Define a useful result
Decide whether you need a verbatim transcript, a translation or a summary. These are different outputs; confirm what your selected model returns before processing a full recording.
STT and transcription workflow
Once audio becomes text, the transcript can feed CRM, search, QA review, summaries or Call Metrics workflows.
App first, API when needed
Teams can validate the workflow in the Space app, then automate it through the API. Format, limits and endpoint details should stay governed by the official documentation.
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 audio source.
2
Transcribe it through the STT API or Space app.
3
Connect the transcript to CRM, search or analytics.
Which workflow fits your use case?
STT language pages
API and developer paths
Use case pages
Related pages
Frequently asked questions
Is Aisha suitable for Audio transcription API?
Start by testing adding audio transcription to products through an API with representative material. Confirm the target language and required features in Space or the API documentation before planning a full integration.
Can this work through an API?
Yes. The speech-to-text API supports audio submission, status checks and transcript retrieval.
Where are exact formats and limits documented?
Supported formats, limits and endpoint details are handled in the official Aisha API documentation.