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Whisper Small Ta - Bharat Ramanathan
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1803
- Wer: 17.1456
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 | Wer |
|---|---|---|---|---|
| 0.3374 | 0.1 | 500 | 0.2579 | 23.3804 |
| 0.29 | 0.2 | 1000 | 0.2260 | 20.9937 |
| 0.2522 | 0.3 | 1500 | 0.2139 | 20.0682 |
| 0.2338 | 0.4 | 2000 | 0.2025 | 19.6785 |
| 0.223 | 0.5 | 2500 | 0.1979 | 18.3147 |
| 0.211 | 0.6 | 3000 | 0.1927 | 17.8276 |
| 0.2032 | 0.7 | 3500 | 0.1865 | 17.3892 |
| 0.1978 | 0.8 | 4000 | 0.1839 | 17.5353 |
| 0.1972 | 0.9 | 4500 | 0.1812 | 17.0969 |
| 0.1894 | 1.0 | 5000 | 0.1803 | 17.1456 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
