---
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- GGarri/241113_newdata
metrics:
- wer
model-index:
- name: Whisper Small ko
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: customdata
      type: GGarri/241113_newdata
    metrics:
    - name: Wer
      type: wer
      value: 0.908879049172687
---

<!-- 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: 0.0506
- Cer: 1.2584
- Wer: 0.9089

## 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: 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 | Cer     | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.1428        | 1.56  | 100  | 0.8829          | 14.7984 | 14.5304 |
| 0.3434        | 3.12  | 200  | 0.2469          | 2.0625  | 1.7828  |
| 0.0286        | 4.69  | 300  | 0.0447          | 1.6430  | 1.4099  |
| 0.011         | 6.25  | 400  | 0.0382          | 1.5148  | 1.1070  |
| 0.0067        | 7.81  | 500  | 0.0409          | 1.4915  | 1.0837  |
| 0.0042        | 9.38  | 600  | 0.0383          | 1.2118  | 0.9438  |
| 0.0018        | 10.94 | 700  | 0.0396          | 1.3866  | 1.0371  |
| 0.0007        | 12.5  | 800  | 0.0445          | 1.4682  | 1.0604  |
| 0.0004        | 14.06 | 900  | 0.0386          | 1.2584  | 0.9089  |
| 0.0002        | 15.62 | 1000 | 0.0431          | 1.1769  | 0.8273  |
| 0.0011        | 17.19 | 1100 | 0.0475          | 1.2701  | 0.9205  |
| 0.0019        | 18.75 | 1200 | 0.0453          | 1.4915  | 1.1419  |
| 0.0012        | 20.31 | 1300 | 0.0437          | 1.2701  | 0.9205  |
| 0.0013        | 21.88 | 1400 | 0.0454          | 1.3284  | 0.9205  |
| 0.0003        | 23.44 | 1500 | 0.0436          | 1.3400  | 0.9438  |
| 0.0001        | 25.0  | 1600 | 0.0460          | 1.3284  | 0.9904  |
| 0.0001        | 26.56 | 1700 | 0.0464          | 1.3517  | 1.0137  |
| 0.0001        | 28.12 | 1800 | 0.0464          | 1.3400  | 1.0021  |
| 0.0001        | 29.69 | 1900 | 0.0467          | 1.3167  | 0.9788  |
| 0.0001        | 31.25 | 2000 | 0.0468          | 1.3167  | 0.9788  |
| 0.0001        | 32.81 | 2100 | 0.0470          | 1.3284  | 0.9904  |
| 0.0001        | 34.38 | 2200 | 0.0473          | 1.2934  | 0.9438  |
| 0.0           | 35.94 | 2300 | 0.0475          | 1.3051  | 0.9555  |
| 0.0           | 37.5  | 2400 | 0.0477          | 1.3051  | 0.9555  |
| 0.0           | 39.06 | 2500 | 0.0478          | 1.3051  | 0.9555  |
| 0.0           | 40.62 | 2600 | 0.0480          | 1.2934  | 0.9438  |
| 0.0           | 42.19 | 2700 | 0.0482          | 1.2818  | 0.9322  |
| 0.0           | 43.75 | 2800 | 0.0483          | 1.2818  | 0.9322  |
| 0.0           | 45.31 | 2900 | 0.0485          | 1.2818  | 0.9322  |
| 0.0           | 46.88 | 3000 | 0.0486          | 1.2584  | 0.9089  |
| 0.0           | 48.44 | 3100 | 0.0487          | 1.2584  | 0.9089  |
| 0.0           | 50.0  | 3200 | 0.0489          | 1.2584  | 0.9089  |
| 0.0           | 51.56 | 3300 | 0.0490          | 1.2584  | 0.9089  |
| 0.0           | 53.12 | 3400 | 0.0491          | 1.2584  | 0.9089  |
| 0.0           | 54.69 | 3500 | 0.0492          | 1.2584  | 0.9089  |
| 0.0           | 56.25 | 3600 | 0.0493          | 1.2584  | 0.9089  |
| 0.0           | 57.81 | 3700 | 0.0493          | 1.2584  | 0.9089  |
| 0.0           | 59.38 | 3800 | 0.0495          | 1.2584  | 0.9089  |
| 0.0           | 60.94 | 3900 | 0.0495          | 1.2584  | 0.9089  |
| 0.0           | 62.5  | 4000 | 0.0496          | 1.2584  | 0.9089  |
| 0.0           | 64.06 | 4100 | 0.0499          | 1.2584  | 0.9089  |
| 0.0           | 65.62 | 4200 | 0.0501          | 1.2584  | 0.9089  |
| 0.0           | 67.19 | 4300 | 0.0502          | 1.2584  | 0.9089  |
| 0.0           | 68.75 | 4400 | 0.0504          | 1.2584  | 0.9089  |
| 0.0           | 70.31 | 4500 | 0.0505          | 1.2584  | 0.9089  |
| 0.0           | 71.88 | 4600 | 0.0506          | 1.2584  | 0.9089  |
| 0.0           | 73.44 | 4700 | 0.0506          | 1.2584  | 0.9089  |
| 0.0           | 75.0  | 4800 | 0.0506          | 1.2584  | 0.9089  |
| 0.0           | 76.56 | 4900 | 0.0506          | 1.2584  | 0.9089  |
| 0.0           | 78.12 | 5000 | 0.0506          | 1.2584  | 0.9089  |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2