Speaker Diarization is the process of identifying and distinguishing the different speakers in a speech/audio file. In this kit, we demonstrate the application of Speaker Diarization concept using open source libraries. To install this kit, scroll down to refer 'Kit Deployment Instructions' section and follow instructions.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Machine Learning

Transformers and Pytorch hub are state of the art libraries that provide pre-trained models for various ML/AI applications.

Kit Solution Source

Speaker Diarization solution created using this kit is added to this section. The entire solution is available as a package to download from the source code repository.

Kit Deployment Instructions

1.Download, extract and double-click kit_installer file to install the kit. 2. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook. 3. To run the kit manually, press 'N' and locate the zip file 'speaker-diarization'. 4.Extract the zip file and navigate to the directory 'speaker-diarization-master' 5. Open command prompt in the extracted directory 'speaker-diarization-master' and run the command 'jupyter notebook' 6. Locate and open the 'SpeakerDiarization.ipynb' notebook from the Jupyter Notebook browser window. 7. Execute cells in the notebook Note: Demo source code will be downloaded to local machine. It is also available here


If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.