DiariZen
Efficiently divide speakers, DiariZen can easily deal with the audio challenges of more speakers, open source and free, and are suitable for scientific research and commercial use!
English
English
What is DiariZen ?
DiariZen is an open source speaker segmentation toolkit based on AudioZen and Pyannote 3.1. It can distinguish different speakers in an audio segment and is a key technology in audio processing. DiariZen has become an ideal choice for researchers and developers for its ease of use, high accuracy and open source features.
Demand population:
DiariZen is mainly aimed at researchers and developers in the field of audio processing, especially those who need to analyze multi-speaker audio. Whether it is academic research or commercial applications, DiariZen can provide efficient solutions.
Example of usage scenarios:
1. Conference minutes: Researchers used DiariZen to segment the conference recordings and analyzed the speech patterns in the conference.
2. Security monitoring: Security agencies use DiariZen to process monitoring recordings, identifying and tracking specific individuals.
3. Real-time application: Developers integrate DiariZen into the application to provide real-time speaker recognition function.
Product Features:
1. Efficient segmentation: Based on AudioZen and Pyannote 3.1, it provides efficient speaker segmentation function.
2. Dataset Support: Supports multiple public datasets such as AMI, AISHELL-4, and AliMeeting for model training and evaluation.
3. Pre-trained model: Provides pre-trained model and estimated RTTM files for users to use directly.
4. Model selection: Supports speaker segmentation using WavLM Base+ and ResNet34-LM models.
5. Detailed instructions: Provide detailed installation and use instructions, which facilitates users to get started quickly.
6. Open source code: Open source code, allowing users to customize and optimize as needed.
Tutorials for use:
1. Create a virtual Python environment and activate it.
2. Install DiariZen and its dependencies.
3. Download and prepare the required data set.
4. Download pre-trained models such as WavLM Base+ and ResNet34-LM.
5. Modify the path to the dataset and configuration file.
6. Run the provided script to split the speaker.
7. Analyze the results and further process or visualize the segmented audio data as needed.
DiariZen 's open source features and high accuracy make it have a wide range of application prospects in the field of audio processing. Whether it is academic research or commercial applications, DiariZen can provide efficient solutions to help users easily divide speakers.
