bioacoustics-model-zoo

Pre-trained models for bioacoustic classification tasks

Set up / Installation

To use the bioacoustics model zoo:

1. Create a python environment (3.9-3.11 supported) using conda or your preferred package manager

For example, using conda:

conda create -n bmz python=3.10

2. Install the repository from GitHub as a package. For instance, to install the bioacousticsmodel-zoo in a python environment (e.g. conda environment) using pip:

pip install git+https://github.com/kitzeslab/bioacoustics-model-zoo

If you want to intall a specific branch or release of the model zoo, for instance release 0.11.0, add an @ then the tag at the end of the command:

pip install git+https://github.com/kitzeslab/[email protected]

3. Install any additional dependencies for the specific models you want to use. Additional dependencies for each model are noted in the Model List below. For example, if using HawkEars you will need to

pip install timm torchaudio

or if using BirdNET you will need to

pip install tensorflow tensorflow-hub

Note that tensorflow installation sometimes requires careful attention to version numbers, see this section below

You can now use the package directly in python:

import bioacoustics_model_zoo as bmz
model = bmz.HawkEars()
model.predict(audio_files) 

See a description of each model and basic usage exmple below. Also see the transfer learning tutorials on OpenSoundscape.org for detailed advice on fine-tuning models from the Bioacoustics Model Zoo.

If you encounter an issue or a bug, or would like to request a new feature, make a new "Issue" on the Github Issues page. You can also reach out to Sam ([email protected]) for more specific inquiries.

Basic usage

List:

List available models in the GitHub repo bioacoustics-model-zoo

import bioacoustics_model_zoo as bm
bmz.utils.list_models()

Load:

Get a ready-to-use model object: choose from the models listed in the previous command

model = bmz.BirdNET()

Inference:

model is an OpenSoundscape CNN object (or other class) which you can use as normal.

For instance, use the model to generate predictions on an audio file:

audio_file_path = './hydrophone_10s.wav'
scores = model.predict([audio_file_path],activation_layer='softmax')
scores

Contributing

To contribute a model to the model zoo, email [email protected] or add a model yourself:

• fork this repository (help)
• add a .py module in the bioacoustics_model_zoo subfolder implementing a class that instantiates your model object
  • implement the predict() and embed() methods with an API matching the other models in the model zoo
  • optionally implement train() method
  • Note: if you have a pytorch model, you may be able to simply subclass opensoundscape.CNN without needing to override these methods
  • in the docstring, provide an example of use
  • in the docstring, also include a suggested citation for others using the model
  • decorate your class with @register_bmz_model
• add an import statement in __init__.py to import your model class into the top-level package API (from bioacoustics_model_zoo.new_model import NewModel)
• add your model to the Model List below in this document, with example usage
• submit a pull request (GitHub's help page)

Check out any of the existing models for examples of how to complete these steps. In particular, pick the current model class most similar to yours (pytorch vs tensorflow) as a starting point.

Model list

BirdNET

Classification and embedding model trained on a large set of annotated bird vocalizations

Additional required packages:

tensorflow, tensorflow_hub

Example:

import bioacoustics_model_zoo as bmz
m = bmz.BirdNET()
m.predict(['test.wav']) # returns dataframe of per-class scores
m.embed(['test.wav']) # returns dataframe of embeddings

Training:

The .train() method trains a shallow fully-connected neural network as a classification head while keeping the feature extractor frozen, since the BirdNET feature extractor is not open-source.

Please see opensoundscape.org documentation and tutorials for detailed walk through. Once you have multi-hot training and validation label dataframes with (file, start_time, end_time) multi-index and a column for each class, training looks like this:

# load the pre-trained BirdNET tensorflow model
m=bmz.BirdNET()
# add a 2-layer PyTorch classification head
m.initialize_custom_classifier(classes=train_df.columns, hidden_layer_sizes=(100,))
# embed the training/validation samples with 5 augmented variations each,
# then fit the classification head
m.train(
  train_df,
  val_df,
  n_augmentation_variants=5,
  embedding_batch_size=64,
  embedding_num_workers=4
)
# save the custom BirdNET model to a file
m.save(save_path)
# later, to reload your fine-tuned BirdNET from the saved object:
# m = bmz.BirdNET.load(save_path)

Perch:

Embedding and bird classification model trained on Xeno Canto

Example:

import bioacoustics_model_zoo as bmz
m = bmz.Perch()
predictions = model.predict(['test.wav']) # predict on the model's classes
embeddings = model.embed(['test.wav']) # generate embeddings on each 5 sec of audio

Training: see BirdNET example above, training is equivalent (only trains shallow classifier on frozen feature extractor).

HawkEars

Bird classification model for 314 North American species

Note that HawkEars internally uses an ensemble of 5 CNNs.

Additional required packages:

timm, torchaudio

Example:

import bioacoustics_model_zoo as bmz
m = bmz.HawkEars()
m.predict(['test.wav']) # returns dataframe of per-class scores
m.embed(['test.wav']) # returns dataframe of embeddings

Training: Training this model is equivalent to training the Opensoundscape.CNN class. Please see documentation on opensoundscape.org for detailed examples and walk-throughs.

