Transcription & segmentation
Verbatim transcripts, timestamps, utterance boundaries, non speech events, and text normalization according to your guidelines.
Audio intelligence · human reviewed
Better speech models start with better human labels.
SarDev Voice Lab provides structured audio and speech annotation for teams training, evaluating, and improving voice AI.
What we do
Audio annotation, built around your model requirements.
From raw recordings to evaluation ready labels, we work to your taxonomy, acceptance criteria, and delivery format.
Diarization & voice attributes
Speaker turns, overlap, vocal delivery, and requested acoustic or linguistic attributes for speech model training.
Speech evaluation & QA
Human review of pronunciation, intelligibility, audio artifacts, instruction adherence, and label consistency.
A practical annotation workflow
Clear guidelines. Consistent judgments. Usable data.
We align on the task definition, annotate a representative sample, review disagreements, and refine the rubric before scaling delivery. Quality checks are tailored to the project rather than assumed from a generic checklist.
- Client supplied taxonomy and output schema
- Sample calibration and exception handling
- Review of ambiguous and low quality audio
- Structured handoff for training or evaluation
Built for voice AI
Support across the speech model lifecycle.
Bring us recordings, generated speech, or model outputs. We can help define the annotation scope for:
- Automatic speech recognition datasets
- Text to speech and conversational voice evaluation
- Call audio and dialogue segmentation
- Pronunciation and speech quality review
Scope, languages, turnaround, security handling, and capacity are agreed for each project.
Start a conversation
Contact SarDev ↗Tell us what your audio needs to teach your model.
Share your data type, labeling requirements, volume, languages, and timeline. We’ll discuss a suitable scope and delivery plan.