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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-large-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cymen-arfor/25awr |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-large-v2-ft-ca-25awr |
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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: cymen-arfor/25awr default |
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type: cymen-arfor/25awr |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.40249424956871765 |
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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-v2-ft-ca-25awr |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the cymen-arfor/25awr default dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8440 |
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- Wer: 0.4025 |
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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: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.4138 | 1.6488 | 1000 | 0.5216 | 0.5082 | |
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| 0.1535 | 3.2976 | 2000 | 0.5362 | 0.4263 | |
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| 0.084 | 4.9464 | 3000 | 0.5920 | 0.4038 | |
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| 0.0185 | 6.5952 | 4000 | 0.7443 | 0.4076 | |
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| 0.0038 | 8.2440 | 5000 | 0.8440 | 0.4025 | |
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### Framework versions |
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- Transformers 4.46.2 |
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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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