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.
Not currently. Output files are plain formats (for example CSV/JSON), so you can read and analyze them in R or other environments.
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.
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.
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.
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.
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.
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.
Runtime depends on hardware and recording duration. GPU inference is often much faster than CPU.
Yes. You can train/fine-tune with your own annotated data and species labels.
Not directly. The workflow assumes audio can be converted to spectrograms from the raw waveform.
Potentially yes, but expect retraining and configuration changes. Open an issue if you want guidance for a specific use case.
No. This project is currently for non-commercial use. See the repository license for details.