greenarcade/wav2vec2-vd-bird-sound-classification
Bird sound classification model trained on my custom dataset. Identifies local bird species from audio recordings.
Usage
from transformers import pipeline
classifier = pipeline("audio-classification", "greenarcade/wav2vec2-vd-bird-sound-classification")
result = classifier("your_audio.wav", top_k=3)
- Developed by: Suvan GS & [Dharanya T]
- Model type: Transformers
- License: MIT
Model Sources [optional]
- Repository: Minor Project
- Paper : Coming Soon
- Demo [optional]: Space
Uses
Used to Classify the sounds for the 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 |
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
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]
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- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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