# CLAP

CLAP (Contrastive Language-Audio Pretraining) is a neural network model that learns acoustic concepts from natural language supervision. It achieves SoTA in “Zero-Shot” classification, Audio-Text & Text-Audio Retrieval, and in some datasets when finetuned.

<img width="832" alt="clap_diagram_v3" src="https://user-images.githubusercontent.com/26778834/199842089-39ef6a2e-8abb-4338-bdfe-680abab70f53.png">

## Setup

You are required to just install the dependencies: `pip install -r requirements.txt` using Python 3 to get started.

If you have [conda](https://www.anaconda.com) installed, you can run the following: 

```shell
git clone https://github.com/microsoft/CLAP.git && \
cd CLAP && \
conda create -n clap python=3.8 && \
conda activate clap && \
pip install -r requirements.txt
```

## CLAP weights
Request CLAP weights: [Pretrained Model \[Zenodo\]](https://zenodo.org/record/7312125#.Y22vecvMIQ9)


## Usage

Please take a look at `src/examples` for usage examples. 

- Load model
```python
from src import CLAP 

clap_model = CLAP("<PATH TO WEIGHTS>", use_cuda=False)
```

- Extract text embeddings
```python
text_embeddings = clap_model.get_text_embeddings(class_labels: List[str])
```

- Extract audio embeddings
```python
audio_embeddings = clap_model.get_audio_embeddings(file_paths: List[str])
```

- Compute similarity 
```python
sim = clap_model.compute_similarity(audio_embeddings, text_embeddings)
```

## Examples
To run zero-shot evaluation on the ESC50 dataset or a single audio file from ESC50, check `CLAP\src\`. For zero-shot evaluation on the ESC50 dataset:
```bash
> cd src && python zero_shot_classification.py
```
Output
```bash
ESC50 Accuracy: 82.6%
```

## Citation
https://arxiv.org/pdf/2206.04769.pdf
```
@article{elizalde2022clap,
  title={Clap: Learning audio concepts from natural language supervision},
  author={Elizalde, Benjamin and Deshmukh, Soham and Ismail, Mahmoud Al and Wang, Huaming},
  journal={arXiv preprint arXiv:2206.04769},
  year={2022}
}
```

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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