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---
language:
- ko
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- gglabs/stt-test2-1223
metrics:
- wer
model-index:
- name: Whisper Small ko
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: custom
type: gglabs/stt-test2-1223
args: 'config: ko, split: test'
metrics:
- name: Wer
type: wer
value: 52.71739130434783
---
<!-- 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 Small ko
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4327
- Wer: 52.7174
## 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: 2
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.0243 | 0.2 | 10 | 1.4327 | 52.7174 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|