wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod9

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the xtreme_s dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6655
  • Wer: 0.8832

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: 0.001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
9.2247 1.0 39 3.1705 1.0
2.9516 2.0 78 2.8732 1.0
2.8953 3.0 117 2.8663 1.0
2.8878 4.0 156 2.8693 1.0
2.8818 5.0 195 2.8656 1.0
2.8814 6.0 234 2.8478 1.0
2.8817 7.0 273 2.8603 1.0
2.8771 8.0 312 2.8647 1.0
2.8811 9.0 351 2.8630 1.0
2.8703 10.0 390 2.8667 1.0
2.8608 11.0 429 2.8429 1.0
2.8578 12.0 468 2.8399 1.0
2.856 13.0 507 2.8531 1.0
2.8438 14.0 546 2.7872 1.0
2.7833 15.0 585 2.7015 1.0
2.7126 16.0 624 2.5606 1.0
2.4797 17.0 663 2.2529 1.0
2.2495 18.0 702 2.1600 1.0
1.8737 19.0 741 1.6194 1.0
1.69 20.0 780 1.4995 0.9999
1.5018 21.0 819 1.3398 0.9834
1.3264 22.0 858 1.2688 0.9692
1.1944 23.0 897 1.2211 0.9585
1.1186 24.0 936 1.1754 0.9517
1.0038 25.0 975 1.2082 0.9758
0.9096 26.0 1014 1.1463 0.9210
0.7954 27.0 1053 1.1530 0.9184
0.7337 28.0 1092 1.1948 0.9208
0.6438 29.0 1131 1.1907 0.9021
0.5933 30.0 1170 1.1994 0.9032
0.5646 31.0 1209 1.2765 0.9019
0.5314 32.0 1248 1.3331 0.9387
0.4208 33.0 1287 1.4003 0.9271
0.3769 34.0 1326 1.4226 0.9635
0.425 35.0 1365 1.3948 0.8890
0.3446 36.0 1404 1.4492 0.8901
0.3411 37.0 1443 1.5271 0.9136
0.3147 38.0 1482 1.4801 0.9139
0.2843 39.0 1521 1.5223 0.9011
0.2908 40.0 1560 1.6087 0.8871
0.2816 41.0 1599 1.5167 0.9097
0.2586 42.0 1638 1.5968 0.9129
0.2428 43.0 1677 1.6335 0.9100
0.2569 44.0 1716 1.5888 0.8967
0.2119 45.0 1755 1.6366 0.8910
0.2496 46.0 1794 1.6392 0.8807
0.2246 47.0 1833 1.6780 0.9197
0.2231 48.0 1872 1.7074 0.8969
0.2083 49.0 1911 1.6566 0.8811
0.2091 50.0 1950 1.6655 0.8832

Framework versions

  • Transformers 4.39.2
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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