Because 5 models are ensembled, training is a bit heavy - you may need small batch sizes, and you might consider removing all but one model.

By default, training HawkEars uses a lower learning rate on the feature extractor than on the classifier - a "fine tuning" paradigm. These values can be modified in the .optimizer_params dictionary.

import bioacoustics_model_zoo as bmz
m = bmz.HawkEars()
m.train(train_df,val_df,epochs=10,batch_size=64,num_workers=4)

MixIT Bird SeparationModel

Separate audio into channels potentially representing separate sources.

This particular model was trained on bird vocalization data.

Additional required packages:

tensorflow, tensorflow_hub

Example:

First, download the checkpoint and metagraph from the MixIt Github repo: install gsutil then run the following command in your terminal:

gsutil -m cp -r gs://gresearch/sound_separation/bird_mixit_model_checkpoints .

Then, use the model in python:

import bioacoustics_model_zoo as bmz
# provide the local path to the checkpoint when creating the object
# this example creates 4 channels; use output_sources8 to separate into 8 channels
model = bmz.SeparationModel(
  checkpoint='/path/to/bird_mixit_model_checkpoints/output_sources4/model.ckpt-3223090',
)

# separate opensoundscape Audio object into 4 channels:
# note that it seems to work best on 5 second segments
a = Audio.from_file('audio.mp3',sample_rate=22050).trim(0,5)
separated = model.separate_audio(a)

# save audio files for each separated channel:
# saves audio files with extensions like _stem0.wav, _stem1.wav, etc
model.load_separate_write('./temp.wav')

YAMNet:

Embedding model trained on AudioSet YouTube

Additional required packages:

tensorflow, tensorflow_hub

Example:

import bioacoustics_model_zoo as bmz
m = bmz.YAMNet()
m.predict(['test.wav']) # returns dataframe of per-class scores
m.embed(['test.wav']) # returns dataframe of embeddings

RanaSierraeCNN:

Detect underwater vocalizations of Rana sierrae, the Sierra Nevada Yellow-legged Frog

example:

import bioacoustics_model_zoo as bmz
m = bmz.RanaSierraeCNN()
m.predict(['test.wav']) # returns dataframe of per-class scores

Other automated detection tools for bioacoustics

RIBBIT

Detect sounds with periodic pulsing patterns.

Implemented in OpenSoundscape as opensoundscape.ribbit.ribbit().

• Python notebooks demonstrating use
• Implementation for R
• Manuscript: Lapp et al 2021

Accelerating and decelerating sequences

Detect pulse trains that accelerate, such as the drumming of Ruffed Grouse (Bonasa umbellus)

Implemented in OpenSoundscape as

opensoundscape.signal_processing.detect_peak_sequence_cwt().

(note that in earlier versions of OpenSoundscape the module is named signal rather than signal_processing)

• Python notebooks demonstrating use
• Manuscript: Lapp et al 2022

Troubleshooting

TensorFlow Installation in Python Environment

Some models in the model zoo require tensorflow (and potentially tensorflow_hub) to be installed in your python environment.

Installing TensorFlow can be tricky, and it may not be possible to have cuda-enabled tensorflow in the same environment as cuda-enabled pytorch. In this case, you can install a cpu-only version of tensorflow (pip install tensorflow-cpu). You may want to start with a fresh environment, or uninstall tensorflow and nvidia-cudnn-cu11 then reinstall pytorch with the appropriate nvidia-cudnn-cu11, to avoid having the wrong cudnn for PyTorch.

Alternatively, if you want to use the TensorFlow Hub models with GPU acceleration, create an environment where you uninstall pytorch and nvidia-cudnn-cu11 and install a cpu-only version (see this page for the correct installation command). Then, you can pip install tensorflow-hub and let it choose the correct nvidia-cudnn so that it can use CUDA and leverage GPU acceleration.

Installing tensorflow: Carefully follow the directions for your system. Note that models provided in this repo might require the specific nvidia-cudnn-cu11 version 8.6.0, which could conflict with the version required for pytorch.

Error while Downloading TF Hub Models

Some of the models provided in this repo are hosted on the Tensorflow model hub.

If you encounter the following error (or similar) when downloading a TensorFlow Hub model:

ValueError: Trying to load a model of incompatible/unknown type. '/var/folders/d8/265wdp1n0bn_r85dh3pp95fh0000gq/T/tfhub_modules/9616fd04ec2360621642ef9455b84f4b668e219e' contains neither 'saved_model.pb' nor 'saved_model.pbtxt'.

You need to delete the folder listed in the error message (something like /var/folders/...tfhub_modules/....). After deleting that folder, downloading the model should work.

The issue occurs because TensorFlow Hub is looking for a cached model in a temporary folder where it was once stored but no longer exists. See relevant GitHub issue here: tensorflow/hub#896