aconeil / w2v2-lmk_org_para

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 2
30-day runs: -1
Model's Last Updated: April 26 2026
automatic-speech-recognition

Introduction of w2v2-lmk_org_para

Model Details of w2v2-lmk_org_para

w2v2-lmk_org_para

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

  • Loss: 1.1860
  • Wer: 0.6934
  • Cer: 0.2422
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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use adamw_torch 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: 300
  • num_epochs: 100
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Wer Cer
9.5202 6.2581 100 4.3511 1.0 1.0
3.2449 12.5161 200 2.9976 1.0 1.0
3.0171 18.7742 300 2.8768 1.0 1.0
2.9274 25.0 400 2.7959 1.0 1.0
2.6596 31.2581 500 2.3278 1.0 0.9025
1.9635 37.5161 600 1.5400 0.9617 0.4646
1.4694 43.7742 700 1.3102 0.8258 0.3343
1.1617 50.0 800 1.1932 0.8188 0.3092
0.9487 56.2581 900 1.2371 0.7700 0.2833
0.7926 62.5161 1000 1.2255 0.7213 0.2589
0.6905 68.7742 1100 1.1964 0.6864 0.2567
0.6536 75.0 1200 1.1720 0.6864 0.2468
0.5637 81.2581 1300 1.1978 0.6934 0.2483
0.5283 87.5161 1400 1.1845 0.6899 0.2384
0.535 93.7742 1500 1.1884 0.6864 0.2422
0.4905 100.0 1600 1.1860 0.6934 0.2422
Framework versions
  • Transformers 4.57.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.0
  • Tokenizers 0.22.1

Runs of aconeil w2v2-lmk_org_para on huggingface.co

4
Total runs
0
24-hour runs
1
3-day runs
2
7-day runs
-1
30-day runs

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