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update model card README.md
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README.md
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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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- hf-asr-leaderboard
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- whisper-event
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- generated_from_trainer
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datasets:
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- kresnik/zeroth_korean
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metrics:
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- wer
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model-index:
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- name: Whisper Medium Korean
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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: Zeroth Korean
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type: kresnik/zeroth_korean
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config: clean
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split: test
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args: 'split: test'
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metrics:
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- name: Wer
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type: wer
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value: 3.6440295136274656
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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 Medium Korean
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Zeroth Korean dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0727
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- Wer: 3.6440
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- Cer: 1.4840
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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: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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: 5000
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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 | Cer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
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| 0.0873 | 0.72 | 1000 | 0.1086 | 7.7549 | 2.5597 |
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| 0.0258 | 1.44 | 2000 | 0.0805 | 4.5475 | 1.7588 |
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| 0.0091 | 2.16 | 3000 | 0.0719 | 3.7946 | 1.5664 |
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| 0.0086 | 2.88 | 4000 | 0.0704 | 3.5537 | 1.5232 |
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| 0.0019 | 3.59 | 5000 | 0.0727 | 3.6440 | 1.4840 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.0a0+d0d6b1f
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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