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wav2vec2-300m-ft-soft-skill

Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
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wav2vec2-300m-ft-soft-skill

This model is a fine-tuned version of glob-asr/xls-r-es-test-lm on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7447
  • Accuracy: 0.6827
  • F1 Micro: 0.3514
  • F1 Macro: 0.6827
  • Precision Micro: 0.6827

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 10
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Micro F1 Macro Precision Micro
0.823 0.51 100 0.6821 0.7589 0.2876 0.7589 0.7589
0.7122 1.02 200 0.6767 0.7589 0.2876 0.7589 0.7589
0.6706 1.52 300 0.6768 0.7589 0.2876 0.7589 0.7589
0.7096 2.03 400 0.6791 0.7589 0.2876 0.7589 0.7589
0.6909 2.54 500 0.6780 0.7589 0.2876 0.7589 0.7589
0.6861 3.05 600 0.6779 0.7589 0.2876 0.7589 0.7589
0.6842 3.55 700 0.6773 0.7589 0.2876 0.7589 0.7589
0.6887 4.06 800 0.6764 0.7589 0.2876 0.7589 0.7589
0.6766 4.57 900 0.6803 0.7589 0.2876 0.7589 0.7589
0.6964 5.08 1000 0.6819 0.7589 0.2876 0.7589 0.7589
0.6515 5.58 1100 0.6788 0.7589 0.2876 0.7589 0.7589
0.6608 6.09 1200 0.6864 0.7589 0.2876 0.7589 0.7589
0.6171 6.6 1300 0.6980 0.7589 0.2876 0.7589 0.7589
0.6292 7.11 1400 0.7172 0.7386 0.3119 0.7386 0.7386
0.6015 7.61 1500 0.6988 0.7462 0.3212 0.7462 0.7462
0.6236 8.12 1600 0.7493 0.6954 0.3432 0.6954 0.6954
0.5643 8.63 1700 0.7250 0.7107 0.3466 0.7107 0.7107
0.6134 9.14 1800 0.7561 0.6751 0.3565 0.6751 0.6751
0.5642 9.64 1900 0.7447 0.6827 0.3514 0.6827 0.6827

Framework versions

  • Transformers 4.21.3
  • Pytorch 1.8.1+cu111
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Evaluation results

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