Home > Preparation > Preparation for Absolute Category Rating (ACR)

Preparation of ACR test

The following steps should be performed to prepare the ACR test setup. For all the resource files (steps 1-4) an example is provided in src/test_inputs using the ITU-T Sup23 Dataset.

Note: make sure to first perform steps listed in the general preparation process.

1. Upload your speech clips in a cloud server and create rating_clips.csv file which contains all URLs in a column named rating_clips (see rating_clips.csv as an example).

   Note about file names:

   • Later in the analysis, the clip's file name will be used as a unique key and appears in the results.
   • In case you have 'conditions' which are represented with more than one clip, you may consider using the condition's name in the clip's file name e.g. xxx_c01_xxxx.wav. When you provide the corresponding pattern, the analysis script will create aggregated results over conditions as well. The name pattern can also be used for creating clip sets using balanced_block design.

2. Upload your training clips in a cloud server and create training_clips.csv file which contains all URLs in a column named training_clips (see training_clips.csv as an example).

   Hint: Training clips are used for anchoring participants' perception, and should represent the entire dataset. They should approximately cover the range from worst to best quality to be expected in the test. It may contain about 5 clips.

3. Upload your gold standard clips in a cloud server and create gold_clips.csv file which contains all URLs in a column named gold_clips and expected answer to each clip in a column named gold_clips_ans (see gold_clips.csv as an example).

   Hint: Gold standard clips are used as a hidden quality control item in each session. It is expected that their answers are so obvious for all participants that they all give the gold_clips_ans rating (+/- 1 deviation is accepted). It is recommended to use clips with excellent (answer 5) or very bad (answer 1) quality. You can use create_gold_clips.py to generate gold clips automatically. See Gold Standard Clips for details.

4. Create trapping stimuli set for your dataset.

   1. Configure the create_trapping_stimuli.py in your config file. See configuration of create_trapping_stimuli script for more information.

   2. Delete all files from trapping_clips_assets\source directory

   cd "src\trapping_clips_assets\source"
   del *.* 
   

   3. Add some clips from your dataset to trapping_clips_assets\source directory. Select clips in a way that

      1. Covers a fair distribution of speakers (best couple of clips per each speaker)
      2. Covers the entire range of quality (some good, fair, and bad ones)

   4. Run create_trapping_stimuli.py

   cd src
   python create_trapping_stimuli.py ^
       --cfg your_config_file.cfg
   

   5. Trapping clips are stored in trapping_clips_assets\output directory. List of clips and their correct answer can be found in trapping_clips_assets\output\output_report.csv. You can replace file names (appears in column named trapping_clips) with the URLs pointing to those files to create the trapping_clips.csv file (see below).

5. Upload your trapping clips in a cloud server and create trapping_clips.csv file which contains all URLs in a column named trapping_clips and expected answer to each clip in a column named trapping_ans (see trapping_clips.csv as an example).

6. Create your custom project by running the master script:

   1. Configure the project in your config file. See master script configuration for more information.

   2. Run master_script.py with all above-mentioned resources as input

      cd src
      python master_script.py ^
          --project YOUR_PROJECT_NAME ^
          --method acr ^
          --cfg your_configuration_file.cfg ^
          --clips rating_clips.csv ^
          --training_clips training_clips.csv ^
          --gold_clips gold_clips.csv ^
          --trapping_clips trapping_clips.csv 
      

      Optionally:

      • Add --check_urls to validate that all links in the CSV files are accessible before creating the project.
      • Add --create_local_test to generate a local preview HTML file for testing. See preview_html for details.

      Note: file paths are expected to be relative to the current working directory.

   3. Double-check the outcome of the script. A folder should be created with YOUR_PROJECT_NAME in current working directory which contains:

   • YOUR_PROJECT_NAME_acr.html: Customized HIT app to be used in Amazon Mechanical Turk (AMT).
   • YOUR_PROJECT_NAME_publish_batch.csv: List of dynamic content to be used during publishing batch in AMT.
   • YOUR_PROJECT_NAME_acr_result_parser.cfg: Customized configuration file to be used by result_parser.py script

Now, you are ready for running the test on Prolific or Amazon Mechanical Turk.