---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: openai/whisper-medium
datasets:
- Marcusxx/cheonanAddresses
model-index:
- name: cheonanAddresses_torchmodel_model
  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. -->

# cheonanAddresses_torchmodel_model

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the Marcusxx/cheonanAddresses dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0531
- Cer: 1.9003

## 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: 100
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Cer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0771        | 0.3101 | 1000  | 0.0758          | 28.1409 |
| 0.0713        | 0.6202 | 2000  | 0.0718          | 2.2685  |
| 0.0629        | 0.9302 | 3000  | 0.0682          | 2.3271  |
| 0.0583        | 1.2403 | 4000  | 0.0637          | 2.1723  |
| 0.0623        | 1.5504 | 5000  | 0.0613          | 2.1513  |
| 0.0568        | 1.8605 | 6000  | 0.0598          | 2.2237  |
| 0.051         | 2.1705 | 7000  | 0.0568          | 2.1897  |
| 0.0448        | 2.4806 | 8000  | 0.0561          | 2.9282  |
| 0.0517        | 2.7907 | 9000  | 0.0539          | 1.9350  |
| 0.0382        | 3.1008 | 10000 | 0.0531          | 1.9003  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1