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--- |
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language: |
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- zh |
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base_model: sit-justin/whisper-small-test |
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tags: |
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- generated_from_trainer |
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datasets: |
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- custom_datset |
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model-index: |
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- name: Whisper Small Chinese MOE Response |
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results: [] |
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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 MOE Response |
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This model is a fine-tuned version of [sit-justin/whisper-small-test](https://huggingface.co/sit-justin/whisper-small-test) on the MOE Response Chinese dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0158 |
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- Cer: 2.5487 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 250 |
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- training_steps: 2000 |
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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 | Cer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.0804 | 0.5405 | 200 | 0.0771 | 5.5753 | |
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| 0.0695 | 1.0811 | 400 | 0.0632 | 5.4650 | |
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| 0.0374 | 1.6216 | 600 | 0.0467 | 4.6808 | |
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| 0.0228 | 2.1622 | 800 | 0.0439 | 4.9994 | |
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| 0.0142 | 2.7027 | 1000 | 0.0339 | 3.4310 | |
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| 0.0068 | 3.2432 | 1200 | 0.0257 | 5.1587 | |
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| 0.005 | 3.7838 | 1400 | 0.0216 | 2.7815 | |
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| 0.0019 | 4.3243 | 1600 | 0.0176 | 2.3772 | |
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| 0.0023 | 4.8649 | 1800 | 0.0158 | 2.5242 | |
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| 0.0012 | 5.4054 | 2000 | 0.0158 | 2.5487 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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