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---
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library_name: transformers
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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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- common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: whisper-small-chinese-tw-minnan
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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_11_0
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type: common_voice_11_0
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config: nan-tw
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split: test
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args: nan-tw
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metrics:
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- name: Wer
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type: wer
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value: 96.9298245614035
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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-chinese-tw-minnan
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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_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1626
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- Wer: 96.9298
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- Cer: 30.1452
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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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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 5000
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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 | Cer |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------:|
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| 0.106 | 3.6364 | 1000 | 0.8017 | 110.0877 | 37.4445 |
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| 0.0094 | 7.2727 | 2000 | 0.9844 | 101.9424 | 37.1209 |
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| 0.0007 | 10.9091 | 3000 | 1.0714 | 95.8020 | 31.5374 |
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| 0.0002 | 14.5455 | 4000 | 1.1310 | 96.0526 | 29.5809 |
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| 0.0001 | 18.1818 | 5000 | 1.1626 | 96.9298 | 30.1452 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 2.4.0+cu124
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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