Instructions to use Shreeyut/Audio-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shreeyut/Audio-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Shreeyut/Audio-Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Shreeyut/Audio-Classification") model = AutoModelForAudioClassification.from_pretrained("Shreeyut/Audio-Classification") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Audio-Classification | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Audio-Classification | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0157 | |
| - Accuracy: 0.9966 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.334 | 1.0 | 83 | 0.2926 | 0.8881 | | |
| | 0.1375 | 2.0 | 166 | 0.0696 | 0.9846 | | |
| | 0.0575 | 3.0 | 249 | 0.0343 | 0.9917 | | |
| | 0.0495 | 4.0 | 332 | 0.0499 | 0.9834 | | |
| | 0.0227 | 5.0 | 415 | 0.0221 | 0.9947 | | |
| | 0.0295 | 6.0 | 498 | 0.0222 | 0.9951 | | |
| | 0.0252 | 7.0 | 581 | 0.0558 | 0.9872 | | |
| | 0.0254 | 8.0 | 664 | 0.0244 | 0.9940 | | |
| | 0.0174 | 9.0 | 747 | 0.0137 | 0.9974 | | |
| | 0.0028 | 10.0 | 830 | 0.0157 | 0.9966 | | |
| ### Framework versions | |
| - Transformers 4.50.2 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |