Whisper Small v3 Burmese - Robust Noisy Training
This model is a fine-tuned version of myatsu/whisper-small-burmese-v2 on the Google FLEURS Burmese + Kaggle Noise (minsithu/audio-noise-dataset) dataset. It achieves the following results on the evaluation set:
- Loss: 0.1440
- Wer: 90.6395
- Cer: 52.9501
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.3093 | 0.4057 | 100 | 0.2936 | 97.5581 | 59.4269 |
| 0.2107 | 0.8114 | 200 | 0.2187 | 95.4360 | 56.5003 |
| 0.1442 | 1.2150 | 300 | 0.1853 | 95.0 | 55.5726 |
| 0.1207 | 1.6207 | 400 | 0.1638 | 93.2558 | 54.6075 |
| 0.0976 | 2.0243 | 500 | 0.1559 | 92.2384 | 53.5169 |
| 0.0784 | 2.4300 | 600 | 0.1503 | 92.0930 | 52.9128 |
| 0.0795 | 2.8357 | 700 | 0.1444 | 91.6570 | 52.9089 |
| 0.0591 | 3.2394 | 800 | 0.1439 | 90.6977 | 52.6892 |
| 0.0492 | 3.6450 | 900 | 0.1440 | 90.6395 | 52.9501 |
| 0.0462 | 4.0487 | 1000 | 0.1428 | 91.25 | 52.8520 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.4.2
- Tokenizers 0.22.1