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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: mit |
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base_model: imTak/whisper_large_v3_turbo_Korean2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imTak/Economy |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper large v3 turbo Korean-Economy |
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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: Economy |
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type: imTak/Economy |
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args: 'config: ko, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 44.99209128911987 |
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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 turbo Korean-Economy |
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This model is a fine-tuned version of [imTak/whisper_large_v3_ko_ft_ft](https://huggingface.co/imTak/whisper_large_v3_ko_ft_ft) on the Economy dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7148 |
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- Wer: 44.9921 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 8000 |
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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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| 0.8263 | 0.4630 | 1000 | 0.8210 | 49.0241 | |
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| 0.7497 | 0.9259 | 2000 | 0.7351 | 47.6006 | |
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| 0.4979 | 1.3889 | 3000 | 0.6992 | 45.6375 | |
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| 0.5197 | 1.8519 | 4000 | 0.6659 | 44.3410 | |
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| 0.4264 | 2.3148 | 5000 | 0.7168 | 46.6459 | |
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| 0.3911 | 2.7778 | 6000 | 0.6988 | 45.0726 | |
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| 0.2565 | 3.2407 | 7000 | 0.7203 | 44.8000 | |
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| 0.2462 | 3.7037 | 8000 | 0.7148 | 44.9921 | |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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