đ greenarcade/wav2vec2-vd-bird-sound-classification
This is a bird sound classification model trained on a custom dataset. It can identify local bird species from audio recordings, offering a practical solution for bird species identification in the field of biology and conservation.
đ Quick Start
đģ Usage Examples
Basic Usage
from transformers import pipeline
classifier = pipeline("audio-classification", "greenarcade/wav2vec2-vd-bird-sound-classification")
result = classifier("your_audio.wav", top_k=3)
đ Documentation
Model Information
Property |
Details |
Developed by |
Suvan GS & [Dharanya T] |
Model Type |
Transformers |
License |
MIT |
Model Sources
Uses
This model is used to classify the sounds of 21 species of birds observed at Vedanthangal Bird Sanctuary.
Out-of-Scope Use
The model will not work for any of the species not listed below:
Species Common Name |
Asian openbill stork |
Blue-tailed bee-eater |
Common kingfisher |
Eurasian spoonbill |
Fulvous whistling duck |
Garganey |
Glossy ibis |
Golden oriole |
Great egret |
Grey Heron |
Indian pond heron |
Indian spot-billed duck |
Little egret |
Northern pintail |
Northern shoveler |
Painted stork |
Rosy starling |
Spot-billed pelican |
Spotted owlet |
White Ibis |
White-throated kingfisher |
đ§ Technical Details
Model Index
- Name: wav2vec2-vd-bird-sound-classification
- Results:
- Task:
- Type: image-classification
- Dataset:
- Name: Custom Bird Dataset
- Type: image-classification
- Metrics:
Name |
Type |
Value |
Accuracy |
accuracy |
91.11 |
F1 Score |
f1 |
89.41 |
Inference Speed (sec) |
inference_time |
0.476 |
Error Rate |
error_rate |
8.89 |
Average ROC AUC |
roc_auc |
98.20 |
Average Precision |
avg_precision |
93.63 |
- Source:
- Name: Custom Evaluation
- URL: https://huggingface.co/greeenboi/wav2vec2-vd-bird-sound-classification
Training Details
- Training Data: [More Information Needed]
- Training Procedure:
- Preprocessing: [More Information Needed]
- Training Hyperparameters:
- Training regime: [More Information Needed]
- Speeds, Sizes, Times: [More Information Needed]
Evaluation
- Testing Data, Factors & Metrics:
- Testing Data: [More Information Needed]
- Factors: [More Information Needed]
- Metrics: [More Information Needed]
- Results:
- Summary: [More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications
- Model Architecture and Objective: [More Information Needed]
- Compute Infrastructure:
- Hardware: [More Information Needed]
- Software: [More Information Needed]
Citation
- BibTeX: [More Information Needed]
- APA: [More Information Needed]
Glossary
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More Information
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Model Card Authors
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Model Card Contact
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