Aisha AI
by voicelab

AI voice workflow

Meeting transcription

Turn a meeting recording into a transcript, then connect reviewed notes to team records and search. Optional summary and speaker-diarization flags are available; review both outputs against the audio.

Aisha AI · Updated

Try STT API docs

Use case

Internal team meetings
Searchable archive of remote discussions
Draft notes on project decisions
Training and seminar recordings

Prepare the meeting recording

Consider the language, microphone quality and overlapping speech in the recording. Use the STT API reference for accepted audio requirements.

Review notes with a person

Compare the transcript with the recording and separately verify decisions, dates, names and actions. Identify participants manually when needed.

What to check before launch

Evaluate results with your own material. Keep separate results for each language and recording or playback condition.

Try a representative sample

Transcribe a short recording. Compare names, numbers, negations and language switches against the audio.

Connect it to the product

Keep API credentials on your server. Check documented formats, limits and response states, and test a failed request before automating the process.

Review each language

Do not generalize one language result to the entire catalog. Ask someone familiar with the target accent and terminology to review the output.

How it works

1

Prepare the meeting recording in an audio format documented by the API.

2

Send the recording through the STT workflow and retrieve the transcript.

3

Verify decisions and actions against the audio, then enter reviewed notes in your team system.

Practical guides

Multilingual audio transcription: a practical guide

Prepare multilingual recordings, separate transcription from translation and check names, numbers and language switches before using the transcript.

Which workflow fits your use case?

Frequently asked questions

Does Aisha create meeting minutes or summaries?

The STT API documents an optional `is_summary=true` field. A generated summary is not a verified set of minutes or action items, so review decisions and tasks against the audio.

Will the transcript identify each speaker by name?

Set `has_diarization=true` to request speaker separation; v1 audio must be at least 15 seconds. Speaker labels distinguish voices but do not identify people by their real names.

Will every language spoken in a meeting be detected?

Check the request language and supported languages in the API reference. Verify language switches against the recording.