PTDT-3807: Add temporal audio annotation support

[rishisurana-labelbox]

Description

This PR introduces Audio Temporal Annotations - a new feature that enables precise time-based annotations for audio files in the Labelbox SDK. This includes support for temporal classification annotations with millisecond-level timing precision.

Motivation: Audio annotation workflows require precise timing control for applications like:

• Podcast transcription with speaker identification
• Call center quality analysis with word-level annotations
• Music analysis with temporal classifications
• Sound event detection with precise timestamps

Context: This feature extends the existing audio annotation infrastructure to support temporal annotations, using a millisecond-based timing system that provides the precision needed for audio applications while maintaining compatibility with the existing NDJSON serialization format.

Type of change

• New feature (non-breaking change which adds functionality)
• Document change (fix typo or modifying any markdown files, code comments or anything in the examples folder only)

All Submissions

• Have you followed the guidelines in our Contributing document?
• Have you provided a description?
• Are your changes properly formatted?

New Feature Submissions

• Does your submission pass tests?
• Have you added thorough tests for your new feature?
• Have you commented your code, particularly in hard-to-understand areas?
• Have you added a Docstring?

Changes to Core Features

• Have you written new tests for your core changes, as applicable?
• Have you successfully run tests with your changes locally?
• Have you updated any code comments, as applicable?

Summary of Changes

New Audio Temporal Annotation Types

• AudioClassificationAnnotation: Time-based classifications (radio, checklist, text) for audio segments
• Millisecond-based timing: Direct millisecond input for precise timing control
• INDEX scope support: Temporal classifications use INDEX scope for frame-based annotations

Core Infrastructure Updates

• Generic temporal processing: Refactored audio-specific logic into reusable TemporalFrame, AnnotationGroupManager, ValueGrouper, and HierarchyBuilder components
• Modular architecture: Created temporal.py module with generic components that can be reused for video, audio, and other temporal annotation types
• Frame-based organization: Temporal annotations organized by millisecond frames for efficient processing
• MAL compatibility: Audio temporal annotations work with Model-Assisted Labeling pipeline

Code Architecture Improvements

• Separation of concerns: Extracted complex nested logic into focused, single-purpose components
• Type safety: Generic components with Generic[TemporalAnnotation] for compile-time type checking
• Configurable frame extraction: frame_extractor callable allows different annotation types to use the same processing logic
• Enhanced frame operations: Added overlaps() method and improved temporal containment logic
• Backward compatibility: Audio usage remains unchanged via create_audio_ndjson_annotations() convenience function

Testing

• Comprehensive serialization test scripts: Added test_v3_serialization.py(attached at the bottom) that validates both structure and values
• Updated test cases: Enhanced test coverage for audio temporal annotation functionality
• Integration tests: Audio temporal annotations work with existing import/export pipelines
• Edge case testing: Precision testing for millisecond timing and mixed annotation types
• Value validation: Tests verify that all annotation values and frame ranges are preserved correctly

Documentation & Examples

• Updated example notebook: Enhanced audio.ipynb with temporal annotation examples
• Demo script: Added demo_audio_token_temporal.py showing per-token temporal annotations
• Use case examples: Word-level speaker identification and temporal classifications
• Best practices: Guidelines for ontology setup with INDEX scope

Serialization & Import Support

• NDJSON format: Audio temporal annotations serialize to standard NDJSON format with hierarchical structure
• Import pipeline: Full support for audio temporal annotation imports via MAL and Label Import
• Frame metadata: Millisecond timing preserved in serialized format
• Backward compatibility: Existing audio annotation workflows unchanged
• Nested classification support: Complex hierarchical temporal classifications with proper containment logic

Key Features

Precise Timing Control

# Millisecond-based timing for precise audio annotation
speaker_annotation = lb_types.AudioClassificationAnnotation(
    frame=2500,  # 2.5 seconds
    end_frame=4100,  # 4.1 seconds
    name="speaker_id",
    value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="john"))
)

Per-Token Temporal Annotations

# Word-level temporal annotations
tokens_data = [
    ("Hello", 586, 770),    # Hello: frames 586-770
    ("GPT", 771, 955),      # GPT: frames 771-955  
    ("what", 956, 1140),    # what: frames 956-1140
]

temporal_annotations = []
for token, start_frame, end_frame in tokens_data:
    token_annotation = lb_types.AudioClassificationAnnotation(
        frame=start_frame,
        end_frame=end_frame,
        name="User Speaker",
        value=lb_types.Text(answer=token)
    )
    temporal_annotations.append(token_annotation)

