laion/clap-htsat-unfused

feature-extraction model by laion

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Model card & documentation

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Model card for CLAP: Contrastive Language-Audio Pretraining The abstract of the paper states that: You can use this model for zero shot audio classification or extracting audio and/or textual features. You can also…

laion/clap-htsat-unfused on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-30T06:29:36.891Z

Inside the original model card — Document outline
  1. Model card for CLAP
  2. Table of Contents
  3. TL;DR
  4. Usage
  5. Uses
  6. Perform zero-shot audio classification
  7. Using pipeline
  8. Run the model:
  9. Run the model on CPU:
  10. Run the model on GPU:
  11. Citation

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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

laion/clap-htsat-unfused is a feature-extraction model repository published by laion on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
feature-extraction
Library
transformers
Recent downloads (30 days)
895,962
Cumulative likes
96
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
ClapModel
License
apache-2.0
Access
Not gated by Hugging Face
Created
2023-02-16T20:47:08.000Z
Last modified
2023-04-24T14:39:57.000Z

Compatibility and lineage

Language coverage was not reported.

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.

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