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wavlm-base-plus-finetuned-linnut-sm

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metadata
library_name: transformers
base_model: microsoft/wavlm-base-plus
tags:
  - generated_from_trainer
datasets:
  - codymd/linnut_audio_sm
metrics:
  - accuracy
  - f1
model-index:
  - name: wavlm-base-plus-finetuned-linnut-sm
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: codymd/linnut_audio_sm
          type: codymd/linnut_audio_sm
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.678
          - name: F1
            type: f1
            value: 0.5581454561830088

wavlm-base-plus-finetuned-linnut-sm

This model is a fine-tuned version of microsoft/wavlm-base-plus on the codymd/linnut_audio_sm dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4633
  • Accuracy: 0.678
  • F1: 0.5581

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Use OptimizerNames.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_ratio: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Accuracy F1 Validation Loss
2.2936 1.0 500 0.222 0.0430 2.4680
1.9148 2.0 1000 0.432 0.1492 1.9393
2.0218 3.0 1500 0.5 0.1981 1.6724
2.1636 4.0 2000 0.526 0.2262 1.6097
1.8098 5.0 2500 0.516 0.2431 2.0782
1.0826 6.0 3000 0.604 0.3281 1.3590
0.6267 7.0 3500 0.606 0.3441 1.3871
0.7986 8.0 4000 0.612 0.3829 1.4410
1.0745 9.0 4500 0.656 0.4504 1.3311
1.094 10.0 5000 0.664 0.4608 1.3141
0.9286 11.0 5500 1.2929 0.69 0.5016
1.1316 12.0 6000 1.5307 0.656 0.4794
0.1818 13.0 6500 1.3146 0.696 0.5485
0.1084 14.0 7000 1.3708 0.682 0.5621
0.3915 15.0 7500 1.4633 0.678 0.5581

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.0