 20.56 | 17600 | 0.1869 | 0.1504 |
| 0.0521 | 21.03 | 18000 | 0.1891 | 0.1504 |
| 0.0497 | 21.5 | 18400 | 0.1909 | 0.1536 |
| 0.0475 | 21.96 | 18800 | 0.1768 | 0.1510 |
| 0.0455 | 22.43 | 19200 | 0.1963 | 0.1543 |
| 0.0472 | 22.9 | 19600 | 0.1837 | 0.1506 |
| 0.0474 | 23.36 | 20000 | 0.1842 | 0.1498 |
| 0.0412 | 23.83 | 20400 | 0.1817 | 0.1461 |
| 0.0421 | 24.3 | 20800 | 0.1831 | 0.1446 |
| 0.039 | 24.77 | 21200 | 0.1857 | 0.1447 |
| 0.0386 | 25.23 | 21600 | 0.1824 | 0.1415 |
| 0.0382 | 25.7 | 22000 | 0.1816 | 0.1397 |
| 0.0341 | 26.17 | 22400 | 0.1839 | 0.1423 |
| 0.0333 | 26.64 | 22800 | 0.1846 | 0.1416 |
| 0.0331 | 27.1 | 23200 | 0.1857 | 0.1436 |
| 0.0319 | 27.57 | 23600 | 0.1891 | 0.1396 |
| 0.0329 | 28.04 | 24000 | 0.1866 | 0.1356 |
| 0.031 | 28.5 | 24400 | 0.1864 | 0.1366 |
| 0.0296 | 28.97 | 24800 | 0.1860 | 0.1357 |
| 0.0298 | 29.44 | 25200 | 0.1836 | 0.1342 |
| 0.0278 | 29.91 | 25600 | 0.1837 | 0.1334 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1