openmmla-audio 0.1.4.post1

Audio module for the OpenMMLA platform

OpenMMLA Audio

Audio module of the mBox multimodal learning analytic system. For more details, please refer to mBox System Design.

Uber Server Setup

Before setting up the audio base, you need to set up a server hosting the InfluxDB, Redis, and Mosquitto services. Please refer to mbox-uber module.

Audio Base & Server Setup

Downloading and Setting up the mbox-audio module is accomplished in three steps:
(1) Clone the repository from GitHub to your local home directory.
(2) Install required system dependencies.
(3) Install openmmla-audio.

1. Clone the repository from GitHub

   git clone https://github.com/ucph-ccs/mbox-audio.git
   

2. Install the required dependencies

   • Mac

     # Install ffmpeg, portaudio-19.7.0, mecab-0.996(required for sacrebleu for NLP collection), llvm-16.0.6
     brew install ffmpeg
     brew install portaudio
     brew install mecab
     brew install llvm
     
     # Export llvm to your PATH, run:
     echo 'export PATH="/opt/homebrew/opt/llvm/bin:$PATH"' >> ~/.zshrc
     echo 'export LDFLAGS="-L/opt/homebrew/opt/llvm/lib"' >> ~/.zshrc
     echo 'export CPPFLAGS="-I/opt/homebrew/opt/llvm/include"' >> ~/.zshrc
     source ~/.zshrc
     

   • Ubuntu 24.04

     sudo apt update && sudo apt upgrade
     sudo apt install build-essential
     sudo apt install git
     sudo apt install ffmpeg
     sudo apt install python3-pyaudio
     sudo apt update && sudo apt install -y libsndfile1
     
     # Install portaudio
     sudo apt install libasound-dev
     # Download the portaudio archive from: http://files.portaudio.com/download.html
     wget https://files.portaudio.com/archives/pa_stable_v190700_20210406.tgz
     # Unzip the archive
     tar -zxvf pa_stable_v190700_20210406.tgz
     # Enter the directory and compile
     cd portaudio
     ./configure && make
     sudo make install
     

   • Raspberry Pi Bullseye or later

     # Install pyaudio
     sudo apt-get install portaudio19-dev
     

3. Install openmmla-audio with conda environment

   • Conda

     # For Raspberry Pi
     wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
     bash Miniforge3-$(uname)-$(uname -m).sh
     
     # For Mac and Linux
     wget "https://repo.anaconda.com/miniconda/Miniconda3-latest-$(uname)-$(uname -m).sh"
     bash Miniconda3-latest-$(uname)-$(uname -m).sh
     

   • Audio Base

     conda create -c conda-forge -n audio-base python==3.10.12 -y
     conda activate audio-base
     pip install openmmla-audio
     

   • Audio Server

     conda create -c conda-forge -n audio-server python==3.10.12 -y
     conda activate audio-server
     pip install openmmla-audio[server] # for linux and raspberry pi
     pip install 'openmmla-audio[server]' # for mac
     

Usage

After successfully installing all required libraries, you can run the audio module on terminal.

1. Run real-time audio analysis system

   • Run audio server and audio bases in distributed mode

   # Run server scripts on your application servers supporting audio bases, specify your audio server cluster on 
   # your uber server by configuring the mbox-uber/conf/nginx.sh file and specify your extra audio upstream services in 
   # mbox-uber/conf/nginx.conf file.
   
   # e.g. our default setting runs audio services on 3 servers, which are server-01.local, server-02.local and 
   # server-03.local. Inside the nginx.conf, we specify 5 audio services related to those three server, which are
   # transcribe, separate, infer, enhance and vad services.
   sudo apt install tmux -y
   ./server.sh
   
   # Run audio bases 
   # :param -b the number of audio base needed to run, default to 3. 
   # :param -s the number of audio base synchronizer need to run, default to 1.
   # :param -l whether to run the audio bases standalone or with application servers, default to false. 
   # :param -p whether to do the speech separation when recognizing, default to false. 
   ./run.sh
   
   # control script to start/stop the session playing
   ./control.sh
   

2. Run the post-time audio analyzer

   1. Create a speaker corpus folder under /audio_db/post-time/ folder, the folder name should be aligned with the name of the audio file to be processed [audio_file_name.wav] without the extension, e.g. /audio_db/post-time/[audio_file_name]/.
   2. Copy the speaker audio files to the speaker corpus folder, the audio files should be named as [speaker_name].wav.
   3. Run audio_post_analyzer.py

      cd examples/
         
      # process a single audio file, supported audio file format: wav, m4a, mp3
      python3 run_audio_post_analyzer.py -f [audio_file_name.wav]
         
      # process all audio files under the ***/audio/post-time/origin/*** folder
      python3 run_audio_post_analyzer.py
      

Visualization

After running, the logs and visualizations are stored in the /logs/ and /visualizations/ folders.

FAQ

Citation

If you use this code in your research, please cite the following paper:

@inproceedings{inproceedings,
author = {Li, Zaibei and Jensen, Martin and Nolte, Alexander and Spikol, Daniel},
year = {2024},
month = {03},
pages = {785-791},
title = {Field report for Platform mBox: Designing an Open MMLA Platform},
doi = {10.1145/3636555.3636872}
}

References

• NeMo
• Silero VAD
• Denoiser
• MossFormer
• Whisper

License

This project is licensed under the MIT License - see the LICENSE file for details.