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hubert-base-libri-demo-feature_extractor_not_frozen

This model is a fine-tuned version of facebook/hubert-base-ls960 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.8505
  • Wer: 1.0

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: 0.00015
  • train_batch_size: 64
  • 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: 3000
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.3787 1.12 500 3.5514 1.0
2.8772 2.24 1000 3.6079 1.0
2.8665 3.36 1500 4.0086 1.0
2.864 4.48 2000 4.0925 1.0
2.8606 5.61 2500 3.8393 1.0
2.8589 6.73 3000 3.7722 1.0
2.8603 7.85 3500 3.8588 1.0
2.8685 8.97 4000 3.8065 1.0
2.8602 10.09 4500 3.8807 1.0
2.858 11.21 5000 3.8724 1.0
2.8582 12.33 5500 3.8091 1.0
2.8579 13.45 6000 3.6785 1.0
2.8569 14.57 6500 3.7339 1.0
2.8574 15.7 7000 3.7972 1.0
2.8564 16.82 7500 3.8758 1.0
2.8569 17.94 8000 3.9114 1.0
2.8569 19.06 8500 3.9208 1.0
2.8565 20.18 9000 3.9229 1.0
2.8571 21.3 9500 3.8876 1.0
2.8569 22.42 10000 3.8732 1.0
2.8557 23.54 10500 3.8587 1.0
2.8569 24.66 11000 3.8505 1.0

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.12.1.dev0
  • Tokenizers 0.13.3
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