Stefan Kahl
95 lines
3.5 KiB
Markdown
95 lines
3.5 KiB
Markdown
# BirdNET-Lite
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TFLite version of BirdNET. Bird sound recognition for more than 6,000 species worldwide.
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Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University
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Go to https://birdnet.cornell.edu to learn more about the project.
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Want to use BirdNET to analyze a large dataset? Don't hesitate to contact us: ccb-birdnet@cornell.edu
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# Setup (Ubuntu 18.04)
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TFLite for x86 platforms comes with the standard Tensorflow package. If you are on a different platform, you need to install a dedicated version of TFLite (e.g., a pre-compiled version for Raspberry Pi).
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We need to setup TF2.3+ for BirdNET. First, we install Python 3 and pip:
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```
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sudo apt-get update
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sudo apt-get install python3-dev python3-pip
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sudo pip3 install --upgrade pip
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```
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Then, we can install Tensorflow with:
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```
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sudo pip3 install tensorflow
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```
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TFLite on x86 platform currently only supports CPUs.
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Note: Make sure to set `CUDA_VISIBLE_DEVICES=""` in your environment variables. Or set `os.environ['CUDA_VISIBLE_DEVICES'] = ''` at the top of your Python script.
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In this example, we use Librosa to open audio files. Install Librosa with:
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```
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sudo pip3 install librosa
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sudo apt-get install ffmpeg
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```
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You can use any other audio lib if you like, or pass raw audio signals to the model.
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If you don't use Librosa, make sure to install NumPy:
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```
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sudo pip3 install numpy
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```
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Note: BirdNET expects 3-second chunks of raw audio data, sampled at 48 kHz.
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# Usage
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You can run BirdNET via the command line. You can add a few parameters that affect the output.
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The input parameters include:
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```
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--i, Path to input file.
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--o, Path to output file. Defaults to result.csv.
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--lat, Recording location latitude. Set -1 to ignore.
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--lon, Recording location longitude. Set -1 to ignore.
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--week, Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.
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--overlap, Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.
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--sensitivity, Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.
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--min_conf, Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.
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--custom_list, Path to text file containing a list of species. Not used if not provided.
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```
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Note: A custom species list needs to contain one species label per line. Take a look at the `model/label.txt` for the correct species label. Only labels from this text file are valid. You can find an example of a valid custom list in the 'example' folder.
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Here are two example commands to run this BirdNET version:
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```
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python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' --lat 35.4244 --lon -120.7463 --week 18
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python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766 --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity 1.25 --custom_list 'example/custom_species_list.txt'
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```
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Note: Please make sure to provide lat, lon, and week. BirdNET will work without these values, but the results might be less reliable.
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The results of the anlysis will be stored in a result file in CSV format. All confidence values are raw prediction scores and should be post-processed to eliminate occasional false-positive results.
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# Contact us
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Please don't hesitate to contact us if you have any issues with the code or if you have any other remarks or questions.
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Our e-mail address: ccb-birdnet@cornell.edu
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We are always open for a collaboration with you.
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# Funding
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This project is supported by Jake Holshuh (Cornell class of ’69). The Arthur Vining Davis Foundations also kindly support our efforts.
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