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--- |
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base_model: openai/whisper-small |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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language: |
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- ps |
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library_name: transformers.js |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- onnx |
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model-index: |
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- name: Whisper Small PS - Hanif Rahman |
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results: [] |
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--- |
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https://huggingface.co/ihanif/whisper-test with ONNX weights to be compatible with Transformers.js. |
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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 Small PS - Hanif Rahman |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.7573 |
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- eval_wer: 46.1819 |
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- eval_runtime: 395.7975 |
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- eval_samples_per_second: 1.294 |
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- eval_steps_per_second: 0.162 |
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- epoch: 5.7143 |
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- step: 2600 |
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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: 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: 4000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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--- |
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
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