Instructions to use Hoo1urk/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hoo1urk/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hoo1urk/audio_cls")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hoo1urk/audio_cls") model = AutoModelForAudioClassification.from_pretrained("Hoo1urk/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
audio_cls
This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5851
- Accuracy: 0.8655
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: 2
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.6361 | 1.0 | 30 | 2.6483 | 0.1513 |
| 2.2127 | 2.0 | 60 | 2.1434 | 0.3529 |
| 1.6976 | 3.0 | 90 | 1.7117 | 0.5546 |
| 1.3022 | 4.0 | 120 | 1.4045 | 0.6639 |
| 0.8453 | 5.0 | 150 | 1.0024 | 0.8487 |
| 0.6061 | 6.0 | 180 | 0.7959 | 0.8739 |
| 0.3722 | 7.0 | 210 | 0.7127 | 0.8739 |
| 0.2689 | 8.0 | 240 | 0.5976 | 0.8571 |
| 0.184 | 9.0 | 270 | 0.5758 | 0.8655 |
| 0.1655 | 10.0 | 300 | 0.5851 | 0.8655 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for Hoo1urk/audio_cls
Base model
Kkonjeong/wav2vec2-base-korean