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Overview

To import annotations in Labelbox, you need to create an annotations payload. In this section, we provide this payload for every supported annotation type.

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Annotation payload types

Labelbox supports two formats for the annotations payload:
  • Python annotation types (recommended)
    • Provides a seamless transition between third-party platforms, machine learning pipelines, and Labelbox.
    • Allows you to build annotations locally with local file paths, numpy arrays, or URLs
    • Easily convert Python Annotation Type format to NDJSON format to quickly import annotations to Labelbox
    • Supports one-level nested classification (radio, checklist, or free-form text) under a tool or classification annotation.
  • JSON
    • Skips formatting annotation payload in the Labelbox Python annotation type
    • Supports any levels of nested classification (radio, checklist, or free-form text) under a tool or classification annotation.

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Label import types

Labelbox additionally supports two types of label imports:
  • Model-assisted labeling (MAL)
    • This workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) as pre-labels on an asset.
  • Ground truth
    • This workflow functionality allows you to bulk import your ground truth annotations from an external or third-party labeling system into Labelbox Annotate. Using the label import API to import external data is a useful way to consolidate and migrate all annotations into Labelbox as a single source of truth.

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Supported Annotations

The following annotations are supported for an audio data row:
  • Radio
  • Checklist
  • Free-form text
  • Temporal classifications (milliseconds): text, radio, checklist

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Classifications

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Radio (single choice)

radio_annotation = lb_types.ClassificationAnnotation(
    name="radio_question",
    value=lb_types.Radio(answer =
        lb_types.ClassificationAnswer(name = "first_radio_answer")
    )
)

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Checklist (multiple choice)

checklist_annotation = lb_types.ClassificationAnnotation(
    name="checklist_question",
    value=lb_types.Checklist(answer = [
        lb_types.ClassificationAnswer(name = "first_checklist_answer"),
        lb_types.ClassificationAnswer(name = "second_checklist_answer"),
        lb_types.ClassificationAnswer(name = "third_checklist_answer")
    ])
  )

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Free-form text

text_annotation = lb_types.ClassificationAnnotation(
    name = "free_text",
    value = lb_types.Text(answer="sample text")
)

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Nested classifications

nested_radio_annotation = lb_types.ClassificationAnnotation(
  name="nested_radio_question",
  value=lb_types.Radio(
    answer=lb_types.ClassificationAnswer(
      name="first_radio_answer",
      classifications=[
        lb_types.ClassificationAnnotation(
          name="sub_radio_question",
          value=lb_types.Radio(
            answer=lb_types.ClassificationAnswer(
              name="first_sub_radio_answer"
            )
          )
        )
      ]
    )
  )
)

nested_checklist_annotation = lb_types.ClassificationAnnotation(
  name="nested_checklist_question",
  value=lb_types.Checklist(
    answer=[lb_types.ClassificationAnswer(
      name="first_checklist_answer",
      classifications=[
        lb_types.ClassificationAnnotation(
          name="sub_checklist_question",
          value=lb_types.Checklist(
            answer=[lb_types.ClassificationAnswer(
            name="first_sub_checklist_answer"
          )]
        ))
      ]
    )]
  )
)

​
Temporal classifications (milliseconds)

Use temporal classifications to attach time-based classification labels to an audio asset. Temporal ranges are represented in milliseconds from the start of the asset. When using NDJSON, temporal classifications use a unified answer: [...] list structure:
  • For temporal radio, answer is a single-item list (unlike global radio, which uses an object).
  • For temporal checklist, answer is a multi-item list.
  • For temporal text, answer is a list of { "value": <text>, "frames": [{"start": <ms>, "end": <ms>}] }.
Temporal classifications support nested hierarchies (for example: Text > Text > Text, or Radio > Radio > Radio).

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Free-form text (temporal)

temporal_text_annotation = lb_types.TemporalClassificationText(
  name="transcription",
  value=[
    (1000, 1100, "Hello"),
    (1500, 2400, "How can I help you?"),
  ],
)

​
Radio (temporal)

temporal_radio_annotation = lb_types.TemporalClassificationQuestion(
  name="speaker",
  value=[
    lb_types.TemporalClassificationAnswer(
      name="user",
      frames=[(200, 1600)],
    )
  ],
)

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Checklist (temporal)

temporal_checklist_annotation = lb_types.TemporalClassificationQuestion(
  name="audio_quality",
  value=[
    lb_types.TemporalClassificationAnswer(
      name="background_noise",
      frames=[(0, 1500), (2000, 3000)],
    ),
    lb_types.TemporalClassificationAnswer(
      name="echo",
      frames=[(2200, 2900)],
    ),
  ],
)

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Nested classifications (temporal)

nested_temporal_radio_annotation = lb_types.TemporalClassificationQuestion(
  name="speaker",
  value=[
    lb_types.TemporalClassificationAnswer(
      name="user",
      frames=[(200, 1600)],
      classifications=[
        lb_types.TemporalClassificationQuestion(
          name="tone",
          value=[
            lb_types.TemporalClassificationAnswer(
              name="professional",
              frames=[(1000, 1600)],
              classifications=[
                lb_types.TemporalClassificationQuestion(
                  name="clarity",
                  value=[
                    lb_types.TemporalClassificationAnswer(
                      name="clear",
                      frames=[(1300, 1600)],
                    )
                  ],
                )
              ],
            )
          ],
        )
      ],
    )
  ],
)

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Example: Import pre-labels or ground truths

The steps to import annotations as pre-labels (machine-assisted learning) are similar to those to import annotations as ground truth labels. However, they vary slightly, and we will describe the differences for each scenario.

