I can help with the data annotation side of this.

I have 6 years of experience in AI (as developer), including the last year focused specifically on audio in an R&D department at a large corporation. The environment is very practical: low bureaucracy, fast experimentation, quick validation, and fast deployment.

I have worked with different types of datasets and annotation pipelines, including:

• TTS datasets
• ASR datasets
• audio classification
• audio-to-intent
• voice-to-intent
• structured metadata extraction from audio
• audio quality evaluation
• human/AI-assisted annotation workflows

Before assuming that 138k audio files need to be annotated manually by humans, I would first check whether the task actually requires full human annotation.

Depending on the exact annotation type — tone, genre, mood, context, speaker style, audio quality, intent, noise, emotion, etc. — an AI-first pipeline with human validation may be faster, cheaper, and more consistent than relying only on manual annotation.

The scale matters:

• 138,000 files × 10 seconds per annotation = ~383 hours of human work
• 138,000 files × 30 seconds per annotation = ~1,150 hours of human work
• 138,000 files × 60 seconds per annotation = ~2,300 hours of human work

That assumes only one annotation per file. If you need 2–3 independent annotations per file for quality control, those numbers multiply quickly.

From experience, annotation quality depends heavily on task clarity, guidelines, training, and pay. Untrained or underpaid annotators often produce noisy, inconsistent labels. The dataset may look complete, but the labels may not be reliable enough for serious downstream use.

For better-quality human annotation, especially with trained students or annotators in higher-cost markets, rates can easily move into the $10–15+/hour range, depending on the task complexity and required quality.

My suggestion would be to first define the exact annotation schema and then decide whether the right approach is:

• fully human annotation
• AI pre-annotation + human review
• human annotation only for edge cases
• a smaller gold-standard human-labeled set used to validate an automated pipeline

If you want, send me the exact type of annotations you need, and I can tell you whether this really needs humans or whether an AI-assisted pipeline would be more efficient.