By Stefan Kahl, Thomas Wilhelm-Stein, Holger Klinck, Danny Kowerko, and Maximilian Eibl
We provide a baseline system for the LifeCLEF bird identification task BirdCLEF2017. We encourage participants to build upon the code base and share their results for future reference. We will keep the repository updated and will add improvements and submission boilerplate in the future.
If you have any questions or problems running the scripts, don't hesitate to contact us.
Contact: Stefan Kahl, Technische Universität Chemnitz, Media Informatics
E-Mail: [email protected]
This project is licensed under the terms of the MIT license.
This is a Thenao/Lasagne implementation in Python 2.7 for the identification of hundreds of bird species based on their vocalizations. This code is tested using Ubuntu 16.04 LTS but should work with other distributions as well.
Before cloning the repository, you need to install CUDA, cuDNN, OpenCV, Libav, Theano and Lasagne. You can find more detailed instructions below. After that, you can use the Python package tool PIP to install missing dependencies after the download of the repository:
git clone https://github.com/kahst/BirdCLEF-Baseline.git
cd BirdCLEF-Baseline
sudo pip install –r requirements.txt
Our workflow consists of four main phases: First, we need to sort the BirdCLEF training data. Secondly, we extract spectrograms from audio recordings. Thirdly, we train a deep neural net based on the resulting spectrograms - we treat the audio classification task as an image processing problem. Finally, we test the trained net given a local validation set of unseen audio recordings.
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The versions that you need for your machine differ, depending on OS and GPU. The installation process listed below should work with Ubuntu 16.04 LTS and any CUDA-capable GPU by NVIDIA.
Download CUDA 9.1 (you might want to use newer versions, if available):
Install CUDA:
sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
Add to environment path:
export PATH=/usr/local/cuda-9.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
Download cuDNN (you need to be registered):
https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
Installing from a Tar File:
Navigate to your directory containing the cuDNN Tar file. Unzip the cuDNN package.
tar -xzvf cudnn-9.0-linux-x64-v7.tgz
Copy the following files into the CUDA Toolkit directory.
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
Prerequisites (incl. Python):
sudo apt-get install python-dev
sudo apt-get install python-pip
sudo pip install numpy
sudo pip install scipy
sudo apt-get install libblas-dev liblapack-dev
sudo apt-get install cmake
sudo pip install cython
Install gpuarray:
https://deeplearning.net/software/libgpuarray/installation.html
git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray
mkdir Build
cd Build
cmake .. -DCMAKE_BUILD_TYPE=Release # or Debug if you are investigating a crash
make
make install
cd ..
python setup.py build
sudo python setup.py install
python setup.py build_ext -L $MY_PREFIX/lib -I $MY_PREFIX/include
sudo ldconfig
Install Theano:
git clone git:https://github.com/Theano/Theano.git
cd Theano
sudo pip install -e .
Adjust .theanorc in your home directory to select a GPU and fix random seeds:
[global]
device=cuda
floatX=float32
optimizer_excluding=low_memory
[mode]=FAST_RUN
[dnn.conv]
algo_bwd_filter=deterministic
algo_bwd_data=deterministic
[gpuarray]
preallocate=0
Clone the repository and install Lasagne:
sudo pip install https://github.com/Lasagne/Lasagne/archive/master.zip
We use OpenCV for image processing; you can install the cv2 package for Python running this command:
sudo apt-get install python-opencv
The audio processing library Librosa uses the Libav tools:
sudo apt-get install libav-tools
If you have trouble with some of the installation steps, you can open an issue or contact us. Thenao and Lasagne offer comprehensive installation guides, too - you should consult them for further information.