Audio Classification
Transformers
Safetensors
English
audio-spectrogram-transformer
gunshot-detection
ast
Instructions to use ranvir-not-found/ast-sda_gunshot-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ranvir-not-found/ast-sda_gunshot-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ranvir-not-found/ast-sda_gunshot-detection")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("ranvir-not-found/ast-sda_gunshot-detection") model = AutoModelForAudioClassification.from_pretrained("ranvir-not-found/ast-sda_gunshot-detection") - Notebooks
- Google Colab
- Kaggle
AST Gunshot Detection Model
Fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 for gunshot detection.
Model Details
- Find-tuned by: Ranabir Saha
- Fine-tuned on: Tropical forest gunshot classification training audio dataset from Automated detection of gunshots in tropical forests using convolutional neural networks (Katsis et al. 2022)
- Raw-Dataset Source: https://doi.org/10.17632/x48cwz364j.3
- Input: Precomputed log-mel spectrograms (.npy files)
- Output: Binary classification (Background/Gunshot)
Usage
from transformers import pipeline
classifier = pipeline(
"audio-classification",
model="ranvir-not-found/ast-sda_gunshot-detection"
)
results = classifier("your_precomputed_spectrogram.npy") # input must be .npy file
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Model tree for ranvir-not-found/ast-sda_gunshot-detection
Base model
MIT/ast-finetuned-audioset-10-10-0.4593