Overview | Setup | CLAP weights | Usage | Examples | 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.

Setup

Install the dependencies: pip install -r requirements.txt using Python 3 to get started.

If you have conda installed, you can run the following:

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]

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

Usage

Zero-Shot Classification and Retrieval

# 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

# 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:

> cd src && python zero_shot_classification.py

Output (version 2023)

ESC50 Accuracy: 93.9%

Citation

Kindly cite our work if you find it useful.

CLAP: Learning Audio Concepts from Natural Language Supervision

@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

@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 provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. 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.