---
library_name: peft
language:
- ko
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
model-index:
- name: Whisper Turbo ko
  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 Turbo 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: 0.0940

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3061        | 0.5405 | 100  | 0.6169          |
| 0.107         | 1.0811 | 200  | 0.3840          |
| 0.0871        | 1.6216 | 300  | 0.3024          |
| 0.0801        | 2.1622 | 400  | 0.2429          |
| 0.0608        | 2.7027 | 500  | 0.2094          |
| 0.0527        | 3.2432 | 600  | 0.1674          |
| 0.0377        | 3.7838 | 700  | 0.1404          |
| 0.0316        | 4.3243 | 800  | 0.1230          |
| 0.0302        | 4.8649 | 900  | 0.1004          |
| 0.0227        | 5.4054 | 1000 | 0.0940          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0