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--- |
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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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metrics: |
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- wer |
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model-index: |
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- name: Whisper_JP |
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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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# Whisper_JP |
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This model is a a Phoneme Level Speech Recognition network, originally a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on a |
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mixture of Different datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2186 |
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- Wer: 21.6707 |
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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: 24 |
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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: 6000 |
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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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| 0.2101 | 0.8058 | 1000 | 0.2090 | 30.1840 | |
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| 0.1369 | 1.6116 | 2000 | 0.1837 | 27.6756 | |
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| 0.0838 | 2.4174 | 3000 | 0.1829 | 26.4036 | |
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| 0.0454 | 3.2232 | 4000 | 0.1922 | 20.9549 | |
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| 0.0434 | 4.0290 | 5000 | 0.2072 | 20.8898 | |
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| 0.021 | 4.8348 | 6000 | 0.2186 | 21.6707 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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