---
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
  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: 0.9811709326272433
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-base-finetuned-ks

This model was trained from scratch on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0799
- Accuracy: 0.9812

## 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: 42
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.222         | 1.0   | 199  | 0.1320          | 0.9748   |
| 0.1763        | 2.0   | 399  | 0.1063          | 0.9762   |
| 0.1502        | 3.0   | 599  | 0.0858          | 0.9798   |
| 0.1234        | 4.0   | 799  | 0.0855          | 0.9804   |
| 0.1402        | 4.98  | 995  | 0.0799          | 0.9812   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3