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165
README.md
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@ -1,49 +1,67 @@
# 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.
<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.
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.
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:
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`
- getting started:
`docs/source/getting_started.md`
- first tutorial:
`docs/source/tutorials/run-inference-on-folder.md`
The current docs default to:
- the current command-line workflow: `batdetect2 predict`
- the current Python workflow: `batdetect2.api_v2.BatDetect2API`
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.
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 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.
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:
* 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:
* If you already have Python installed (version >= 3.10,< 3.14), you can create
a fresh environment with:
```bash
python -m venv .venv
@ -57,7 +75,8 @@ You can use pip to install `batdetect2`:
pip install batdetect2
```
Alternatively, download this code from the repository (by clicking on the green button on top right) and unzip it.
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
@ -68,11 +87,11 @@ 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.
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:
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
@ -87,32 +106,37 @@ batdetect2 predict directory \
outputs
```
This will scan the audio files in `example_data/audio`
and save model outputs to `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`.
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.
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 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.
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).
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.
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.
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.
@ -133,23 +157,35 @@ 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.
`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:
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`
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.
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.
If you prefer to process your data within a Python script then you can use the
`batdetect2` Python API.
```python
from batdetect2 import api
@ -170,25 +206,32 @@ detections, features = api.process_spectrogram(spec)
# Do something else ...
```
You can integrate the detections or the extracted features to your custom analysis pipeline.
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`.
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).
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.
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
@ -196,7 +239,9 @@ 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):
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},
@ -207,10 +252,6 @@ If you find our work useful in your research please consider citing our paper wh
```
## Acknowledgements
Thanks to all the contributors who spent time collecting and annotating audio data.
### TODOs
- [x] Release the code and pretrained model
- [ ] Release the datasets and annotations used the experiments in the paper
- [ ] Add the scripts used to generate the tables and figures from the paper
Thanks to all the contributors who spent time collecting and annotating audio
data.

124
src/batdetect2/api_v2.py
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@ -54,7 +54,8 @@ class BatDetect2API:
evaluate predictions, and train or fine-tune models.
In most cases, start with :meth:`from_checkpoint` to load a trained model.
Use :meth:`from_config` when you want to build a new model with custom configs.
Use :meth:`from_config` when you want to build a new model with custom
configs.
Examples
--------
@ -93,7 +94,7 @@ class BatDetect2API:
):
"""Create a fully configured API instance.
This initializer is mainly for internal wiring.
This initializer is mainly for internal use.
In most cases, users should create the API with
:meth:`from_checkpoint` or :meth:`from_config`.
@ -264,7 +265,7 @@ class BatDetect2API:
targets_config: TargetConfig,
val_annotations: Sequence[data.ClipAnnotation] | None = None,
trainable: Literal[
"all", "heads", "classifier_head", "bbox_head"
"all", "heads", "classifier_head", "size_head"
] = "heads",
train_workers: int = 0,
val_workers: int = 0,
@ -279,11 +280,11 @@ class BatDetect2API:
logger_config: LoggerConfig | None = None,
logging_callbacks: Sequence[LoggingCallback[TrainLoggingContext]] = (),
) -> "BatDetect2API":
"""Fine-tune the current model with a new target definition.
"""Fine-tune the current model for new target sounds.
Use this when you want to keep the existing model weights but change
the target sounds. You can fine-tune the whole model or just the
classifier heads.
heads.
Parameters
----------
@ -293,7 +294,7 @@ class BatDetect2API:
Target definition to train against.
val_annotations : Sequence[data.ClipAnnotation] | None, optional
Validation annotations.
trainable : {"all", "heads", "classifier_head", "bbox_head"}, optional
trainable : {"all", "heads", "classifier_head", "size_head"}, optional
Which model parameters remain trainable.
train_workers : int, optional
Number of worker processes for training data loading.
@ -509,19 +510,63 @@ class BatDetect2API:
return metrics
def load_audio(self, path: data.PathLike) -> np.ndarray:
"""Load one audio file into a waveform array."""
"""Load one audio file into a waveform array.
Parameters
----------
path : data.PathLike
Path to the audio file.
Returns
-------
np.ndarray
Audio waveform loaded from disk.
"""
return self.audio_loader.load_file(path)
def load_recording(self, recording: data.Recording) -> np.ndarray:
"""Load one recording object into a waveform array."""
"""Load one recording object into a waveform array.
Parameters
----------
recording : data.Recording
Recording object describing the audio to load.
Returns
-------
np.ndarray
Audio waveform for the requested recording.
"""
return self.audio_loader.load_recording(recording)
def load_clip(self, clip: data.Clip) -> np.ndarray:
"""Load one clip object into a waveform array."""
"""Load one clip object into a waveform array.
Parameters
----------
clip : data.Clip
Clip object describing the section of audio to load.
Returns
-------
np.ndarray
Audio waveform for the requested clip.
"""
return self.audio_loader.load_clip(clip)
def get_top_class_name(self, detection: Detection) -> str:
"""Get highest-confidence class name for one detection."""
"""Get the name of the highest-confidence class for one detection.
Parameters
----------
detection : Detection
Detection whose class scores will be inspected.
Returns
-------
str
Class name with the highest score.
"""
import numpy as np
@ -535,7 +580,22 @@ class BatDetect2API:
include_top_class: bool = True,
sort_descending: bool = True,
) -> list[tuple[str, float]]:
"""Get class score list as ``(class_name, score)`` pairs."""
"""Get class scores as ``(class_name, score)`` pairs.
Parameters
----------
detection : Detection
Detection whose class scores will be returned.
include_top_class : bool, optional
If ``False``, omit the highest-scoring class from the result.
sort_descending : bool, optional
If ``True``, sort scores from highest to lowest.
Returns
-------
list[tuple[str, float]]
Class-score pairs for the detection.
"""
scores = [
(class_name, float(score))
@ -559,17 +619,22 @@ class BatDetect2API:
if class_name != top_class_name
]
@staticmethod
def get_detection_features(detection: Detection) -> np.ndarray:
"""Get extracted feature vector for one detection."""
return detection.features
def generate_spectrogram(
self,
audio: np.ndarray,
) -> torch.Tensor:
"""Convert a waveform array into a model spectrogram."""
"""Convert a waveform array into a spectrogram tensor.
Parameters
----------
audio : np.ndarray
Audio waveform.
Returns
-------
torch.Tensor
Spectrogram tensor ready for model inference.
"""
import torch
tensor = torch.tensor(audio).unsqueeze(0)
@ -703,7 +768,20 @@ class BatDetect2API:
audio_dir: data.PathLike,
detection_threshold: float | None = None,
) -> list[ClipDetections]:
"""Run inference on all supported audio files in a directory."""
"""Run inference on all supported audio files in a directory.
Parameters
----------
audio_dir : data.PathLike
Directory containing audio files.
detection_threshold : float | None, optional
Detection score threshold override.
Returns
-------
list[ClipDetections]
Predictions for all supported audio files found in the directory.
"""
from soundevent.audio.files import get_audio_files
files = list(get_audio_files(audio_dir))
@ -904,8 +982,8 @@ class BatDetect2API:
) -> "BatDetect2API":
"""Build an API instance from config objects.
Use this when you want to create a new model stack without loading a
saved checkpoint.
Use this when you want to create a new model without loading a saved
checkpoint.
Parameters
----------
@ -1161,7 +1239,7 @@ class BatDetect2API:
def _set_trainable_parameters(
self,
trainable: Literal["all", "heads", "classifier_head", "bbox_head"],
trainable: Literal["all", "heads", "classifier_head", "size_head"],
) -> None:
detector = self.model.detector
@ -1177,6 +1255,6 @@ class BatDetect2API:
for parameter in detector.classifier_head.parameters():
parameter.requires_grad = True
if trainable in {"heads", "bbox_head"}:
if trainable in {"heads", "size_head"}:
for parameter in detector.size_head.parameters():
parameter.requires_grad = True

