---
license: apache-2.0
base_model: Abdalla312/wav2vec2-base-finetuned-ks
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9999221365724519
---

<!-- 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-finetuned-ks

This model is a fine-tuned version of [Abdalla312/wav2vec2-base-finetuned-ks](https://huggingface.co./Abdalla312/wav2vec2-base-finetuned-ks) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Accuracy: 0.9999

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0069        | 1.0   | 511  | 0.0376          | 0.9914   |
| 0.0           | 2.0   | 1023 | 0.0003          | 0.9999   |
| 0.0           | 3.0   | 1533 | 0.0006          | 0.9999   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2