---
library_name: transformers
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- GGarri/241109_newdata
metrics:
- wer
model-index:
- name: Whisper Small ko
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: customdata
      type: GGarri/241109_newdata
    metrics:
    - name: Wer
      type: wer
      value: 1.3576367064739157
---

<!-- 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 customdata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0892
- Cer: 1.7222
- Wer: 1.3576

## 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: 8
- seed: 42
- optimizer: Use 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Cer    | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 1.0297        | 1.9231  | 100  | 0.8542          | 5.7071 | 5.2420 |
| 0.2945        | 3.8462  | 200  | 0.2539          | 3.1678 | 2.6524 |
| 0.022         | 5.7692  | 300  | 0.0721          | 2.3633 | 1.8982 |
| 0.0066        | 7.6923  | 400  | 0.0744          | 2.1999 | 1.7348 |
| 0.0055        | 9.6154  | 500  | 0.0681          | 1.9485 | 1.5462 |
| 0.0021        | 11.5385 | 600  | 0.0743          | 2.1622 | 1.7096 |
| 0.0019        | 13.4615 | 700  | 0.0723          | 2.1747 | 1.7096 |
| 0.0005        | 15.3846 | 800  | 0.0733          | 1.8856 | 1.4833 |
| 0.0001        | 17.3077 | 900  | 0.0738          | 1.9233 | 1.4582 |
| 0.0001        | 19.2308 | 1000 | 0.0748          | 1.9233 | 1.4582 |
| 0.0001        | 21.1538 | 1100 | 0.0759          | 1.8353 | 1.4708 |
| 0.0001        | 23.0769 | 1200 | 0.0765          | 1.8102 | 1.4456 |
| 0.0001        | 25.0    | 1300 | 0.0770          | 1.7976 | 1.4331 |
| 0.0001        | 26.9231 | 1400 | 0.0773          | 1.7976 | 1.4331 |
| 0.0001        | 28.8462 | 1500 | 0.0776          | 1.7976 | 1.4331 |
| 0.0001        | 30.7692 | 1600 | 0.0780          | 1.7976 | 1.4331 |
| 0.0001        | 32.6923 | 1700 | 0.0782          | 1.7976 | 1.4331 |
| 0.0001        | 34.6154 | 1800 | 0.0786          | 1.7850 | 1.4205 |
| 0.0001        | 36.5385 | 1900 | 0.0790          | 1.7850 | 1.4205 |
| 0.0001        | 38.4615 | 2000 | 0.0794          | 1.7599 | 1.3953 |
| 0.0001        | 40.3846 | 2100 | 0.0804          | 1.7599 | 1.3953 |
| 0.0001        | 42.3077 | 2200 | 0.0811          | 1.7599 | 1.3953 |
| 0.0           | 44.2308 | 2300 | 0.0816          | 1.7599 | 1.3953 |
| 0.0           | 46.1538 | 2400 | 0.0821          | 1.7473 | 1.3828 |
| 0.0           | 48.0769 | 2500 | 0.0825          | 1.7473 | 1.3828 |
| 0.0           | 50.0    | 2600 | 0.0829          | 1.7473 | 1.3828 |
| 0.0           | 51.9231 | 2700 | 0.0832          | 1.7473 | 1.3828 |
| 0.0           | 53.8462 | 2800 | 0.0836          | 1.7473 | 1.3828 |
| 0.0           | 55.7692 | 2900 | 0.0840          | 1.7473 | 1.3828 |
| 0.0           | 57.6923 | 3000 | 0.0843          | 1.7473 | 1.3828 |
| 0.0           | 59.6154 | 3100 | 0.0846          | 1.7473 | 1.3828 |
| 0.0           | 61.5385 | 3200 | 0.0849          | 1.7473 | 1.3828 |
| 0.0           | 63.4615 | 3300 | 0.0853          | 1.7473 | 1.3828 |
| 0.0           | 65.3846 | 3400 | 0.0855          | 1.7348 | 1.3702 |
| 0.0           | 67.3077 | 3500 | 0.0858          | 1.7348 | 1.3702 |
| 0.0           | 69.2308 | 3600 | 0.0860          | 1.7348 | 1.3702 |
| 0.0           | 71.1538 | 3700 | 0.0863          | 1.7348 | 1.3702 |
| 0.0           | 73.0769 | 3800 | 0.0866          | 1.7348 | 1.3702 |
| 0.0           | 75.0    | 3900 | 0.0868          | 1.7348 | 1.3702 |
| 0.0           | 76.9231 | 4000 | 0.0870          | 1.7348 | 1.3702 |
| 0.0           | 78.8462 | 4100 | 0.0877          | 1.7473 | 1.3828 |
| 0.0           | 80.7692 | 4200 | 0.0881          | 1.7222 | 1.3576 |
| 0.0           | 82.6923 | 4300 | 0.0884          | 1.7222 | 1.3576 |
| 0.0           | 84.6154 | 4400 | 0.0886          | 1.7222 | 1.3576 |
| 0.0           | 86.5385 | 4500 | 0.0888          | 1.7222 | 1.3576 |
| 0.0           | 88.4615 | 4600 | 0.0889          | 1.7222 | 1.3576 |
| 0.0           | 90.3846 | 4700 | 0.0890          | 1.7222 | 1.3576 |
| 0.0           | 92.3077 | 4800 | 0.0891          | 1.7222 | 1.3576 |
| 0.0           | 94.2308 | 4900 | 0.0891          | 1.7222 | 1.3576 |
| 0.0           | 96.1538 | 5000 | 0.0892          | 1.7222 | 1.3576 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.4.0
- Datasets 2.18.0
- Tokenizers 0.20.3