birdnetr
This is a wrapper for the birdnet Python package for automated bird sound ID
Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
1 of 4 committers (25.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (19.0%) to scientific vocabulary
Keywords
Repository
This is a wrapper for the birdnet Python package for automated bird sound ID
Basic Info
- Host: GitHub
- Owner: birdnet-team
- License: other
- Language: R
- Default Branch: main
- Homepage: https://birdnet-team.github.io/birdnetR/
- Size: 6.59 MB
Statistics
- Stars: 21
- Watchers: 9
- Forks: 1
- Open Issues: 2
- Releases: 7
Topics
Metadata Files
README.md
birdnetR 
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birdnetR integrates BirdNET, a state‐of‐the‐art deep learning classifier for automated (bird) sound identification, into an R-workflow.
This package will simplify the analysis of (large) audio datasets from bioacoustic projects, allowing researchers to easily apply machine learning techniques—even without a background in computer science.
birdnetR is an R wrapper around the birdnet Python package. It provides the core functionality to analyze audio using the pre-trained 'BirdNET' model or a custom classifier, and to predict bird species occurrence based on location and week of the year.
However, it does not include all the advanced features available in the BirdNET Analyzer. For advanced applications, such as training custom classifiers and validation, users should use the 'BirdNET Analyzer' directly.
birdnetR is under active development, and changes may affect existing workflows.
Installation
Install the released version from CRAN:
r
install.packages("birdnetR")
or install the development version from GitHub with:
r
pak::pak("birdnet-team/birdnetR")
Python dependencies are installed on demand, meaning they are installed when you use them for the first time. This will result in longer initial setup.
Example use
This is a simple example using the tflite BirdNET model to predict species in an audio file.
```r
Load the package
library(birdnetR)
Initialize a BirdNET model
model <- birdnetmodeltflite()
Path to the audio file (replace with your own file path)
audio_path <- system.file("extdata", "soundscape.mp3", package = "birdnetR")
Predict species within the audio file
predictions <- predictspeciesfromaudiofile(model, audio_path)
Get most probable prediction within each time interval
gettopprediction(predictions)
```
Citation
Feel free to use birdnetR for your acoustic analyses and research. If you do, please cite as:
bibtex
@article{kahl2021birdnet,
title={BirdNET: A deep learning solution for avian diversity monitoring},
author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger},
journal={Ecological Informatics},
volume={61},
pages={101236},
year={2021},
publisher={Elsevier}
}
License
- Source Code: The source code for this project is licensed under the MIT License.
- Models: The models used in this project are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
Please ensure you review and adhere to the specific license terms provided with each model. Note that educational and research purposes are considered non-commercial use cases.
Funding
This project is supported by Jake Holshuh (Cornell class of '69) and The Arthur Vining Davis Foundations. Our work in the K. Lisa Yang Center for Conservation Bioacoustics is made possible by the generosity of K. Lisa Yang to advance innovative conservation technologies to inspire and inform the conservation of wildlife and habitats.
The development of BirdNET is supported by the German Federal Ministry of Education and Research through the project “BirdNET+” (FKZ 01|S22072). The German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety contributes through the “DeepBirdDetect” project (FKZ 67KI31040E). In addition, the Deutsche Bundesstiftung Umwelt supports BirdNET through the project “RangerSound” (project 39263/01).
Partners
BirdNET is a joint effort of partners from academia and industry. Without these partnerships, this project would not have been possible. Thank you!
Owner
- Name: BirdNET-Team
- Login: birdnet-team
- Kind: organization
- Location: Germany
- Repositories: 1
- Profile: https://github.com/birdnet-team
GitHub Events
Total
- Create event: 6
- Release event: 3
- Issues event: 15
- Watch event: 7
- Delete event: 4
- Issue comment event: 10
- Push event: 50
- Pull request event: 6
Last Year
- Create event: 6
- Release event: 3
- Issues event: 15
- Watch event: 7
- Delete event: 4
- Issue comment event: 10
- Push event: 50
- Pull request event: 6
Committers
Last synced: 11 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| fegue | f****r@g****m | 131 |
| Sunny Tseng | s****g@g****m | 24 |
| Stefan Kahl | k****t@h****e | 15 |
| Melissa Weidlich-Rau | 1****x | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 20
- Total pull requests: 12
- Average time to close issues: about 1 month
- Average time to close pull requests: 8 days
- Total issue authors: 4
- Total pull request authors: 2
- Average comments per issue: 1.05
- Average comments per pull request: 1.08
- Merged pull requests: 12
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 11
- Pull requests: 8
- Average time to close issues: 2 months
- Average time to close pull requests: 5 days
- Issue authors: 3
- Pull request authors: 1
- Average comments per issue: 1.09
- Average comments per pull request: 0.63
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- fegue (17)
- jaymwin (1)
- vjjan91 (1)
- r02ls22 (1)
Pull Request Authors
- fegue (15)
- SunnyTseng (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- cran 247 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 0
(may contain duplicates) - Total versions: 15
- Total maintainers: 1
proxy.golang.org: github.com/birdnet-team/birdnetr
- Documentation: https://pkg.go.dev/github.com/birdnet-team/birdnetr#section-documentation
- License: other
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Latest release: v0.3.2
published about 1 year ago
Rankings
proxy.golang.org: github.com/birdnet-team/birdnetR
- Documentation: https://pkg.go.dev/github.com/birdnet-team/birdnetR#section-documentation
- License: other
-
Latest release: v0.3.2
published about 1 year ago
Rankings
cran.r-project.org: birdnetR
Deep Learning for Automated (Bird) Sound Identification
- Homepage: https://birdnet-team.github.io/birdnetR/
- Documentation: http://cran.r-project.org/web/packages/birdnetR/birdnetR.pdf
- License: MIT + file LICENSE
-
Latest release: 0.3.2
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
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- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
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- JamesIves/github-pages-deploy-action v4.5.0 composite
- actions/checkout v4 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
- reticulate * imports
- knitr * suggests
- rmarkdown * suggests
- testthat >= 3.0.0 suggests