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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: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Bingsu/zeroth-korean |
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- mozilla-foundation/common_voice_17_0 |
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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 - imTak |
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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: Bingsu/zeroth-korean |
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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: 5.270290618882698 |
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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 - imTak |
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This model is a fine-tuned version of [imTak/whisper_large_v3_ko_ft](https://huggingface.co/imTak/whisper_large_v3_ko_ft) on the Zeroth-Korean dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0670 |
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- Wer: 5.2703 |
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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 | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.1068 | 0.7184 | 1000 | 0.1216 | 8.6132 | |
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| 0.0388 | 1.4368 | 2000 | 0.0905 | 5.3606 | |
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| 0.0089 | 2.1552 | 3000 | 0.0707 | 4.7282 | |
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| 0.0082 | 2.8736 | 4000 | 0.0670 | 5.2703 | |
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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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