3.1 KiB
FAQ
Installation and setup
Do I need Python knowledge to use batdetect2?
Not much.
If you only want to run the model on your own recordings, you can use the CLI and follow the steps in {doc}getting_started.
Some command-line familiarity helps, but you do not need to write Python code for standard inference workflows.
Are there plans for an R version?
Not currently. Output files are plain formats (for example CSV/JSON), so you can read and analyze them in R or other environments.
I cannot get installation working. What should I do?
First, re-check {doc}getting_started and confirm your environment is active.
If it still fails, open an issue with your OS, install method, and full error output: GitHub Issues.
Model behavior and performance
The model does not perform well on my data
This usually means your data distribution differs from training data. The best next step is to validate on reviewed local data and then fine-tune/train on your own annotations if needed.
The model confuses insects/noise with bats
This can happen, especially when recording conditions differ from training conditions. Threshold tuning and training with local annotations can improve results.
See {doc}how_to/tune-detection-threshold.
The model struggles with feeding buzzes or social calls
This is a known limitation of available training data in some settings. If you have high-quality annotated examples, they are valuable for improving models.
Calls in the same sequence are predicted as different species
Currently we do not do any sophisticated post processing on the results output by the model. We return a probability associated with each species for each call. You can use these predictions to clean up the noisy predictions for sequences of calls.
Can I trust model outputs for biodiversity conclusions?
The models developed and shared as part of this repository should be used with caution. While they have been evaluated on held out audio data, great care should be taken when using the model outputs for any form of biodiversity assessment. Your data may differ, and as a result it is very strongly recommended that you validate the model first using data with known species to ensure that the outputs can be trusted.
The pipeline is slow
Runtime depends on hardware and recording duration. GPU inference is often much faster than CPU.
Training and scope
Can I train on my own species set?
Yes. You can train/fine-tune with your own annotated data and species labels.
Does this work on frequency-division or zero-crossing recordings?
Not directly. The workflow assumes audio can be converted to spectrograms from the raw waveform.
Can this be used for non-bat bioacoustics (for example insects or birds)?
Potentially yes, but expect retraining and configuration changes. Open an issue if you want guidance for a specific use case.
Usage and licensing
Can I use this for commercial purposes?
No. This project is currently for non-commercial use. See the repository license for details.