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hubert-large-ls960-ft-waxal-keyword-spotting

Audio Classification
Transformers
PyTorch
hubert
Generated from Trainer
Inference Endpoints
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hubert-large-ls960-ft-waxal-keyword-spotting / README.md
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metadata
license: apache-2.0
tags:
  - audio-classification
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - f1
model-index:
  - name: hubert-large-ls960-ft
    results: []

hubert-large-ls960-ft

This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the galsenai/waxal_dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3272
  • Accuracy: 0.9413
  • Precision: 0.9865
  • F1: 0.9628

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: 3e-05
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 0
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 48
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 32.0

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision F1
4.7142 1.01 500 5.2765 0.0 0.0 0.0
4.396 2.02 1000 5.4145 0.0 0.0 0.0
3.8883 3.04 1500 4.4336 0.0474 0.0408 0.0104
2.7848 4.05 2000 3.9772 0.1300 0.1281 0.0964
1.8649 5.06 2500 3.4482 0.1576 0.3339 0.1547
1.3084 6.07 3000 2.9703 0.3081 0.5296 0.3402
0.9868 7.08 3500 2.3985 0.4687 0.8032 0.5353
0.7679 8.1 4000 1.7937 0.6521 0.8389 0.7095
0.6232 9.11 4500 1.4768 0.7389 0.8698 0.7847
0.5126 10.12 5000 1.0542 0.8287 0.9443 0.8763
0.4453 11.13 5500 0.9050 0.8518 0.9511 0.8960
0.3775 12.15 6000 0.6996 0.8928 0.9662 0.9266
0.3568 13.16 6500 0.6157 0.8958 0.9743 0.9285
0.3165 14.17 7000 0.4925 0.9151 0.9764 0.9436
0.2951 15.18 7500 0.4992 0.9038 0.9773 0.9369
0.2763 16.19 8000 0.5212 0.9072 0.9821 0.9404
0.2634 17.21 8500 0.5201 0.9087 0.9817 0.9418
0.2422 18.22 9000 0.4504 0.9235 0.9840 0.9514
0.236 19.23 9500 0.3829 0.9257 0.9825 0.9518
0.2272 20.24 10000 0.4632 0.9155 0.9822 0.9451
0.226 21.25 10500 0.4731 0.9159 0.9837 0.9470
0.2129 22.27 11000 0.3814 0.9299 0.9832 0.9549
0.2009 23.28 11500 0.4119 0.9257 0.9814 0.9515
0.1973 24.29 12000 0.4310 0.9216 0.9843 0.9493
0.1965 25.3 12500 0.3272 0.9413 0.9865 0.9628
0.1989 26.32 13000 0.4231 0.9242 0.9878 0.9528
0.1916 27.33 13500 0.3978 0.9284 0.9876 0.9559
0.1849 28.34 14000 0.4529 0.9216 0.9865 0.9507
0.1844 29.35 14500 0.3854 0.9314 0.9864 0.9566
0.1831 30.36 15000 0.4178 0.9257 0.9853 0.9528
0.1778 31.38 15500 0.3737 0.9360 0.9884 0.9606

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.9.1.dev0
  • Tokenizers 0.13.2