Insect Sound Classification

Requirements

• anaconda3-2019.03 (python 3.7.3)
• pytorch==1.1.0
• configargparse==0.14.0
• pydub==0.23.1
• flask==1.0.2
• sox
• ffmpeg
• lame

Data Organization

An example workspace is in demo directory. It contains the training set (all_data) and the learned model (after training).

demo
└── all_data
    ├── 20190722
    │   ├── label.txt
    │   └── sound
    │       ├── 13_黄脸油葫芦.mp3
    │       ├── 测试01_迷卡斗蟋.m4a
    │       ├── 测试02_迷卡斗蟋.m4a
    │       ├── 测试03_迷卡斗蟋.m4a
    │       ├── 测试04_黄脸油葫芦.m4a
    │       ├── 测试05_黄脸油葫芦.m4a
    │       ├── 测试06_黄脸油葫芦.m4a
    │       └── 测试07_黄脸油葫芦.m4a
    └── 20190724
        ├── label.txt
        └── sound
            ├── ...

For serving the incremental training, the all_data directory contains subdirectories (e.g., 20190722, 20190724), each subdirectory is a batch of data (e.g., collected every one or two days).

• sound contains sound files (with format wav/mp3/m4a)
• label.txt contains annotations in column format
  • Each row is annotation which has two columns separated by a \t.
  • The first column is the name of an audio file in sound folder.
  • The second column is the label of the audio file.

Training

To train a model,

./train demo v1

where argument demo is the workspace above and v1 is the prefix of the output model (e.g., version numbers) If it sucesses, we will see the following information and the training process is started.

Building adam optimizer...
Building training data iterator...
train data size: 98
Building validation data iterator...
valid data size: 38
Starting training on CPU, could be very slow
Epoch: 0 Step: 1 Loss: 10.077764 Instances per Sec: 1.220545
Evaluating validation set...
  * Epoch: 0 Step: 1 P@1  : 0.500000 Instances per Sec: 143.867318
  * Epoch: 0 Step: 1 P@10 : 1.000000 Instances per Sec: 143.867318
  * Epoch: 0 Step: 1 P@50 : 1.000000 Instances per Sec: 143.867318
  * Epoch: 0 Step: 1 P@100: 1.000000 Instances per Sec: 143.867318
  * Epoch: 0 MAP  : 0.750000 Instances per Sec: 143.867318
  * Achieving best score on validation set...
Saving checkpoint ../demo/v1-model_best.pt
Epoch: 1 Step: 1 Loss: 0.683140 Instances per Sec: 1.680890
Evaluating validation set...
  * Epoch: 1 Step: 1 P@1  : 0.500000 Instances per Sec: 144.663466
  * Epoch: 1 Step: 1 P@10 : 1.000000 Instances per Sec: 144.663466
  * Epoch: 1 Step: 1 P@50 : 1.000000 Instances per Sec: 144.663466
  * Epoch: 1 Step: 1 P@100: 1.000000 Instances per Sec: 144.663466
  * Epoch: 1 MAP  : 0.750000 Instances per Sec: 144.663466

When it finishes, a model file v1-model_best.pt will be generated in demo.

Deployment

Starting the Server

Given a model demo/v1-model_best.pt, we can use deploy/run_server.py to deploy a REST API.

cd deploy
python run_server.py -model ../demo/v1-model_best.pt -version v1

It will start serving at http://127.0.0.1:5000/predict/v1

Submitting Requests

python simple_request.py -audio_path 测试04_黄脸油葫芦.m4a -version v1

It will output

1. 黄脸油葫芦: 0.6078
2. 迷卡斗蟋: 0.3922

Full Options

Full options of run_server.py

% python3 run_server.py -h
usage: run_server.py [-h] --model MODEL [--gpu GPU] [--host HOST]
                     [--port PORT] [--version VERSION]

run_server.py

optional arguments:
  -h, --help            show this help message and exit

Model:
  --model MODEL, -model MODEL
                        Path to model .pt file(s).
  --gpu GPU, -gpu GPU   Device to run on

SERVER:
  --host HOST, -host HOST
                        The host url
  --port PORT, -port PORT
                        The port
  --version VERSION, -version VERSION
                        The version of model

Full options of simple_request.py

% python3 simple_request.py -h
usage: simple_request.py [-h] --audio_path AUDIO_PATH
                         [--rest_api_url REST_API_URL] [--top_num TOP_NUM]
                         [--version VERSION]

simple_request.py

optional arguments:
  -h, --help            show this help message and exit

Data:
  --audio_path AUDIO_PATH, -audio_path AUDIO_PATH
                        Path to audio file

Server:
  --rest_api_url REST_API_URL, -rest_api_url REST_API_URL
                        Initialize the PyTorch REST API endpoint URL
  --top_num TOP_NUM, -top_num TOP_NUM
                        Return top predictions
  --version VERSION, -version VERSION
                        The version of model