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

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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