Instructions to use CianKim/whisper-tiny-kor_eng_tiny_oc_pr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CianKim/whisper-tiny-kor_eng_tiny_oc_pr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="CianKim/whisper-tiny-kor_eng_tiny_oc_pr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_oc_pr") model = AutoModelForSpeechSeq2Seq.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_oc_pr") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-kor_eng_tiny_oc_pr
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5788
- Cer: 6.0926
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: 3e-05
- train_batch_size: 12
- eval_batch_size: 6
- seed: 42
- optimizer: Use OptimizerNames.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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.5208 | 8.3333 | 100 | 0.3326 | 6.5800 |
| 0.0245 | 16.6667 | 200 | 0.4374 | 5.6052 |
| 0.001 | 25.0 | 300 | 0.4592 | 5.4427 |
| 0.0003 | 33.3333 | 400 | 0.4810 | 5.1990 |
| 0.0002 | 41.6667 | 500 | 0.4961 | 5.1990 |
| 0.0001 | 50.0 | 600 | 0.5080 | 5.1990 |
| 0.0001 | 58.3333 | 700 | 0.5156 | 4.9553 |
| 0.0 | 66.6667 | 800 | 0.5231 | 4.9553 |
| 0.0 | 75.0 | 900 | 0.5324 | 5.1990 |
| 0.0 | 83.3333 | 1000 | 0.5352 | 5.1990 |
| 0.0 | 91.6667 | 1100 | 0.5416 | 5.1990 |
| 0.0 | 100.0 | 1200 | 0.5451 | 5.1990 |
| 0.0 | 108.3333 | 1300 | 0.5479 | 5.2803 |
| 0.0 | 116.6667 | 1400 | 0.5507 | 5.2803 |
| 0.0 | 125.0 | 1500 | 0.5521 | 5.2803 |
| 0.0 | 133.3333 | 1600 | 0.5544 | 5.2803 |
| 0.0 | 141.6667 | 1700 | 0.5540 | 5.2803 |
| 0.0 | 150.0 | 1800 | 0.5573 | 6.1738 |
| 0.0 | 158.3333 | 1900 | 0.5578 | 6.1738 |
| 0.0 | 166.6667 | 2000 | 0.5575 | 6.1738 |
| 0.0 | 175.0 | 2100 | 0.5622 | 6.1738 |
| 0.0 | 183.3333 | 2200 | 0.5640 | 6.1738 |
| 0.0 | 191.6667 | 2300 | 0.5657 | 6.1738 |
| 0.0 | 200.0 | 2400 | 0.5675 | 6.1738 |
| 0.0 | 208.3333 | 2500 | 0.5706 | 6.1738 |
| 0.0 | 216.6667 | 2600 | 0.5710 | 6.1738 |
| 0.0 | 225.0 | 2700 | 0.5715 | 6.1738 |
| 0.0 | 233.3333 | 2800 | 0.5720 | 6.1738 |
| 0.0 | 241.6667 | 2900 | 0.5728 | 6.1738 |
| 0.0 | 250.0 | 3000 | 0.5744 | 6.1738 |
| 0.0 | 258.3333 | 3100 | 0.5750 | 6.1738 |
| 0.0 | 266.6667 | 3200 | 0.5765 | 6.1738 |
| 0.0 | 275.0 | 3300 | 0.5770 | 6.1738 |
| 0.0 | 283.3333 | 3400 | 0.5781 | 6.1738 |
| 0.0 | 291.6667 | 3500 | 0.5783 | 6.1738 |
| 0.0 | 300.0 | 3600 | 0.5789 | 6.1738 |
| 0.0 | 308.3333 | 3700 | 0.5792 | 6.1738 |
| 0.0 | 316.6667 | 3800 | 0.5787 | 6.0926 |
| 0.0 | 325.0 | 3900 | 0.5785 | 6.0926 |
| 0.0 | 333.3333 | 4000 | 0.5788 | 6.0926 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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