How can I create a multi-speaker transcript with speaker diarization using Whisper?

When it comes to reliably separating speakers, the two main ingredients are:

1. Speech Transcription: Converting audio into text (this is what Whisper excels at).
2. Speaker Diarization: Distinguishing “who spoke when” in the audio.

Why Whisper Alone Isn't Enough

Whisper is designed for transcription rather than speaker diarization. It might pick up on some cues in the text (especially if speakers use distinctive phrases or if the conversation is structured), but it doesn't use the underlying audio characteristics—like voice timbre—to robustly differentiate speakers. This can limit its accuracy, especially in less structured or noisier dialogues.

The Standard Approach

Most robust transcription tools, such as fireflies.ai, typically follow this workflow:

1. Diarization Stage:
   Use a dedicated speaker diarization model (e.g., models available in pyannote.audio or other diarization systems) that analyzes the raw audio. This stage outputs time-stamped speaker segments, which tell you which parts of the audio belong to which speaker.

2. Transcription Stage:
   Run a transcription model like Whisper on the audio. The transcription can then be segmented based on the diarization output.

3. Integration:
   Combine the diarized segments with the transcribed text. This way, you can format the output as:

   PLAINTEXTCopied
   Speaker 1: [transcribed text for segment]
   Speaker 2: [transcribed text for segment]
   Speaker 1: [transcribed text for next segment]

Why Use Audio-Based Diarization?

By analyzing the sound directly, the diarization model leverages features such as:

• Voice timbre: Recognizing unique characteristics in each speaker’s voice.
• Pauses and overlaps: Determining turn-taking even when speakers interrupt, which is hard to capture from the transcript alone.

Even if you're tempted to work solely with text (as your experiment with Whisper hinted at), the lack of direct audio cues means you're less likely to capture the true speaker boundaries, especially in less structured or more spontaneous dialogues.

Conclusion

To achieve a fully formatted multi-speaker transcript like:

PLAINTEXTCopied
Speaker 1: …  
Speaker 2: …  
Speaker 1: …

the recommended approach is to use a dedicated speaker diarization tool to preprocess the audio and identify speaker segments, then integrate those segments with the transcription (from Whisper or another ASR system). This hybrid approach gives you the most reliable identification and formatting of speakers in your transcript.