---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- didiudom94/gentlemen2
metrics:
- wer
model-index:
- name: Whisper Large-V3 Ko to En
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Gentlemen
      type: didiudom94/gentlemen2
      args: 'split: train'
    metrics:
    - name: Wer
      type: wer
      value: 0.7630289773857083
---

<!-- 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 Ko to En

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Gentlemen dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1304
- Wer: 0.7630

## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.2511        | 0.4507 | 1000 | 1.2619          | 0.7806 |
| 1.1681        | 0.9013 | 2000 | 1.1703          | 0.7751 |
| 0.9217        | 1.3520 | 3000 | 1.1486          | 0.7768 |
| 0.9093        | 1.8026 | 4000 | 1.1304          | 0.7630 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3