RaspberryPi.md

Running on Raspberry Pi

This implementation uses full of CPU, not specialized to use GPU on it. You will need Raspberry Pi 3.

Install modeles

Followings are my installation log, you might be able to install on fresh Raspbian image.

sudo apt-get install python3-numpy
sudo apt-get install python3-scipy
sudo apt-get install python3-pandas
sudo apt-get install python3-h5py
sudo apt install libatlas-base-dev
sudo apt-get install python3-pyaudio
sudo pip3 install resampy==0.1.5 librosa==0.5.1
pip3 install tensorflow
pip3 install keras
pip3 install easydict
git clone https://github.com/daisukelab/ml-sound-classifier.git
cd ml-sound-classifier/ext
./download.sh

Tensorflow 1.9 will be installed as of now.

LibROSA has to be the version above, thanks to the web article (in Japanese) "Raspbian Stretch with Desktopにlibrosaをインストールする@Qiita".

You can confirm by running off-line inference as follows:

cd ml-sound-classifier
python3 premitive_file_predictor.py sample/fireworks.wav

** This uses MobileNetV2 model, and it's not fast enough.

Run realtime inference

cd ml-sound-classifier/rpi
python3 ../realtime_predictor.py -i 2

The rpi folder has appropriate config.py for using alexbased_small_fsd2018_41cls.h5 pre-trained model.

• 0.5s interval to output results; conf.rt_process_count / conf.rt_oversamples = 5 / 10 = 0.5
• It still calculates ensemble of 10 predictions.