Overview
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.
![clap_diagrams](/microsoft/msclap/resolve/main/docs/clap2_diagram.png)
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
CLAP weights: versions 2022, 2023, and clapcap
clapcap is the audio captioning model that uses the 2023 encoders.
Usage
CLAP code is in https://github.com/microsoft/CLAP
- 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])
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}
}
Trademarks
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