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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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datasets: |
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- iFaz/Whisper_Compatible_SER_benchmark |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-SER-base-v1 |
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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: Whisper_Compatible_SER_benchmark(Not train_augmented) |
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type: iFaz/Whisper_Compatible_SER_benchmark |
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args: 'config: en, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 105.45094152626362 |
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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-SER-base-v1 |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Whisper_Compatible_SER_benchmark(Not train_augmented) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8757 |
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- Wer: 105.4509 |
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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: 32 |
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- eval_batch_size: 1 |
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- seed: 42 |
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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: 6000 |
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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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| 0.1761 | 2.4450 | 1000 | 0.5625 | 48.9594 | |
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| 0.0796 | 4.8900 | 2000 | 0.5905 | 87.2151 | |
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| 0.0201 | 7.3350 | 3000 | 0.7191 | 125.5203 | |
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| 0.0054 | 9.7800 | 4000 | 0.7985 | 127.7998 | |
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| 0.0012 | 12.2249 | 5000 | 0.8611 | 108.0278 | |
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| 0.0008 | 14.6699 | 6000 | 0.8757 | 105.4509 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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