| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | - automatic-speech-recognition |
| | - NbAiLab/NPSC |
| | - robust-speech-event |
| | - false |
| | - nb-NO |
| | - hf-asr-leaderboard |
| | datasets: |
| | - NbAiLab/NPSC |
| | language: |
| | - nb-NO |
| | model-index: |
| | - name: XLSR-300M-bokmaal |
| | results: |
| | - task: |
| | name: Automatic Speech Recognition |
| | type: automatic-speech-recognition |
| | dataset: |
| | name: NPSC |
| | type: NbAiLab/NPSC |
| | args: 16K_mp3_bokmaal |
| | metrics: |
| | - name: "Test (Bokm\xE5l) WER" |
| | type: wer |
| | value: 0.07699635320946434 |
| | - name: "Test (Bokm\xE5l) CER" |
| | type: cer |
| | value: 0.0284288464829 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # XLSR-300M-bokmaal |
| |
|
| | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1635 |
| | - Wer: 0.1005 |
| |
|
| | ## 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: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 2000 |
| | - num_epochs: 15.0 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | | 3.0307 | 0.32 | 500 | 3.0026 | 1.0 | |
| | | 2.7865 | 0.64 | 1000 | 2.4849 | 0.9926 | |
| | | 0.7522 | 0.95 | 1500 | 0.4567 | 0.3594 | |
| | | 0.5703 | 1.27 | 2000 | 0.3440 | 0.2586 | |
| | | 0.4762 | 1.59 | 2500 | 0.2925 | 0.2178 | |
| | | 0.4585 | 1.91 | 3000 | 0.2442 | 0.1981 | |
| | | 0.4013 | 2.23 | 3500 | 0.2495 | 0.1818 | |
| | | 0.449 | 2.54 | 4000 | 0.2152 | 0.1808 | |
| | | 0.355 | 2.86 | 4500 | 0.2179 | 0.1670 | |
| | | 0.3142 | 3.18 | 5000 | 0.1953 | 0.1542 | |
| | | 0.3242 | 3.5 | 5500 | 0.2103 | 0.1526 | |
| | | 0.3016 | 3.82 | 6000 | 0.1911 | 0.1477 | |
| | | 0.2713 | 4.13 | 6500 | 0.1836 | 0.1422 | |
| | | 0.2807 | 4.45 | 7000 | 0.1924 | 0.1447 | |
| | | 0.2929 | 4.77 | 7500 | 0.1848 | 0.1402 | |
| | | 0.2595 | 5.09 | 8000 | 0.1783 | 0.1330 | |
| | | 0.2289 | 5.41 | 8500 | 0.1901 | 0.1313 | |
| | | 0.2567 | 5.72 | 9000 | 0.1784 | 0.1298 | |
| | | 0.2401 | 6.04 | 9500 | 0.1956 | 0.1298 | |
| | | 0.2098 | 6.36 | 10000 | 0.1748 | 0.1277 | |
| | | 0.2246 | 6.68 | 10500 | 0.1777 | 0.1254 | |
| | | 0.2197 | 7.0 | 11000 | 0.1703 | 0.1222 | |
| | | 0.2122 | 7.32 | 11500 | 0.1917 | 0.1221 | |
| | | 0.2746 | 7.63 | 12000 | 0.1769 | 0.1215 | |
| | | 0.2148 | 7.95 | 12500 | 0.1736 | 0.1193 | |
| | | 0.1915 | 8.27 | 13000 | 0.1814 | 0.1161 | |
| | | 0.2462 | 8.59 | 13500 | 0.1748 | 0.1166 | |
| | | 0.1872 | 8.91 | 14000 | 0.1769 | 0.1133 | |
| | | 0.1886 | 9.22 | 14500 | 0.1852 | 0.1143 | |
| | | 0.1789 | 9.54 | 15000 | 0.1696 | 0.1126 | |
| | | 0.1692 | 9.86 | 15500 | 0.1817 | 0.1122 | |
| | | 0.1765 | 10.18 | 16000 | 0.1769 | 0.1093 | |
| | | 0.1699 | 10.5 | 16500 | 0.1604 | 0.1084 | |
| | | 0.1591 | 10.81 | 17000 | 0.1777 | 0.1080 | |
| | | 0.1499 | 11.13 | 17500 | 0.1645 | 0.1074 | |
| | | 0.163 | 11.45 | 18000 | 0.1704 | 0.1065 | |
| | | 0.1597 | 11.77 | 18500 | 0.1576 | 0.1064 | |
| | | 0.1484 | 12.09 | 19000 | 0.1637 | 0.1041 | |
| | | 0.1464 | 12.4 | 19500 | 0.1631 | 0.1047 | |
| | | 0.156 | 12.72 | 20000 | 0.1686 | 0.1029 | |
| | | 0.1625 | 13.04 | 20500 | 0.1648 | 0.1023 | |
| | | 0.1395 | 13.36 | 21000 | 0.1688 | 0.1027 | |
| | | 0.1387 | 13.68 | 21500 | 0.1670 | 0.1013 | |
| | | 0.1434 | 13.99 | 22000 | 0.1677 | 0.1017 | |
| | | 0.1442 | 14.31 | 22500 | 0.1688 | 0.1008 | |
| | | 0.1439 | 14.63 | 23000 | 0.1647 | 0.1004 | |
| | | 0.137 | 14.95 | 23500 | 0.1636 | 0.1006 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.17.0.dev0 |
| | - Pytorch 1.10.1+cu102 |
| | - Datasets 1.18.2.dev0 |
| | - Tokenizers 0.11.0 |
| |
|
