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---

language:
- ko
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-base
datasets:
- AIHub/noise
model-index:
- name: Whisper Base Noise Ko - Dearlie
  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 Base Noise Ko - Dearlie

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noise Data dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3670
- Cer: 57.4924

## 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: 4000

- mixed_precision_training: Native AMP



### Training results



| Training Loss | Epoch  | Step | Validation Loss | Cer     |

|:-------------:|:------:|:----:|:---------------:|:-------:|

| 1.6034        | 0.8780 | 1000 | 1.6217          | 75.3884 |

| 1.4053        | 1.7559 | 2000 | 1.4598          | 60.7893 |

| 1.2681        | 2.6339 | 3000 | 1.3881          | 61.1636 |

| 1.1608        | 3.5119 | 4000 | 1.3670          | 57.4924 |





### Framework versions



- Transformers 4.41.0.dev0

- Pytorch 2.3.0+cu121

- Datasets 2.19.0

- Tokenizers 0.19.1