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Overview

This repo contains code for two main parts:

  • TraGen - the part of the solution focused on generating transcript from an audio recording of interview/focus group.
    • essentially everything except for travis folder is falling under TraGen
  • TraVis - the part of the solution focused on visualising the output of TraGen (the transcript).

TraGen

TraGen - Transcript Generator, is written in Python.

It is not only about generating transcripts, but also about evaluating the segmentation, diarization and transcription.

The main idea is that we have implementations of:

  • segmentation
  • diarization
  • transcription

All of these can then be chained to form a pipeline, e.g. the only working implemented pipeline is "ina-dummy-aws", meaning

  • INA speech segmenting
  • dummy diarization (diarizing only based on sex information from INA)
  • AWS transcribing

The file ina_dummy_aws.py can then be simply run (modifying the bit of code at the end, which determines which audio and how much of it will be transcribed). In the process of running the code:

  • a new folder in ../data/pipelines would be created, initially with the raw recording audio file (raw.mp3)
  • subsequently, audio segments would appear after the segmentation stage, diarization info would be added, transcription and eventually a final.json file would be created, containing all the information from the process.

The raw.mp3 and final.json are then inputs for TraVis app.

Finally, Huw Fulcher started some work on evaluating the current transcription based on the ICSI dataset (containing recordings and manually created precise transcripts). See the evaluation folder.

Installation

The basic structure of this project is:

  • root_project_folder
    • src - source files (this is version controlled - the rest isn't)
      • notebooks - jupyter notebooks
      • requirements.txt, .gitignore...
      • other files...
    • data - recordings and other data..
    • other files...

So start off by creating the root folder and the src & data folders. Then

  • cd to src dir
  • install pipenv if not installed (pip install pipenv)
    • we won't use pipenv to install packages (takes freaking long time), just to easily create/activate virtual environment
  • run pipenv --python 3.6.8 shell - will create/activate a new virtual env
    • we use this version - 3.6.8. This is cause
      • 3.6 is what is used on ONS machines
      • some packages rely on Tensorflow which had troubles running with 3.7
    • now install pip install -r requirements.txt
    • ignore the Pipfile created by pipenv - pipenv is too slow for now
    • manually install pyAudioAnalysis as per https://github.com/tyiannak/pyAudioAnalysis#installation
  • install ffmpeg https://tecadmin.net/install-ffmpeg-on-linux/
  • the project also needs to be setup to be able to connect to AWS. After installing boto3 package, one should be able to do this from the command line. (same for GCP cloud, although that one is not used by this solution at the moment)

Alternative to pipenv is virtualenv:

  • virtualenv --system-site-packages -p python3 ../venv - create a new virtual env
  • source ../venv/bin/activate in the src dir to activate the venv

Data

Download the "recordings" folder from https://drive.google.com/open?id=18KeJYoHWOeWEMKHhZoLl9hBT8ptaBml-

Then unzip and move the recordings folder to the data folder. I.e.:

  • root_project_folder
    • data
      • recordings
        • harvard
        • bbc_interview
        • ...

Main audio libraries used - overview

  • librosa
    • package for audio analysis, feature extraction etc.
    • "provides the building blocks necessary to create music information retrieval systems".
    • link
  • pydub
    • audio manipulation. E.g audio[30000:59000].export(...) and things like that
    • link
  • speechrecognition
    • package for speech reco. Is more of a unified wrapper for several APIs
    • not sure we will use much
    • link
  • inaSpeechSegmenter
    • Neural network based segmentation tool. Works nicely!
    • Needs Tensorflow and ffmpeg
    • link)
  • pyAudioAnalysis
    • another one for feature extraction, segmentation, classification...
    • this one has diarization too. Does not work too well, but the methodology seems sound.
  • google-cloud-speech
    • the python API for GCP speech2text
    • currently, I use a test tier on GCP where I put my own debit card which has a free credit for a year.

TraVis

The TraVis - Transcript Visualiser - is a JavaScript web app based on React.

Using React makes for a nice file structure that easily corresponds to the visuals on the webpage

It is recommended to use VS code to develop this app.

The raw.mp3 and final.json (the JSON file made using TraGen from the raw.mp3) are the inputs for TraVis app.

Installation & run

The dependencies are managed via NPM. Thus going into the travis dir, one should:

  • install the packages with npm install
  • run npm run build to re-build the dist folder
  • run with bash run-dev.sh and access the app in the browser

The solution uses Webpack to build the final bundle.js, compile SCSS stylesheets etc.