analist / xlsr-ewe

huggingface.co
Total runs: 0
24-hour runs: 0
7-day runs: 0
30-day runs: -2
Model's Last Updated: March 13 2026
automatic-speech-recognition

Introduction of xlsr-ewe

Model Details of xlsr-ewe

xlsr-ewe

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2997
  • Wer: 0.2635
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: 2
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 40
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Wer
169.4589 1.0 69 14.2354 1.0
60.2340 2.0 138 5.9731 1.0
31.7164 3.0 207 3.5702 1.0
25.3039 4.0 276 3.0823 1.0
24.5229 5.0 345 3.0302 1.0
24.5389 6.0 414 2.9540 1.0
17.9817 7.0 483 1.7917 0.9957
8.9278 8.0 552 0.8225 0.6369
7.1229 9.0 621 0.6046 0.5514
5.0350 10.0 690 0.5255 0.4525
5.6587 11.0 759 0.4622 0.4232
4.3718 12.0 828 0.4218 0.3913
4.2471 13.0 897 0.3807 0.3971
3.1870 14.0 966 0.3713 0.3589
3.6097 15.0 1035 0.3483 0.3485
3.1309 16.0 1104 0.3327 0.3340
2.9087 17.0 1173 0.3241 0.3249
2.7514 18.0 1242 0.3218 0.3244
2.8296 19.0 1311 0.3053 0.3123
2.2216 20.0 1380 0.3092 0.3032
2.5109 21.0 1449 0.2995 0.2971
2.7112 22.0 1518 0.3010 0.2964
2.0306 23.0 1587 0.3137 0.3006
2.2682 24.0 1656 0.3008 0.2847
2.2246 25.0 1725 0.3068 0.2889
2.0131 26.0 1794 0.2919 0.2999
2.3610 27.0 1863 0.2961 0.2884
1.7638 28.0 1932 0.2992 0.2799
2.1672 29.0 2001 0.2931 0.2786
1.6068 30.0 2070 0.2900 0.2706
1.6683 31.0 2139 0.3041 0.2693
1.6144 32.0 2208 0.2998 0.2752
1.5779 33.0 2277 0.2984 0.2676
1.4730 34.0 2346 0.2977 0.2643
1.2034 35.0 2415 0.3010 0.2615
1.3116 36.0 2484 0.3034 0.2685
1.4723 37.0 2553 0.3026 0.2661
1.1795 38.0 2622 0.3012 0.2654
1.2413 39.0 2691 0.2997 0.2622
1.2913 40.0 2760 0.2997 0.2635
Framework versions
  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

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