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---
library_name: transformers
license: apache-2.0
base_model: davidilag/wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-1b-E2-faroese-100h-30-epochs_20250124_v3
results: []
---
<!-- 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-1b-E2-faroese-100h-30-epochs_20250124_v3
This model is a fine-tuned version of [davidilag/wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123](https://huggingface.co/davidilag/wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1178
- Wer: 19.6766
- Cer: 4.2984
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------:|
| 3.0158 | 0.4877 | 1000 | 3.2867 | 100.1278 | 90.1077 |
| 0.8828 | 0.9754 | 2000 | 0.6226 | 64.6429 | 19.5448 |
| 0.5525 | 1.4628 | 3000 | 0.3515 | 42.8030 | 12.1094 |
| 0.4761 | 1.9505 | 4000 | 0.2862 | 37.6437 | 10.2765 |
| 0.3943 | 2.4379 | 5000 | 0.2760 | 35.6831 | 9.6572 |
| 0.3827 | 2.9256 | 6000 | 0.2715 | 34.3834 | 9.4094 |
| 0.3386 | 3.4131 | 7000 | 0.2373 | 32.3964 | 8.4524 |
| 0.2965 | 3.9008 | 8000 | 0.2105 | 30.3476 | 7.8638 |
| 0.2807 | 4.3882 | 9000 | 0.2004 | 29.8145 | 7.6445 |
| 0.2881 | 4.8759 | 10000 | 0.1956 | 29.4444 | 7.5324 |
| 0.2609 | 5.3633 | 11000 | 0.1880 | 28.4046 | 7.2800 |
| 0.2518 | 5.8510 | 12000 | 0.1842 | 28.1667 | 7.0835 |
| 0.2191 | 6.3385 | 13000 | 0.1790 | 27.5984 | 6.8847 |
| 0.2159 | 6.8261 | 14000 | 0.1894 | 28.2416 | 7.1790 |
| 0.2076 | 7.3136 | 15000 | 0.1714 | 26.7612 | 6.7245 |
| 0.2007 | 7.8013 | 16000 | 0.1805 | 27.0653 | 6.9486 |
| 0.1662 | 8.2887 | 17000 | 0.1792 | 26.1136 | 6.4886 |
| 0.1764 | 8.7764 | 18000 | 0.1626 | 26.3823 | 6.5478 |
| 0.1426 | 9.2638 | 19000 | 0.1623 | 25.3646 | 6.2456 |
| 0.1406 | 9.7515 | 20000 | 0.1642 | 25.4747 | 6.2275 |
| 0.144 | 10.2390 | 21000 | 0.1620 | 25.1002 | 6.1880 |
| 0.1328 | 10.7267 | 22000 | 0.1558 | 24.8227 | 6.0854 |
| 0.1366 | 11.2141 | 23000 | 0.1521 | 24.2235 | 5.9079 |
| 0.1223 | 11.7018 | 24000 | 0.1461 | 24.0913 | 5.7777 |
| 0.1195 | 12.1892 | 25000 | 0.1378 | 23.9855 | 5.7580 |
| 0.1218 | 12.6769 | 26000 | 0.1347 | 23.8137 | 5.5623 |
| 0.1069 | 13.1644 | 27000 | 0.1350 | 23.3159 | 5.5276 |
| 0.1037 | 13.6520 | 28000 | 0.1400 | 23.0735 | 5.4740 |
| 0.0885 | 14.1395 | 29000 | 0.1432 | 23.1528 | 5.4030 |
| 0.0934 | 14.6272 | 30000 | 0.1321 | 22.8841 | 5.3430 |
| 0.0836 | 15.1146 | 31000 | 0.1285 | 22.4303 | 5.1742 |
| 0.0875 | 15.6023 | 32000 | 0.1237 | 22.3422 | 5.1260 |
| 0.0741 | 16.0897 | 33000 | 0.1345 | 22.3333 | 5.2176 |
| 0.0712 | 16.5774 | 34000 | 0.1348 | 22.1439 | 5.0929 |
| 0.0747 | 17.0649 | 35000 | 0.1269 | 21.7650 | 4.9777 |
| 0.0735 | 17.5525 | 36000 | 0.1262 | 21.7782 | 4.9280 |
| 0.0598 | 18.0400 | 37000 | 0.1253 | 21.7518 | 4.9730 |
| 0.0564 | 18.5277 | 38000 | 0.1196 | 21.5095 | 4.9020 |
| 0.0574 | 19.0151 | 39000 | 0.1187 | 21.1438 | 4.7939 |
| 0.0526 | 19.5028 | 40000 | 0.1218 | 21.1394 | 4.7655 |
| 0.0495 | 19.9905 | 41000 | 0.1188 | 21.0116 | 4.6905 |
| 0.056 | 20.4779 | 42000 | 0.1160 | 20.7913 | 4.6392 |
| 0.0524 | 20.9656 | 43000 | 0.1180 | 20.7693 | 4.6140 |
| 0.0474 | 21.4531 | 44000 | 0.1211 | 20.5446 | 4.6100 |
| 0.054 | 21.9407 | 45000 | 0.1152 | 20.4036 | 4.5588 |
| 0.0339 | 22.4282 | 46000 | 0.1181 | 20.3595 | 4.4885 |
| 0.0437 | 22.9159 | 47000 | 0.1179 | 20.2273 | 4.4562 |
| 0.0408 | 23.4033 | 48000 | 0.1172 | 20.1524 | 4.4538 |
| 0.0418 | 23.8910 | 49000 | 0.1197 | 20.1260 | 4.4317 |
| 0.0415 | 24.3784 | 50000 | 0.1174 | 20.0423 | 4.4041 |
| 0.0353 | 24.8661 | 51000 | 0.1128 | 19.9410 | 4.3804 |
| 0.0385 | 25.3536 | 52000 | 0.1152 | 19.9674 | 4.3797 |
| 0.0365 | 25.8413 | 53000 | 0.1143 | 19.8132 | 4.3449 |
| 0.0355 | 26.3287 | 54000 | 0.1175 | 19.8088 | 4.3355 |
| 0.0324 | 26.8164 | 55000 | 0.1184 | 19.7956 | 4.3260 |
| 0.032 | 27.3038 | 56000 | 0.1186 | 19.7515 | 4.3229 |
| 0.032 | 27.7915 | 57000 | 0.1174 | 19.7163 | 4.3173 |
| 0.0387 | 28.2790 | 58000 | 0.1175 | 19.7295 | 4.3221 |
| 0.0345 | 28.7666 | 59000 | 0.1180 | 19.6634 | 4.3055 |
| 0.0451 | 29.2541 | 60000 | 0.1179 | 19.6546 | 4.2992 |
| 0.0476 | 29.7418 | 61000 | 0.1178 | 19.6766 | 4.2984 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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