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    Models/Voice Activity Detection/johnislarry/cloned-pyannote-speaker-diarization-endpoint
    Voice Activity Detectionpyannote-audiomit

    cloned-pyannote-speaker-diarization-endpoint

    by johnislarry

    Identifier
    Model ID
    johnislarry/cloned-pyannote-speaker-diarization-endpoint

    Tags

    pyannote-audiopyannotepyannote-audio-pipelineaudiovoicespeechspeakerspeaker-diarizationspeaker-change-detectionvoice-activity-detectionoverlapped-speech-detectiondataset:amidataset:diharddataset:voxconversedataset:aishelldataset:reperedataset:voxcelebarxiv:2012.01477license:mitendpoints_compatibleregion:us

    Use cloned-pyannote-speaker-diarization-endpoint 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, cloned-pyannote-speaker-diarization-endpoint 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.