• Original version: pip install tensorflow==1.9 keras==2.1.5
• Updated 2020.04.20: pip install tensorflow==1.15.2 keras
• or pipenv install is you have pipenv

• Download ffmpeg from ffmpeg, you should select Static linking and get a zip file.
• extract the zip file into ffmpeg folder, so that there exists ffmpeg/bin/ffmeg.exe.

• Download sox from SOund eXchange, you should get a zip file.
• extract zip file into the sox folder. so that there exists sox/sox.exe.

Convert recorded audio files to *.wav files

python ./convert_file.py <Data Folder>

The Data Folder should contains many subfolders where your audios files reside. Typically, one of your audio file could be <Data Folder>/group1/a.mp3.

The results of conversion are within ./data/train/. Your should manually move some of them to ./data/test to accomplish training-validation separation. The fraction of moved files depends on yourself.

The data augmentation server is implemented by grpc.

pip install grpcio

or for some version of python3

pip3 install grpcio

Training involves two files: train.py and augmentation/.

python -m augmentation will start a augmentation server that provide train data and test data. train.py will connect to augmentation server and request data.

augmentation/config.py is used for configuring the batch size/thread size/data source/...

Before training, there are several things you should do.

You have done it in Data preparation. Now check it again.

• put train data into data/train/

• put validate data into data/test/

• NOTE: the wav file must be encoded by 16 bit signed integer, mono-channeled and at a sampling rate of 16000.

• You should got things correct if you obtained them from convert_file.py

• You should got sox in sox/, now check it again.

server side: python -m augmentation

• this will start an augmentation server utilizing sox.

client side: python train.py

• this will start trainig with data requested from augmentation server.

• NOTE: run it from the folder audioNet

** Resume a interrupted training process. You can resume from certain checkpoint, modify the last line of train.py, set -1(Negtive 1) as your start point.

modify webfront.py, change MODEL_ID to yours.

open a web browser and input URL:http://127.0.0.1:5000/predict.

*It requires [ffmpeg](https://ffmpeg.org/) for audio file format convertion.

** Select Checkpoint for Evaluation modify webfront.py, change MODEL_ID to yours.

See Run python webfront.py

• Choose an ID of checkpoint by yourself from models/save_<ID>.h5.
• Run python ./create_pb.py <ID>. This will create file models/model.pb
• Place your model.pb file where you want to deploy. Typically, see Android mobile example: androidAudioRecg