Estimated duration
~1h
Deadline
Mon, Sep 14, 2026
Location
📍 ZA (Remote OK)
USD
We need native Afrikaans speakers to transcribe conversational audio recordings for an AI training dataset. Transcripts must be fully human-generated or human QA-ed — no machine-only transcripts accepted.
What you'll do: • Transcribe conversational Afrikaans audio files (2 minutes to 1 hour per file). • Deliver transcripts in JSON format with: → Time-coding (no overlaps across speakers) → Speaker diarization (each speaker individually labeled, tied to Speaker IDs) → Sequential ordering of utterances → Minimum 95% precision • Confirm each transcript is fully human-generated or human QA-ed.
Workflow: • We provide the audio file plus a machine-generated draft transcript (from our Amazon Transcribe pre-pass) where the ASR handles the language reasonably. For languages where ASR performance is poor, you'll transcribe from scratch. • You listen to the full audio, correct every error in the draft (or transcribe from scratch), verify speaker labels and timestamps, and output the corrected JSON. • You attach an attestation confirming the transcript is human-generated or human QA-ed.
Requirements: • Native or near-native speaker of Afrikaans. • Comfortable working with JSON output (schema and conventions provided in our Transcriber Guide). • Access to a laptop or desktop computer. • Ability to deliver on rolling schedule aligned with 20-day tranches.
What you'll be paid: • $5 per accepted audio hour transcribed, paid per accepted submission. • Accepted = passes our internal QA sample check (we spot-check a portion of your files).
Timeline: Rolling submissions from now through Sep 14, 2026. Tranche 1 due August 4th.
What to submit: • JSON transcript file per audio file (schema in the Transcriber Guide we'll share on acceptance). • Attestation that the transcript is human-generated or human QA-ed.
Transcriber has delivered a JSON transcript per audio file matching schema requirements (time-coding, speaker diarization, sequential ordering, min 95% precision), with an attestation that it is human-generated or human QA-ed, and has passed our internal QA spot-check.
USD
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