Convert and optimize BirdNET models for ONNX Runtime inference on GPUs, CPUs, and embedded devices
🐦 Convert and optimize BirdNET models to ONNX for efficient inference on GPUs, CPUs, and embedded devices like Raspberry Pi.
Simple code I made in python as an amateur in bioacoustics and programming (with a little help from ChatGPT) to analize data from a homemade Hydrophone. Gets clip length, recording frecuency, cuts to 5 minutes, visualizes and saves spectrogram, RMS in re dB, Sound origin and percentage (Physical, Human, Biological) and comparative spectrum
BirdCLEF+ 2026 (Kaggle): multi-label species detection in audio soundscapes with handcrafted features and LightGBM — leakage-free grouped validation, co-occurrence correction, soundscape sub-window features (public macro-AUC 0.783)
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