Whisper Tiny Japanese Combine 4k - Chee Li
This model is a fine-tuned version of openai/whisper-tiny on the Meta JSON Japanese Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.8869
- Wer: 396.6874
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.441 | 4.1322 | 1000 | 2.4726 | 406.5217 |
| 1.8098 | 8.2645 | 2000 | 2.0185 | 462.4224 |
| 1.2666 | 12.3967 | 3000 | 1.5918 | 404.3478 |
| 0.8324 | 16.5289 | 4000 | 1.2738 | 460.8696 |
| 0.5744 | 20.6612 | 5000 | 1.0687 | 607.0393 |
| 0.3308 | 24.7934 | 6000 | 0.9561 | 532.7122 |
| 0.242 | 28.9256 | 7000 | 0.9024 | 461.0766 |
| 0.1651 | 33.0579 | 8000 | 0.8869 | 396.6874 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1
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Base model
openai/whisper-tiny