Instructions to use CianKim/whisper-tiny-kor_eng_tiny_ed_ob 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_ed_ob 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_ed_ob")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_ed_ob") model = AutoModelForSpeechSeq2Seq.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_ed_ob") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-kor_eng_tiny_ed_ob
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2284
- Cer: 6.6456
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 | Cer | Validation Loss |
|---|---|---|---|---|
| 0.8207 | 16.6667 | 100 | 6.9620 | 0.9476 |
| 0.0353 | 33.3333 | 200 | 7.9114 | 0.8010 |
| 0.0002 | 50.0 | 300 | 7.9114 | 0.8538 |
| 0.0001 | 66.6667 | 400 | 7.9114 | 0.8916 |
| 0.0 | 83.3333 | 500 | 7.9114 | 0.9169 |
| 0.0 | 100.0 | 600 | 5.9072 | 0.9309 |
| 0.0 | 116.6667 | 700 | 5.8017 | 0.9514 |
| 0.0 | 133.3333 | 800 | 6.1181 | 0.9646 |
| 0.0 | 150.0 | 900 | 6.1181 | 0.9769 |
| 0.0 | 166.6667 | 1000 | 6.1181 | 0.9847 |
| 0.0 | 183.3333 | 1100 | 6.5401 | 1.0037 |
| 0.0 | 200.0 | 1200 | 6.5401 | 1.0220 |
| 0.0 | 216.6667 | 1300 | 6.6456 | 1.0314 |
| 0.0 | 233.3333 | 1400 | 7.1730 | 1.0465 |
| 0.0 | 250.0 | 1500 | 7.1730 | 1.0657 |
| 0.0 | 266.6667 | 1600 | 7.1730 | 1.0768 |
| 0.0 | 283.3333 | 1700 | 7.1730 | 1.0807 |
| 0.0 | 300.0 | 1800 | 7.5949 | 1.1066 |
| 0.0 | 316.6667 | 1900 | 7.5949 | 1.1196 |
| 0.0 | 333.3333 | 2000 | 7.5949 | 1.1301 |
| 0.0 | 350.0 | 2100 | 6.9620 | 1.1401 |
| 0.0 | 366.6667 | 2200 | 6.9620 | 1.1537 |
| 0.0 | 383.3333 | 2300 | 6.9620 | 1.1606 |
| 0.0 | 400.0 | 2400 | 6.9620 | 1.1631 |
| 0.0 | 416.6667 | 2500 | 6.3291 | 1.1769 |
| 0.0 | 433.3333 | 2600 | 6.3291 | 1.1780 |
| 0.0 | 450.0 | 2700 | 7.1730 | 1.1860 |
| 0.0 | 466.6667 | 2800 | 6.0127 | 1.1920 |
| 0.0 | 483.3333 | 2900 | 6.1181 | 1.1972 |
| 0.0 | 500.0 | 3000 | 6.3291 | 1.2019 |
| 0.0 | 516.6667 | 3100 | 6.5401 | 1.2073 |
| 0.0 | 533.3333 | 3200 | 6.7511 | 1.2118 |
| 0.0 | 550.0 | 3300 | 6.6456 | 1.2163 |
| 0.0 | 566.6667 | 3400 | 6.6456 | 1.2171 |
| 0.0 | 583.3333 | 3500 | 6.6456 | 1.2232 |
| 0.0 | 600.0 | 3600 | 1.2269 | 6.6456 |
| 0.0 | 616.6667 | 3700 | 1.2264 | 6.6456 |
| 0.0 | 633.3333 | 3800 | 1.2278 | 6.6456 |
| 0.0 | 650.0 | 3900 | 1.2286 | 6.6456 |
| 0.0 | 666.6667 | 4000 | 1.2284 | 6.6456 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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