# BatDetect2
<img style="display:
block-inline;" width="64" height="64" src="assets/bat_icon.png">Code for
                                                                detecting and
                                                                classifying bat
                                                                echolocation
                                                                calls in high
                                                                frequency audio
                                                                recordings.

> [!WARNING]
> BatDetect2 2.0.1 is out.
> There are many changes and new recommended workflows.
> We have left the previous `batdetect2.api` module intact, but if you run
> into issues or want to upgrade, see the migration guide in the docs site.
>
> This update also ships with a refreshed default model.
> It was trained in the same way and on the same data as before, but you
> should still expect small output differences in some cases.

## What BatDetect2 is useful for

BatDetect2 can help you screen recordings for bat calls, find recordings that
need expert review, and compare model outputs across sites or projects with
appropriate caution.

It is best used as a tool to support ecological work, not as a replacement for
validation or expert interpretation.

## Start here

If you want the simplest current workflow, use the documentation site and start
with:

- getting started:
  `docs/source/getting_started.md`
- first tutorial:
  `docs/source/tutorials/run-inference-on-folder.md`

If you need the previous workflow based on `batdetect2 detect` or
`batdetect2.api`, use the legacy docs section and migration guide in the docs
site.

## Install BatDetect2

If you already use Python, activate the environment where you want BatDetect2 to
live.

If not, create a fresh one first so BatDetect2 stays separate from other
software on your machine.

Two common options are:

* Install the Anaconda Python 3.10 distribution for your operating system from
  [here](https://www.continuum.io/downloads).
  Create a new environment and activate it:

```bash
conda create -y --name batdetect2 python==3.10
conda activate batdetect2
```

* If you already have Python installed (version >= 3.10,< 3.14), you can create
  a fresh environment with:

```bash
python -m venv .venv
source .venv/bin/activate
```

### Installing BatDetect2
You can use pip to install `batdetect2`:

```bash
pip install batdetect2
```

Alternatively, download this code from the repository (by clicking on the green
button on top right) and unzip it.
Once unzipped, run this from extracted folder.

```bash
pip install .
```

Make sure you have the environment activated before installing `batdetect2`.

## Run BatDetect2 on a folder of recordings

Once installed, the simplest current workflow is to run BatDetect2 on a folder
of `.wav` files.

If you are working from this repository checkout, you can use this example
checkpoint path:

```text
src/batdetect2/models/checkpoints/Net2DFast_UK_same.pth.tar
```

Example command:

```bash
batdetect2 predict directory \
  src/batdetect2/models/checkpoints/Net2DFast_UK_same.pth.tar \
  example_data/audio \
  outputs
```

This will scan the audio files in `example_data/audio` and save model outputs to
`outputs`.

For the full beginner walkthrough, use
`docs/source/tutorials/run-inference-on-folder.md`.

## Legacy workflow

The sections below are kept only for people maintaining older BatDetect2 scripts
and analysis pipelines.

If you are new to BatDetect2, stop here and use the current docs and command
above.

If you really do need the older workflow, the reference material is below.


## Try the model
1) You can try a demo of the model (for UK species) on
   [huggingface](https://huggingface.co/spaces/macaodha/batdetect2).

2) Alternatively, click
   [here](https://colab.research.google.com/github/macaodha/batdetect2/blob/master/batdetect2_notebook.ipynb)
   to run the model using Google Colab.
   You can also run this notebook locally.


## Running the model on your own data

After following the above steps to install the code you can run the model on
your own data.

The remainder of this section is legacy reference material.


### Using the command line

The commands below describe the legacy CLI workflow.

For new work, prefer the current docs and `batdetect2 predict`.

You can run the model by opening the command line and typing:
```bash
batdetect2 detect AUDIO_DIR ANN_DIR DETECTION_THRESHOLD
```
e.g.
```bash
batdetect2 detect example_data/audio/ example_data/anns/ 0.3
```

`AUDIO_DIR` is the path on your computer to the audio wav files of interest.
`ANN_DIR` is the path on your computer where the model predictions will be
saved.
The model will output both `.csv` and `.json` results for each audio file.
`DETECTION_THRESHOLD` is a number between 0 and 1 specifying the cut-off
threshold applied to the calls.
A smaller number will result in more calls detected, but with the chance of
introducing more mistakes.

There are also optional arguments, e.g. you can request that the model outputs
features (i.e. estimated call parameters) such as duration, max_frequency, etc.
by setting the flag `--spec_features`.
These will be saved as `*_spec_features.csv` files:
`batdetect2 detect example_data/audio/ example_data/anns/ 0.3 --spec_features`

You can also specify which model to use by setting the `--model_path` argument.
If not specified, it will default to using a model trained on UK data e.g.
`batdetect2 detect example_data/audio/ example_data/anns/ 0.3 --model_path
models/Net2DFast_UK_same.pth.tar`


### Using the Python API

The examples below describe the legacy Python API.

For new work, prefer `batdetect2.api_v2.BatDetect2API` and the current docs
site.

If you prefer to process your data within a Python script then you can use the
`batdetect2` Python API.

```python
from batdetect2 import api

AUDIO_FILE = "example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav"

# Process a whole file
results = api.process_file(AUDIO_FILE)

# Or, load audio and compute spectrograms
audio = api.load_audio(AUDIO_FILE)
spec = api.generate_spectrogram(audio)

# And process the audio or the spectrogram with the model
detections, features, spec = api.process_audio(audio)
detections, features = api.process_spectrogram(spec)

# Do something else ...
```

You can integrate the detections or the extracted features to your custom
analysis pipeline.


## Training the model on your own data
Take a look at the training tutorial in the docs site first.

If you are working from this repository checkout, start with
`docs/source/tutorials/train-a-custom-model.md`.


## Data and annotations
The raw audio data and annotations used to train the models in the paper will be
added soon.
The audio interface used to annotate audio data for training and evaluation is
available [here](https://github.com/macaodha/batdetect2_GUI).


## Warning
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.


## FAQ
For more information please consult our [FAQ](docs/source/faq.md).


## Reference
If you find our work useful in your research please consider citing our paper
which you can find
[here](https://www.biorxiv.org/content/10.1101/2022.12.14.520490v1):
```
@article{batdetect2_2022,
    title     = {Towards a General Approach for Bat Echolocation Detection and Classification},
    author    = {Mac Aodha, Oisin and  Mart\'{i}nez Balvanera, Santiago and  Damstra, Elise and  Cooke, Martyn and  Eichinski, Philip and  Browning, Ella and  Barataudm, Michel and  Boughey, Katherine and  Coles, Roger and  Giacomini, Giada and MacSwiney G., M. Cristina and  K. Obrist, Martin and Parsons, Stuart and  Sattler, Thomas and  Jones, Kate E.},
    journal   = {bioRxiv},
    year      = {2022}
}
```

## Acknowledgements

Thanks to all the contributors who spent time collecting and annotating audio
data.