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---
language:
- ko
license: apache-2.0
base_model: openai/whisper-large
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- younghoonKIM/MAICON2023_noise_preprocessd
model-index:
- name: whisper_large
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_large
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co./openai/whisper-large) on the MAICON2023_noise dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2609
- Cer: 27.9801
## 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: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6254 | 0.36 | 1000 | 0.5211 | 39.0406 |
| 0.3894 | 0.71 | 2000 | 0.3733 | 23.1574 |
| 0.0932 | 1.07 | 3000 | 0.2990 | 24.4794 |
| 0.0952 | 1.43 | 4000 | 0.2609 | 27.9801 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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