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---
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language:
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- ko
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license: apache-2.0
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tags:
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- generated_from_trainer
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base_model: openai/whisper-base
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datasets:
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- AIHub/noise
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model-index:
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- name: Whisper Base Noise Ko - Dearlie
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Whisper Base Noise Ko - Dearlie
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noise Data dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3670
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- Cer: 57.4924
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 4000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Cer |
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|:-------------:|:------:|:----:|:---------------:|:-------:|
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| 1.6034 | 0.8780 | 1000 | 1.6217 | 75.3884 |
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| 1.4053 | 1.7559 | 2000 | 1.4598 | 60.7893 |
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| 1.2681 | 2.6339 | 3000 | 1.3881 | 61.1636 |
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| 1.1608 | 3.5119 | 4000 | 1.3670 | 57.4924 |
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### Framework versions
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- Transformers 4.41.0.dev0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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