self-trained Whisper-Medium Yue model (to be fine-tuned by other datasets) #JL
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2796
- Cer: 8.3321
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.0187 | 5.5556 | 1000 | 0.2198 | 9.1962 |
| 0.0006 | 11.1111 | 2000 | 0.2458 | 8.8610 |
| 0.0001 | 16.6667 | 3000 | 0.2670 | 8.4587 |
| 0.0001 | 22.2222 | 4000 | 0.2757 | 8.3544 |
| 0.0001 | 27.7778 | 5000 | 0.2796 | 8.3321 |
Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1