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wav2vec2-large-xls-r-1b-nl

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Dutch
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results
Inference Endpoints
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wav2vec2-large-xls-r-1b-nl / README.md
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metadata
language:
  - nl
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - nl
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-1b-nl
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: nl
        metrics:
          - name: Test WER
            type: wer
            value: 11.55
          - name: Test CER
            type: cer
            value: 3.37
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: nl
        metrics:
          - name: Test WER
            type: wer
            value: tbc
          - name: Test CER
            type: cer
            value: tbc

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1479
  • Wer: 0.1156

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 5
  • total_train_batch_size: 40
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.2223 0.52 500 0.3866 0.3425
1.0748 1.03 1000 0.2574 0.2169
1.0416 1.55 1500 0.2177 0.1946
0.9951 2.06 2000 0.2008 0.1760
0.975 2.58 2500 0.1961 0.1751
0.9461 3.1 3000 0.1989 0.1782
0.9381 3.61 3500 0.1928 0.1699
0.934 4.13 4000 0.1923 0.1633
0.9322 4.64 4500 0.1871 0.1634
0.9012 5.16 5000 0.1890 0.1702
0.9045 5.68 5500 0.1882 0.1740
0.8826 6.19 6000 0.1856 0.1575
0.8848 6.71 6500 0.1861 0.1617
0.8723 7.22 7000 0.1927 0.1646
0.8725 7.74 7500 0.1798 0.1531
0.8573 8.26 8000 0.1781 0.1587
0.8633 8.77 8500 0.1852 0.1628
0.8603 9.29 9000 0.1833 0.1601
0.8421 9.8 9500 0.1788 0.1543
0.8404 10.32 10000 0.1844 0.1556
0.8342 10.84 10500 0.1770 0.1538
0.8161 11.35 11000 0.1821 0.1567
0.8371 11.87 11500 0.1909 0.1629
0.8083 12.38 12000 0.1778 0.1498
0.806 12.9 12500 0.1802 0.1547
0.8013 13.42 13000 0.1859 0.1584
0.7913 13.93 13500 0.1875 0.1517
0.8063 14.45 14000 0.1799 0.1571
0.7991 14.96 14500 0.1792 0.1538
0.7843 15.48 15000 0.1753 0.1464
0.7905 16.0 15500 0.1784 0.1508
0.7808 16.51 16000 0.1771 0.1485
0.7743 17.03 16500 0.1795 0.1491
0.7833 17.54 17000 0.1722 0.1484
0.7763 18.06 17500 0.1767 0.1518
0.7698 18.58 18000 0.1720 0.1460
0.7571 19.09 18500 0.1735 0.1478
0.7673 19.61 19000 0.1817 0.1511
0.7415 20.12 19500 0.1763 0.1481
0.751 20.64 20000 0.1742 0.1484
0.7563 21.16 20500 0.1810 0.1611
0.7423 21.67 21000 0.1817 0.1557
0.7242 22.19 21500 0.1690 0.1446
0.7251 22.7 22000 0.1684 0.1446
0.7302 23.22 22500 0.1735 0.1430
0.733 23.74 23000 0.1720 0.1454
0.7128 24.25 23500 0.1668 0.1383
0.7184 24.77 24000 0.1635 0.1377
0.7015 25.28 24500 0.1646 0.1389
0.7198 25.8 25000 0.1775 0.1462
0.7178 26.32 25500 0.1705 0.1419
0.7199 26.83 26000 0.1649 0.1416
0.6981 27.35 26500 0.1724 0.1418
0.6886 27.86 27000 0.1633 0.1382
0.6922 28.38 27500 0.1698 0.1420
0.6833 28.9 28000 0.1611 0.1351
0.6798 29.41 28500 0.1639 0.1365
0.6711 29.93 29000 0.1668 0.1358
0.6762 30.44 29500 0.1682 0.1355
0.6594 30.96 30000 0.1629 0.1345
0.6664 31.48 30500 0.1625 0.1321
0.6838 31.99 31000 0.1597 0.1372
0.6603 32.51 31500 0.1583 0.1302
0.6468 33.02 32000 0.1595 0.1322
0.6464 33.54 32500 0.1609 0.1315
0.6623 34.06 33000 0.1622 0.1366
0.6414 34.57 33500 0.1587 0.1330
0.6242 35.09 34000 0.1614 0.1337
0.632 35.6 34500 0.1568 0.1272
0.6346 36.12 35000 0.1583 0.1274
0.6143 36.64 35500 0.1576 0.1264
0.6208 37.15 36000 0.1621 0.1263
0.6185 37.67 36500 0.1623 0.1270
0.6128 38.18 37000 0.1604 0.1268
0.6151 38.7 37500 0.1593 0.1246
0.6082 39.22 38000 0.1532 0.1238
0.6 39.73 38500 0.1524 0.1224
0.6032 40.25 39000 0.1521 0.1212
0.6016 40.76 39500 0.1551 0.1215
0.6009 41.28 40000 0.1523 0.1215
0.5875 41.8 40500 0.1541 0.1216
0.608 42.31 41000 0.1536 0.1209
0.5876 42.83 41500 0.1567 0.1211
0.5714 43.34 42000 0.1532 0.1217
0.5756 43.86 42500 0.1516 0.1196
0.5719 44.38 43000 0.1491 0.1191
0.5829 44.89 43500 0.1497 0.1193
0.5664 45.41 44000 0.1487 0.1173
0.5707 45.92 44500 0.1470 0.1164
0.5696 46.44 45000 0.1479 0.1161
0.5767 46.96 45500 0.1492 0.1175
0.5573 47.47 46000 0.1471 0.1165
0.5625 47.99 46500 0.1484 0.1168
0.5671 48.5 47000 0.1474 0.1162
0.5484 49.02 47500 0.1479 0.1158
0.555 49.54 48000 0.1477 0.1157

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0