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    Models/Audio Classification/soniqo/WeSpeaker-ResNet34-LM-LiteRT
    Audio Classificationlitertcc-by-4.0

    WeSpeaker-ResNet34-LM-LiteRT

    by soniqo

    Identifier
    Model ID
    soniqo/WeSpeaker-ResNet34-LM-LiteRT

    Tags

    literttflitespeaker-embeddingspeaker-recognitiondiarizationon-devicesoniqospeech-cloudspeech-coreaudio-classificationmultilingualbase_model:pyannote/wespeaker-voxceleb-resnet34-LMbase_model:finetune:pyannote/wespeaker-voxceleb-resnet34-LMlicense:cc-by-4.0region:us

    Use WeSpeaker-ResNet34-LM-LiteRT 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, WeSpeaker-ResNet34-LM-LiteRT runs as a managed extractor inside a processing pipeline. Point a bucket of audio classification 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.