AudioCLIP Extending CLIP to Image, Text and Audio

Last update: Jan 02, 2023

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Overview

AudioCLIP

Extending CLIP to Image, Text and Audio

This repository contains implementation of the models described in the paper arXiv:2106.13043. This work based on our previous works:

ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio (2021).
ESResNet: Environmental Sound Classification Based on Visual Domain Models (2020).

Abstract

In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models.

In this work, we present an extension of the CLIP model that handles audio in addition to text and images. Our proposed model incorporates the ESResNeXt audio-model into the CLIP framework using the AudioSet dataset. Such a combination enables the proposed model to perform bimodal and unimodal classification and querying, while keeping CLIP's ability to generalize to unseen datasets in a zero-shot inference fashion.

AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90.07% on the UrbanSound8K and 97.15% on the ESC-50 datasets. Further it sets new baselines in the zero-shot ESC-task on the same datasets (68.78% and 69.40%, respectively).

Finally, we also assess the cross-modal querying performance of the proposed model as well as the influence of full and partial training on the results. For the sake of reproducibility, our code is published.

How to Run the Model

The required Python version is >= 3.7.

AudioCLIP

On the ESC-50 dataset

python main.py --config protocols/audioclip-esc50.json --Dataset.args.root /path/to/ESC50

On the UrbanSound8K dataset

python main.py --config protocols/audioclip-us8k.json --Dataset.args.root /path/to/UrbanSound8K

Cite Us

@misc{guzhov2021audioclip,
      title={AudioCLIP: Extending CLIP to Image, Text and Audio}, 
      author={Andrey Guzhov and Federico Raue and Jörn Hees and Andreas Dengel},
      year={2021},
      eprint={2106.13043},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

Comments

Make project usable by other python projects: remove git lfs and move files into an audioclip folder

Git lfs was giving problems, so I removed all assets files from it - the files can be found in the "Release" anyways.

Also it was a bit problematic to use this project in other projects because the folder structure was lacking. I moved all files into an "audioclip" folder to fix python pathing for external projects.

I renamed master to main, but I doubt that this change is going to stay once this pull request is merged.

opened by NotNANtoN 0

Releases(v0.1)

v0.1(Jun 29, 2021)
Text embeddings' vocabulary and PyTorch' state_dicts containing weights of the AudioCLIP model trained on AudioSet:

bpe_simple_vocab_16e6.txt.gz – CLIP's vocabulary (origin)

CLIP.pt – vanilla CLIP (text Transformer & ResNet-50 image-head, origin)

ESRNXFBSP.pt – ESResNeXt trained on AudioSet (standalone)

AudioCLIP trained on AudioSet (+ video frames)

AudioCLIP-Full-Training.pt – training of all three heads (text, image and audio)

AudioCLIP-Partial-Training.pt – training of the audio-head only

Source code(tar.gz)
Source code(zip)
AudioCLIP-Full-Training.pt(512.41 MB)
AudioCLIP-Partial-Training.pt(512.41 MB)
bpe_simple_vocab_16e6.txt.gz(1.29 MB)
CLIP.pt(389.49 MB)
ESRNXFBSP.pt(119.01 MB)

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AudioCLIP Extending CLIP to Image, Text and Audio

Related tags

Overview

AudioCLIP

Extending CLIP to Image, Text and Audio

Abstract

How to Run the Model

AudioCLIP

On the ESC-50 dataset

On the UrbanSound8K dataset

Cite Us

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Comments

Make project usable by other python projects: remove git lfs and move files into an audioclip folder

Releases(v0.1)

v0.1(Jun 29, 2021)

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