Zooniverse campaign

Summary
- Workflow
- Repository structure
Preparing samples
Retrieving classifications
- Matching classifications back to the metadata

Summary

The present repository showcases the organization of a Zooniverse campaign using ChildProject and DataLad.

This campaign requires citizens to listen to 500 ms audio clips and to perform the following tasks:

Decide whether they hear speech from either a Baby, a Child, an Adolescent, an Adult, or no speech.
Guess the gender of the speaker in case of non-baby speech.
Classify the type of sound among four categories (Canonical, Non-Canonical, Laughing, Crying).

Workflow

We used DataLad to manage this campaign.
The primary dataset (containing the audio and the metadata) was included in this repository as a subdataset. It was structured according to ChildProject's standards.
ChildProject was used to generate the samples, to upload the audio chunks to zooniverse, and to retrieve the classifications.

This repository contains all the scripts that we used to implement this workflow. You are welcome to re-use this code and adapt it to your needs.

Repository structure

annotations contains annotations built from the classifications retrieved from Zooniverse.
classifications contains the classifications retrieved from Zooniverse.
samples contains the samples that were selected as well as the chunks generated from them.
vandam-data is a subdataset containing VanDam Daylong corpus, structed according to ChildProject's standards.

Preparing samples

Sampling

Sampling consists in selecting which portions of the recordings should be annotated by humans. It can be done through using the samplers provided in the ChildProject package.

Here, we sample 50 vocalizations per recording among all those detected and attributed to the key child (CHI) or a female adult (FEM) by the Voice Type Classifier:

# download VTC annotations
datalad get vandam-data/annotations/vtc/converted

# sample random CHI and FEM vocalizations from these annotations
child-project sampler vandam-data samples/chi_fem/ random-vocalizations \
  --annotation-set vtc \
  --target-speaker-type CHI FEM \
  --sample-size 50 \
  --by recording_filename

The sampler produces a CSV dataframe as samples/chi_fem/segments_YYYYMMDD_HHMMSS.csv, e.g. samples/chi_fem/segments_20210716_184443.csv.

Preparing the chunks for Zooniverse

After the samples have been generated, they have to be extracted from the audio and uploaded to Zooniverse, which can be done with ChildProject's extract-chunks function. However, these samples may contain private information about the participants. Therefore, they cannot be shared as is on a public crowd-sourcing platform. We therefore configure extract-chunks to split these samples into 500ms chunks, which will be classified in random order, thus preventing the recovery of sensitive information by the contributors:

datalad get vandam-data/recordings/converted/standard

child-project zooniverse extract-chunks vandam-data \
 --keyword chi_fem \
 --chunks-length 500 \
 --segments samples/chi_fem/segments_20210716_184443.csv \
 --destination samples/chi_fem/chunks \
 --profile standard

This will extract the audio chunks into samples/chi_fem/chunks/chunks/ and write a metadata file into samples/chi_fem/chunks (in our case, as samples/chi_fem/chunks/chunks_20210716_191944.csv).

See ChildProject's documentation for more information about the Zooniverse pipeline.

Uploading audio chunks to Zooniverse

Once the chunks have been extracted, the next step is to upload them to Zooniverse. Note that due to quotas, it is recommended to upload only a few at time (e.g. 1000 per day).

You will need to provide the numerical id of your Zooniverse project; you will also need to set Zooniverse credentials as environment variables:

export ZOONIVERSE_LOGIN=""
export ZOONIVERSE_PWD=""
export PROJECT_ID=14957

child-project zooniverse upload-chunks \
 --chunks samples/chi_fem/chunks/chunks_20210716_191944.csv \
 --project-id $PROJECT_ID \
 --set-name vandam_chi_fem \
 --amount 1000

This will display a message for each chunk:

...
uploading chunk BN32_010007.mp3 (25153080,25153580)
uploading chunk BN32_010007.mp3 (45016146,45016646)
uploading chunk BN32_010007.mp3 (46794141,46794641)
uploading chunk BN32_010007.mp3 (14107752,14108252)
uploading chunk BN32_010007.mp3 (35709983,35710483)
uploading chunk BN32_010007.mp3 (45433933,45434433)
uploading chunk BN32_010007.mp3 (35711483,35711983)
uploading chunk BN32_010007.mp3 (38737938,38738438)
uploading chunk BN32_010007.mp3 (24586156,24586656)
uploading chunk BN32_010007.mp3 (15556956,15557456)
uploading chunk BN32_010007.mp3 (28439601,28440101)
uploading chunk BN32_010007.mp3 (27317629,27318129)
uploading chunk BN32_010007.mp3 (38391252,38391752)
...

