alisonbma / clap-microsoft

Learning audio concepts from natural language supervision

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.

Setup

You are required to just 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=3.8 && \
conda activate clap && \
pip install -r requirements.txt

CLAP weights

Request CLAP weights: Pretrained Model [Zenodo]

Usage

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

• Load model

from src import CLAP 

clap_model = CLAP("<PATH TO WEIGHTS>", 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

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:

> cd src && python zero_shot_classification.py

Output

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.

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.