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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-base |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- aimpower/mandarin_stutter_speech |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Base ZH - Dongim Lee |
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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: AImpower Mandarin Stutter Speech |
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type: aimpower/mandarin_stutter_speech |
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config: zh |
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split: test |
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metrics: |
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- name: Wer |
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type: wer |
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value: 87.24489795918367 |
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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 Base ZH - Dongim Lee |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the AImpower Mandarin Stutter Speech dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4500 |
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- Wer: 87.2449 |
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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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| 0.2036 | 2.0704 | 1000 | 0.3756 | 86.5816 | |
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| 0.0885 | 4.1408 | 2000 | 0.3903 | 86.2245 | |
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| 0.0367 | 6.2112 | 3000 | 0.4295 | 86.8878 | |
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| 0.0242 | 8.2816 | 4000 | 0.4500 | 87.2449 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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