Instructions to use jongturkson/car_fault with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jongturkson/car_fault with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="jongturkson/car_fault")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("jongturkson/car_fault") model = AutoModelForAudioClassification.from_pretrained("jongturkson/car_fault", device_map="auto") - Notebooks
- Google Colab
- Kaggle
car_fault
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.9935
- Accuracy: 0.0
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 1.8015 | 0.2 |
| No log | 2.0 | 4 | 1.8574 | 0.4 |
| No log | 3.0 | 6 | 1.8964 | 0.0 |
| No log | 4.0 | 8 | 1.9233 | 0.0 |
| 1.0616 | 5.0 | 10 | 1.9403 | 0.0 |
| 1.0616 | 6.0 | 12 | 1.9618 | 0.0 |
| 1.0616 | 7.0 | 14 | 1.9757 | 0.0 |
| 1.0616 | 8.0 | 16 | 1.9836 | 0.0 |
| 1.0616 | 9.0 | 18 | 1.9904 | 0.0 |
| 0.9504 | 10.0 | 20 | 1.9935 | 0.0 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for jongturkson/car_fault
Base model
facebook/wav2vec2-baseEvaluation results
- Accuracy on audiofolderself-reported0.000