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--- |
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library_name: transformers |
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language: |
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- en |
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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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- generated_from_trainer |
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metrics: |
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- wer |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: ./whisper-base-ea_base |
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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-base-ea_base |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Afrispeech-200 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6880 |
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- Wer Ortho: 0.2755 |
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- Wer: 0.2202 |
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- Cer: 0.0998 |
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- Precision: 0.8628 |
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- Recall: 0.8622 |
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- F1: 0.8616 |
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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: Use OptimizerNames.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: constant_with_warmup |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 500 |
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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 Ortho | Wer | Cer | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:---------:|:------:|:------:| |
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| 0.9241 | 0.4237 | 100 | 0.8794 | 0.2973 | 0.2468 | 0.1060 | 0.8428 | 0.8469 | 0.8443 | |
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| 0.7528 | 0.8475 | 200 | 0.7464 | 0.2903 | 0.2354 | 0.1032 | 0.8583 | 0.8593 | 0.8581 | |
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| 0.5275 | 1.2712 | 300 | 0.7158 | 0.2778 | 0.2285 | 0.1000 | 0.8619 | 0.8627 | 0.8616 | |
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| 0.5686 | 1.6949 | 400 | 0.6956 | 0.2805 | 0.2255 | 0.1021 | 0.8638 | 0.8632 | 0.8626 | |
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| 0.3472 | 2.1186 | 500 | 0.6880 | 0.2755 | 0.2202 | 0.0998 | 0.8628 | 0.8622 | 0.8616 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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