---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: whister test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: ko
      split: None
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 34.87394957983193
---

<!-- 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. -->

# whister test

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5798
- Wer: 34.8739

## 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer     |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.0008        | 22.2222  | 1000 | 0.4828          | 33.4989 |
| 0.0003        | 44.4444  | 2000 | 0.5277          | 34.2246 |
| 0.0001        | 66.6667  | 3000 | 0.5551          | 34.6830 |
| 0.0001        | 88.8889  | 4000 | 0.5718          | 34.7976 |
| 0.0001        | 111.1111 | 5000 | 0.5798          | 34.8739 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1