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XLSR-300M-bokmaal

Automatic Speech Recognition
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metadata
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 (Bokmål) WER
            type: wer
            value: 0.07699635320946434
          - name: Test (Bokmål) CER
            type: cer
            value: 0.0284288464829

XLSR-300M-bokmaal

This model is a fine-tuned version of 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