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--- |
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library_name: transformers |
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language: |
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- zh |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Taiwanese Small3 - Steven Wang |
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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: Common Voice 17.0 |
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type: mozilla-foundation/common_voice_17_0 |
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config: nan-tw |
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split: None |
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args: 'config: zh, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 90.87361803598526 |
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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 Taiwanese Small3 - Steven Wang |
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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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- Loss: 0.7512 |
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- Wer: 90.8736 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:----:|:---------------:|:-------:| |
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| 0.083 | 2.9240 | 1000 | 0.6382 | 93.2582 | |
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| 0.0111 | 5.8480 | 2000 | 0.6895 | 91.6323 | |
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| 0.0019 | 8.7719 | 3000 | 0.7383 | 91.1771 | |
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| 0.0009 | 11.6959 | 4000 | 0.7512 | 90.8736 | |
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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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