Canadian French Audio Transcriber

About OpenTrain

OpenTrain is the centralized platform where people start and grow careers in AI training and data labeling. We help freelancers find projects, build a unified portfolio, and track work across the AI training ecosystem.

OpenTrain connects contributors with real projects that teach AI systems how language, speech, and other data behave—making it easier to build a durable freelance career in this fast-growing field.

Why AI training matters

AI systems learn from human-created examples. That means careful transcription, tagging, and annotation are essential to build accurate speech and language models. This work is remote, often flexible, and a direct way to shape how modern AI understands Canadian French.

• Work remotely on real-world language data used to train speech and NLP models.
• Flexible, part-time contractor roles are common—fit work around other commitments.
• Many projects require no prior AI experience; language expertise and attention to detail are often enough.

The role

OpenTrain is recruiting Canadian French transcription and annotation contractors to review, correct, and enrich speech transcripts. This is a remote, part-time contractor role expected to run at 20+ hours per week.

You will focus on Canadian French linguistic accuracy and metadata that improves downstream model quality.

• Employment type: Contractor, Part-time.
• Location: Remote (worldwide).
• Time requirement: 20+ hours/week.
• Languages: Canadian French (fr).
• Data type: Audio; Label types: Transcription and Entity/NER classification.
• Tooling: Other transcription/annotation platforms.
• Pay: $15–$25 USD per hour (PAY_PER_HOUR).

What you'll do

Your day-to-day work centers on improving machine-generated transcripts and adding rich annotations that capture linguistic detail and entities in Canadian French audio.

• Review and transcribe machine-generated French transcripts for Canadian French accuracy.
• Correct errors and inconsistencies; perform careful quality control.
• Add and refine metadata tags and detailed annotations for speech and text files.
• Document changes and annotations to meet project standards.
• Share feedback on language nuances, cultural context, and workflow improvements.
• Work under confidentiality and defined quality guidelines while collaborating with the project team.

Requirements

Candidates must meet the core linguistic and quality standards below. These are essential for ensuring high-quality training data.

• Fluent proficiency in Canadian French, including grammar, spelling, and regional vocabulary.
• Strong transcription and editing ability, especially with machine-generated content.
• Experience with metadata tagging and detailed annotation for audio or text files.
• Strong written and verbal communication skills.
• Ability to work independently in a remote environment and maintain confidentiality.
• High attention to detail and commitment to accuracy and quality control.

Helpful background

The following experiences make you especially competitive but are not required.

• Professional transcriber or audio engineer experience.
• Prior data annotator or language-data pipeline experience.
• Familiarity with AI training workflows and transcription platforms.

Pay, schedule, and logistics

This contractor role pays between $15 and $25 USD per hour, as reflected in the OpenTrain budget. Work is remote and typically requires at least 20 hours per week; exact scheduling is set with the project team.

No prior AI experience is required. Transcription tools or platforms are helpful but not mandatory.

How to apply

If you meet the requirements and are interested, apply to the OpenTrain project through the OpenTrain listing. Include examples of past transcription or annotation work when available.

Qualified applicants will be assessed for language accuracy and annotation quality before beginning paid tasks.

• Prepare a brief summary of relevant experience and any sample transcripts.
• Expect a short qualification or test task focused on Canadian French transcription standards.