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.
Project description
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
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
In CLAP\src\
:
- Zero-Shot Classification and Retrieval
from msclap import CLAP
# Load model (Choose between versions '2022' or '2023')
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
from msclap import CLAP
# Load model (Choose version 'clapcap')
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 examples 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 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for msclap-1.3.2.post1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a587028e102b5b2bd3fec7b91f8291f31b5bdb9e357d764c69050b8c7dddbcd5 |
|
MD5 | 65f4ef0e14ee214d4e5b2e6991e882f9 |
|
BLAKE2b-256 | 34be2dd322041102a30bf5daa51d53e737f151631f85047545655143d17e94c1 |