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README.md

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-# Bird Classifier
-Auto deployed system ✅
+---
+language:
+- en
+license: mit
+tags:
+- bioacoustics
+- audio-classification
+- bird-sound-identification
+- pytorch
+- vision-transformers
+- protoclr
+- umap
+- hdbscan
+pipeline_tag: audio-classification
+library_name: pytorch
+metrics:
+- accuracy
+---
+
+# 🦅 Edge-Optimized Bioacoustic Atlas & Real-Time Avian Classifier
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EL5VS_vAKvojPf5UPuQVFbK5gkgP51hB?usp=sharing)
+[![Hugging Face Repository](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repository-blue)](https://huggingface.co/sukriramli/tiny-bird-diffusion)
+[![Framework: PyTorch](https://img.shields.io/badge/Framework-PyTorch-ee4c2c?logo=pytorch)](https://pytorch.org/)
+[![Optimization: UMAP + HDBSCAN](https://img.shields.io/badge/Optimization-UMAP%20%2B%20HDBSCAN-green)]()
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+A decoupled, ultra-lightweight machine learning pipeline designed for low-latency edge deployment, automated avian species tracking, and interactive audio streaming.
+
+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.
+
+---
+
+## 🛠️ The System Architecture Problem & Our Solution
+
+### Our Decoupled Geometric Solution
+This repository implements a **decoupled mathematical pattern**. Heavy feature extraction is processed upfront. The complex high-dimensional latent space is then permanently compressed into a frozen geometric lookup coordinate plane. The client device only runs low-compute spatial distance algorithms, achieving zero-lag edge inference.
+
+---
+
+## 🔬 Core Engineering Pillars
+
+### 1. High-Ratio Manifold Compression
+Instead of forcing edge hardware to hold dense classification layer weights, we isolate the 512-dimensional floating-point latent vectors generated by the transformer. We utilize **UMAP (Uniform Manifold Approximation and Projection)** to topology-map this high-dimensional array down to a highly constrained 2D coordinate vector (X, Y). This slashes the database RAM footprint by over **99%** while preserving semantic biological boundaries.
+
+### 2. Acoustic Domain Shift Mitigation (augment.py)
+Pre-trained foundation models are typically trained on pristine, studio-grade wildlife audio recordings, causing them to fail frequently in noisy consumer spaces. To bridge this gap, our data preparation pipeline routes clean data shards through a custom acoustic corruption environment mimicking real-world conditions.
+
+---
+
+## 📁 Modular Codebase Layout
+
+* `augment.py`: Digital Signal Processing (DSP) environment warping functions (Noise, Echo, Muffling filters).
+* `pipeline.py`: Low-latency engineering pipeline managing model configuration and UMAP coordinate projection.
+* `api.py`: Clean, production-ready prediction endpoint designed for real-time app integration.