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Setup environment

With conda env
  • To create conda environment:
conda create --name yamnet-env python=3.8
conda activate yamnet-env
  • To install some libs for audio task:
sudo apt-get update
sudo apt-get install libsndfile1 -y
sudo apt-get install ffmpeg -y
  • To install requirements:
pip install -r requirements.txt
With Docker container
  • Updated soon

Dataset

Prepare data
  • To download esc50 dataset:
python3 main.py --scenario  download_data --dataset_name esc-50

  • Flag:

    • --dataset_name: select dataset (esc-50, speech-commands, background)

  • To unzip dataset:

unzip ./dataset-esc50/esc-50.zip
  • To create category dataset:
python3 main.py --scenario  create_category_dataset
  • To create splited dataset:
python3 main.py --scenario  split_dataset --length_audio 2 --step 0.15
  • Flag:
    • --length_audio: length of the sliding window
    • --step: length of the hope of the sliding window
Split dataset
  • To train test split:
python3 main.py --scenario train_test_split --test_data_ratio 0.2
  • Flags:
    • --test_data_ratio: ratio of test and train data

Train and Export

  • To run training:
python3 main.py --scenario train \
--model Yamnet \
--use_custom_dataset \
--train_data_ratio 0.8 \
--epochs 100 \
--batch_size 16 \
--tflite_file_name esc_2s_015_new.tflite \
--save_path ./model_2s_015_new

  • Flag:

    • --model: select model (Yamnet, BrowserFft)
    • --train_data_ratio: ratio of train data and dev data
    • --epochs: num epochs
    • --batch_size: num batch size
    • --tflite_file_name: the tflite model name
    • --save_path: path to directory contains model

  • To check tflite_model_info:

python3 main.py --scenario tflite_model_info

Notes:

  • To modify parameters, go to scenario/args.py or through command.

Build APK file:

Install Android Studio
  • To install, read this tutorial https://linuxhint.com/install-android-studio-linux-mint-and-ubuntu/
  • Or run the following commands:
sudo apt update
sudo apt install openjdk-11-jdk
sudo snap install android-studio –classic
Run default audio_classification app
  • To get the repo https://github.com/tensorflow/examples/tree/master/lite/examples/audio_classification/android:
git clone https://github.com/tensorflow/examples.git
cp ./examples/lites/examples/audio_classification ./
cd audio_classification
  • Start Android Studio, open the project located in audio_classification/android, run app with default model:
- Select target device menu.
- Click `Run`.
Copy your model to assets
  • To run with yourself model, copy path/to/esc_model.tflite to the android app:
cp path/to/esc_model.tflite  audio_classification/android/app/src/main/assets/
Modify params on Android Studio

Go to /android/app/src/main/java/org/tensorflow/lite/examples/audio/AudioClassificationHelper.kt.

  • To change model name, at line 136:
const val YAMNET_MODEL = "path/to/esc_model.tflite"
  • To change length recordings, (change 1000ms->2000ms), at line 105:
val lengthInMilliSeconds = ((classifier.requiredInputBufferSize * 1.0f) /
                classifier.requiredTensorAudioFormat.sampleRate) * 2000
  • To get the result of custom model, change output index from 0->1 (0: result from original yamnet, 1: result from custom yamnet), at line 122:
listener.onResult(output[1].categories, inferenceTime)
Build APK file
  • Click Run to build app. In the toolbar, to build APK file, click `Build>Build Bunder(s)/APK(s)>Build APK(s)
  • Get the APK file at /audio_classification/android/app/build/intermediates/apk/debug
  • Copy the APK file to the android phone and install.