# Speaker Segmentation

DiariZen
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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.
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Reverb
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Reverb is an open-source inference codebase for speech recognition and speaker segmentation models, utilizing the WeNet framework for ASR and the Pyannote framework for speaker segmentation. It offers detailed model descriptions and allows users to download models from Hugging Face. Reverb aims to provide developers and researchers with high-quality tools for various speech processing tasks.
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whisper-diarization
Whisper Diarization
whisper-diarization is an open-source project that integrates Whisper's automatic speech recognition (ASR) capabilities, Voice Activity Detection (VAD), and speaker embedding technology. It improves the accuracy of speaker embeddings by extracting the audible portions of audio, generating transcriptions using Whisper, and correcting timestamps and alignment through WhisperX to minimize segmentation errors caused by temporal offsets. Subsequently, MarbleNet is employed for VAD and segmentation to eliminate silence, while TitaNet is used to extract speaker embeddings for identifying speakers in each segment. Finally, the results are correlated with the timestamps generated by WhisperX, determining the speaker of each word based on timestamps and realigning with a punctuation model to compensate for minor timing offsets.
AI speech recognition
70.9K
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