Instructions to use juran123/audio_kws_1019 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juran123/audio_kws_1019 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="juran123/audio_kws_1019")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("juran123/audio_kws_1019") model = AutoModelForAudioClassification.from_pretrained("juran123/audio_kws_1019", device_map="auto") - Notebooks
- Google Colab
- Kaggle
audio_kws_1019
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: 1.4075
- Accuracy: 0.6923
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 2.0192 | 0.0 |
| No log | 2.0 | 4 | 1.9373 | 0.0769 |
| No log | 3.0 | 6 | 1.8649 | 0.3077 |
| No log | 4.0 | 8 | 1.7997 | 0.3846 |
| No log | 5.0 | 10 | 1.7116 | 0.5385 |
| No log | 6.0 | 12 | 1.6421 | 0.6154 |
| No log | 7.0 | 14 | 1.5258 | 0.6154 |
| No log | 8.0 | 16 | 1.4744 | 0.6154 |
| No log | 9.0 | 18 | 1.4488 | 0.6923 |
| No log | 10.0 | 20 | 1.4075 | 0.6923 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
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Model tree for juran123/audio_kws_1019
Base model
Kkonjeong/wav2vec2-base-korean