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--- |
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library_name: transformers |
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language: |
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- spa |
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license: apache-2.0 |
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base_model: openai/whisper-tiny |
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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 Tiny 1000 Audios - vfranchis |
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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 Tiny 1000 Audios - vfranchis |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the 1000 audios 1.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5691 |
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- Wer: 30.7692 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 25 |
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- training_steps: 300 |
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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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| 1.4694 | 0.4 | 25 | 1.0082 | 38.4615 | |
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| 0.2677 | 0.8 | 50 | 0.7480 | 46.1538 | |
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| 0.1034 | 1.2 | 75 | 0.6340 | 46.1538 | |
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| 0.0672 | 1.6 | 100 | 0.6319 | 46.1538 | |
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| 0.0547 | 2.0 | 125 | 0.5773 | 30.7692 | |
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| 0.0299 | 2.4 | 150 | 0.5612 | 30.7692 | |
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| 0.022 | 2.8 | 175 | 0.5784 | 30.7692 | |
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| 0.0218 | 3.2 | 200 | 0.5702 | 30.7692 | |
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| 0.0127 | 3.6 | 225 | 0.5721 | 30.7692 | |
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| 0.013 | 4.0 | 250 | 0.5554 | 30.7692 | |
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| 0.0084 | 4.4 | 275 | 0.5680 | 30.7692 | |
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| 0.0102 | 4.8 | 300 | 0.5691 | 30.7692 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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