openai/whisper-base
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6082
- Wer: 16.5259
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: 64
- eval_batch_size: 32
- seed: 42
- 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 |
|---|---|---|---|---|
| 0.2939 | 4.02 | 1000 | 0.3712 | 14.9737 |
| 0.1381 | 8.04 | 2000 | 0.4280 | 16.5207 |
| 0.0248 | 13.01 | 3000 | 0.5326 | 16.9985 |
| 0.0063 | 17.02 | 4000 | 0.5855 | 16.4293 |
| 0.0048 | 21.04 | 5000 | 0.6082 | 16.5259 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- WER on rishabhjain16/infer_pfstest set self-reported42.200
- WER on rishabhjain16/infer_mysttest set self-reported16.170
- WER on rishabhjain16/libritts_dev_cleantest set self-reported10.650