Precise Temporal Event Labeling
Select exact time ranges on the waveform, apply ontology-guided event classes, and review boundaries in synchronized audio context.
Time rangesEvent classesBoundary review
Audio Annotation Platform
Audio Annotation Platform for Speech and Sound AI
Annotate speech, transcripts, speaker diarization, speaker labels, sound events, and acoustic properties with precise temporal ranges, synchronized playback, and scalable review workflows.
No credit card required
Audio annotation features
Everything You Need for Complex Audio Annotation
Label exact time ranges, review synchronized waveform and spectrogram context, align transcripts, and navigate long recordings without losing context.
Waveform and Spectrogram Context
Play, pause, scrub, and zoom while the waveform, spectrogram, selected ranges, and playhead remain synchronized.
Time-Aligned Transcription Review
Review transcript spans, speaker labels, confidence, and exact timestamps directly against the source audio.
Nested ontologies and dynamic properties
Define nested speaker and event classes, properties, Item Properties, and Relations, then review temporal values on the timeline.
Complete audio annotation timelines
Review temporal events, speaker segments, transcript spans, class attributes, and item-level states together without losing context.
Example sequence10 h 35 min
Long-Recording Review at Exact-Time Precision
Navigate a 10 h 35 min recording with overview navigation, waveform zoom, stable playback, and exact time selection.
Long-form navigationExact time selection
Built for AI Data at Scale
Annotate complex audio datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
20×
Faster audio data annotation
AI-assisted labeling and shared timelines streamline audio data annotation.
95%
Automated audio annotation
On average, 95% of audio labels are pre-labeled automatically, then reviewed and refined by humans.
15×
Lower Training Data Costs
The cost per accepted label can be up to 15× lower as curation, annotation, and QA are automated.
Supported audio annotation types
All audio annotation types in one platform.
Label time ranges, segment recordings, align transcripts, classify speakers and events, apply properties, and connect related annotations.
01
Temporal Event / Time Range
Select precise start and end times for speech, sound events, and other temporal labels.
02
Audio Segmentation
Partition recordings into meaningful speech, silence, music, noise, or event regions.
03
Transcription
Align transcript spans and words to exact audio time ranges for review.
04
Speaker Labeling
Assign speaker roles and identities to conversational or diarized segments.
05
Event Classification
Classify speech acts, sound events, acoustic conditions, and review outcomes.
06
Event Properties
Capture sentiment, quality, confidence, or other attributes on selected events.
07
Class Attributes
Define reusable, ontology-guided attributes for speakers, events, and segments.
08
Item Properties
Label properties of the complete recording, including source, environment, language, and quality.
09
Relations
Connect speakers, temporal events, transcript spans, and other related audio annotations.
Audio annotation quality assurance
Build quality into every audio annotation.
Compare labels on the same recording, check approved references, and resolve issues with the waveform and transcript in view. Keep each quality decision connected to its source interval.
Audio Annotation Consensus
Compare independent annotations on the same recording and time axis. Review disagreements in event intervals, speaker labels, and supported annotation properties.
Recording and intervalAgreement checksReview disagreements
Quality Gate (Honeypot)
Check audio labels against approved references hidden from annotators. Evaluate supported event classes and time boundaries using the configured quality criteria.
Validation & Issue Resolution
Inspect missing properties, transcript errors, and disputed audio intervals in context. Correct the affected labels and resolve reviewer feedback before approval.
Audio QA Analytics
Track benchmark results, consensus outcomes, and review decisions across audio tasks. Prioritize recordings and intervals that need additional listening and review.
Benchmark resultsReview outcomes
Audio dataset curation & annotation workflows
Curate audio data and automate annotation workflows.
Search, version, and inspect audio datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as audio data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Explore large audio datasets through semantic understanding instead of manual filters. Find relevant clips, events, speakers, and transcript cases across diverse conditions.
Embedding View
Visualize dataset structure, identify outliers and labeling issues, and improve audio-data quality before training.
Integrated Workflows
Build workflows that connect models, annotation, review, and quality assurance in one continuous loop. Reduce handoffs and keep datasets moving from labeling to approval.
Bring Your Audio Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world audio datasets.
Solutions
Audio annotation solutions for speech and sound AI
Prepare time-aligned, ontology-driven audio datasets for transcription, speaker analysis, acoustic events, and contact-center intelligence.
Speech Recognition and Transcription
Build time-aligned speech datasets with accurate transcripts, clear segment boundaries, and recording context. Review words against the original audio before exporting labeled examples for ASR models.
Explore speech recognition and transcription
Speaker Diarization
Label who spoke when with consistent speaker IDs and reviewed turn boundaries. Prepare meetings, interviews, and conversations for diarization training and evaluation.
Explore speaker diarization
Sound Event Detection
Mark when relevant sounds begin and end, with consistent event classes and acoustic context. Prepare reviewed recordings for environmental, industrial, and media sound-recognition models.
Explore sound event detection
Contact Center Intelligence
Prepare call recordings with transcripts, speaker roles, and defined conversation labels. Build training data for intent understanding, issue resolution, and speech analytics with the original audio in context.
Explore contact center intelligenceQuestions, answered
Audio Annotation Platform FAQs
Answers about audio and speech annotation, transcription, speaker diarization, speaker labeling, sound-event annotation, temporal ranges, long recordings, ontologies, and quality workflows in Unitlab.
Talk with the Unitlab teamWhat audio annotation types does Unitlab support?+
Unitlab supports temporal events and time ranges, audio segmentation, transcription, speaker labeling, event classification, class attributes, Item Properties, and Relations. Teams define the required labels and rules in reusable ontologies for consistent speech and sound datasets.
Audio annotation documentationHow do temporal events and audio segmentation work in Unitlab?+
Annotators select precise start and end times on the waveform, apply ontology-guided event classes, and review boundaries with synchronized playback and spectrogram context. Segments remain editable throughout review and QA.
Annotation workflow documentationCan Unitlab annotate long audio recordings?+
Yes. Unitlab combines stable playback, scrubbing, waveform zoom, spectrogram context, and exact time selection for long-form audio annotation. Annotators can move between the full recording and focused segments without losing context.
Annotation workflow documentationHow do ontologies and dynamic properties work in audio annotation?+
Nested ontologies define speakers, event classes, properties, Item Properties, and Relations. Temporal properties capture changing conditions as reviewable time ranges on the audio timeline.
Properties and relations documentationHow does Unitlab manage audio annotation quality and review?+
Configurable workflows route audio tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for audio pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version audio datasets in one platform?
Yes. Teams can curate audio data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationAUDIO ANNOTATION PLATFORM
Build Production-Ready Audio Datasets with Unitlab
Annotate, transcribe, review, and manage complex audio data in one AI-assisted workspace. Move faster from raw recordings to production-ready datasets with temporal labeling and built-in quality control.
