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---
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base_model: openai/whisper-small
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datasets:
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- common_voice_13_0
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library_name: transformers
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license: apache-2.0
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metrics:
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- wer
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tags:
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- generated_from_trainer
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model-index:
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- name: whisper-small-Cantonese-test
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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: common_voice_13_0
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type: common_voice_13_0
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config: zh-HK
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split: test
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args: zh-HK
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metrics:
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- type: wer
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value: 71.49724051985046
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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-small-Cantonese-test
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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_13_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3400
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- Wer: 71.4972
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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: 5e-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: 10
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- training_steps: 200
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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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| 0.5135 | 0.1140 | 100 | 0.4197 | 78.8143 |
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| 0.4537 | 0.2281 | 200 | 0.3400 | 71.4972 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.4.0+cu118
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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