This example illustrates how to solve typical side-by-side task with Crowdom.

In our example, we ask workers which transcript is more suitable for the given audio.

You may want to first study image classification example because it contains more detailed comments of overall process.

Setup¶

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%pip install crowdom
%pip install pyyaml
%pip install markdown2

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from datetime import timedelta
import logging.config
import pandas as pd

import markdown2
import yaml

from crowdom import base, datasource, client, objects, pricing, params as labeling_params

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with open('logging.yaml') as f:
    logging.config.dictConfig(yaml.full_load(f.read()))

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from IPython.display import clear_output, display

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toloka_client = client.create_toloka_client(token=os.getenv('TOLOKA_TOKEN') or input('Enter your token: '))
clear_output()

Task definition¶

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lang = 'EN'

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instruction = {}
for worker_lang in ['EN', 'RU']:
    with open(f'instruction_{worker_lang}.md') as f:
        instruction[worker_lang] = markdown2.markdown(f.read())

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task_spec = base.TaskSpec(
    id='audio-transcript-sbs',
    function=base.SbSFunction(inputs=(objects.Text,), hints=(objects.Audio,)),
    name={
        'EN': 'Audio transcripts comparison', 
        'RU': 'Сравнение расшифровок аудиозаписей',
    },
    description={
        'EN': 'From the two transcripts, choose the more suitable for given audio recording',
        'RU': 'Из двух расшифровок выберите более подходящую аудиозаписи',
    },
    instruction=instruction,
)

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task_spec_en = client.PreparedTaskSpec(task_spec, lang)

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client.define_task(task_spec_en, toloka_client)

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task_duration_hint = timedelta(seconds=10)  # audios are about 1-5 seconds each

Data preparation¶

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input_objects = datasource.read_tasks('tasks.json', task_spec_en.task_mapping)
control_objects = datasource.read_tasks('control_tasks.json', task_spec_en.task_mapping, has_solutions=True)

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client.TaskPreview(input_objects[0], task_spec=task_spec_en).display_link()

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task preview

Launch configuration¶

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params_form = labeling_params.get_interface(task_spec_en, task_duration_hint, toloka_client)

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params = params_form.get_params()

Launch¶

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artifacts = client.launch_sbs(
    task_spec_en,
    params,
    input_objects,
    control_objects,
    toloka_client,
    interactive=True,
)

clear formula, which does not account edge cases like min commission and incomplete assignments

$\displaystyle TotalPrice_{clear} = TaskCount * PricePerTask_\$ * Overlap * (1 + TolokaCommission) = 5 * 0.0022\$ * 1 * 1.3 = 0.01\$.$

more precise formula, which accounts more edge cases

$\displaystyle TotalPrice_{precise} = AssignmentCount * PricePerAssignment_\$ * Overlap = \left \lceil \frac {TaskCount} {TasksOnAssignment} \right \rceil * (PricePerAssignment_\$ + max(PricePerAssignment_\$ * TolokaCommission, MinTolokaCommission_\$) * Overlap = \lceil 5 / 4 \rceil * (0.009\$ + max(0.009\$ * 0.3, 0.001\$) * 1 = 2 * 0.012 * 1 = 0.02\$.$

Output()

2022-10-31 12:44:52,918 - crowdom.client.launch:_launch:193 - INFO: - classification has started

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results = artifacts.results

Results study¶

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with pd.option_context('max_colwidth', 100):
    display(results.predict())

	audio_hint	text_a	text_b	result	confidence	overlap
0	https://tlk.s3.yandex.net/ext_dataset/noisy_speech/noisy_tested_wav/p257_026.wav	is was accurate	is this accurate	b	1.0	1
1	https://tlk.s3.yandex.net/ext_dataset/noisy_speech/noisy_tested_wav/p232_300.wav	what has altered	has altered	a	1.0	1
2	https://tlk.s3.yandex.net/ext_dataset/noisy_speech/noisy_tested_wav/p257_120.wav	does this	does this mean	b	1.0	1
3	https://tlk.s3.yandex.net/ext_dataset/noisy_speech/noisy_tested_wav/p232_044.wav	we will push	we will pull	a	1.0	1
4	https://tlk.s3.yandex.net/ext_dataset/noisy_speech/noisy_tested_wav/p232_405.wav	we have not received a letter from the danish	we have not yet received a letter from the irish	b	1.0	1