Speaker Diarization | AssemblyAI

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The Speaker Diarization model lets you detect multiple speakers in an audio file and what each speaker said.

If you enable Speaker Diarization, the resulting transcript will return a list of utterances, where each utterance corresponds to an uninterrupted segment of speech from a single speaker.

Speaker Diarization and multichannel

Speaker Diarization doesn’t support multichannel transcription. Enabling both Speaker Diarization and multichannel will result in an error.

Quickstart

Python SDK

Python

TypeScript SDK

TypeScript

C#

Ruby

PHP

To enable Speaker Diarization, set speaker_labels to True in the transcription config.

1 import assemblyai as aai
2 
3 aai.settings.api_key = "<YOUR_API_KEY>"
4 
5 # You can use a local filepath:
6 # audio_file = "./example.mp3"
7 
8 # Or use a publicly-accessible URL:
9 audio_file = (
10     "https://assembly.ai/wildfires.mp3"
11 )
12 
13 config = aai.TranscriptionConfig(
14   speaker_labels=True,
15 )
16 
17 transcript = aai.Transcriber().transcribe(audio_file, config)
18 
19 for utterance in transcript.utterances:
20   print(f"Speaker {utterance.speaker}: {utterance.text}")

Set number of speakers expected

You can set the number of speakers expected in the audio file by setting the speakers_expected parameter.

Only use this parameter if you are certain about the number of speakers in the audio file.

Example

$ curl https://api.assemblyai.com/v2/transcript \
> --header "Authorization: <YOUR_API_KEY>" \
> --header "Content-Type: application/json" \
> --data '{
>   "audio_url": "YOUR_AUDIO_URL",
>   "speaker_labels": true,
>   "speakers_expected": 3
> }'

Set a range of possible speakers

You can set a range of possible speakers in the audio file by setting the speaker_options parameter.

This parameter is suitable for use cases where there is a known minimum/maximum number of speakers in the audio file.

Setting max_speakers_expected higher than is necessary may hurt model accuracy.

Example

$ curl https://api.assemblyai.com/v2/transcript \
> --header "Authorization: <YOUR_API_KEY>" \
> --header "Content-Type: application/json" \
> --data '{
>   "audio_url": "YOUR_AUDIO_URL",
>   "speaker_labels": true,
>   "speaker_options": {
>     "min_speakers_expected": 3,
>     "max_speakers_expected": 5
>   }
> }'

API reference

Request

$ curl https://api.assemblyai.com/v2/transcript \
> --header "Authorization: <YOUR_API_KEY>" \
> --header "Content-Type: application/json" \
> --data '{
>   "audio_url": "YOUR_AUDIO_URL",
>   "speaker_labels": true,
>   "speakers_expected": 3
> }'

Key	Type	Description
`speaker_labels`	boolean	Enable Speaker Diarization.
`speakers_expected`	number	Set number of speakers.
`speaker_options`	object	Set range of possible speakers.
`speaker_options.min_speakers_expected`	number	The minimum number of speakers expected in the audio file.
`speaker_options.max_speakers_expected`	number	The maximum number of speakers expected in the audio file.

Response

Key	Type	Description
`utterances`	array	A turn-by-turn temporal sequence of the transcript, where the i-th element is an object containing information about the i-th utterance in the audio file.
`utterances[i].confidence`	number	The confidence score for the transcript of this utterance.
`utterances[i].end`	number	The ending time, in milliseconds, of the utterance in the audio file.
`utterances[i].speaker`	string	The speaker of this utterance, where each speaker is assigned a sequential capital letter. For example, “A” for Speaker A, “B” for Speaker B, and so on.
`utterances[i].start`	number	The starting time, in milliseconds, of the utterance in the audio file.
`utterances[i].text`	string	The transcript for this utterance.
`utterances[i].words`	array	A sequential array for the words in the transcript, where the j-th element is an object containing information about the j-th word in the utterance.
`utterances[i].words[j].text`	string	The text of the j-th word in the i-th utterance.
`utterances[i].words[j].start`	number	The starting time for when the j-th word is spoken in the i-th utterance, in milliseconds.
`utterances[i].words[j].end`	number	The ending time for when the j-th word is spoken in the i-th utterance, in milliseconds.
`utterances[i].words[j].confidence`	number	The confidence score for the transcript of the j-th word in the i-th utterance.
`utterances[i].words[j].speaker`	string	The speaker who uttered the j-th word in the i-th utterance.

The response also includes the request parameters used to generate the transcript.

Frequently asked questions & troubleshooting

How can I improve the performance of the Speaker Diarization model?

To improve the performance of the Speaker Diarization model, it’s recommended to ensure that each speaker speaks for at least 30 seconds uninterrupted. Avoiding scenarios where a person only speaks a few short phrases like “Yeah”, “Right”, or “Sounds good” can also help. If possible, avoiding cross-talking can also improve performance.

How many speakers can the model handle?

By default, the upper limit on the number of speakers for Speaker Diarization is 10. If you expect more than 10 speakers, you can use speaker_options to set a range of possible speakers. Please note, setting max_speakers_expected higher than necessary may hurt model accuracy.

How accurate is the Speaker Diarization model?

The accuracy of the Speaker Diarization model depends on several factors, including the quality of the audio, the number of speakers, and the length of the audio file. Ensuring that each speaker speaks for at least 30 seconds uninterrupted and avoiding scenarios where a person only speaks a few short phrases can improve accuracy. However, it’s important to note that the model isn’t perfect and may make mistakes, especially in more challenging scenarios.

Why is the speaker diarization not performing as expected?

The speaker diarization may be performing poorly if a speaker only speaks once or infrequently throughout the audio file. Additionally, if the speaker speaks in short or single-word utterances, the model may struggle to create separate clusters for each speaker. Lastly, if the speakers sound similar, there may be difficulties in accurately identifying and separating them. Background noise, cross-talk, or an echo may also cause issues.