Instructions to use hwirang/korean_kws2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hwirang/korean_kws2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hwirang/korean_kws2")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("hwirang/korean_kws2") model = AutoModelForAudioClassification.from_pretrained("hwirang/korean_kws2") - Notebooks
- Google Colab
- Kaggle
korean_kws2
This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4359
- Accuracy: 0.9474
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 1.9692 | 0.1579 |
| No log | 2.0 | 4 | 1.9958 | 0.2632 |
| No log | 3.0 | 6 | 2.0024 | 0.2105 |
| No log | 4.0 | 8 | 1.9453 | 0.2105 |
| No log | 5.0 | 10 | 1.9165 | 0.2105 |
| No log | 6.0 | 12 | 1.8630 | 0.2105 |
| No log | 7.0 | 14 | 1.7510 | 0.4211 |
| No log | 8.0 | 16 | 1.6911 | 0.4211 |
| No log | 9.0 | 18 | 1.5667 | 0.5789 |
| No log | 10.0 | 20 | 1.4755 | 0.5789 |
| No log | 11.0 | 22 | 1.2824 | 0.6316 |
| No log | 12.0 | 24 | 1.1149 | 0.7368 |
| No log | 13.0 | 26 | 1.0276 | 0.8421 |
| No log | 14.0 | 28 | 0.8625 | 0.9474 |
| No log | 15.0 | 30 | 0.7777 | 0.9474 |
| No log | 16.0 | 32 | 0.7167 | 0.9474 |
| No log | 17.0 | 34 | 0.6685 | 0.9474 |
| No log | 18.0 | 36 | 0.6067 | 0.9474 |
| No log | 19.0 | 38 | 0.5757 | 0.9474 |
| No log | 20.0 | 40 | 0.5455 | 0.9474 |
| No log | 21.0 | 42 | 0.5235 | 0.9474 |
| No log | 22.0 | 44 | 0.4995 | 0.9474 |
| No log | 23.0 | 46 | 0.4815 | 0.9474 |
| No log | 24.0 | 48 | 0.4675 | 0.9474 |
| No log | 25.0 | 50 | 0.4569 | 0.9474 |
| No log | 26.0 | 52 | 0.4512 | 0.9474 |
| No log | 27.0 | 54 | 0.4476 | 0.9474 |
| No log | 28.0 | 56 | 0.4425 | 0.9474 |
| No log | 29.0 | 58 | 0.4379 | 0.9474 |
| No log | 30.0 | 60 | 0.4359 | 0.9474 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for hwirang/korean_kws2
Base model
Kkonjeong/wav2vec2-base-korean