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Before you start

The below imports are needed to use the code examples in this section. 
import labelbox as lb
import uuid
import labelbox.types as lb_types
Replace the value of API_KEY with a valid API key to connect to the Labelbox client. 
API_KEY = None
client = lb.Client(API_KEY)

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Step 1: Import data rows

Data rows must first be uploaded to Catalog to attach annotations. This example shows how to create a data row in Catalog by attaching it to a dataset . 
global_key = "sample-audio-1.mp3"

asset = {
  "row_data": "https://storage.googleapis.com/labelbox-datasets/audio-sample-data/sample-audio-1.mp3",
  "global_key": global_key
}

dataset = client.create_dataset(name="audio_annotation_import_demo_dataset")
task = dataset.create_data_rows([asset])

task.wait_till_done()

print("Errors:", task.errors)
print("Failed data rows: ", task.failed_data_rows)

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Step 2: Set up ontology

Your project ontology should support the tools and classifications required by your annotations. To ensure accurate schema feature mapping, the value used as the name parameter should match the value of the name field in your annotation. For example, when we created an annotation above, we provided a nameannotation_name. Now, when we set up our ontology, we must ensure that the name of our bounding box tool is also anotations_name. The same alignment must hold true for the other tools and classifications we create in our ontology. This example shows how to create an ontology containing all supported annotation types . 
ontology_builder = lb.OntologyBuilder(
  classifications=[
    lb.Classification(
      class_type=lb.Classification.Type.TEXT,
      name="text_audio"),
    lb.Classification(
      class_type=lb.Classification.Type.CHECKLIST,
      name="checklist_audio",
      options=[
        lb.Option(value="first_checklist_answer"),
        lb.Option(value="second_checklist_answer")
      ]
    ),
    lb.Classification(
      class_type=lb.Classification.Type.RADIO,
      name="radio_audio",
      options=[
        lb.Option(value="first_radio_answer"),
        lb.Option(value="second_radio_answer")
      ]
    ),
    # Temporal classifications for audio require INDEX scope
    lb.Classification(
      class_type=lb.Classification.Type.TEXT,
      name="transcription",
      scope=lb.Classification.Scope.INDEX
    ),
  ]
)

ontology = client.create_ontology("Ontology Audio Annotations",
                                  ontology_builder.asdict(),
                                  media_type=lb.MediaType.Audio)

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Step 3: Set Up a Labeling Project

# Create Labelbox project
project = client.create_project(name="audio_project",
                                    media_type=lb.MediaType.Audio)

# Setup your ontology
project.connect_ontology(ontology)

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Step 4: Send Data Rows to Project

# Create a batch to send to your MAL project
batch = project.create_batch(
  "first-batch-audio-demo", # Each batch in a project must have a unique name
  global_keys=[global_key], # Paginated collection of data row objects, list of data row ids or global keys
  priority=5 # priority between 1(Highest) - 5(lowest)
)

print("Batch: ", batch)

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Step 5: Create annotation payloads

For help understanding annotation payloads, see overview. To declare payloads, you can use Python annotation types (preferred) or NDJSON objects. For annotations that you want to import as ground truth labels, you can also specify benchmarks using the is_benchmark_reference flag. These examples demonstrate each format and how to compose annotations into labels attached to data rows. 
label = []
label.append(
  lb_types.Label(
    data={"global_key" : global_key },
    annotations=[
      text_annotation,
      checklist_annotation,
      radio_annotation,
      temporal_text_annotation,
      temporal_radio_annotation,
      temporal_checklist_annotation
    ],
    # Optional: set the label as a benchmark
    # Only supported for groud truth imports
    is_benchmark_reference = True
  )
)

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Step 6: Import annotation payload

For prelabeled (model-assisted labeling) scenarios, pass your payload as the value of the predictions parameter. For ground truths, pass the payload to the labels parameter.

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Option A: Upload as prelabels (model assisted labeling)

This option is helpful for speeding up the initial labeling process and reducing the manual labeling workload for high-volume datasets. 
# Upload MAL label for this data row in project
upload_job = lb.MALPredictionImport.create_from_objects(
    client = client,
    project_id = project.uid,
    name="mal_job"+str(uuid.uuid4()),
    predictions=label
)

print(f"Errors: {upload_job.errors}", )
print(f"Status of uploads: {upload_job.statuses}")

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Option B: Upload to a labeling project as ground truth

This option is helpful for loading high-confidence labels from another platform or previous projects that just need review rather than manual labeling effort. 
# Upload label for this data row in project
upload_job = lb.LabelImport.create_from_objects(
    client = client,
    project_id = project.uid,
    name="label_import_job"+str(uuid.uuid4()),
    labels=label
)

print(f"Errors: {upload_job.errors}", )
print(f"Status of uploads: {upload_job.statuses}")