Instructions to use gnh950/audio_cls_hhd2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gnh950/audio_cls_hhd2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="gnh950/audio_cls_hhd2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("gnh950/audio_cls_hhd2") model = AutoModelForAudioClassification.from_pretrained("gnh950/audio_cls_hhd2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
audio_cls_hhd2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5060
- Accuracy: 0.8908
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 15 | 0.9455 | 0.8067 |
| No log | 2.0 | 30 | 0.8462 | 0.7815 |
| No log | 3.0 | 45 | 0.6772 | 0.8487 |
| No log | 4.0 | 60 | 0.5531 | 0.8655 |
| No log | 5.0 | 75 | 0.5060 | 0.8908 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1
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