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wav2vec2-large-waxal-keyword-spotting

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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wav2vec2-large-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: wav2vec2-large
    results: []

wav2vec2-large

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

  • Loss: 0.3413
  • Accuracy: 0.9443
  • Precision: 0.9780
  • F1: 0.9604

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
4.6314 1.01 500 4.9165 0.0205 0.0028 0.0049
3.7739 2.02 1000 4.4491 0.0356 0.0750 0.0252
2.5035 3.04 1500 4.1429 0.1129 0.2672 0.1114
1.5633 4.05 2000 3.1973 0.3676 0.6598 0.3830
1.0538 5.06 2500 2.5479 0.5889 0.8417 0.6557
0.7422 6.07 3000 1.4494 0.7825 0.8921 0.8194
0.5762 7.08 3500 1.3168 0.7726 0.9277 0.8267
0.46 8.1 4000 0.8783 0.8564 0.9532 0.8982
0.4007 9.11 4500 0.7524 0.8738 0.9637 0.9137
0.3374 10.12 5000 0.6386 0.8852 0.9678 0.9221
0.3108 11.13 5500 0.5049 0.9106 0.9681 0.9373
0.2735 12.15 6000 0.6097 0.8905 0.9624 0.9226
0.2716 13.16 6500 0.4543 0.9000 0.9569 0.9206
0.2484 14.17 7000 0.3965 0.9272 0.9742 0.9489
0.228 15.18 7500 0.6807 0.8856 0.9777 0.9257
0.2307 16.19 8000 0.5219 0.9174 0.9802 0.9464
0.2169 17.21 8500 0.4630 0.9121 0.9677 0.9338
0.1997 18.22 9000 0.5152 0.9128 0.9740 0.9398
0.1921 19.23 9500 0.5105 0.9144 0.9867 0.9476
0.1825 20.24 10000 0.6302 0.9053 0.9832 0.9407
0.1786 21.25 10500 0.4602 0.9272 0.9813 0.9524
0.1671 22.27 11000 0.5443 0.9147 0.9794 0.9444
0.1623 23.28 11500 0.3413 0.9443 0.9780 0.9604
0.1595 24.29 12000 0.4478 0.9288 0.9813 0.9531
0.151 25.3 12500 0.4178 0.9360 0.9818 0.9571
0.1472 26.32 13000 0.4154 0.9356 0.9833 0.9578
0.1473 27.33 13500 0.4549 0.9318 0.9837 0.9561
0.131 28.34 14000 0.3574 0.9424 0.9845 0.9621
0.134 29.35 14500 0.4475 0.9333 0.9840 0.9568
0.1282 30.36 15000 0.4012 0.9382 0.9837 0.9591
0.1307 31.38 15500 0.3552 0.9428 0.9847 0.9624

Framework versions

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