---
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small KO
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13.0
      type: mozilla-foundation/common_voice_13_0
      config: ko
      split: test
      args: 'config: ko, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.9687814702920443
---

<!-- 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-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3779
- Cer: 1.0055
- Wer: 0.9688

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.2858        | 5.26  | 100  | 0.4512          | 1.1681 | 0.9869 |
| 0.0058        | 10.53 | 200  | 0.3578          | 1.0637 | 0.8610 |
| 0.0012        | 15.79 | 300  | 0.3699          | 0.9535 | 0.9225 |
| 0.0009        | 21.05 | 400  | 0.3759          | 1.0149 | 0.9809 |
| 0.0008        | 26.32 | 500  | 0.3779          | 1.0055 | 0.9688 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1