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.
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
Request CLAP weights: Pretrained Model [Zenodo]
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)
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%
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}
}
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