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--- |
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license: apache-2.0 |
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tags: |
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- whisper-small |
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- asr |
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- zh-TW |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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model-index: |
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- name: Whisper Small TW |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: zh-TW |
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split: test |
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metrics: |
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- type: wer |
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value: 9.78 |
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name: WER |
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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 Medium TW |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 dataset. |
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## Training and evaluation data |
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Training: |
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- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation) |
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Evaluation: |
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- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test) |
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## Training procedure |
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- Datasets were augmented using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift, TimeStretch, Gain, AddGaussianNoise transformations at `p=0.3`. |
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- A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case. |
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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: 16 |
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- gradient_accumulation_steps: 1 |
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- optimizer: Adam |
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- generation_max_length: 225 |
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- warmup_steps: 500 |
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- max_steps: 2400 |
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- fp16: True |
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- evaluation_strategy: "steps" |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 2.0.1+cu120 |
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- Datasets 2.13.1 |
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