---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- korean_samll_dataset4
model-index:
- name: korean-small_t35
  results: []
---

<!-- 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. -->

# korean-small_t35

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the korean_samll_dataset4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1385
- Cer: 5.2553

## 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
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2501        | 0.06  | 200   | 0.2322          | 8.7579 |
| 0.217         | 0.12  | 400   | 0.2118          | 8.6072 |
| 0.1947        | 0.18  | 600   | 0.2011          | 7.4035 |
| 0.1938        | 0.24  | 800   | 0.1941          | 7.3665 |
| 0.1878        | 0.3   | 1000  | 0.1826          | 7.0860 |
| 0.192         | 0.36  | 1200  | 0.1786          | 6.8894 |
| 0.1768        | 0.42  | 1400  | 0.1739          | 6.5072 |
| 0.1777        | 0.48  | 1600  | 0.1708          | 6.4205 |
| 0.1714        | 0.54  | 1800  | 0.1675          | 6.6288 |
| 0.171         | 0.6   | 2000  | 0.1637          | 6.3026 |
| 0.1678        | 0.66  | 2200  | 0.1638          | 6.4964 |
| 0.1606        | 0.72  | 2400  | 0.1604          | 6.4205 |
| 0.1541        | 0.78  | 2600  | 0.1580          | 6.1524 |
| 0.1578        | 0.84  | 2800  | 0.1550          | 5.8736 |
| 0.1524        | 0.9   | 3000  | 0.1535          | 5.9458 |
| 0.153         | 0.96  | 3200  | 0.1512          | 5.8205 |
| 0.112         | 1.02  | 3400  | 0.1492          | 5.7590 |
| 0.0833        | 1.08  | 3600  | 0.1491          | 5.7022 |
| 0.0928        | 1.14  | 3800  | 0.1495          | 5.6578 |
| 0.1005        | 1.2   | 4000  | 0.1480          | 6.0906 |
| 0.0918        | 1.26  | 4200  | 0.1475          | 5.8175 |
| 0.0929        | 1.32  | 4400  | 0.1470          | 5.7632 |
| 0.091         | 1.38  | 4600  | 0.1460          | 5.6557 |
| 0.0858        | 1.44  | 4800  | 0.1445          | 5.6947 |
| 0.0889        | 1.5   | 5000  | 0.1435          | 5.6632 |
| 0.0903        | 1.56  | 5200  | 0.1442          | 5.6412 |
| 0.0894        | 1.61  | 5400  | 0.1426          | 5.5711 |
| 0.0842        | 1.67  | 5600  | 0.1426          | 5.4424 |
| 0.0926        | 1.73  | 5800  | 0.1419          | 5.4171 |
| 0.0801        | 1.79  | 6000  | 0.1400          | 5.3960 |
| 0.0843        | 1.85  | 6200  | 0.1397          | 5.5648 |
| 0.0909        | 1.91  | 6400  | 0.1386          | 5.4677 |
| 0.0816        | 1.97  | 6600  | 0.1384          | 5.6586 |
| 0.0484        | 2.03  | 6800  | 0.1421          | 5.4541 |
| 0.0506        | 2.09  | 7000  | 0.1408          | 5.4424 |
| 0.0475        | 2.15  | 7200  | 0.1410          | 5.5565 |
| 0.0477        | 2.21  | 7400  | 0.1406          | 5.5453 |
| 0.0465        | 2.27  | 7600  | 0.1407          | 5.3383 |
| 0.0487        | 2.33  | 7800  | 0.1404          | 5.4192 |
| 0.0438        | 2.39  | 8000  | 0.1400          | 5.4088 |
| 0.0432        | 2.45  | 8200  | 0.1404          | 5.4022 |
| 0.0457        | 2.51  | 8400  | 0.1410          | 5.3852 |
| 0.0468        | 2.57  | 8600  | 0.1398          | 5.2881 |
| 0.0456        | 2.63  | 8800  | 0.1390          | 5.2789 |
| 0.0426        | 2.69  | 9000  | 0.1390          | 5.3619 |
| 0.0437        | 2.75  | 9200  | 0.1385          | 5.2553 |
| 0.0467        | 2.81  | 9400  | 0.1386          | 5.3404 |
| 0.044         | 2.87  | 9600  | 0.1383          | 5.3180 |
| 0.0445        | 2.93  | 9800  | 0.1382          | 5.3072 |
| 0.0453        | 2.99  | 10000 | 0.1380          | 5.3267 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2