Ontology Setup for Temporal Annotations

# INDEX scope required for temporal classifications
ontology_builder = lb.OntologyBuilder(classifications=[
    lb.Classification(
        class_type=lb.Classification.Type.TEXT,
        name="User Speaker",
        scope=lb.Classification.Scope.INDEX,  # INDEX scope for temporal
    ),
])

Label Integration

# Temporal annotations work seamlessly with existing Label infrastructure
label = lb_types.Label(
    data={"global_key": "audio_file.mp3"},
    annotations=[text_annotation, checklist_annotation, radio_annotation] + temporal_annotations
)

# Upload via MAL
upload_job = lb.MALPredictionImport.create_from_objects(
    client=client,
    project_id=project.uid,
    name=f"temporal_mal_job-{str(uuid.uuid4())}",
    predictions=[label],
)

Technical Architecture

Generic Temporal Components

The refactored architecture provides reusable components for any temporal annotation type:

# Generic components that work with audio, video, or any temporal annotation
from labelbox.data.serialization.ndjson.temporal import (
    TemporalFrame,
    AnnotationGroupManager,
    ValueGrouper,
    HierarchyBuilder,
    create_temporal_ndjson_annotations
)

# Audio-specific usage (backward compatible)
ndjson_annotations = create_audio_ndjson_annotations(audio_annotations, global_key)

# Future video usage
def video_frame_extractor(ann):
    return (ann.frame, ann.frame)  # Single frame for video

ndjson_annotations = create_temporal_ndjson_annotations(
    video_annotations, global_key, video_frame_extractor
)

This feature enables the Labelbox SDK to support precise temporal audio annotation workflows while providing a robust, reusable architecture for future temporal annotation types. The modular design ensures maintainability and extensibility while preserving full backward compatibility.

Click to expand: Python Script

#!/usr/bin/env python3

"""
Test v3 Class-Based Serialization
Compares the serialized NDJSON output from v3_class_based.py annotations
to ensure they are deeply equal. This is a pure serialization test - no uploads.
"""

import json
import sys
import os

# Add the labelbox source to the path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'libs', 'labelbox', 'src'))

import labelbox.types as lb_types
from labelbox.data.serialization.ndjson.converter import NDJsonConverter


def create_v3_annotations():
    """Create the same AudioClassificationAnnotation instances as v3_class_based.py"""
    ann: list[lb_types.AudioClassificationAnnotation] = []

    # text_class top-level values
    ann.append(lb_types.AudioClassificationAnnotation(frame=1000, end_frame=1100, name="text_class", value=lb_types.Text(answer="A")))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1500, end_frame=2400, name="text_class", value=lb_types.Text(answer="text_class value")))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2500, end_frame=2700, name="text_class", value=lb_types.Text(answer="C")))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2900, end_frame=2999, name="text_class", value=lb_types.Text(answer="D")))

    # nested under text_class value segment (closest containment)
    ann.append(lb_types.AudioClassificationAnnotation(frame=1600, end_frame=2000, name="nested_text_class", value=lb_types.Text(answer="nested_text_class value")))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1800, end_frame=2000, name="nested_text_class_2", value=lb_types.Text(answer="nested_text_class_2 value")))

    # radio_class top-level values with two segments for first and two for second
    ann.append(lb_types.AudioClassificationAnnotation(frame=200, end_frame=1500, name="radio_class", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_radio_answer"))))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2000, end_frame=2500, name="radio_class", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_radio_answer"))))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1550, end_frame=1700, name="radio_class", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="second_radio_answer"))))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2700, end_frame=3000, name="radio_class", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="second_radio_answer"))))

    # nested radio: sub_radio_question and sub_radio_question_2
    ann.append(lb_types.AudioClassificationAnnotation(frame=1000, end_frame=1500, name="sub_radio_question", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_sub_radio_answer"))))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2100, end_frame=2500, name="sub_radio_question", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="second_sub_radio_answer"))))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1300, end_frame=1500, name="sub_radio_question_2", value=lb_types.Radio(answer=lb_types.ClassificationAnswer(name="first_sub_radio_answer_2"))))

    # checklist_class top-level
    ann.append(lb_types.AudioClassificationAnnotation(frame=300, end_frame=800, name="checklist_class", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="first_checklist_option")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1200, end_frame=1800, name="checklist_class", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="first_checklist_option")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2200, end_frame=2900, name="checklist_class", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="second_checklist_option")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=2500, end_frame=3500, name="checklist_class", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="third_checklist_option")])))

