NEWVectors or files. Pick a path.Start →
    Models/Voice Activity Detection/kamilakesbi/speaker-segmentation-test

    speaker-segmentation-test

    by kamilakesbi

    Identifier
    Model ID
    kamilakesbi/speaker-segmentation-test

    Tags

    pyannotepytorchpyannote.audiopyannote-audio-modelaudiovoicespeechspeakerspeaker-diarizationspeaker-change-detectionspeaker-segmentationvoice-activity-detectionoverlapped-speech-detectionresegmentationspeaker-recognitionspeaker-verificationspeaker-identificationspeaker-embeddingPyTorchwespeakerregion:us

    Use speaker-segmentation-test on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, speaker-segmentation-test runs as a managed extractor inside a processing pipeline. Point a bucket of voice activity detection data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.