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README.md
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# 🦅 Edge-Optimized Bioacoustic Atlas & Real-Time Avian Classifier
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[](https://pytorch.org/)
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[]()
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[](https://opensource.org/licenses/MIT)
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A decoupled, ultra-lightweight machine learning pipeline designed for low-latency edge deployment, automated avian species tracking, and interactive audio streaming.
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By leveraging **Prototypical Contrastive Learning (ProtoCLR)** backbones combined with advanced topological manifold compression (**UMAP & HDBSCAN**), this system projects complex audio waveforms onto a dense, 2D geometric map. The resulting production architecture handles **168 unique biological species** across **149 autonomous eco-acoustic clusters** natively in a client browser window with sub-second latency—completely bypassing the need for compute-heavy cloud inferencing heads.
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* `augment.py`: Digital Signal Processing (DSP) environment warping functions (Noise, Echo, Muffling filters).
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* `pipeline.py`: Low-latency engineering pipeline managing model configuration and UMAP coordinate projection.
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* `api.py`: Clean, production-ready prediction endpoint designed for real-time app integration.
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---
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language:
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- en
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license: mit
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tags:
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- bioacoustics
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- audio-classification
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- bird-sound-identification
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- pytorch
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- vision-transformers
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- protoclr
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- umap
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- hdbscan
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pipeline_tag: audio-classification
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library_name: pytorch
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metrics:
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- accuracy
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---
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# 🦅 Edge-Optimized Bioacoustic Atlas & Real-Time Avian Classifier
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[%F0%9F%93%93%20Open%20In%20Colab](https://colab.research.google.com/drive/1EL5VS_vAKvojPf5UPuQVFbK5gkgP51hB?usp=sharing)
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[![]_blue)](https://colab.research.google.com/drive/1EL5VS_vAKvojPf5UPuQVFbK5gkgP51hB?usp=sharing)
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[](https://pytorch.org/)
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[]()
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[](https://opensource.org/licenses/MIT)
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A decoupled, ultra-lightweight machine learning pipeline designed for low-latency edge deployment, automated avian species tracking, and interactive audio streaming.
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By leveraging **Prototypical Contrastive Learning (ProtoCLR)** backbones combined with advanced topological manifold compression (**UMAP & HDBSCAN**), this system projects complex audio waveforms onto a dense, 2D geometric map. The resulting production architecture handles **168 unique biological species** across **149 autonomous eco-acoustic clusters** natively in a client browser window with sub-second latency—completely bypassing the need for compute-heavy cloud inferencing heads.
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* `augment.py`: Digital Signal Processing (DSP) environment warping functions (Noise, Echo, Muffling filters).
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* `pipeline.py`: Low-latency engineering pipeline managing model configuration and UMAP coordinate projection.
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* `api.py`: Clean, production-ready prediction endpoint designed for real-time app integration.
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