Instructions to use eastman94/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eastman94/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="eastman94/audio_cls")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("eastman94/audio_cls") model = AutoModelForAudioClassification.from_pretrained("eastman94/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: 0.5575
- 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 |
|---|---|---|---|---|
| 2.6412 | 1.0 | 30 | 2.6438 | 0.0504 |
| 2.5256 | 2.0 | 60 | 2.4761 | 0.1597 |
| 1.9412 | 3.0 | 90 | 1.8575 | 0.5966 |
| 1.4527 | 4.0 | 120 | 1.4392 | 0.6891 |
| 1.0052 | 5.0 | 150 | 1.1511 | 0.7395 |
| 0.6206 | 6.0 | 180 | 0.8657 | 0.7731 |
| 0.4973 | 7.0 | 210 | 0.6832 | 0.8319 |
| 0.3152 | 8.0 | 240 | 0.6594 | 0.8319 |
| 0.2418 | 9.0 | 270 | 0.5456 | 0.8739 |
| 0.236 | 10.0 | 300 | 0.5575 | 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 eastman94/audio_cls
Base model
Kkonjeong/wav2vec2-base-korean