---
library_name: transformers
language:
- ko
license: mit
base_model: imTak/whisper_large_v3_turbo_Korean2
tags:
- generated_from_trainer
datasets:
- imTak/Economy
metrics:
- wer
model-index:
- name: Whisper large v3 turbo Korean-Economy
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Economy
      type: imTak/Economy
      args: 'config: ko, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 44.99209128911987
---

<!-- 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 large v3 turbo Korean-Economy

This model is a fine-tuned version of [imTak/whisper_large_v3_ko_ft_ft](https://huggingface.co/imTak/whisper_large_v3_ko_ft_ft) on the Economy dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7148
- Wer: 44.9921

## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.8263        | 0.4630 | 1000 | 0.8210          | 49.0241 |
| 0.7497        | 0.9259 | 2000 | 0.7351          | 47.6006 |
| 0.4979        | 1.3889 | 3000 | 0.6992          | 45.6375 |
| 0.5197        | 1.8519 | 4000 | 0.6659          | 44.3410 |
| 0.4264        | 2.3148 | 5000 | 0.7168          | 46.6459 |
| 0.3911        | 2.7778 | 6000 | 0.6988          | 45.0726 |
| 0.2565        | 3.2407 | 7000 | 0.7203          | 44.8000 |
| 0.2462        | 3.7037 | 8000 | 0.7148          | 44.9921 |


### Framework versions

- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3