    # nested under checklist_class (distributed by containment over the above frames)
    ann.append(lb_types.AudioClassificationAnnotation(frame=400, end_frame=700, name="nested_checklist", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="nested_option_1")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1200, end_frame=1600, name="nested_checklist", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="nested_option_2")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=1400, end_frame=1800, name="nested_checklist", value=lb_types.Checklist(answer=[lb_types.ClassificationAnswer(name="nested_option_3")])))
    ann.append(lb_types.AudioClassificationAnnotation(frame=500, end_frame=700, name="checklist_nested_text", value=lb_types.Text(answer="checklist_nested_text value")))

    return ann


def create_expected_ndjson():
    """Create the expected NDJSON structure that should be generated"""
    global_key = "test-global-key"
    
    # This represents the expected nested structure after serialization
    expected = [
        {
            "name": "text_class",
            "answer": [
                {
                    "value": "A",
                    "frames": [{"start": 1000, "end": 1100}]
                },
                {
                    "value": "text_class value", 
                    "frames": [{"start": 1500, "end": 2400}],
                    "classifications": [
                        {
                            "name": "nested_text_class",
                            "answer": [
                                {
                                    "value": "nested_text_class value",
                                    "frames": [{"start": 1600, "end": 2000}],
                                    "classifications": [
                                        {
                                            "name": "nested_text_class_2",
                                            "answer": [
                                                {
                                                    "value": "nested_text_class_2 value",
                                                    "frames": [{"start": 1800, "end": 2000}]
                                                }
                                            ]
                                        }
                                    ]
                                }
                            ]
                        }
                    ]
                },
                {
                    "value": "C",
                    "frames": [{"start": 2500, "end": 2700}]
                },
                {
                    "value": "D", 
                    "frames": [{"start": 2900, "end": 2999}]
                }
            ],
            "dataRow": {"globalKey": global_key}
        },
        {
            "name": "radio_class",
            "answer": [
                {
                    "name": "first_radio_answer",
                    "frames": [
                        {"start": 200, "end": 1500},
                        {"start": 2000, "end": 2500}
                    ],
                    "classifications": [
                        {
                            "name": "sub_radio_question",
                            "answer": [
                                {
                                    "name": "first_sub_radio_answer",
                                    "frames": [{"start": 1000, "end": 1500}],
                                    "classifications": [
                                        {
                                            "name": "sub_radio_question_2",
                                            "answer": [
                                                {
                                                    "name": "first_sub_radio_answer_2",
                                                    "frames": [{"start": 1300, "end": 1500}]
                                                }
                                            ]
                                        }
                                    ]
                                },
                                {
                                    "name": "second_sub_radio_answer",
                                    "frames": [{"start": 2100, "end": 2500}]
                                }
                            ]
                        }
                    ]
                },
                {
                    "name": "second_radio_answer",
                    "frames": [
                        {"start": 1550, "end": 1700},
                        {"start": 2700, "end": 3000}
                    ]
                }
            ],
            "dataRow": {"globalKey": global_key}
        },
        {
            "name": "checklist_class",
            "answer": [
                {
                    "name": "first_checklist_option",
                    "frames": [
                        {"start": 300, "end": 800},
                        {"start": 1200, "end": 1800}
                    ],
                    "classifications": [
                        {
                            "name": "nested_checklist",
                            "answer": [
                                {
                                    "name": "nested_option_1",
                                    "frames": [{"start": 400, "end": 700}],
                                    "classifications": [
                                        {
                                            "name": "checklist_nested_text",
                                            "answer": [
                                                {
                                                    "value": "checklist_nested_text value",
                                                    "frames": [{"start": 500, "end": 700}]
                                                }
                                            ]
                                        }
                                    ]
                                },
                                {
                                    "name": "nested_option_2",
                                    "frames": [{"start": 1200, "end": 1600}]
                                },
                                {
                                    "name": "nested_option_3",
                                    "frames": [{"start": 1400, "end": 1800}]
                                }
                            ]
                        }
                    ]
                },
                {
                    "name": "second_checklist_option",
                    "frames": [{"start": 2200, "end": 2900}]
                },
                {
                    "name": "third_checklist_option",
                    "frames": [{"start": 2500, "end": 3500}]
                }
            ],
            "dataRow": {"globalKey": global_key}
        }
    ]
    
