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We need native Oromo speakers to transcribe conversational audio recordings for an AI training dataset. Transcripts must be fully human-generated or human QA-ed β no machine-only transcripts accepted. What you'll do: β’ Transcribe conversational Oromo audio files (2 minutes to 1 hour per file). β’ Deliver transcripts in JSON format with: β Time-coding (no overlaps across speakers) β Speaker diarization (each speaker individually labeled, tied to Speaker IDs) β Sequential ordering of utterances β Minimum 95% precision β’ Confirm each transcript is fully human-generated or human QA-ed. Workflow: β’ 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. β’ 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. β’ You attach an attestation confirming the transcript is human-generated or human QA-ed. Requirements: β’ Native or near-native speaker of Oromo. β’ Comfortable working with JSON output (schema and conventions provided in our Transcriber Guide). β’ Access to a laptop or desktop computer. β’ Ability to deliver on rolling schedule aligned with 20-day tranches. What you'll be paid: β’ $5 per accepted audio hour transcribed, paid per accepted submission. β’ 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: β’ JSON transcript file per audio file (schema in the Transcriber Guide we'll share on acceptance). β’ Attestation that the transcript is human-generated or human QA-ed.
We need native Haitian Creole speakers to transcribe conversational audio recordings for an AI training dataset. Transcripts must be fully human-generated or human QA-ed β no machine-only transcripts accepted. What you'll do: β’ Transcribe conversational Haitian Creole audio files (2 minutes to 1 hour per file). β’ Deliver transcripts in JSON format with: β Time-coding (no overlaps across speakers) β Speaker diarization (each speaker individually labeled, tied to Speaker IDs) β Sequential ordering of utterances β Minimum 95% precision β’ Confirm each transcript is fully human-generated or human QA-ed. Workflow: β’ 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. β’ 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. β’ You attach an attestation confirming the transcript is human-generated or human QA-ed. Requirements: β’ Native or near-native speaker of Haitian Creole. β’ Comfortable working with JSON output (schema and conventions provided in our Transcriber Guide). β’ Access to a laptop or desktop computer. β’ Ability to deliver on rolling schedule aligned with 20-day tranches. What you'll be paid: β’ $5 per accepted audio hour transcribed, paid per accepted submission. β’ 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: β’ JSON transcript file per audio file (schema in the Transcriber Guide we'll share on acceptance). β’ Attestation that the transcript is human-generated or human QA-ed.
We need native Sinhalese speakers to transcribe conversational audio recordings for an AI training dataset. Transcripts must be fully human-generated or human QA-ed β no machine-only transcripts accepted. What you'll do: β’ Transcribe conversational Sinhalese audio files (2 minutes to 1 hour per file). β’ Deliver transcripts in JSON format with: β Time-coding (no overlaps across speakers) β Speaker diarization (each speaker individually labeled, tied to Speaker IDs) β Sequential ordering of utterances β Minimum 95% precision β’ Confirm each transcript is fully human-generated or human QA-ed. Workflow: β’ 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. β’ 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. β’ You attach an attestation confirming the transcript is human-generated or human QA-ed. Requirements: β’ Native or near-native speaker of Sinhalese. β’ Comfortable working with JSON output (schema and conventions provided in our Transcriber Guide). β’ Access to a laptop or desktop computer. β’ Ability to deliver on rolling schedule aligned with 20-day tranches. What you'll be paid: β’ $5 per accepted audio hour transcribed, paid per accepted submission. β’ 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: β’ JSON transcript file per audio file (schema in the Transcriber Guide we'll share on acceptance). β’ Attestation that the transcript is human-generated or human QA-ed.
We need native Burmese speakers to transcribe conversational audio recordings for an AI training dataset. Transcripts must be fully human-generated or human QA-ed β no machine-only transcripts accepted. What you'll do: β’ Transcribe conversational Burmese audio files (2 minutes to 1 hour per file). β’ Deliver transcripts in JSON format with: β Time-coding (no overlaps across speakers) β Speaker diarization (each speaker individually labeled, tied to Speaker IDs) β Sequential ordering of utterances β Minimum 95% precision β’ Confirm each transcript is fully human-generated or human QA-ed. Workflow: β’ 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. β’ 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. β’ You attach an attestation confirming the transcript is human-generated or human QA-ed. Requirements: β’ Native or near-native speaker of Burmese. β’ Comfortable working with JSON output (schema and conventions provided in our Transcriber Guide). β’ Access to a laptop or desktop computer. β’ Ability to deliver on rolling schedule aligned with 20-day tranches. What you'll be paid: β’ $5 per accepted audio hour transcribed, paid per accepted submission. β’ 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: β’ JSON transcript file per audio file (schema in the Transcriber Guide we'll share on acceptance). β’ Attestation that the transcript is human-generated or human QA-ed.