Whisper Small Sr - Sagicc
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3675
- Wer Ortho: 28.9565
- Wer: 18.0930
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0728 | 2.87 | 500 | 0.2978 | 29.5435 | 18.8749 |
| 0.0318 | 5.75 | 1000 | 0.3675 | 28.9565 | 18.0930 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for Sagicc/whisper-small-sr
Base model
openai/whisper-smallDataset used to train Sagicc/whisper-small-sr
Evaluation results
- Wer on Common Voice 13test set self-reported18.093