---
base_model: openai/whisper-base
datasets:
- Jpep26/NoErrorDataset
language:
- ko
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Test
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: NoErrorDataset
      type: Jpep26/NoErrorDataset
      args: 'config: ko, split: valid'
    metrics:
    - type: wer
      value: 0.48766120044578887
      name: Wer
---

<!-- 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. -->

# Test

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the NoErrorDataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6020
- Cer: 0.4962
- Wer: 0.4877
- Mean: 0.4919

## 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: 32
- eval_batch_size: 16
- 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer    | Wer    | Mean   |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| 2.3893        | 0.3817 | 50   | 2.0607          | 0.4421 | 0.7020 | 0.5720 |
| 1.2402        | 0.7634 | 100  | 1.0999          | 0.3408 | 0.5773 | 0.4591 |
| 0.7512        | 1.1450 | 150  | 0.7303          | 0.7268 | 0.5418 | 0.6343 |
| 0.593         | 1.5267 | 200  | 0.6020          | 0.4962 | 0.4877 | 0.4919 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.19.1