A python api for BirdNET-Analyzer and BirdNET-Lite
birdnetlib requires Python 3.7+ and prior installation of Tensorflow Lite, librosa and ffmpeg. See BirdNET-Analyzer for more details on installing the Tensorflow-related dependencies.
pip install birdnetlib
birdnetlib provides a common interface for BirdNET-Analyzer and BirdNET-Lite.
To use the newer 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)
To use the BirdNET-Lite model, use the LiteAnalyzer class.
from birdnetlib import Recording
from birdnetlib.analyzer_lite import LiteAnalyzer
from datetime import datetime
# Load and initialize the BirdNET-Lite models.
analyzer = LiteAnalyzer()
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) # Returns list of 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},
{'common_name': 'House Finch',
'confidence': 0.4496,
'end_time': 15.0,
'scientific_name': 'Haemorhous mexicanus',
'start_time': 12.0}]
DirectoryAnalyzer can process a directory and analyze contained files.
def on_analyze_complete(recording):
print(recording.path)
pprint(recording.detections)
directory = DirectoryAnalyzer(
"/Birds/mp3_dir",
patterns=["*.mp3", "*.wav"]
)
directory.on_analyze_complete = on_analyze_complete
directory.process()
See the full example for analyzer options and error handling callbacks.
DirectoryWatcher can watch a directory and analyze new files as they are created.
def on_analyze_complete(recording):
print(recording.path)
pprint(recording.detections)
watcher = DirectoryWatcher("/Birds/mp3_dir")
watcher.on_analyze_complete = on_analyze_complete
watcher.watch()
See the full example for analyzer options and error handling callbacks.
SpeciesList uses BirdNET-Analyzer to predict species lists from location and date.
species = SpeciesList()
species_list = species.return_list(
lon=-120.7463, lat=35.4244, date=datetime(year=2022, month=5, day=10)
)
print(species_list)
# [{'scientific_name': 'Haemorhous mexicanus', 'common_name': 'House Finch', 'threshold': 0.8916686}, ...]
- Watch a directory for new files, then analyze with multiple analyzer models as files are saved
- Watch a directory for new files, and apply datetimes by parsing file names (eg 2022-08-15-birdnet-21:05:52.wav) prior to analyzing This example can also be used to modify lat/lon, min_conf, etc., based on file name prior to analyzing.
- Limit detections to certain species by passing a predefined species list to the analyzer Useful when searching for a particular set of bird detections.
- Extract detections as audio file samples and/or spectrograms Supports audio extractions as .flac, .wav and .mp3. Spectrograms exported as .png, .jpg, or other matplotlib.pyplot supported formats. Can be filtered to only extract files above a separate minimum confidence value.
birdnetlib uses models provided by BirdNET-Lite and BirdNET-Analyzer under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.
BirdNET-Lite and BirdNET-Analyzer 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.
birdnetlib is maintained by Joe Weiss. Contributions are welcome.
- 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