msclap 1.3.4
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.
License: MIT
Requires Python: <4.0,>=3.8

###### [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="docs/clap2_diagram.png">

## Setup

First, install python 3.8 or higher (3.11 recommended). Then, install CLAP using either of the following:

```shell
# Install pypi pacakge
pip install msclap

# Or Install latest (unstable) git source
pip install git+https://github.com/microsoft/CLAP.git
```

## CLAP weights
CLAP weights are downloaded automatically (choose between versions _2022_, _2023_, and _clapcap_), but are also available at: [Zenodo](https://zenodo.org/record/8378278) or [HuggingFace](https://huggingface.co/microsoft/msclap)

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

## Usage

- Zero-Shot Classification and Retrieval
```python
from msclap import CLAP

# Load model (Choose between versions '2022' or '2023')
# The model weight will be downloaded automatically if `model_fp` is not specified
clap_model = CLAP(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
from msclap import CLAP

# Load model (Choose version 'clapcap')
clap_model = CLAP(version = 'clapcap', use_cuda=False)

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

## Examples
Take a look at [examples](./examples/) for usage examples. 

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

```bash
> cd examples && 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.

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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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