Whisper Medium Indonesian for Disaster Response
This model is a fine-tuned version of openai/whisper-medium on the Indonesian Speech Dataset (InaVoCript, Fleurs, OpenSLR Javanese) dataset. It achieves the following results on the evaluation set:
- Loss: 0.2632
- Wer: 9.9675
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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1029 | 1.8692 | 1000 | 0.2180 | 11.2987 |
| 0.0165 | 3.7383 | 2000 | 0.2327 | 10.0649 |
| 0.0035 | 5.6075 | 3000 | 0.2444 | 10.3571 |
| 0.0008 | 7.4766 | 4000 | 0.2615 | 9.8377 |
| 0.0006 | 9.3458 | 5000 | 0.2632 | 9.9675 |
Framework versions
- Transformers 4.45.0
- Pytorch 2.8.0+cu128
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for octava/whisper-medium-indonesian-disaster
Base model
openai/whisper-mediumDataset used to train octava/whisper-medium-indonesian-disaster
Evaluation results
- Wer on Indonesian Speech Dataset (InaVoCript, Fleurs, OpenSLR Javanese)self-reported9.968