---
language:
- ko
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- SsongSsong/cp-final-project
model-index:
- name: whisper_ko_finetune100k
  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. -->

# whisper_ko_finetune100k

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the ksponspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4177
- Cer: 15.0287

## 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 | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7481        | 0.03  | 200  | 0.7555          | 44.8484 |
| 0.5393        | 0.06  | 400  | 0.5608          | 20.6969 |
| 0.5012        | 0.1   | 600  | 0.5293          | 17.3680 |
| 0.504         | 0.13  | 800  | 0.5075          | 17.5291 |
| 0.4934        | 0.16  | 1000 | 0.4927          | 15.6846 |
| 0.4958        | 0.19  | 1200 | 0.4838          | 17.7085 |
| 0.4697        | 0.22  | 1400 | 0.4768          | 18.5884 |
| 0.4501        | 0.26  | 1600 | 0.4688          | 16.5509 |
| 0.4505        | 0.29  | 1800 | 0.4617          | 17.2548 |
| 0.4434        | 0.32  | 2000 | 0.4566          | 15.7144 |
| 0.4482        | 0.35  | 2200 | 0.4515          | 16.0023 |
| 0.4333        | 0.38  | 2400 | 0.4465          | 14.8264 |
| 0.433         | 0.42  | 2600 | 0.4444          | 15.2950 |
| 0.4027        | 0.45  | 2800 | 0.4406          | 15.3715 |
| 0.4181        | 0.48  | 3000 | 0.4376          | 14.8904 |
| 0.4209        | 0.51  | 3200 | 0.4336          | 14.7853 |
| 0.4535        | 0.54  | 3400 | 0.4303          | 15.2584 |
| 0.4301        | 0.58  | 3600 | 0.4280          | 14.8824 |
| 0.4474        | 0.61  | 3800 | 0.4255          | 15.2287 |
| 0.4024        | 0.64  | 4000 | 0.4244          | 15.2515 |
| 0.3891        | 0.67  | 4200 | 0.4224          | 15.8675 |
| 0.411         | 0.7   | 4400 | 0.4206          | 15.0230 |
| 0.383         | 0.74  | 4600 | 0.4194          | 14.9293 |
| 0.4008        | 0.77  | 4800 | 0.4182          | 14.9818 |
| 0.4164        | 0.8   | 5000 | 0.4177          | 15.0287 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0