wav2vec2-xlsr-luganda
This model is a fine-tuned version of sulaimank/wav2vec2-xlsr-CV_Fleurs_AMMI_ALFFA-swahili-200hrs on the None dataset. It achieves the following results on the evaluation set:
- Cer: 0.0223
- Loss: 0.2018
- Wer: 0.1123
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|---|---|---|---|---|---|
| 0.8458 | 1.0 | 16873 | 0.0601 | 0.2206 | 0.3081 |
| 0.4665 | 2.0 | 33746 | 0.0503 | 0.1822 | 0.2534 |
| 0.3881 | 3.0 | 50619 | 0.0442 | 0.1644 | 0.2235 |
| 0.3411 | 4.0 | 67492 | 0.0403 | 0.1545 | 0.2041 |
| 0.3080 | 5.0 | 84365 | 0.0378 | 0.1489 | 0.1922 |
| 0.2828 | 6.0 | 101238 | 0.0359 | 0.1406 | 0.1830 |
| 0.2626 | 7.0 | 118111 | 0.0346 | 0.1372 | 0.1770 |
| 0.2443 | 8.0 | 134984 | 0.0345 | 0.1338 | 0.1736 |
| 0.2290 | 9.0 | 151857 | 0.0324 | 0.1327 | 0.1651 |
| 0.2146 | 10.0 | 168730 | 0.0319 | 0.1308 | 0.1642 |
| 0.2027 | 11.0 | 185603 | 0.0310 | 0.1289 | 0.1600 |
| 0.1907 | 12.0 | 202476 | 0.0314 | 0.1315 | 0.1593 |
| 0.1800 | 13.0 | 219349 | 0.0303 | 0.1275 | 0.1557 |
| 0.1703 | 14.0 | 236222 | 0.0301 | 0.1323 | 0.1534 |
| 0.1614 | 15.0 | 253095 | 0.0294 | 0.1319 | 0.1510 |
| 0.1531 | 16.0 | 269968 | 0.0291 | 0.1333 | 0.1461 |
| 0.1453 | 17.0 | 286841 | 0.0280 | 0.1324 | 0.1438 |
| 0.1388 | 18.0 | 303714 | 0.0282 | 0.1350 | 0.1443 |
| 0.1318 | 19.0 | 320587 | 0.0280 | 0.1405 | 0.1425 |
| 0.1250 | 20.0 | 337460 | 0.0274 | 0.1419 | 0.1385 |
| 0.1200 | 21.0 | 354333 | 0.0271 | 0.1431 | 0.1377 |
| 0.1148 | 22.0 | 371206 | 0.0266 | 0.1485 | 0.1362 |
| 0.1094 | 23.0 | 388079 | 0.0262 | 0.1470 | 0.1338 |
| 0.1045 | 24.0 | 404952 | 0.0265 | 0.1534 | 0.1341 |
| 0.1010 | 25.0 | 421825 | 0.0258 | 0.1549 | 0.1305 |
| 0.0971 | 26.0 | 438698 | 0.0252 | 0.1574 | 0.1285 |
| 0.0926 | 27.0 | 455571 | 0.0256 | 0.1623 | 0.1306 |
| 0.0892 | 28.0 | 472444 | 0.0251 | 0.1620 | 0.1266 |
| 0.0862 | 29.0 | 489317 | 0.0249 | 0.1673 | 0.1253 |
| 0.0834 | 30.0 | 506190 | 0.0247 | 0.1671 | 0.1245 |
| 0.0800 | 31.0 | 523063 | 0.0245 | 0.1705 | 0.1237 |
| 0.0778 | 32.0 | 539936 | 0.0244 | 0.1767 | 0.1232 |
| 0.0757 | 33.0 | 556809 | 0.0244 | 0.1711 | 0.1217 |
| 0.0731 | 34.0 | 573682 | 0.0238 | 0.1778 | 0.1204 |
| 0.0705 | 35.0 | 590555 | 0.0234 | 0.1758 | 0.1188 |
| 0.0686 | 36.0 | 607428 | 0.0233 | 0.1754 | 0.1183 |
| 0.0662 | 37.0 | 624301 | 0.0234 | 0.1824 | 0.1172 |
| 0.0648 | 38.0 | 641174 | 0.0231 | 0.1818 | 0.1163 |
| 0.0634 | 39.0 | 658047 | 0.0232 | 0.1834 | 0.1174 |
| 0.0614 | 40.0 | 674920 | 0.0232 | 0.1844 | 0.1170 |
| 0.0603 | 41.0 | 691793 | 0.0230 | 0.1864 | 0.1165 |
| 0.0587 | 42.0 | 708666 | 0.0232 | 0.1926 | 0.1173 |
| 0.0573 | 43.0 | 725539 | 0.0232 | 0.1943 | 0.1175 |
| 0.0562 | 44.0 | 742412 | 0.0229 | 0.1976 | 0.1152 |
| 0.0551 | 45.0 | 759285 | 0.0227 | 0.1952 | 0.1142 |
| 0.0537 | 46.0 | 776158 | 0.0227 | 0.1963 | 0.1136 |
| 0.0532 | 47.0 | 793031 | 0.0225 | 0.2008 | 0.1138 |
| 0.0522 | 48.0 | 809904 | 0.0224 | 0.2005 | 0.1130 |
| 0.0520 | 49.0 | 826777 | 0.0224 | 0.2009 | 0.1126 |
| 0.0509 | 50.0 | 843650 | 0.0223 | 0.2018 | 0.1123 |
Framework versions
- Transformers 5.4.0
- Pytorch 2.11.0+cu130
- Datasets 3.6.0
- Tokenizers 0.22.2