The subject set and its subjects (i.e. the chunks) now appears in the project:

Retrieving classifications

The classifications performed by citizens on Zooniverse for this project can be retrieved with ChildProject's retrieve-classifications command:

child-project zooniverse retrieve-classifications \
  --destination classifications/classifications.csv \
  --project-id $PROJECT_ID

$ head classifications/classifications.csv 
id,user_id,subject_id,task_id,answer_id,workflow_id,answer
335442480,2202359,61513545,T1,4,17576,Junk
335442502,2202359,61513542,T1,4,17576,Junk
335442523,2202359,61513540,T1,4,17576,Junk
335442614,2202359,61513546,T1,4,17576,Junk
335442630,2202359,61513544,T1,4,17576,Junk
335443247,2221541,61513546,T1,4,17576,Junk
335443443,2202359,61513544,T1,4,17576,Junk
335450309,1078249,61513540,T1,4,17576,Junk
335457868,2202359,61514279,T1,4,17576,Junk

Two special notes about the output of retrieve-classifications:

It contains no information related to the metadata of the input dataset, which is the desired behavior (Zooniverse should not store any data that might compromise the privacy of the participants)
It contains all the classifications for this project, including those for data outside of the current campaign.

Matching classifications back to the metadata

Classifications can be matched to the original metadata (the recording from which the clips were extracted, the timestamps of the clips, etc.) with classifications/match.py:

$ python classifications/match.py --help
usage: match.py [-h] --chunks CHUNKS [CHUNKS ...] classifications output

match classifications with extracted chunks

positional arguments:
  classifications       classifications file
  output                output

optional arguments:
  -h, --help            show this help message and exit
  --chunks CHUNKS [CHUNKS ...]
                        list of chunks

python classifications/match.py \
 classifications/classifications.csv \ 
 classifications/matched.csv \ 
 --chunks samples/chi_fem/chunks/chunks*.csv

id	user_id	subject_id	task_id	answer_id	workflow_id	answer	index	recording_filename	onset	offset	segment_onset	segment_offset	wav	mp3	date_extracted	uploaded	project_id	subject_set	zooniverse_id	keyword
346474371	2202359.0	64210633	T1	4	17576	Junk	38	BN32_010007.mp3	24587156	24587656	24584902	24588410	BN32_010007_24587156_24587656.wav	BN32_010007_24587156_24587656.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210633	chi_fem
346474611	2202359.0	64210607	T1	4	17576	Junk	30	BN32_010007.mp3	14108752	14109252	14107993	14109011	BN32_010007_14108752_14109252.wav	BN32_010007_14108752_14109252.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210607	chi_fem
346474648	2202359.0	64210647	T1	4	17576	Junk	43	BN32_010007.mp3	4713603	4714103	4713263	4714944	BN32_010007_4713603_4714103.wav	BN32_010007_4713603_4714103.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210647	chi_fem
346474683	2202359.0	64210890	T1	4	17576	Junk	125	BN32_010007.mp3	4713103	4713603	4713263	4714944	BN32_010007_4713103_4713603.wav	BN32_010007_4713103_4713603.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210890	chi_fem
346474902	2202359.0	64210872	T1	4	17576	Junk	119	BN32_010007.mp3	39329854	39330354	39328511	39331697	BN32_010007_39329854_39330354.wav	BN32_010007_39329854_39330354.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210872	chi_fem
346475101	2202359.0	64210613	T1	4	17576	Junk	32	BN32_010007.mp3	4149238	4149738	4148967	4149510	BN32_010007_4149238_4149738.wav	BN32_010007_4149238_4149738.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210613	chi_fem
346475237	2202359.0	64210774	T1	4	17576	Junk	86	BN32_010007.mp3	17157819	17158319	17157511	17158127	BN32_010007_17157819_17158319.wav	BN32_010007_17157819_17158319.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210774	chi_fem
346474475	2202359.0	64210728	T1	0	17576	Baby	70	BN32_010007.mp3	37978055	37978555	37978100	37979011	BN32_010007_37978055_37978555.wav	BN32_010007_37978055_37978555.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210728	chi_fem
346474475	2202359.0	64210728	T3	1	17576	Non-Canonical	70	BN32_010007.mp3	37978055	37978555	37978100	37979011	BN32_010007_37978055_37978555.wav	BN32_010007_37978055_37978555.mp3	2021-07-16 19:19:44	True	14957	vandam_chi_fem	64210728	chi_fem

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