These are lightweight classification heads trained on BirdNET v2.4 embeddings using transfer learning. They identify bat species from ultrasonic echolocation recordings.

Attribution

Powered by BirdNET (https://birdnet.cornell.edu/). These bat classifiers are built on BirdNET v2.4 embeddings via transfer learning and inherit BirdNET's CC BY-NC-SA 4.0 terms. The classifier heads originate from BattyBirdNET-Analyzer by rdz-oss. Crediting BirdNET is required wherever these models are used.

How It Works

BattyBirdNET uses a two-stage inference pipeline:

BirdNET v2.4 extracts 1024-dim embeddings from audio
Regional bat classifier (this repo) maps embeddings to bat species

The "slow-down trick": bat recordings at 256 kHz are fed directly to BirdNET without resampling. BirdNET's spectrogram pipeline (trained on 48 kHz bird audio) treats the samples as 48 kHz, shifting ultrasonic bat calls (20-120 kHz) into the audible frequency range where its learned features work.

Model Variants

Three precision formats are provided for each regional classifier:

Format	Directory	Use Case
FP32	`fp32/`	Standard precision for GPU and desktop CPU
FP16	`fp16/`	Half precision for Raspberry Pi 5, modern GPUs
INT8	`int8/`	Quantized for low-power devices, embedded ARM

Available Regions

Region	Model	Species	Coverage
Bavaria	`BattyBirdNET-Bavaria-256kHz`	32	Germany, Central Europe
Bavaria (high)	`BattyBirdNET-Bavaria-256kHz-high`	24	Germany, stricter thresholds
EU	`BattyBirdNET-EU-256kHz`	30	Broad European coverage
Scotland	`BattyBirdNET-Scotland-256kHz`	11	Scotland
South Wales	`BattyBirdNET-SouthWales-256kHz`	29	South Wales
Sweden	`BattyBirdNET-Sweden-256kHz`	23	Sweden, Nordic
UK	`BattyBirdNET-UK-256kHz`	20	United Kingdom
USA	`BattyBirdNET-USA-256kHz`	38	United States (full)
USA East	`BattyBirdNET-USA-EAST-256kHz`	23	Eastern United States
USA East (high)	`BattyBirdNET-USA-EAST-256kHz-high`	17	Eastern US, stricter thresholds
USA West	`BattyBirdNET-USA-WEST-256kHz`	28	Western United States

Model Architecture

Input: [batch, 1024] float32 (BirdNET v2.4 embedding vectors)
Output: [batch, N_species] float32 (raw logits, apply sigmoid for probabilities)
Activation: Sigmoid (same as BirdNET)
Labels: One species per line in corresponding _Labels.txt file

Usage with Birda (Rust CLI)

bash

# Install models
mkdir -p ~/.local/share/birda/models/bat/
cp fp32/BattyBirdNET-Bavaria-256kHz_fp32.onnx ~/.local/share/birda/models/bat/
cp labels/BattyBirdNET-Bavaria-256kHz_Labels.txt ~/.local/share/birda/models/bat/

# Run bat detection
birda analyze -m birdnet-v24-embeddings --bat bavaria bat_recording.wav

See birda for full documentation.

Usage with Python (ONNX Runtime)

python

import numpy as np
import onnxruntime as ort

# Load BirdNET v2.4 (with embeddings exposed) and bat classifier
birdnet = ort.InferenceSession("birdnet-v24-embeddings.onnx")
bat_model = ort.InferenceSession("fp32/BattyBirdNET-Bavaria-256kHz_fp32.onnx")

# Load 256kHz bat audio (144,000 samples = 0.5625s)
audio = np.random.randn(1, 144000).astype(np.float32)  # replace with real audio

# Stage 1: Extract embeddings
outputs = birdnet.run(None, {"input": audio})
embeddings = outputs[1]  # [1, 1024]

# Stage 2: Classify bat species
logits = bat_model.run(None, {"input": embeddings})[0]
scores = 1 / (1 + np.exp(-logits))  # sigmoid

# Load labels
with open("labels/BattyBirdNET-Bavaria-256kHz_Labels.txt") as f:
    labels = [line.strip() for line in f if line.strip()]

for i, (label, score) in enumerate(zip(labels, scores[0])):
    if score > 0.1:
        print(f"{label}: {score:.1%}")

Conversion Details

Converted from BattyBirdNET-Analyzer v1.0 TFLite models using birdnet-onnx-converter:

TFLite to ONNX via tf2onnx (opset 17)
Optimized with onnxslim (graph simplification, dynamic batching)
FP16 and INT8 quantization via onnxconverter-common and onnxruntime

Audio Requirements

Sample rate: 256 kHz (standard for AudioMoth and similar bat detectors)
Chunk size: 144,000 samples (0.5625 seconds at 256 kHz)
Overlap: 25% recommended (36,000 samples)
Format: WAV recommended; FLAC, MP3 also supported

Training Data Sources

The original BattyBirdNET models were trained on data from:

NABat Machine Learning (North America)
xeno-canto (bird and bat recordings)
ChiroVox (public bat call library)
Animal Sound Archive Berlin (Humboldt University)

License

CC-BY-NC-SA-4.0 (non-commercial use only), following the original BattyBirdNET-Analyzer license.

tphakala/
BattyBirdNET-onnx

BattyBirdNET ONNX Models

Attribution

How It Works

Model Variants

Available Regions

Model Architecture

Usage with Birda (Rust CLI)

Usage with Python (ONNX Runtime)

Conversion Details

Audio Requirements

Training Data Sources

License

Citation

Acknowledgments

tphakala/BattyBirdNET-onnx

BattyBirdNET ONNX Models

Attribution

How It Works

Model Variants

Available Regions

Model Architecture

Usage with Birda (Rust CLI)

Usage with Python (ONNX Runtime)

Conversion Details

Audio Requirements

Training Data Sources

License

Citation

Acknowledgments

tphakala/
BattyBirdNET-onnx