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---
library_name: transformers
license: apache-2.0
base_model: davidilag/wav2vec2-xls-r-1b-scandinavian-E5-100h-30-epochs-20250124
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-1b-E5-faroese-100h-30-epochs_20250124
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-E5-faroese-100h-30-epochs_20250124
This model is a fine-tuned version of [davidilag/wav2vec2-xls-r-1b-scandinavian-E5-100h-30-epochs-20250124](https://huggingface.co/davidilag/wav2vec2-xls-r-1b-scandinavian-E5-100h-30-epochs-20250124) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1020
- Wer: 18.7866
- Cer: 4.0428
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
| 0.6462 | 0.4877 | 1000 | 0.4463 | 49.8480 | 14.3603 |
| 0.4739 | 0.9754 | 2000 | 0.2744 | 35.1720 | 9.4284 |
| 0.3953 | 1.4628 | 3000 | 0.2147 | 31.1671 | 8.0981 |
| 0.3758 | 1.9505 | 4000 | 0.2073 | 31.1847 | 7.9119 |
| 0.3123 | 2.4379 | 5000 | 0.2123 | 29.9423 | 7.7486 |
| 0.3032 | 2.9256 | 6000 | 0.1951 | 29.8277 | 7.5569 |
| 0.2866 | 3.4131 | 7000 | 0.1822 | 28.2372 | 7.1364 |
| 0.2601 | 3.9008 | 8000 | 0.1833 | 27.0432 | 6.8018 |
| 0.2259 | 4.3882 | 9000 | 0.1809 | 26.6996 | 6.7458 |
| 0.2474 | 4.8759 | 10000 | 0.1606 | 26.1312 | 6.4500 |
| 0.2131 | 5.3633 | 11000 | 0.1674 | 26.1929 | 6.5005 |
| 0.214 | 5.8510 | 12000 | 0.1550 | 24.8888 | 6.0910 |
| 0.181 | 6.3385 | 13000 | 0.1583 | 24.7918 | 6.1367 |
| 0.1703 | 6.8261 | 14000 | 0.1457 | 24.9592 | 6.0444 |
| 0.1816 | 7.3136 | 15000 | 0.1578 | 24.6024 | 5.9979 |
| 0.1594 | 7.8013 | 16000 | 0.1482 | 24.3997 | 5.8661 |
| 0.1373 | 8.2887 | 17000 | 0.1485 | 24.0428 | 5.7170 |
| 0.1497 | 8.7764 | 18000 | 0.1383 | 23.8049 | 5.7265 |
| 0.1119 | 9.2638 | 19000 | 0.1379 | 23.0956 | 5.5213 |
| 0.1218 | 9.7515 | 20000 | 0.1504 | 23.6815 | 5.7186 |
| 0.1177 | 10.2390 | 21000 | 0.1395 | 23.4392 | 5.6199 |
| 0.1128 | 10.7267 | 22000 | 0.1383 | 23.3643 | 5.5813 |
| 0.1198 | 11.2141 | 23000 | 0.1360 | 22.7783 | 5.3438 |
| 0.1105 | 11.7018 | 24000 | 0.1375 | 22.5977 | 5.2996 |
| 0.1035 | 12.1892 | 25000 | 0.1252 | 22.4391 | 5.2736 |
| 0.092 | 12.6769 | 26000 | 0.1323 | 22.2629 | 5.2397 |
| 0.0783 | 13.1644 | 27000 | 0.1286 | 22.2717 | 5.1442 |
| 0.0835 | 13.6520 | 28000 | 0.1298 | 21.6284 | 4.9619 |
| 0.0702 | 14.1395 | 29000 | 0.1192 | 21.5447 | 4.9091 |
| 0.0807 | 14.6272 | 30000 | 0.1177 | 21.3773 | 4.9493 |
| 0.0714 | 15.1146 | 31000 | 0.1254 | 21.3112 | 4.8972 |
| 0.0734 | 15.6023 | 32000 | 0.1216 | 21.2980 | 4.8554 |
| 0.0621 | 16.0897 | 33000 | 0.1191 | 20.8618 | 4.7118 |
| 0.0601 | 16.5774 | 34000 | 0.1134 | 20.7913 | 4.6747 |
| 0.0631 | 17.0649 | 35000 | 0.1148 | 20.6327 | 4.6384 |
| 0.0655 | 17.5525 | 36000 | 0.1106 | 20.4697 | 4.5769 |
| 0.0492 | 18.0400 | 37000 | 0.1172 | 20.4520 | 4.5880 |
| 0.0485 | 18.5277 | 38000 | 0.1180 | 20.3066 | 4.6022 |
| 0.0455 | 19.0151 | 39000 | 0.1102 | 20.0511 | 4.4349 |
| 0.0422 | 19.5028 | 40000 | 0.1143 | 20.0511 | 4.4467 |
| 0.0412 | 19.9905 | 41000 | 0.1109 | 19.8749 | 4.3978 |
| 0.0469 | 20.4779 | 42000 | 0.1110 | 20.0203 | 4.4428 |
| 0.0388 | 20.9656 | 43000 | 0.1084 | 19.7163 | 4.3410 |
| 0.0357 | 21.4531 | 44000 | 0.1081 | 19.5356 | 4.3016 |
| 0.043 | 21.9407 | 45000 | 0.1043 | 19.2404 | 4.2211 |
| 0.027 | 22.4282 | 46000 | 0.1074 | 19.2801 | 4.2250 |
| 0.0344 | 22.9159 | 47000 | 0.1091 | 19.3374 | 4.2124 |
| 0.0306 | 23.4033 | 48000 | 0.1083 | 19.2096 | 4.1982 |
| 0.033 | 23.8910 | 49000 | 0.1037 | 19.1259 | 4.1611 |
| 0.0309 | 24.3784 | 50000 | 0.1071 | 19.1743 | 4.1840 |
| 0.0246 | 24.8661 | 51000 | 0.0986 | 19.1127 | 4.1438 |
| 0.0299 | 25.3536 | 52000 | 0.1045 | 18.9673 | 4.1098 |
| 0.0296 | 25.8413 | 53000 | 0.1013 | 18.9717 | 4.0901 |
| 0.0272 | 26.3287 | 54000 | 0.1023 | 18.7822 | 4.0404 |
| 0.0225 | 26.8164 | 55000 | 0.1032 | 18.7690 | 4.0380 |
| 0.0206 | 27.3038 | 56000 | 0.1020 | 18.7734 | 4.0436 |
| 0.0273 | 27.7915 | 57000 | 0.1020 | 18.8131 | 4.0483 |
| 0.0267 | 28.2790 | 58000 | 0.1015 | 18.8131 | 4.0499 |
| 0.0268 | 28.7666 | 59000 | 0.1020 | 18.7866 | 4.0428 |
| 0.0307 | 29.2541 | 60000 | 0.1020 | 18.7822 | 4.0436 |
| 0.033 | 29.7418 | 61000 | 0.1020 | 18.7866 | 4.0428 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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