Audio Edit Quality Annotation for Speech Dataset Training

About OpenTrain

OpenTrain is a central job board for data-labeling and AI-training work, aggregating openings from many AI companies and labeling platforms so contributors can find relevant projects in one place. Creating an OpenTrain account is free and applying usually takes only a few minutes.

We help match skilled annotators with short-term and contract projects that improve how AI systems understand and handle real-world audio, text, and visual data.

About AI Training Work

AI training (also called data labeling or human feedback work) is the human effort behind modern machine learning. For speech systems, people listen, transcribe, rate, and annotate audio so models learn to produce natural, correct output.

This project contributes directly to making speech editing and synthesis models more accurate and natural by identifying where edits sound incorrect or unnatural and marking the exact sections that need attention.

The Role

You will review and annotate 5 hours of audio recordings to assess the quality of edits. For each edited audio file you will compare the edited audio to its unedited transcription and waveform, classify overall edit quality, mark specific issues, and segment regions with problematic edits.

This is a contract position, open worldwide, and uses Label Studio as the labeling platform. Familiarity with audio editing and detailed evaluation is required.

What You'll Do

Follow detailed annotation instructions to evaluate edited speech recordings and record your findings in Label Studio.

• Compare edited audio to the unedited transcription and waveform to find discrepancies or unnatural edits.
• Assign classification labels that capture overall naturalness and correctness of edits.
• Mark and segment specific problematic regions (temporal/bounding-box style annotations) where edits are bad or disruptive.
• Document the types of issues observed according to the provided schema (e.g., cut artifacts, timing errors, missing words) as instructed.

Requirements

All role facts come from the project brief; do not apply unless you meet the stated requirements.

• Some experience with audio editing is required.
• Basic proficiency using audio editing software and comfort viewing waveforms.
• Strong attention to detail and ability to follow thorough instructions.
• Familiarity with labeling tools such as Label Studio is a plus.
• Reliable internet connection and the ability to meet project deadlines.

Compensation & Logistics

This project pays per label at a rate of USD 0.1275 per label. The dataset to annotate is 5 hours of audio. Work is contract-based and contributors are responsible for their own taxes and expenses.

• Data type: Audio.
• Label types used: Classification and Bounding Box (temporal segmentation) in Label Studio.
• Employment type: Contractor.
• Open to applicants worldwide.

Who Should Apply

Apply if you have hands-on audio editing experience, an eye for subtle editing errors, and previous exposure to annotation workflows. This role is well suited for editors, audio technicians, and experienced annotators who can make fine-grained judgments about speech naturalness.

• You do not need advanced degrees; practical experience with audio editing tools and careful listening skills are essential.
• Familiarity with Label Studio or similar annotation platforms is advantageous but not mandatory.

How To Apply

Create a free OpenTrain account if you don't already have one, then submit your application for this project. Applications typically take only a few minutes.

Be prepared to confirm your audio-editing experience and your ability to meet project deadlines. If accepted, you will receive labeling instructions and access to the Label Studio workspace where you will complete the annotations.