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# Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
###  CVPR 2024


[![Website](https://img.shields.io/badge/DenseAV-%F0%9F%8C%90Website-purple?style=flat)](https://aka.ms/denseav) [![arXiv](https://img.shields.io/badge/arXiv-2406.05629-b31b1b.svg)](https://arxiv.org/abs/2406.05629) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mhamilton723/DenseAV/blob/main/demo.ipynb)

[![Huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DenseAV-orange)](https://huggingface.co/spaces/mhamilton723/DenseAV) 

[//]: # ([![Huggingface]&#40;https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper%20Page-orange&#41;]&#40;https://huggingface.co/papers/2403.10516&#41;)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/separating-the-chirp-from-the-chat-self/speech-prompted-semantic-segmentation-on)](https://paperswithcode.com/sota/speech-prompted-semantic-segmentation-on?p=separating-the-chirp-from-the-chat-self)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/separating-the-chirp-from-the-chat-self/sound-prompted-semantic-segmentation-on)](https://paperswithcode.com/sota/sound-prompted-semantic-segmentation-on?p=separating-the-chirp-from-the-chat-self)


[Mark Hamilton](https://mhamilton.net/),
[Andrew Zisserman](https://www.robots.ox.ac.uk/~az/),
[John R. Hershey](https://research.google/people/john-hershey/),
[William T. Freeman](https://billf.mit.edu/about/bio)

![DenseAV Overview Graphic](https://mhamilton.net/images/hero_fig_black.jpg)

**TL;DR**:Our model, DenseAV, learns the meaning of words and the location of sounds (visual grounding) without supervision or text.

https://github.com/mhamilton723/DenseAV/assets/6456637/ba908ab5-9618-42f9-8d7a-30ecb009091f


## Contents
<!--ts-->
   * [Install](#install)
   * [Model Zoo](#model-zoo)
   * [Getting Datasets](#getting-atasets)
   * [Evaluate Models](#evaluate-models)
   * [Train a Model](#train-model)
   * [Local Gradio Demo](#local-gradio-demo)
   * [Coming Soon](coming-soon)
   * [Citation](#citation)
   * [Contact](#contact)
<!--te-->

## Install

To use DenseAV locally clone the repository:

```shell script

git clone https://github.com/mhamilton723/DenseAV.git

cd DenseAV

pip install -e .

```


## Model Zoo

To see examples of pretrained model usage please see our [Collab notebook](https://colab.research.google.com/github/mhamilton723/DenseAV/blob/main/demo.ipynb). We currently supply the following pretrained models:

| Model Name                    | Checkpoint                                                                                                                       | Torch Hub Repository | Torch Hub Name     |
|-------------------------------|----------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------------|
| Sound                         | [Download](https://marhamilresearch4.blob.core.windows.net/denseav-public/hub/denseav_sound.ckpt) | mhamilton723/DenseAV | sound              |
| Language                      | [Download](https://marhamilresearch4.blob.core.windows.net/denseav-public/hub/denseav_language.ckpt) | mhamilton723/DenseAV | language           |
| Sound + Language (Two Headed) | [Download](https://marhamilresearch4.blob.core.windows.net/denseav-public/hub/denseav_2head.ckpt)   | mhamilton723/DenseAV | sound_and_language |

For example, to load the model trained on both sound and language:

```python

model = torch.hub.load("mhamilton723/DenseAV", 'sound_and_language')

```

### Load from HuggingFace

```python

from denseav.train import LitAVAligner



model1 = LitAVAligner.from_pretrained("mhamilton723/DenseAV-sound")

model2 = LitAVAligner.from_pretrained("mhamilton723/DenseAV-language")

model3 = LitAVAligner.from_pretrained("mhamilton723/DenseAV-sound-language")

```


## Getting Datasets

Our code assumes that all data lives in a common directory on your system, in these examples we use `/path/to/your/data`. Our code will often reference this directory as the `data_root`

### Speech and Sound Prompted ADE20K

To download our new Speech and Sound prompted ADE20K Dataset:

```bash

cd /path/to/your/data

wget https://marhamilresearch4.blob.core.windows.net/denseav-public/datasets/ADE20KSoundPrompted.zip

unzip ADE20KSoundPrompted.zip

wget https://marhamilresearch4.blob.core.windows.net/denseav-public/datasets/ADE20KSpeechPrompted.zip

unzip ADE20KSpeechPrompted.zip

```

### Places Audio

First download the places audio dataset from its [original source](https://groups.csail.mit.edu/sls/downloads/placesaudio/downloads.cgi).

To run the code the data will need to be processed to be of the form:

```

[Instructions coming soon]

```

### Audioset

Because of copyright issues we cannot make [Audioset](https://research.google.com/audioset/dataset/index.html) easily availible to download.
First download this dataset through appropriate means. [This other project](https://github.com/ktonal/audioset-downloader) appears to make this simple.

To run the code the data will need to be processed to be of the form:

```

[Instructions coming soon]

```


## Evaluate Models

To evaluate a trained model first clone the repository for
[local development](#local-development). Then run

```shell

cd featup

python evaluate.py

```

After evaluation, see the results in tensorboard's hparams tab. 

```shell

cd ../logs/evaluate

tensorboard --logdir .

```

Then visit [https://localhost:6006](https://localhost:6006) and click on hparams to browse results. We report "advanced" speech metrics and "basic" sound metrics in our paper.


## Train a Model

```shell

cd denseav

python train.py

```

## Local Gradio Demo

To run our [HuggingFace Spaces hosted DenseAV demo](https://huggingface.co/spaces/mhamilton723/FeatUp) locally first install DenseAV for local development. Then  run:

```shell

python gradio_app.py

```

Wait a few seconds for the demo to spin up, then navigate to [http://localhost:7860/](http://localhost:7860/) to view the demo.


## Coming Soon:

- Bigger models!

## Citation

```

@misc{hamilton2024separating,

      title={Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language}, 

      author={Mark Hamilton and Andrew Zisserman and John R. Hershey and William T. Freeman},

      year={2024},

      eprint={2406.05629},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```

## Contact

For feedback, questions, or press inquiries please contact [Mark Hamilton](mailto:[email protected])