DiariZen

A toolkit for speaker segmentation.

CommonProductProgrammingSpeaker SegmentationAudio Processing

DiariZen is a speaker segmentation toolkit powered by AudioZen and Pyannote 3.1. Speaker segmentation is a crucial step in audio processing, allowing the differentiation of various speakers within a segment of audio. This technology is widely applicable in fields such as meeting transcription, call monitoring, and security surveillance. Key advantages of DiariZen include its user-friendliness, high accuracy, and open-source nature, enabling researchers and developers to freely utilize and enhance it. DiariZen is available on GitHub under the MIT license, meaning it is completely free and can be used commercially.

Provides efficient speaker segmentation based on AudioZen and Pyannote 3.1.
Supports various public datasets
such as AMI
AISHELL-4
and AliMeeting
for model training and evaluation.
Offers pre-trained models and estimated RTTM files for convenient user access.
Supports speaker segmentation using WavLM Base+ and ResNet34-LM models.
Includes detailed installation and usage instructions to facilitate quick onboarding for users.
Open-source code allowing customization and optimization according to user needs.

\The target audience primarily consists of researchers and developers in the field of audio processing
especially those who require speaker segmentation for analyzing multi-speaker audio. DiariZen's ease of use and accuracy make it an ideal choice for both academic research and commercial applications.\

Researchers use DiariZen to segment audio from meetings to analyze speaking patterns.
Security agencies utilize DiariZen to process surveillance recordings for identifying and tracking specific individuals.
Developers integrate DiariZen into their applications to provide real-time speaker identification functionalities.

1. Create and activate a virtual Python environment.
2. Install DiariZen and its dependencies.
3. Download and prepare the required datasets.
4. Download pre-trained models such as WavLM Base+ and ResNet34-LM.
5. Modify the paths in the dataset and configuration files.

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