Tiny Bird Diffusion
A decoupled, ultra-lightweight machine learning pipeline designed for low-latency edge deployment, automated avian species tracking, and interactive audio streaming.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 60,9 KB–75,1 MB
- Versão
- vProtoclr
- Atualizado
- 31 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
🦅 Edge-Optimized Bioacoustic Atlas & Real-Time Avian Classifier
Open In Colab Hugging Face Repository Framework: PyTorch Optimization: UMAP + HDBSCAN License: 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.
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2 trechos--- 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 [](https://colab.research.google.com/drive/1EL5VS_vAKvojPf5UPuQVFbK5gkgP51hB?usp=sharing) [](https://huggingface.co/sukriramli/tiny-bird-diffusion) [](https://pytorch.org/) []() [](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...
Source context: 16 downloads · 0 likes · Pipeline audio-classification · Library pytorch · Repo sukriramli/tiny-bird-diffusion
