ms_clap/README.md

###### [Overview](#CLAP) | [Setup](#Setup) | [CLAP weights](#CLAP-weights) | [Usage](#Usage) | [Examples](#Examples) | [Citation](#Citation)

# CLAP

CLAP (Contrastive Language-Audio Pretraining) is a model that learns acoustic concepts from natural language supervision and enables “Zero-Shot” inference. The model has been extensively evaluated in 26 audio downstream tasks achieving SoTA in several of them including classification, retrieval, and captioning.

<img width="832" alt="clap_diagrams" src="https://github.com/bmartin1/CLAP/assets/26778834/c5340a09-cc0c-4e41-ad5a-61546eaa824c">

## Setup

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=3.10 && \
conda activate clap && \
pip install -r requirements.txt
```

## NEW CLAP weights
Download CLAP weights: versions _2022_, _2023_, and _clapcap_: [Pretrained Model \[Zenodo\]](https://zenodo.org/record/8378278)

_clapcap_ is the audio captioning model that uses the 2023 encoders.

## Usage

- Zero-Shot Classification and Retrieval
```python
# Load model (Choose between versions '2022' or '2023')
from src import CLAP 

clap_model = CLAP("<PATH TO WEIGHTS>", version = '2023', use_cuda=False)

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

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

# Compute similarity between audio and text embeddings 
similarities = clap_model.compute_similarity(audio_embeddings, text_embeddings)
```

- Audio Captioning
```python
# Load model (Choose version 'clapcap')
from src import CLAP 

clap_model = CLAP("<PATH TO WEIGHTS>", version = 'clapcap', use_cuda=False)

# Generate audio captions
captions = clap_model.generate_caption(file_paths: List[str])
```

## Examples
Take a look at `CLAP\src\` for usage examples. 

To run Zero-Shot Classification on the ESC50 dataset try the following:

```bash
> cd src && python zero_shot_classification.py
```
Output (version 2023)
```bash
ESC50 Accuracy: 93.9%
```

## Citation

Kindly cite our work if you find it useful.

[CLAP: Learning Audio Concepts from Natural Language Supervision](https://ieeexplore.ieee.org/abstract/document/10095889)
```
@inproceedings{CLAP2022,
  title={Clap learning audio concepts from natural language supervision},
  author={Elizalde, Benjamin and Deshmukh, Soham and Al Ismail, Mahmoud and Wang, Huaming},
  booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}
```

[Natural Language Supervision for General-Purpose Audio Representations](https://arxiv.org/abs/2309.05767)
```
@misc{CLAP2023,
      title={Natural Language Supervision for General-Purpose Audio Representations}, 
      author={Benjamin Elizalde and Soham Deshmukh and Huaming Wang},
      year={2023},
      eprint={2309.05767},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2309.05767}
}
```

## 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.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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 [[email protected]](mailto:[email protected]) with any additional questions or comments.

## Trademarks

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trademarks or logos is subject to and must follow 
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.