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--- |
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library_name: transformers |
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language: |
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- vi |
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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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- capleaf/viVoice |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Vi - finetune viVoice - 70000 |
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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: viVoice |
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type: capleaf/viVoice |
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config: default |
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split: test |
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args: 'split: train' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 14.076664076664077 |
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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 Vi - finetune viVoice - 70000 |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the viVoice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.7260 |
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- Wer: 14.0767 |
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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: 1.25e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 16 |
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- optimizer: Use OptimizerNames.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: 1000 |
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- training_steps: 80000 |
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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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| 0.1892 | 0.05 | 4000 | 3.5308 | 18.7775 | |
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| 0.1551 | 0.1 | 8000 | 4.2465 | 18.1171 | |
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| 0.1444 | 0.15 | 12000 | 4.4830 | 16.9775 | |
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| 0.1097 | 1.0266 | 16000 | 4.4955 | 16.1357 | |
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| 0.0966 | 1.0766 | 20000 | 4.8873 | 15.6825 | |
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| 0.0915 | 1.1266 | 24000 | 4.8408 | 15.6177 | |
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| 0.0853 | 2.0032 | 28000 | 5.0293 | 15.1904 | |
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| 0.065 | 2.0532 | 32000 | 5.0290 | 15.8120 | |
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| 0.0644 | 2.1032 | 36000 | 5.1940 | 14.5299 | |
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| 0.0584 | 2.1532 | 40000 | 5.3418 | 15.1515 | |
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| 0.0466 | 3.0298 | 44000 | 5.2564 | 15.2422 | |
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| 0.0405 | 3.0798 | 48000 | 5.4065 | 14.7112 | |
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| 0.0412 | 3.1298 | 52000 | 5.5395 | 14.1414 | |
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| 0.0344 | 4.0064 | 56000 | 5.6079 | 14.5947 | |
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| 0.0288 | 4.0564 | 60000 | 5.5141 | 14.4911 | |
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| 0.0257 | 4.1064 | 64000 | 5.6983 | 14.7242 | |
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| 0.0249 | 4.1564 | 68000 | 5.7079 | 14.0378 | |
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| 0.0209 | 5.033 | 72000 | 5.5744 | 13.8177 | |
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| 0.0192 | 5.083 | 76000 | 5.7272 | 14.1803 | |
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| 0.0185 | 5.133 | 80000 | 5.7260 | 14.0767 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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