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README.md
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---
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library_name: transformers
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language:
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- ko
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license: apache-2.0
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base_model: openai/whisper-large-v3
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tags:
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- generated_from_trainer
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datasets:
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- didiudom94/gentlemen2
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metrics:
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- wer
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model-index:
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- name: Whisper Large-V3 Ko to En
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Gentlemen
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type: didiudom94/gentlemen2
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args: 'split: train'
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metrics:
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- name: Wer
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type: wer
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value: 0.7630289773857083
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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 Large-V3 Ko to En
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Gentlemen dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1304
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- Wer: 0.7630
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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: 5e-06
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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 | Wer |
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|:-------------:|:------:|:----:|:---------------:|:------:|
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| 1.2511 | 0.4507 | 1000 | 1.2619 | 0.7806 |
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| 1.1681 | 0.9013 | 2000 | 1.1703 | 0.7751 |
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| 0.9217 | 1.3520 | 3000 | 1.1486 | 0.7768 |
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| 0.9093 | 1.8026 | 4000 | 1.1304 | 0.7630 |
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### Framework versions
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- Transformers 4.45.2
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- Pytorch 2.5.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.20.3
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