davidilag
/

wav2vec2-xls-r-1b-faroese-100h-30-epochs_20250126_v2

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-1b
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: wav2vec2-xls-r-1b-faroese-100h-30-epochs_20250126_v2
+  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-faroese-100h-30-epochs_20250126_v2
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1065
+- Wer: 18.8527
+- Cer: 4.1045
+## 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     |
+|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
+| 1.4856        | 0.4877  | 1000  | 0.8786          | 82.4118 | 26.5338 |
+| 0.6959        | 0.9754  | 2000  | 0.4059          | 49.6850 | 13.2626 |
+| 0.4978        | 1.4628  | 3000  | 0.2672          | 36.8991 | 9.6228  |
+| 0.4215        | 1.9505  | 4000  | 0.2359          | 33.7005 | 8.5821  |
+| 0.3261        | 2.4379  | 5000  | 0.2273          | 31.9910 | 8.2815  |
+| 0.3388        | 2.9256  | 6000  | 0.2162          | 31.0261 | 7.9927  |
+| 0.2987        | 3.4131  | 7000  | 0.1988          | 29.2814 | 7.4294  |
+| 0.2824        | 3.9008  | 8000  | 0.1919          | 28.5192 | 7.1311  |
+| 0.257         | 4.3882  | 9000  | 0.1861          | 27.9861 | 7.1059  |
+| 0.2668        | 4.8759  | 10000 | 0.1758          | 27.2679 | 6.8108  |
+| 0.2438        | 5.3633  | 11000 | 0.1699          | 26.8450 | 6.6419  |
+| 0.2218        | 5.8510  | 12000 | 0.1704          | 26.7040 | 6.6222  |
+| 0.194         | 6.3385  | 13000 | 0.1623          | 25.9638 | 6.3760  |
+| 0.1997        | 6.8261  | 14000 | 0.1550          | 25.5805 | 6.3216  |
+| 0.186         | 7.3136  | 15000 | 0.1493          | 25.5584 | 6.1243  |
+| 0.1686        | 7.8013  | 16000 | 0.1597          | 25.3998 | 6.2198  |
+| 0.1433        | 8.2887  | 17000 | 0.1493          | 24.6552 | 5.8813  |
+| 0.1581        | 8.7764  | 18000 | 0.1459          | 24.7962 | 5.9847  |
+| 0.1245        | 9.2638  | 19000 | 0.1425          | 24.3865 | 5.8190  |
+| 0.1396        | 9.7515  | 20000 | 0.1576          | 24.5407 | 5.8900  |
+| 0.1188        | 10.2390 | 21000 | 0.1410          | 23.7300 | 5.6407  |
+| 0.1131        | 10.7267 | 22000 | 0.1373          | 23.4524 | 5.5586  |
+| 0.1267        | 11.2141 | 23000 | 0.1397          | 23.7741 | 5.6075  |
+| 0.1192        | 11.7018 | 24000 | 0.1346          | 23.4084 | 5.4482  |
+| 0.1073        | 12.1892 | 25000 | 0.1325          | 22.8312 | 5.3811  |
+| 0.098         | 12.6769 | 26000 | 0.1227          | 22.3642 | 5.1681  |
+| 0.0939        | 13.1644 | 27000 | 0.1307          | 22.7695 | 5.2856  |
+| 0.0929        | 13.6520 | 28000 | 0.1274          | 22.2629 | 5.1854  |
+| 0.0787        | 14.1395 | 29000 | 0.1265          | 21.9853 | 5.0268  |
+| 0.0805        | 14.6272 | 30000 | 0.1239          | 22.1131 | 5.0923  |
+| 0.0766        | 15.1146 | 31000 | 0.1226          | 21.7386 | 4.9905  |
+| 0.0745        | 15.6023 | 32000 | 0.1189          | 21.4786 | 4.9124  |
+| 0.0663        | 16.0897 | 33000 | 0.1224          | 21.7650 | 4.9250  |
+| 0.0638        | 16.5774 | 34000 | 0.1188          | 21.0689 | 4.8185  |
+| 0.0609        | 17.0649 | 35000 | 0.1177          | 20.9058 | 4.7420  |
+| 0.0677        | 17.5525 | 36000 | 0.1140          | 21.0116 | 4.7546  |
+| 0.0516        | 18.0400 | 37000 | 0.1180          | 20.8794 | 4.7025  |
+| 0.0488        | 18.5277 | 38000 | 0.1177          | 20.4653 | 4.6307  |
+| 0.0512        | 19.0151 | 39000 | 0.1135          | 20.1789 | 4.5447  |
+| 0.0464        | 19.5028 | 40000 | 0.1163          | 20.2758 | 4.5013  |
+| 0.0465        | 19.9905 | 41000 | 0.1125          | 20.1877 | 4.4635  |
+| 0.0536        | 20.4779 | 42000 | 0.1129          | 20.0247 | 4.4477  |
+| 0.0417        | 20.9656 | 43000 | 0.1131          | 19.7383 | 4.3830  |
+| 0.038         | 21.4531 | 44000 | 0.1117          | 19.8881 | 4.4027  |
+| 0.0454        | 21.9407 | 45000 | 0.1105          | 19.6942 | 4.3514  |
+| 0.031         | 22.4282 | 46000 | 0.1130          | 19.6193 | 4.3388  |
+| 0.0373        | 22.9159 | 47000 | 0.1100          | 19.4563 | 4.2788  |
+| 0.0319        | 23.4033 | 48000 | 0.1107          | 19.4519 | 4.2717  |
+| 0.035         | 23.8910 | 49000 | 0.1112          | 19.2933 | 4.2299  |
+| 0.0327        | 24.3784 | 50000 | 0.1111          | 19.1964 | 4.1999  |
+| 0.0261        | 24.8661 | 51000 | 0.1056          | 19.0906 | 4.1842  |
+| 0.0321        | 25.3536 | 52000 | 0.1059          | 18.9496 | 4.1447  |
+| 0.0301        | 25.8413 | 53000 | 0.1058          | 19.0906 | 4.1558  |
+| 0.0281        | 26.3287 | 54000 | 0.1091          | 18.9893 | 4.1344  |
+| 0.0246        | 26.8164 | 55000 | 0.1062          | 18.8836 | 4.1100  |
+| 0.0238        | 27.3038 | 56000 | 0.1064          | 18.8615 | 4.1076  |
+| 0.0305        | 27.7915 | 57000 | 0.1068          | 18.9188 | 4.1195  |
+| 0.0293        | 28.2790 | 58000 | 0.1067          | 18.8791 | 4.1060  |
+| 0.0288        | 28.7666 | 59000 | 0.1066          | 18.8836 | 4.1131  |
+| 0.0345        | 29.2541 | 60000 | 0.1065          | 18.8439 | 4.1029  |
+| 0.0365        | 29.7418 | 61000 | 0.1065          | 18.8527 | 4.1045  |
+### Framework versions
+- Transformers 4.48.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

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