AudioMosaic-vit-b16-finetune-esc-split5

<div align="center"> <a href="https://arxiv.org/abs/2605.14231" target="_blank"><img src="https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv" alt="arXiv"></a> <a href="https://huggingface.co/collections/hanxunh/audiomosaic" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-AudioMosaic-blue.svg" alt="HuggingFace"></a> <a href="https://github.com/HanxunH/AudioMosaic/blob/main/LICENSE" target="_blank"><img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green"></a> <a><img alt="Made with Python" src="https://img.shields.io/badge/made_with-Python-blue"></a> </div>

AudioMosaic: Contrastive Masked Audio Representation Learning

Code: https://github.com/HanxunH/AudioMosaic

Pretrained encoder: hanxunh/AudioMosaic-vit-b16-pretrained

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AudioMosaic — ESC-50 (fold 5) Classifier

This is the AudioMosaic ViT-B/16 encoder fine-tuned on ESC-50 (fold 5) for single-label audio classification over 50 classes.

Model Details

• Architecture: ViT-B/16 with linear classifier head
• Embedding dim: 768, Depth: 12, Heads: 12
• Input: log-mel spectrogram of size 1024 × 128
• Patch size: 16 × 16
• Pooling: average over patch tokens
• Classes: 50

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Model Usage

import sys, torch
from huggingface_hub import snapshot_download

local_dir = snapshot_download("hanxunh/AudioMosaic-vit-b16-finetune-esc-split5")
sys.path.insert(0, local_dir)

from load_model import load_classifier
model = load_classifier(device="cuda")

# Forward a log-mel spectrogram batch of shape [B, 1, 1024, 128]
fbank = torch.randn(2, 1, 1024, 128).cuda()
with torch.no_grad():
    logits = model(fbank)              # [B, 50]
    pred = logits.argmax(dim=-1)       # predicted class id

The release contains:

• model.safetensors — fine-tuned classifier weights
• config.json — architecture hyperparameters
• modeling.py — vendored model architecture (no need to install AudioMosaic)
• load_model.py — convenience loader

Required dependencies: torch, timm, torchlibrosa, safetensors, huggingface_hub.

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Citation

@inproceedings{huang2026audiomosaic,
  title={AudioMosaic: Contrastive Masked Audio Representation Learning},
  author={Hanxun Huang and Qizhou Wang and Xingjun Ma and Cihang Xie and Christopher Leckie and Sarah Erfani},
  booktitle={ICML},
  year={2026}
}