whisper-small-ur
This model is a fine-tuned version of openai/whisper-small on the Urdu dataset. It achieves the following results on the evaluation set:
- Loss: 0.4843
- Wer: 33.3110
Training and evaluation data
Dataset included two rows; transcription & audio. The model was prepared using a dataset of 6500 rows. Train-test split was applied, 82% training (5324) and 18% testing (1176).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5907 | 0.6 | 400 | 0.6646 | 44.5644 |
| 0.2862 | 1.2 | 800 | 0.5806 | 38.1544 |
| 0.251 | 1.8 | 1200 | 0.4843 | 33.3110 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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