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  # 🦅 Edge-Optimized Bioacoustic Atlas & Real-Time Avian Classifier
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- [![Hugging Face Repository](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repository-blue)](https://huggingface.co/sukriramli/tiny-bird-diffusion)
 
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  [![Framework: PyTorch](https://img.shields.io/badge/Framework-PyTorch-ee4c2c?logo=pytorch)](https://pytorch.org/)
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  [![Optimization: UMAP + HDBSCAN](https://img.shields.io/badge/Optimization-UMAP%20%2B%20HDBSCAN-green)]()
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  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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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+
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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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  [![Framework: PyTorch](https://img.shields.io/badge/Framework-PyTorch-ee4c2c?logo=pytorch)](https://pytorch.org/)
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  [![Optimization: UMAP + HDBSCAN](https://img.shields.io/badge/Optimization-UMAP%20%2B%20HDBSCAN-green)]()
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  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](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.