birdnetlib
A python api for BirdNET-Analyzer and BirdNET-Lite
Installation
birdnetlib requires Python 3.9+ and prior installation of Tensorflow Lite, librosa and ffmpeg. See BirdNET-Analyzer for more details on installing the Tensorflow-related dependencies.
Documentation
birdnetlib provides a common interface for BirdNET-Analyzer and BirdNET-Lite.
Using BirdNET-Analyzer
To use the latest BirdNET-Analyzer model, use the Analyzer class.
from birdnetlib import Recording
from birdnetlib.analyzer import Analyzer
from datetime import datetime
# Load and initialize the BirdNET-Analyzer models.
analyzer = Analyzer()
recording = Recording(
analyzer,
"sample.mp3",
lat=35.4244,
lon=-120.7463,
date=datetime(year=2022, month=5, day=10), # use date or week_48
min_conf=0.25,
)
recording.analyze()
print(recording.detections)
recording.detections contains a list of detected species, along with time ranges and confidence value.
[{'common_name': 'House Finch',
'confidence': 0.5744,
'end_time': 12.0,
'scientific_name': 'Haemorhous mexicanus',
'start_time': 9.0,
'label': 'Haemorhous mexicanus_House Finch'},
{'common_name': 'House Finch',
'confidence': 0.4496,
'end_time': 15.0,
'scientific_name': 'Haemorhous mexicanus',
'start_time': 12.0,
'label': 'Haemorhous mexicanus_House Finch'}]
The Recording class takes a file path as an argument. You can also use RecordingFileObject to analyze an in-memory object, or RecordingBuffer for handling an array buffer.
About BirdNET-Analyzer
birdnetlib uses models provided by BirdNET-Analyzer and BirdNET-Lite under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.
BirdNET-Analyzer and BirdNET-Lite were developed by the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology.
For more information on BirdNET analyzers, please see the project repositories below:
birdnetlib is not associated with BirdNET-Lite, BirdNET-Analyzer or the K. Lisa Yang Center for Conservation Bioacoustics.
About birdnetlib
birdnetlib is maintained by Joe Weiss. Contributions are welcome.
Project Goals
- Establish a unified API for interacting with Tensorflow-based BirdNET analyzers
- Enable python-based test cases for BirdNET analyzers
- Make it easier to use BirdNET in python-based projects
- Make it easier to migrate to new BirdNET versions/models as they become available