I'm building a meeting notes feature and need diarization plus timestamps. Which API fits best?

For meeting notes with diarization + timestamps, the best fit is usually OpenAI’s speech-to-text/transcription API, specifically a transcription endpoint that supports:

• word-level or segment-level timestamps
• speaker diarization (who spoke when), if available in the model/workflow you choose
• long-form meeting audio handling

Quick recommendation

If your main goal is accurate meeting transcription with timestamps, start with an API designed for transcription rather than generic speech recognition.

What to look for in the API

You want these features:

1. Diarization — labels speakers like Speaker 1 / Speaker 2
2. Timestamps — ideally word-level, otherwise segment-level
3. Robustness for noisy, multi-speaker meetings
4. JSON output — easier to map notes back to audio

Important note

Not every transcription API provides diarization natively. Some give:

• timestamps only
• speaker labels only
• or require a separate diarization pass using another service/model

Best practical options

• OpenAI transcription API: good choice if you want high-quality transcription and structured outputs.
• If diarization is essential and must be very accurate, you may also consider combining:
  • a transcription API
  • plus a dedicated diarization service/model

If you want the simplest architecture

A common pipeline is:

1. Upload meeting audio
2. Transcribe with timestamps
3. Run diarization
4. Merge speaker labels with transcript segments
5. Generate notes/action items

My suggestion

If you want one API to start with, use OpenAI’s transcription API and verify whether its current output format meets your diarization needs. If not, pair it with a dedicated diarization tool.

If you want, I can also give you:

• a feature-by-feature comparison of OpenAI vs Deepgram vs AssemblyAI vs Google
• or a recommended architecture for building meeting notes with speaker labels and timestamps.