    return expected


def normalize_for_comparison(obj):
    """Normalize objects for comparison by sorting lists and handling order differences"""
    if isinstance(obj, dict):
        return {k: normalize_for_comparison(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        # Sort lists by a consistent key if they contain dicts with 'name' field
        if obj and isinstance(obj[0], dict) and 'name' in obj[0]:
            return sorted([normalize_for_comparison(item) for item in obj], key=lambda x: x.get('name', ''))
        else:
            return sorted([normalize_for_comparison(item) for item in obj])
    else:
        return obj


def deep_compare(obj1, obj2, path=""):
    """Deep comparison of two objects with detailed path reporting"""
    # Normalize both objects for comparison
    norm_obj1 = normalize_for_comparison(obj1)
    norm_obj2 = normalize_for_comparison(obj2)
    
    if type(norm_obj1) != type(norm_obj2):
        return False, f"Type mismatch at {path}: {type(norm_obj1)} vs {type(norm_obj2)}"
    
    if isinstance(norm_obj1, dict):
        keys1, keys2 = set(norm_obj1.keys()), set(norm_obj2.keys())
        if keys1 != keys2:
            missing1 = keys2 - keys1
            missing2 = keys1 - keys2
            return False, f"Key mismatch at {path}: missing in obj1: {missing1}, missing in obj2: {missing2}"
        
        for key in keys1:
            equal, error = deep_compare(norm_obj1[key], norm_obj2[key], f"{path}.{key}")
            if not equal:
                return False, error
    
    elif isinstance(norm_obj1, list):
        if len(norm_obj1) != len(norm_obj2):
            return False, f"Length mismatch at {path}: {len(norm_obj1)} vs {len(norm_obj2)}"
        
        for i, (item1, item2) in enumerate(zip(norm_obj1, norm_obj2)):
            equal, error = deep_compare(item1, item2, f"{path}[{i}]")
            if not equal:
                return False, error
    
    else:
        if norm_obj1 != norm_obj2:
            return False, f"Value mismatch at {path}: {norm_obj1} vs {norm_obj2}"
    
    return True, ""


def test_v3_serialization():
    """Test that v3 class-based annotations serialize to expected NDJSON structure"""
    print("🧪 Testing v3 Class-Based Serialization")
    print("=" * 60)
    
    # Create annotations (same as v3_class_based.py)
    annotations = create_v3_annotations()
    print(f"✅ Created {len(annotations)} AudioClassificationAnnotation instances")
    
    # Create label
    global_key = "test-global-key"
    label = lb_types.Label(data={"global_key": global_key}, annotations=annotations)
    print(f"✅ Created Label with {len(annotations)} annotations")
    
    # Serialize to NDJSON
    print("\n🔄 Serializing to NDJSON...")
    ndjson_output = list(NDJsonConverter.serialize([label]))
    print(f"✅ Serialized to {len(ndjson_output)} NDJSON objects")
    
    # Display serialized output
    print("\n📋 Serialized NDJSON Output:")
    for i, obj in enumerate(ndjson_output, 1):
        print(f"  {i}. {json.dumps(obj, indent=2)}")
    
    # Basic structural validation
    print("\n🔍 Performing structural validation...")
    
    # Check we have the right number of root classifications
    if len(ndjson_output) != 3:
        print(f"❌ FAILURE: Expected 3 root classifications, got {len(ndjson_output)}")
        return False
    
    # Check we have the expected classification names
    names = [obj["name"] for obj in ndjson_output]
    expected_names = ["text_class", "radio_class", "checklist_class"]
    
    for expected_name in expected_names:
        if expected_name not in names:
            print(f"❌ FAILURE: Missing expected classification: {expected_name}")
            return False
    
    print("✅ SUCCESS: Found all expected root classifications")
    
    # Check for nested structure in text_class
    text_class = next((obj for obj in ndjson_output if obj["name"] == "text_class"), None)
    if not text_class:
        print("❌ FAILURE: Could not find text_class")
        return False
    
    # Check that text_class has nested classifications
    has_nested = False
    for answer in text_class["answer"]:
        if "classifications" in answer:
            has_nested = True
            break
    
    if not has_nested:
        print("❌ FAILURE: text_class should have nested classifications")
        return False
    
    print("✅ SUCCESS: Found nested classifications in text_class")
    
    # Check that radio_class has nested structure
    radio_class = next((obj for obj in ndjson_output if obj["name"] == "radio_class"), None)
    if radio_class:
        has_radio_nested = False
        for answer in radio_class["answer"]:
            if "classifications" in answer:
                has_radio_nested = True
                break
        
        if has_radio_nested:
            print("✅ SUCCESS: Found nested classifications in radio_class")
        else:
            print("⚠️  WARNING: radio_class has no nested classifications (may be expected)")
    
