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

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

# 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 or HuggingFace

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

Usage

• Zero-Shot Classification and Retrieval

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

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 for usage examples.

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

> cd examples && 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.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com 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.