How to Use Amazon Transcribe Speaker Diarization

Before you start

Amazon Transcribe speaker diarization runs as a cloud service. You need an AWS account with permission to start transcription jobs and access the input audio in an Amazon S3 bucket. The audio file must be in a format supported by Amazon Transcribe, and the media should contain speech from two or more speakers for useful labeling.

Check the input device and recording setup before uploading. Set the input level so speech is clear without clipping, use a stable sample rate, and reduce background noise as much as possible. Diarization identifies speaker changes from the audio, so consistently recorded voices make the labels more reliable.

Step-by-step instructions

Open the Amazon Transcribe console and choose Create job. Select the language for the audio and point the job to the S3 URI of the input file. For the output, choose an S3 location where you have write access.

In Audio settings, enable Audio identification. Select Speaker diarization and specify the number of speakers if the audio uses a known fixed number of speakers. If the number varies, use speaker clustering and let Amazon Transcribe detect the speaker turns.

Keep the change reversible by saving a separate output prefix for the diarized result. You can run another job with different speaker settings without overwriting the first transcription.

Check the output

Open the completed job and download the full transcript from the output S3 location. In the JSON result, look for items that include a speaker_label field such as spk_0 and spk_1. Each segment should contain the transcript text and its associated speaker label.

Verify that speaker changes align approximately with the audio. Diarization may split a single speaker into more than one label or merge similar voices. Short speaker turns and overlapping speech are common limitations.

Common problems and next steps

If speaker labels seem inaccurate, first reduce background noise and check whether the recording has consistent input levels. Avoid compressed files with heavy processing if possible. Then rerun the job with a known speaker count when the number of talkers is fixed.

If you need to control speaker identification by channel, use a separate recording channel for each talker and select channel-based transcription instead of speaker diarization. This gives more predictable labels but requires the right output device and recording setup from the start.