Audio Embeddings with Wav2CLIP

Multimodal Embeddings Part 2 (of 4)

Learn how to use PyTorch to embed audio clips into the same semantic vector space as text and images with CLIP.

i.e. directly compare audio waveforms with images and text.

No metadata like titles or descriptions. Just the magic of neural networks and embeddings.

Check out the video on YouTube

e.g. you can find audio for images. Below, I’m searching with a photo of fire 👇

Topics

• Objective

  • Compare audio clips with images and text for semantic similarity.

  • Applications include finding relevant audio files for images or search queries.

• Methodology

  • Using Wave to CLIP for generating audio embeddings from environmental audio recordings.

  • Dataset: ECS 50 (Environmental Sound Classification).

  • Wave to CLIP trained using contrastive learning.

• Contrastive Learning

  • Ensures audio clips and corresponding text descriptions are close in embedding space.

  • Uses triplet loss with anchor, positive, and negative data points.

  • Objective: Distance between anchor and positive should be smaller than between anchor and negative.

• Implementation

  • Load ECS 50 dataset and generate audio embeddings.

  • Find corresponding images for audio files using pre-collected images from Unsplash.

  • Compare audio files with text descriptions.

• Technical Details

  • Tools and libraries: NumPy, PyTorch, Librosa, Transformers from Hugging Face.

  • Steps: Load audio files, create embeddings, and batch processing.

  • Data storage: Efficient storage in tensors.

• Examples and Demonstrations

  • Matching audio files with images:

    • Examples: Rain sounds matched with images of rain.

    • Demonstrated with various audio files and corresponding images.

  • Matching images with audio files:

    • Examples: Fire image matched with crackling sounds.

    • Demonstrated with various images and corresponding audio files.

• Grand Example

  • Combined comparison of audio, images, and text.

  • Demonstrated with specific text queries like "helicopter" and "water".

• Conclusion

  • Impressed with the model's performance in matching audio with images and text.

Namaste,

Alex

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The task is … not so much to see what no one has yet seen; but to think what nobody has yet thought, about that which everybody sees.

Erwin Schrödinger

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