Whisper Medium Hindi -megha sharma
This model is a fine-tuned version of openai/whisper-medium on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4333
- Wer: 18.0203
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 25000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0669 | 3.3898 | 1000 | 0.2086 | 20.9684 |
| 0.0115 | 6.7797 | 2000 | 0.2637 | 19.7579 |
| 0.0034 | 10.1695 | 3000 | 0.3012 | 19.6408 |
| 0.0026 | 13.5593 | 4000 | 0.3179 | 19.2893 |
| 0.0014 | 16.9492 | 5000 | 0.3242 | 18.7817 |
| 0.0024 | 20.3390 | 6000 | 0.3348 | 19.1624 |
| 0.0024 | 23.7288 | 7000 | 0.3421 | 19.7774 |
| 0.0006 | 27.1186 | 8000 | 0.3511 | 18.6939 |
| 0.0008 | 30.5085 | 9000 | 0.3632 | 18.8989 |
| 0.0007 | 33.8983 | 10000 | 0.3600 | 18.7622 |
| 0.0006 | 37.2881 | 11000 | 0.3470 | 18.4791 |
| 0.0002 | 40.6780 | 12000 | 0.3548 | 18.2936 |
| 0.0001 | 44.0678 | 13000 | 0.3711 | 18.0594 |
| 0.0006 | 47.4576 | 14000 | 0.3733 | 18.2839 |
| 0.0003 | 50.8475 | 15000 | 0.3766 | 18.1667 |
| 0.0 | 54.2373 | 16000 | 0.3745 | 18.0203 |
| 0.0 | 57.6271 | 17000 | 0.3914 | 17.8739 |
| 0.0 | 61.0169 | 18000 | 0.4003 | 17.9032 |
| 0.0 | 64.4068 | 19000 | 0.4081 | 17.8641 |
| 0.0 | 67.7966 | 20000 | 0.4153 | 17.8544 |
| 0.0 | 71.1864 | 21000 | 0.4219 | 17.8544 |
| 0.0 | 74.5763 | 22000 | 0.4281 | 18.0105 |
| 0.0 | 77.9661 | 23000 | 0.4333 | 18.0203 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Base model
openai/whisper-medium