Instructions to use CianKim/whisper-tiny-kor_eng_tiny_ed_tx 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_tx 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_tx")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_ed_tx") model = AutoModelForSpeechSeq2Seq.from_pretrained("CianKim/whisper-tiny-kor_eng_tiny_ed_tx") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-kor_eng_tiny_ed_tx
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2171
- Cer: 1.9969
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 |
|---|---|---|---|---|
| 0.1851 | 14.2857 | 100 | 0.1529 | 3.5330 |
| 0.0008 | 28.5714 | 200 | 0.1691 | 2.4578 |
| 0.0002 | 42.8571 | 300 | 0.1754 | 2.3041 |
| 0.0001 | 57.1429 | 400 | 0.1805 | 1.9969 |
| 0.0 | 71.4286 | 500 | 0.1856 | 2.4578 |
| 0.0 | 85.7143 | 600 | 0.1890 | 2.4578 |
| 0.0 | 100.0 | 700 | 0.1915 | 2.7650 |
| 0.0 | 114.2857 | 800 | 0.1932 | 2.7650 |
| 0.0 | 128.5714 | 900 | 0.1956 | 2.7650 |
| 0.0 | 142.8571 | 1000 | 0.1972 | 2.7650 |
| 0.0 | 157.1429 | 1100 | 0.1984 | 2.1505 |
| 0.0 | 171.4286 | 1200 | 0.2001 | 2.1505 |
| 0.0 | 185.7143 | 1300 | 0.2011 | 2.1505 |
| 0.0 | 200.0 | 1400 | 0.2022 | 2.1505 |
| 0.0 | 214.2857 | 1500 | 0.2033 | 2.1505 |
| 0.0 | 228.5714 | 1600 | 0.2035 | 2.1505 |
| 0.0 | 242.8571 | 1700 | 0.2048 | 2.1505 |
| 0.0 | 257.1429 | 1800 | 0.2058 | 2.1505 |
| 0.0 | 271.4286 | 1900 | 0.2064 | 2.1505 |
| 0.0 | 285.7143 | 2000 | 0.2079 | 2.1505 |
| 0.0 | 300.0 | 2100 | 0.2093 | 2.1505 |
| 0.0 | 314.2857 | 2200 | 0.2100 | 2.1505 |
| 0.0 | 328.5714 | 2300 | 0.2102 | 2.1505 |
| 0.0 | 342.8571 | 2400 | 0.2108 | 2.1505 |
| 0.0 | 357.1429 | 2500 | 0.2111 | 2.1505 |
| 0.0 | 371.4286 | 2600 | 0.2116 | 2.1505 |
| 0.0 | 385.7143 | 2700 | 0.2131 | 1.9969 |
| 0.0 | 400.0 | 2800 | 0.2134 | 1.9969 |
| 0.0 | 414.2857 | 2900 | 0.2138 | 1.9969 |
| 0.0 | 428.5714 | 3000 | 0.2144 | 1.9969 |
| 0.0 | 442.8571 | 3100 | 0.2147 | 1.9969 |
| 0.0 | 457.1429 | 3200 | 0.2153 | 1.9969 |
| 0.0 | 471.4286 | 3300 | 0.2159 | 1.9969 |
| 0.0 | 485.7143 | 3400 | 0.2161 | 1.9969 |
| 0.0 | 500.0 | 3500 | 0.2161 | 1.9969 |
| 0.0 | 514.2857 | 3600 | 0.2167 | 1.9969 |
| 0.0 | 528.5714 | 3700 | 0.2168 | 1.9969 |
| 0.0 | 542.8571 | 3800 | 0.2168 | 1.9969 |
| 0.0 | 557.1429 | 3900 | 0.2168 | 1.9969 |
| 0.0 | 571.4286 | 4000 | 0.2171 | 1.9969 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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