---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wavlm-base-plus-finetuned-linnut-sm

This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/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