---
language:
- ko
license: apache-2.0
base_model: INo0121/whisper-base-ko-callvoice
tags:
- generated_from_trainer
datasets:
- kresnik/zeroth_korean
metrics:
- wer
model-index:
- name: tmtms/whisper_checkpoints
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Zeroth-Korean
      type: kresnik/zeroth_korean
      args: 'config: ko, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 10.856798674898359
---

<!-- 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. -->

# tmtms/whisper_checkpoints

This model is a fine-tuned version of [INo0121/whisper-base-ko-callvoice](https://huggingface.co/INo0121/whisper-base-ko-callvoice) on the Zeroth-Korean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1501
- Wer: 10.8568

## 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: 24
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1644        | 1.08  | 1000 | 0.2571          | 22.2406 |
| 0.0822        | 2.16  | 2000 | 0.1818          | 14.2448 |
| 0.0528        | 3.23  | 3000 | 0.1575          | 11.1128 |
| 0.0383        | 4.31  | 4000 | 0.1501          | 10.8568 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.15.2