Audio AI Labeling Workflow Guidance

I’m setting up a full-cycle workflow for labeling audio data that will later feed our AI models. The recordings include calls and free-form speech, and every file must end up with:

• A word-level transcript
• Accurate speaker diarization (who spoke and when)
• Tags for sentiment, topics, and other key events

I already have the raw audio and a basic storage setup; what I need now is your guidance on the most efficient, scalable way to handle annotation, quality control, and hand-off to model training. That covers tool selection (Label Studio, PyTorch, Whisper, pyannote, or similar), process design, automation scripts, and clear documentation so my in-house team can keep the pipeline running after you’re done.

Please be comfortable working with large audio datasets, ASR, speaker diarization, and sentiment/topic classification, and be able to share concrete examples of past labeling pipelines you’ve built. I’m based in South-East Asia and would like to collaborate closely, so overlapping hours with India, Pakistan, Europe, or the wider Asian region will be ideal.

Deliverables
• A written workflow/architecture plan
• Setup scripts or notebooks for the chosen labeling platform
• QA checklist and sampling strategy for label validation
• Handoff guide so my team can maintain and extend the process independently

If you can help me turn a pile of raw audio into clean, richly labeled data ready for model training, let’s talk.