Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use Hemg/violence-audio-Recognition-1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/violence-audio-Recognition-1111 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/violence-audio-Recognition-1111")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemg/violence-audio-Recognition-1111") model = AutoModelForAudioClassification.from_pretrained("Hemg/violence-audio-Recognition-1111") - Notebooks
- Google Colab
- Kaggle
violence-audio-Recognition-1111
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: 0.0926
- Accuracy: 0.9764
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4554 | 1.0 | 61 | 0.2254 | 0.9150 |
| 0.2139 | 2.0 | 122 | 0.1620 | 0.9508 |
| 0.1447 | 3.0 | 183 | 0.1170 | 0.9641 |
| 0.1113 | 4.0 | 244 | 0.0926 | 0.9764 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Hemg/violence-audio-Recognition-1111
Base model
facebook/wav2vec2-baseEvaluation results
- Accuracy on audiofolderself-reported0.976