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    Voice Activity Detectionpyannote-audioopenrail

    brouhaha

    by pyannote

    Identifier
    Model ID
    pyannote/brouhaha

    Tags

    pyannote-audiopytorchpyannotepyannote-audio-modelaudiovoicespeechvoice-activity-detectionspeech-to-noise ratiosnrroom acousticsc50dataset:LibriSpeechdataset:AudioSetdataset:EchoThiefdataset:MIT-Acoustical-Reverberation-Scenearxiv:2210.13248license:openrailregion:us

    Use brouhaha 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, brouhaha 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.