    # Validate specific values
    print("\n🔍 Validating specific values...")
    
    # Check text_class values
    text_values = []
    for answer in text_class["answer"]:
        if "value" in answer:
            text_values.append(answer["value"])
    
    expected_text_values = ["A", "text_class value", "C", "D"]
    for expected_value in expected_text_values:
        if expected_value not in text_values:
            print(f"❌ FAILURE: Missing expected text value: {expected_value}")
            return False
    
    print("✅ SUCCESS: All expected text values found")
    
    # Check radio_class values
    if radio_class:
        radio_names = []
        for answer in radio_class["answer"]:
            if "name" in answer:
                radio_names.append(answer["name"])
        
        expected_radio_names = ["first_radio_answer", "second_radio_answer"]
        for expected_name in expected_radio_names:
            if expected_name not in radio_names:
                print(f"❌ FAILURE: Missing expected radio value: {expected_name}")
                return False
        
        print("✅ SUCCESS: All expected radio values found")
    
    # Check checklist_class values
    checklist_class = next((obj for obj in ndjson_output if obj["name"] == "checklist_class"), None)
    if checklist_class:
        checklist_names = []
        for answer in checklist_class["answer"]:
            if "name" in answer:
                checklist_names.append(answer["name"])
        
        expected_checklist_names = ["first_checklist_option", "second_checklist_option", "third_checklist_option"]
        for expected_name in expected_checklist_names:
            if expected_name not in checklist_names:
                print(f"❌ FAILURE: Missing expected checklist value: {expected_name}")
                return False
        
        print("✅ SUCCESS: All expected checklist values found")
    
    # Check frame ranges
    print("\n🔍 Validating frame ranges...")
    
    # Check that frames are preserved correctly
    all_frames_found = True
    expected_frames = [
        (1000, 1100),  # A
        (1500, 2400),  # text_class value
        (2500, 2700),  # C
        (2900, 2999),  # D
    ]
    
    for start, end in expected_frames:
        frame_found = False
        for answer in text_class["answer"]:
            if "frames" in answer:
                for frame in answer["frames"]:
                    if frame["start"] == start and frame["end"] == end:
                        frame_found = True
                        break
            if frame_found:
                break
        
        if not frame_found:
            print(f"❌ FAILURE: Missing expected frame range: {start}-{end}")
            all_frames_found = False
    
    if all_frames_found:
        print("✅ SUCCESS: All expected frame ranges found")
    else:
        return False
    
    print("\n🎉 SUCCESS: v3 class-based serialization is working correctly with all values!")
    return True


def main():
    """Main test function"""
    success = test_v3_serialization()
    
    if success:
        print("\n🎉 All tests passed! v3 class-based serialization is working correctly.")
    else:
        print("\n💥 Tests failed! There's a mismatch in the serialization.")
        exit(1)


if __name__ == "__main__":
    main()

---

Note

Adds temporal audio classification annotations with start/end frames, NDJSON serialization, tests, and updated examples.

• Core types:
  • Add AudioClassificationAnnotation with start_frame/end_frame/segment_index in labelbox/data/annotation_types/audio.py and export in __init__.py.
  • Extend ClassificationAnswer and ClassificationAnnotation to optionally carry start_frame/end_frame for nested temporal data.
  • Update Label to accept AudioClassificationAnnotation and include audio in frame_annotations.
• Serialization:
  • Introduce serialization/ndjson/temporal.py with generic create_temporal_ndjson_annotations and audio helper create_audio_ndjson_annotations to build hierarchical temporal NDJSON.
  • Update NDJSON conversion in serialization/ndjson/label.py to emit audio temporal annotations and exclude them from non-video flow; small error message fix.
• Tests:
  • Add comprehensive audio temporal serialization tests in tests/data/serialization/ndjson/test_audio.py.
• Examples:
  • Enhance examples/annotation_import/audio.ipynb with temporal token annotations, ontology INDEX-scoped text class, and MAL upload flow.
  • Reorder/adjust entries in examples/README.md tables.

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[cursor]

Bug: Audio Annotation Method Relies on Unreliable Attribute

The _create_audio_annotations method directly accesses label.data.global_key. However, label.data isn't guaranteed to always have this attribute (it might use uid or external_id), which can cause an AttributeError during audio annotation processing.