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Shield: CC BY-NC-SA 4.0

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

Simple think-aloud data processing with Amazon Web Services

An almost automated workflow to process think-aloud data powered by Amazon Web Services (S3, Lambda, Transcribe, Lex).

The simple workflow: transcription -> segmentation -> encoding

workflow

This is a part of MSc. Thesis A Solution to Analyze Mobile Eye-tracking Data for GI User Research by Yuhao (Markie) Jiang, ITC-University of Twente, Enschede, The Netherlands, June 2020.

Requirements

  • AWS account
  • Configured AWS CLI
  • Boto3

Languages

AWS Transcribe supports multiple languages, but Lex only supports English. Thus this whole workflow only works for English (US), while the transcription part can be used for other languages supported by Amazon.

Prepare the data

All inputs files should be stored in an S3 bucket with the following structure:

S3 Bucket 
├── intents  
│   └── code_name.txt  
│   └── code_name.txt    
│   └── ...  
├── custom_vocabulary.txt  
├── think_aloud_audio.mp3  
├── another_audio.mp3  
├── ...

custom_vocabulary.txt should be in the Table Format

Audio files can be: .mp3, .mp4, .wav, .flac

intents contains the .txt files

Note: Intents don't have to be in the same buckets as the audio and vocabulary files, but they should be all stored under the sub-folder of intents
Intents should follow the naming of code_name.txt, where code is a simple code you want to use for your intent, and name is the name of the intent. For example, I_mapinteraction.txt represents an intent called "mapinteraction" with the code I.
The intent file should contain sample utterances separated by line break. For example, the I_mapinteraction.txt has the following content:

zoom in.
I'll zoom out a little bit more.
I'm click the button to...
...

Some AWS access configuration

You will need your AWS region name to run transcribe.py
You will also need a IAM role that has the following policies attached:

  • AWSLambdaExecute
  • AWSLambdaBasicExecutionRole
  • AmazonLexRunBotsOnly

This can be done via AWS CLI or from the IAM console.

Note: transcribe.py will create a temporary data-access-role that allow Transcribe to read and write into your S3 bucket when jobs are queued. This role is attached with these policies provided by Amazon. This role will be deleted as the script finishes.

Run

Make sure the lambda_function folder is in the same directory as the scripts before running the scripts!

transcribe.py

Arguments:

  • InputBucket (in): name of the bucket where you store your custom vocabulary and audio files
  • OutputBucket (out): name of the bucket to store transcripts, can be the same with InputBucket
  • Region (rg): the region of your CLI, has to be the same with the region of the buckets!
  • Role (rl): the IAM role that has the accesses mentioned above.

For example: $ python transcribe.py bucket1 bucket2 us-west-2, FullAccessRole

build_bot_s3.py

Arguments:

  • Bucket (b): name of the bucket where the intent folder is stored.
  • BotName (n): name of the bot

For example: $ python build_bot_s3.py mybucket mybot

encode.py

Arguments:

  • InputBucket (in): name of the bucket that stores the transcript segments produced by transcribe.py
  • Role (rl): the same role name used for transcribe.py
  • BotName (bn): name of the Lex bot
  • BotAlias (ba): alias name of the Lex bot. If the bot is created with build_bot_s3.py, then the alias is [botname]_alias

For example: $ python encode.py mybucket FullAccessRole mybot mybot_alias

Outputs and intermediate results

transcribe.py will write transcripts in .json as the AWS default format, as well as segmented transcripts (by sentence) in .tsv to your output S3 bucket. Segmented transcripts follow the format of:

start_time end_time content
2.11 7.17 and therefore I also need to old map

build_bot_s3.py will create intents and build a Lex chatbot. You can test, modify and rebuild the bot at the Lex Console.

encode.py will write coded transcript sentences to your S3 bucket as tsv files. They follow the format of:

start_time end_time content BotCode
2.11 7.17 and therefore I also need to old map A

Human intervention

Amazon is quite smart but not perfect. Thus human intervention is recommended as intermediate results are produced.

Modifying the transcripts: you can modify the tsv transcript segments by downloading and editing it in your text/sheet editor, or you can use this tool to modify the json transcript. Once you upload the modified json to the same bucket, an new segment file will be automatically generated.

Testing the bot and modifying the intents: this is easiest done through the Lex console. Do not run the build bot script again after you have created intents or bots with the same name.

Sources and AWS docs