17
src/batdetect2/cli/finetune.py
View File

@ -1,11 +1,10 @@
from pathlib import Path
from typing import Literal, cast
from typing import Literal
import click
from loguru import logger
from batdetect2.cli.base import cli
from batdetect2.train.checkpoints import DEFAULT_CHECKPOINT
__all__ = ["finetune_command"]
@ -20,8 +19,7 @@ __all__ = ["finetune_command"]
type=str,
help=(
"Path to a checkpoint, bundled checkpoint alias, or a Hugging Face "
"URI to fine-tune from. Defaults to "
f"'{DEFAULT_CHECKPOINT}'."
"URI to fine-tune from. Defaults to uk_same"
),
)
@click.option(
@ -61,7 +59,7 @@ __all__ = ["finetune_command"]
)
@click.option(
"--trainable",
type=click.Choice(["all", "heads", "classifier_head", "bbox_head"]),
type=click.Choice(["all", "heads", "classifier_head", "size_head"]),
default="heads",
show_default=True,
help="Which model parameters remain trainable during fine-tuning.",
@ -119,7 +117,9 @@ def finetune_command(
training_config: Path | None = None,
audio_config: Path | None = None,
logging_config: Path | None = None,
trainable: str = "heads",
trainable: Literal[
"all", "heads", "classifier_head", "size_head"
] = "heads",
seed: int | None = None,
num_epochs: int | None = None,
train_workers: int = 0,
@ -196,10 +196,7 @@ def finetune_command(
train_annotations=train_annotations,
val_annotations=val_annotations,
targets_config=target_conf,
trainable=cast(
Literal["all", "heads", "classifier_head", "bbox_head"],
trainable,
),
trainable=trainable,
train_workers=train_workers,
val_workers=val_workers,
checkpoint_dir=ckpt_dir,

9
tests/test_api_v2/test_api_v2.py
View File

@ -200,13 +200,14 @@ def test_user_can_read_extracted_features_per_detection(
) -> None:
"""User story: inspect extracted feature vectors per detection."""
# Given
prediction = api_v2.process_file(example_audio_files[0])
assert len(prediction.detections) > 0
# When
feature_vectors = [det.features for det in prediction.detections]
feature_vectors = [
api_v2.get_detection_features(det) for det in prediction.detections
]
# Then
assert len(prediction.detections) > 0
assert len(feature_vectors) == len(prediction.detections)
assert all(vec.ndim == 1 for vec in feature_vectors)
assert all(vec.size > 0 for vec in feature_vectors)