---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
base_model: openai/whisper-large-v3
model-index:
- name: whisper_finetune
  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. -->

# whisper_finetune

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the aihub_100000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4970
- Cer: 5.4843
- Wer: 22.9248

## 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: 1e-08
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 0.9923        | 0.9   | 1000 | 0.5893          | 6.0827 | 25.3866 |
| 0.9389        | 1.79  | 2000 | 0.4970          | 5.4843 | 22.9248 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.17.1
- Tokenizers 0.15.2