Trusted by teams building voice AI
Over 100,000 hours of audio labeled
Turn your raw audio into training-ready datasets: diarization, timecodes, sentiment, intent, and more — delivered in pipeline-ready formats.
Clear guidelines, edge-case rules, and repeatable segmentation so outputs stay stable across batches.
Quality checks and batch summaries so you can trust the dataset before training and evaluation.
JSONL/RTTM/CSV exports aligned to your schema, naming conventions, and IDs.
Pipeline-ready exports in JSONL, RTTM, or your schema — clean, structured, and consistent.
SPEAKER SPEAKER_00 1 12.450 3.210 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 15.820 5.140 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 21.300 2.890 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 24.450 4.320 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 29.100 6.870 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 36.220 3.450 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 40.100 5.280 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 45.750 2.940 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 48.990 4.110 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 53.420 6.330 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 60.100 3.780 <NA> <NA> Agent <NA> SPEAKER SPEAKER_01 1 64.250 2.560 <NA> <NA> Customer <NA> SPEAKER SPEAKER_00 1 67.180 4.920 <NA> <NA> Agent <NA>
Choose the labels you need. Combine multiple services into one delivery to save time and resources.
Structure the audio: speakers + timecodes.
Labels speakers and aligns every turn to time (e.g., SPEAKER_01, Agent, Customer).
Maps speaker IDs to roles and consistent naming rules across the dataset.
Transcript aligned by segments with start/end times (utterance-level or turn-level).
Word-by-word timing for precise alignment and analysis.
Train voice systems with utterance-level labels.
Utterance-level emotion labels designed to enrich transcripts for conversational AI.
Sentiment assigned per utterance (not only overall conversation sentiment).
Per-utterance intent and dialog acts (e.g., ask, confirm, escalate).
Annotates entities/slots alongside intents and dialog acts (e.g., date, product, account issue).
Model what actually happens in live conversations.
Pragmatic labels to capture indirect language and tone that changes meaning.
Labels fillers/disfluencies and conversation events (false starts, interruptions, barge-in).
If you have a unique labeling schema or dataset requirement, we'll adapt the workflow and deliver to your spec. Examples we can support include:
Clear acceptance criteria, batch summaries, and controlled handling for sensitive audio.
Versioned guidelines + change log
Calibration + consistency checks
Batch QC report (issues + fixes)
Optional adjudication / second pass
Schema validation (timestamps, speakers, labels)
NDA-ready + role-based access
Retention controls + deletion confirmation
Restricted project access (scoped)
Secure delivery via approved method
Audit-friendly handling on request
Accurate labeling requires linguistic and cultural context. Our global network of native speakers ensures precision across every language.
Native speakers across major world languages, regional dialects, and low-resource languages for comprehensive coverage.
Understanding context, idioms, slang, and cultural references that machine translation and non-native speakers miss.
Match labelers to specific regional variants (e.g., Mexican Spanish, Quebecois French) for accurate transcription and annotation.
Send a sample and target labels. We validate segmentation rules, schema fields, and edge cases.
We finalize label guides and lock a versioned schema to ensure consistency across all future batches.
Repeatable batch deliveries with stable IDs, QC summaries, and change control.
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