Freelance Transcriptionist — Sinhalese

Posted 55 Minutes Ago
Be an Early Applicant
6 Locations
Remote
Entry level
Artificial Intelligence • Machine Learning • Robotics • Software
The Role
Transcribe two-speaker Sinhalese audio recordings manually, producing accurate sequential transcripts with speaker labels and HH:MM:SS timestamps. Maintain a word error rate of 5% or less and zero speaker-label errors, correct returned files, meet daily output targets, and work independently for at least four hours per day. Prior transcription experience is optional, but contributors must pass a qualification test.
Summary Generated by Built In

About Perle

Perle is an AI infrastructure company building expert-driven training data, evaluation systems, and applied AI products for the world's leading labs and enterprises. Headquartered in San Francisco with experts across more than forty markets, we specialize in the work that requires real human judgment: domain expertise, linguistic nuance, and cultural fidelity that generic data vendors cannot deliver.


Perle is hiring a freelance transcriptionist fluent in Sinhalese for a remote transcription role. This is a paid contract position open to qualified contributors worldwide.


1. Role Overview

You will transcribe 2-speaker, channel-separated audio recordings in Sinhalese. Each file has two clearly separated speakers. Your job is to produce a clean, accurate, sequentially ordered transcript with speaker labels and timestamps.


2. What You Will Do

  • Listen to 2-speaker audio recordings in Sinhalese
  • Type out what each speaker says, accurately and in full
  • Label each speaker turn as Speaker 1 or Speaker 2, matching the audio channel
  • Add timestamps at the start of each speaker turn (format: HH:MM:SS)
  • Ensure transcripts are sequential with no unexplained gaps


3. Requirements

  • Native or near-native fluency in Sinhalese (spoken and written)
  • Strong written Sinhalese — accurate spelling, punctuation, and grammar
  • Ability to work independently and meet daily output targets
  • Access to a computer with a stable internet connection
  • Availability for at least 4 hours per day
  • Prior transcription experience is a plus but not required — all contributors must pass a qualification test


4. Quality Standard

  • Word Error Rate (WER) of 5% or less on all delivered transcripts
  • Zero speaker label errors — Speaker 1 must always correspond to Channel 1
  • Transcripts reviewed on a rolling basis; feedback provided promptly
  • Files that do not meet the quality threshold will be returned for correction


5. Project Details

Detail

Information

Language required

Sinhalese

Project type

Audio transcription (manual)

Commitment

~at least 4 hours/day (remote, flexible schedule)

Start date

As soon as qualified — rolling onboarding


6. How to Apply

Submit your application through the Perle platform. You will be asked to complete a short qualification task — a 5-minute audio transcription sample in Sinhalese. Applicants who pass the qualification test will be onboarded immediately and assigned to production tasks.


Perle is an equal opportunity employer. We welcome applicants from all backgrounds.


Skills Required

  • Native or near-native fluency in Sinhalese, spoken and written
  • Strong written Sinhalese, including accurate spelling, punctuation, and grammar
  • Ability to work independently and meet daily output targets
  • Access to a computer with a stable internet connection
  • Availability for at least 4 hours per day
  • Pass the Sinhalese transcription qualification test
  • Prior transcription experience
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The Company
26 Employees
Year Founded: 2024

What We Do

Perle is an AI infrastructure company that builds expert-driven training data, evaluation systems, and applied AI products for leading labs and enterprises. Based in San Francisco with experts across more than forty markets, it provides human-judgment-intensive domain expertise, linguistic nuance, and cultural fidelity. Its platform supports end-to-end training data, evaluation, labeling, and multimodal data programs for teams developing AI and robotics.

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