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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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base_model: openai/whisper-base
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tags:
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- hf-asr-leaderboard
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_13_0
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model-index:
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- name: oceanstar-bridze
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results: []
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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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# oceanstar-bridze
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the bridzeDataset dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1880
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- Cer: 7.3894
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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: 8
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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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### Training results
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| Training Loss | Epoch | Step | Cer | Validation Loss |
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|:-------------:|:-----:|:----:|:-------:|:---------------:|
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| 0.3652 | 0.06 | 500 | 11.3504 | 0.3574 |
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| 0.2788 | 0.13 | 1000 | 9.1325 | 0.2645 |
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| 0.2213 | 0.1 | 1500 | 0.2388 | 9.3132 |
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| 0.2257 | 0.13 | 2000 | 0.2194 | 8.6295 |
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| 0.1941 | 0.16 | 2500 | 0.2068 | 7.5109 |
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| 0.1395 | 0.19 | 3000 | 0.1969 | 7.3247 |
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| 0.1787 | 0.23 | 3500 | 0.1905 | 7.5517 |
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| 0.1639 | 0.26 | 4000 | 0.1880 | 7.3894 |
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### Framework versions
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- Transformers 4.32.0.dev0
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- Pytorch 1.10.1
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- Datasets 2.14.2
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- Tokenizers 0.13.3
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