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    Models/Embeddings/laion/clap-htsat-fused
    HFAudio Embeddingsapache-2.0

    clap-htsat-fused

    by laion

    Contrastive Language-Audio Pretraining for audio-text retrieval

    8.3Mdl/month
    126likes
    154Mparams
    Identifiers
    Model ID
    laion/clap-htsat-fused
    Feature URI
    mixpeek://audio_extractor@v1/laion_clap_fused_v1

    Overview

    CLAP learns aligned audio and text representations through contrastive learning, similar to how CLIP works for images and text. The HTSAT-fused variant uses the HTS-AT audio transformer fused with RoBERTa text embeddings.

    On Mixpeek, CLAP enables semantic audio search, find audio segments matching natural language descriptions like "crowd cheering" or "rain on a roof."

    Architecture

    HTS-AT (Hierarchical Token-Semantic Audio Transformer) as audio encoder, RoBERTa as text encoder. Trained on AudioSet, Clotho, and other audio-text pair datasets with contrastive loss. Outputs 512-dim joint embedding space.

    Mixpeek SDK Integration

    // No extractor parameter takes a Hugging Face model id (checked against
    // GET /v1/discovery/extractors, which returns 13), so clap-htsat-fused runs
    // on your side and the output is upserted through POST
    // /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
    // path is to upload the weights instead: POST /v1/namespaces/{id}/models
    // accepts the huggingface format and a custom plugin loads them.
    const res = await fetch(
      "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
      {
        method: "POST",
        headers: {
          Authorization: "Bearer API_KEY",
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          collection_id: "col_your_collection",
          documents: [
            {
              document_id: "asset-00412",
              // The vector name has to match a vector index on the collection.
              vectors: { "audio-embedding": yourVector },
              payload: { source_key: "archive/2026/asset-00412" },
            },
          ],
        }),
      },
    );
    
    // Managed alternative, if this exact model is not the requirement:
    // audio_fingerprint_extractor@v1 runs laion/clap-htsat-tiny
    // (512-d) over a bucket, with no inference of your own.

    Capabilities

    • Audio-text cross-modal retrieval
    • 512-dimensional audio embeddings
    • Zero-shot audio classification
    • Environmental sound recognition

    Use Cases on Mixpeek

    Sound effect search, find audio by description
    Music discovery, semantic similarity across audio tracks
    Environmental monitoring, classify ambient sounds

    Benchmarks

    DatasetMetricScoreSource
    ESC-50Accuracy (zero-shot)93.7%Wu et al., 2023: Table 2
    AudioCaps (text→audio)Recall@136.7%Wu et al., 2023: Table 3

    Performance

    Input Sizevariable audio (10s chunks typical)
    Embedding Dim512
    GPU Latency~6ms / chunk (A100)
    GPU Throughput~165 chunks/sec (A100)
    GPU Memory~0.5 GB

    Specification

    FrameworkHF
    Organizationlaion
    FeatureAudio Embeddings
    Output512-dim vector
    Modalitiesvideo, audio
    RetrieverAudio Similarity
    Parameters154M
    Licenseapache-2.0
    Downloads/mo8.3M
    Likes126

    Research Paper

    Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation

    arxiv.org

    Build a pipeline with clap-htsat-fused

    Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.

    Run it on your own data, free