---
license: apache-2.0
base_model: laion/larger_clap_general
tags: [coreml, clap, audio, embeddings]
---

# CLAP (larger_clap_general) for Core ML — Gridshift

Core ML conversion of [`laion/larger_clap_general`](https://huggingface.co/laion/larger_clap_general), used by Gridshift's smart sample search.

- `GridshiftCLAP.mlpackage.zip`: audio encoder. Input `audio`: fp32 [1, 480000], mono 48 kHz, 10 s (HF `repeatpad` for shorter files). Output `embedding`: fp32 [1, 512], L2-normalized. Log-mel front-end in the graph (matches `ClapFeatureExtractor`), front-end ops fp32, int8 weights (convolutions excepted).
- `GridshiftCLAPText.mlpackage.zip`: text encoder. Inputs `input_ids`, `attention_mask`: int32 [1, 64] (RoBERTa tokenizer in `tokenizer.tokenizer.zip`). Output `embedding`: fp32 [1, 512], same space.

Validated against the PyTorch reference (`transformers` ClapModel): audio cosine ≥ 0.9995 per sample, pairwise structure Δ ≤ 0.012; text cosine ≥ 0.999. SHA-256s are in `manifest.json` and `text_manifest.json`.

Upstream: LAION-CLAP, Apache-2.0. Wu et al., 2022, *Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation* (arXiv:2211.06687).