whisper-nm-normal
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: 0.1729
- Wer: 3.8911
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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 132
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 5.5714 | 100 | 0.1699 | 6.2257 |
| 0.685 | 11.1143 | 200 | 0.2757 | 15.9533 |
| 0.685 | 16.6857 | 300 | 0.1657 | 5.0584 |
| 0.058 | 22.2286 | 400 | 0.1940 | 5.4475 |
| 0.058 | 27.8 | 500 | 0.1729 | 3.8911 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for susmitabhatt/whisper-nm-normal
Base model
openai/whisper-small