---
library_name: transformers
base_model: fleek/wav2vec-large-xlsr-korean
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xlsr-korean-dialect-recognition
  results: []
---

<!-- 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-xlsr-korean-dialect-recognition

This model is a fine-tuned version of [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Accuracy: 0.9783

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8264        | 0.0794 | 500  | 0.3492          | 0.8641   |
| 0.674         | 0.1588 | 1000 | 0.2810          | 0.8985   |
| 0.3338        | 0.2382 | 1500 | 0.2596          | 0.9269   |
| 0.3121        | 0.3176 | 2000 | 0.2037          | 0.9403   |
| 0.2074        | 0.3970 | 2500 | 0.1472          | 0.9494   |
| 0.4901        | 0.4764 | 3000 | 0.1448          | 0.9582   |
| 0.2544        | 0.5558 | 3500 | 0.1676          | 0.9535   |
| 0.2138        | 0.6352 | 4000 | 0.1057          | 0.9684   |
| 0.1705        | 0.7146 | 4500 | 0.1463          | 0.9551   |
| 0.4207        | 0.7940 | 5000 | 0.0907          | 0.9722   |
| 0.0229        | 0.8734 | 5500 | 0.0887          | 0.9738   |
| 0.203         | 0.9528 | 6000 | 0.0752          | 0.9783   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0