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---
library_name: transformers
language:
- ko
license: mit
base_model: imTak/whisper_large_v3_ko_ft_ft
tags:
- generated_from_trainer
datasets:
- imTak/korean-speak-Develop
metrics:
- wer
model-index:
- name: Whisper large v3 turbo Korean-Develop
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Develop
type: imTak/korean-speak-Develop
args: 'config: ko, split: test'
metrics:
- name: Wer
type: wer
value: 16.64530599166934
---
<!-- 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-Develop
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 Develop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2410
- Wer: 16.6453
## 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: 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 | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2119 | 1.9455 | 500 | 0.2721 | 22.6690 |
| 0.0714 | 3.8911 | 1000 | 0.2542 | 19.9135 |
| 0.0145 | 5.8366 | 1500 | 0.2417 | 18.5037 |
| 0.0018 | 7.7821 | 2000 | 0.2410 | 16.6453 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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