whisper-SER-base-v1
This model is a fine-tuned version of openai/whisper-base on the Whisper_Compatible_SER_benchmark(Not train_augmented) dataset. It achieves the following results on the evaluation set:
- Loss: 0.8757
- Wer: 105.4509
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: 32
- eval_batch_size: 1
- seed: 42
- 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: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1761 | 2.4450 | 1000 | 0.5625 | 48.9594 |
| 0.0796 | 4.8900 | 2000 | 0.5905 | 87.2151 |
| 0.0201 | 7.3350 | 3000 | 0.7191 | 125.5203 |
| 0.0054 | 9.7800 | 4000 | 0.7985 | 127.7998 |
| 0.0012 | 12.2249 | 5000 | 0.8611 | 108.0278 |
| 0.0008 | 14.6699 | 6000 | 0.8757 | 105.4509 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for iFaz/whisper-SER-base-v1
Base model
openai/whisper-baseDataset used to train iFaz/whisper-SER-base-v1
Evaluation results
- Wer on Whisper_Compatible_SER_benchmark(Not train_augmented)self-reported105.451