Audio Modalities

Get accurate audio data for AI model training through AI labelling engines

Transcribe speech, diarize speakers, tag events and segment audios with AI-assited engine to consolidate audio data at 10x pace

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One layer for every type of audio labelling

From word-level transcription to speaker diarization, the data labelling layer routes every clip to the right audio modelities, auto-labelling engines s provide results with confidence, while those above threeshold are passed automatically others are directly sent to a human reviewer so nothing gets shipped unverified.

Task Types

A complete toolkit for automated audio labelling

Transcription — AI-generated timestamped transcript with confidence score
Transcription
Convert speech to accurate, timestamped text, ready to train or fine-tune speech models.
Audio classification — AI-assigned category label with confidence score
Audio Classification
Categorize entire audio files by content, source, or context with a single global label.
Speaker diarization — per-speaker timeline with transcript turns
Speaker Diarization
Identify and separate individual speakers across a recording, segment by segment.
Emotion detection — detected emotion, sentiment, and intensity with transcript
Emotion Detection
Detect emotional tone and sentiment shifts across speech segments, timestamp by timestamp.
Sound event detection — polyphonic event timeline with detected events
Sound Event Detection
Flag specific sound events — alarms, glass breaking, applause — wherever they occur in the file.
Audio segmentation — labeled temporal sections across a recording
Audio Segmentation
Break long recordings into labeled temporal segments for structured downstream training.
Built-In Automation

Everything you need to scale audio labeling

Ontologies
Customizable ontologies for every audio project

Build nested classification schemas specific to your domain, from simple tags to multi-level attribute hierarchies.

 AI Assistance
Native AI integration with Whisper, AST & pyannote

Access AI engines integrated with Whisper for transcription, AST for segmentation and pyannote for speaker diarization natively faster, more accurate first-pass labels before any human intervention.

 Analytics
In-depth performance analytics

Uncover insights on label quality and engine performance to optimize efficency, quality, and workforce efficiency.

 Workflows
Configurable workflows for quality control

Guarantee quality throughout labeling pipelines with customizable review stages, consensus routing, and approval gates.
FAQ

Common questions

Transcription, audio classification, speaker diarization, emotion detection, sound event detection, and audio segmentation all routed through the same Label layer.

Every clip is transcribed and scored by the engine best suited to its task. Segments above the configured confidence threshold are accepted automatically everything else is queued for a review.

Yes. Ontologies are configured per project, from flat tag lists to nested, multi-level classification schemas.

Every delivery includes data-level and task-level quality metrics, plus a full audit trail suppourted by lineage report.

Labeled audio data flows directly into Version for lineage tracking and Observe for production drift monitoring no separate export intervention.

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