frechet-audio-distance 0.1.2

A lightweight library of Frechet Audio Distance calculation.

Frechet Audio Distance in PyTorch

A lightweight library of Frechet Audio Distance calculation.

Currently, we support embedding from:

• VGGish by S. Hershey et al.
• PANN by Kong et al..

Installation

pip install frechet_audio_distance

Demo

from frechet_audio_distance import FrechetAudioDistance

# to use `vggish`
frechet = FrechetAudioDistance(
    model_name="vggish",
    use_pca=False, 
    use_activation=False,
    verbose=False
)
# to use `PANN`
frechet = FrechetAudioDistance(
    model_name="pann",
    use_pca=False, 
    use_activation=False,
    verbose=False
)
fad_score = frechet.score("/path/to/background/set", "/path/to/eval/set", dtype="float32")

When computing the Frechet Audio Distance, you can choose to save the embeddings for future use. This capability not only ensures consistency across evaluations but can also significantly reduce computation time, especially if you're evaluating multiple times using the same dataset.

# Specify the paths to your saved embeddings
background_embds_path = "/path/to/saved/background/embeddings.npy"
eval_embds_path = "/path/to/saved/eval/embeddings.npy"

# Compute FAD score while reusing the saved embeddings (or saving new ones if paths are provided and embeddings don't exist yet)
fad_score = frechet.score(
    "/path/to/background/set",
    "/path/to/eval/set",
    background_embds_path=background_embds_path,
    eval_embds_path=eval_embds_path,
    dtype="float32"
)

Result validation

Test 1: Distorted sine waves on vggish (as provided here) [notes]

FAD scores comparison w.r.t. to original implementation in google-research/frechet-audio-distance

	baseline vs test1	baseline vs test2
google-research	12.4375	4.7680
frechet_audio_distance	12.7398	4.9815

Test 2: Distorted sine waves on PANN

	baseline vs test1	baseline vs test2
frechet_audio_distance	0.000465	0.00008594

To contribute

• Run python3 -m build to build your version locally. The built wheel should be in dist/.
• pip install your local wheel version, and run pytest test/ to validate your changes.

References

VGGish in PyTorch: https://github.com/harritaylor/torchvggish

Frechet distance implementation: https://github.com/mseitzer/pytorch-fid

Frechet Audio Distance paper: https://arxiv.org/abs/1812.08466

PANN paper: https://arxiv.org/abs/1912.10211