Whisper tiny En - SF test
This model is a fine-tuned version of openai/whisper-tiny on the SF 200 dataset. It achieves the following results on the evaluation set:
- Loss: 1.7031
- Wer: 62.9291
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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5292 | 8.8889 | 100 | 1.0957 | 59.9542 |
| 0.0462 | 17.7778 | 200 | 1.2314 | 57.6659 |
| 0.0058 | 26.6667 | 300 | 1.4473 | 63.6156 |
| 0.0029 | 35.5556 | 400 | 1.5361 | 63.6156 |
| 0.0017 | 44.4444 | 500 | 1.6016 | 60.6407 |
| 0.0012 | 53.3333 | 600 | 1.6367 | 62.9291 |
| 0.0009 | 62.2222 | 700 | 1.6670 | 63.6156 |
| 0.0008 | 71.1111 | 800 | 1.6875 | 63.6156 |
| 0.0007 | 80.0 | 900 | 1.6992 | 63.6156 |
| 0.0007 | 88.8889 | 1000 | 1.7031 | 62.9291 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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