---
base_model: microsoft/wavlm-base
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-ft-keyword-spotting
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: superb
      type: superb
      config: ks
      split: validation
      args: ks
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9694027655192704
---

<!-- 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. -->

# wav2vec2-base-ft-keyword-spotting

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2270
- Accuracy: 0.9694

## 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: 64
- eval_batch_size: 64
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3203        | 1.0   | 199  | 1.2906          | 0.6328   |
| 0.9587        | 2.0   | 399  | 0.7793          | 0.7355   |
| 0.6218        | 3.0   | 599  | 0.3858          | 0.9289   |
| 0.4379        | 4.0   | 799  | 0.2581          | 0.9688   |
| 0.3779        | 4.98  | 995  | 0.2270          | 0.9694   |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.0.post302
- Datasets 2.14.5
- Tokenizers 0.13.3