speaker-diarization

Maintainer: pyannote - Last updated 4/28/2024

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Model overview

The speaker-diarization model is an open-source pipeline created by pyannote, a company that provides AI consulting services. The model is used for speaker diarization, which is the process of partitioning an audio recording into homogeneous segments according to the speaker identity. This is useful for applications like meeting transcription, where it's important to know which speaker said what.

The model relies on the pyannote.audio library, which provides a set of neural network-based building blocks for speaker diarization. The pipeline comes pre-trained and can be used off-the-shelf without the need for further fine-tuning.

Model inputs and outputs

Inputs

  • Audio file: The audio file to be processed for speaker diarization.

Outputs

  • Diarization: The output of the speaker diarization process, which includes information about the start and end times of each speaker's turn, as well as the speaker labels. The output can be saved in the RTTM (Rich Transcription Time Marked) format.

Capabilities

The speaker-diarization model is a fully automatic pipeline that doesn't require any manual intervention, such as manual voice activity detection or manual specification of the number of speakers. It is benchmarked on a growing collection of datasets and achieves high accuracy, with low diarization error rates even in the presence of overlapped speech.

What can I use it for?

The speaker-diarization model can be used in various applications that involve audio processing, such as meeting transcription, audio indexing, and speaker attribution in podcasts or interviews. By automatically separating the audio into speaker turns, the model can greatly simplify the process of transcribing and analyzing audio recordings.

Things to try

One interesting aspect of the speaker-diarization model is its ability to handle a variable number of speakers. If the number of speakers is known in advance, you can provide this information to the model using the num_speakers option. Alternatively, you can specify a range for the number of speakers using the min_speakers and max_speakers options.

Another feature to explore is the model's real-time performance. The pipeline is benchmarked to have a real-time factor of around 2.5%, meaning it can process a one-hour conversation in approximately 1.5 minutes. This makes the model suitable for near-real-time applications, where fast processing is essential.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Total Score

659

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