01
Define the dataset
Name the languages, accents, and speakers your model is weakest on. Set target speaker counts, hours of audio, and the turnaround you need.
AuraOne · Human Data · Voice
Voice data from real speakers.
Input
Speakers
Check
Consent
Output
Audio files
Build speech datasets across languages, accents, and speaker groups. Each program defines the speakers, the hours, the rights, the quality checks, and the file your model team receives.
02
Set the rights
Every recording gets a consent record: who spoke, in what language, under what rights, and whether the voice can be reused. The rights ride with the data, not just a worker contract.
03
Review for the job
Speech-to-text and text-to-speech rubrics, scored accuracy, resolved disagreements, and voice-agent safety cases. The data is checked for your task, not just collected.
04
Deliver the dataset
One file from speaker to delivery, with the consent and licensing details attached. When a speaker revokes, you can see which recordings are affected.
What you can request
Voice is biometric. The consent has to be clear.
A speaker can revoke. The EU AI Act starts enforcing training-data source tracking for AI systems in August 2026, and a voice is a biometric identifier. A competitor lost terabytes once, including who its workers were. That is why every recording carries its own consent record, and why your data is never pooled in one store a single breach can drain.
Multilingual ASR training
Accent and dialect coverage
TTS voice fonts
Per-recording consent records
Voice-agent safety and red-team
Speaker demographics on demand
Tell us the languages, the accents, the speaker count, and the hours. A neutral source you can defend under audit, not aligned with any one lab. Public voice programs open by waitlist.