clap-htsat-fused
by laion
Contrastive Language-Audio Pretraining for audio-text retrieval
laion/clap-htsat-fusedmixpeek://audio_extractor@v1/laion_clap_fused_v1Overview
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
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| ESC-50 | Accuracy (zero-shot) | 93.7% | Wu et al., 2023: Table 2 |
| AudioCaps (text→audio) | Recall@1 | 36.7% | Wu et al., 2023: Table 3 |
Performance
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Specification
Research Paper
Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation
arxiv.orgBuild 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