Whisper Small es
This model is a fine-tuned version of openai/whisper-small on the Speech-MASSIVE dataset. It achieves the following results on the evaluation set:
- Loss: 0.2010
- Wer Ortho: 9.7478
- Wer: 9.6823
Model description
More information needed
Intended uses & limitations
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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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0168 | 3.7037 | 500 | 0.1860 | 9.7856 | 9.7154 |
| 0.0021 | 7.4074 | 1000 | 0.2010 | 9.7478 | 9.6823 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Base model
openai/whisper-small