Quick start

This guide will help you get started with BirdNET quickly. It covers installation, basic usage, and running benchmarks to evaluate performance.

Installation

To install BirdNET, you can use pip. Open your terminal and run the following command:

pip install birdnet --user

This command installs the BirdNET package along with its dependencies.

Predict species from audio file

import birdnet

model = birdnet.load("acoustic", "2.4", "tf")

predictions = model.predict("example/soundscape.wav")

predictions.to_csv("example/predictions.csv")

Predict species from multiple audio files in a directory

import birdnet
from birdnet import AudioDataset

model = birdnet.load("acoustic", "2.4", "tf")

predictions = model.predict("example/soundscapes/")

predictions.to_csv("example/predictions.csv")

Predict species for a given location and time

import birdnet

model = birdnet.load("geo", "2.4", "tf")

predictions = model.predict(42.5, -76.45, week=4)

predictions.to_txt("example/species.txt")

Predict species with a custom species list

import birdnet

model = birdnet.load("acoustic", "2.4", "tf")

predictions = model.predict(
    "example/soundscape.wav",
    custom_species_list="example/species.txt",
)

predictions.to_csv("example/predictions.csv")

Use a different model version or backend

The version and backend arguments of birdnet.load select the model. Besides V2.4, the V3.0 (preview) acoustic model is available in the tf, pb, pt and onnx backends, and the V3.0 geo model in the tf and pb backends. See Models for the full support matrix.

import birdnet

# V3.0 acoustic model via the ONNX backend (requires: pip install birdnet[onnx])
model = birdnet.load("acoustic", "3.0", "onnx")

predictions = model.predict("example/soundscape.wav")

predictions.to_csv("example/predictions.csv")