This repository contains a python package to compute distribution-based quality measures for audio data using embeddings, with a focus on music.
-
Fréchet Distance (see https://arxiv.org/abs/1812.08466 )
-
Kernel Distance/Maximum Mean Discrepancy (see https://arxiv.org/abs/1812.08466 )
-
Density and Coverage (see https://arxiv.org/abs/2002.09797 )
-
Audio Prompt Adherence (see https://arxiv.org/abs/2404.00775 )
The measures have in common that they compare a set of candidate audio tracks against a set of reference tracks, rather than evaluating individual tracks, and they all work on embedding representations of audio, obtained from models pretrained on tasks like audio classification.
The first two measures are typically used to measure audio quality (i.e. the naturalness of the sound, and the absence of acoustic artifacts). Density and Coverage explicitly measure how well the candidate set coincides with the reference set by comparing the embedding manifolds.
The Audio Prompt Adherence measures operates on sets whose elements are pairs of audio tracks, typically a mix and an accompaniment, and quantifies how well the accompaniment fits to the mix.
The measures can be combined with embeddings from any of the following models:
-
VGGish - https://arxiv.org/abs/1609.09430 Trained on audio event classification. 128-dimensional embeddings from the last feature layer before the classification layer.
-
OpenL3 - https://github.com/marl/openl3 Trained on music
-
Laion CLAP - https://github.com/LAION-AI/CLAP Trained on Music; Embeddings from the last three layers (512, 512, and 128-dimensional)
Download this repo your computer, and in the root directory of the repo, run:
pip install .
The following examples demonstrate the use of the package. Both examples are
also included under the ./examples directory.
The following code computes FAD and Kernel Distance for a (unrealistically small) set of audio samples:
from pathlib import Path
import json
import torch
from audio_metrics import (
async_audio_loader,
multi_audio_slicer,
EmbedderPipeline,
AudioMetrics,
CLAP,
)
from audio_metrics.example_utils import generate_audio_samples
audio_dir = Path("audio_samples")
win_dur = 5.0
n_pca = 64
dev = torch.device("cuda")
print("generating 'real' and 'fake' audio samples")
generate_audio_samples(audio_dir)
# load audio samples from files in `audio_dir`
real_items = async_audio_loader(audio_dir / "real")
fake_items = async_audio_loader(audio_dir / "fake")
# iterate over windows
real_items = multi_audio_slicer(real_items, win_dur)
fake_items = multi_audio_slicer(fake_items, win_dur)
print("creating embedder")
embedder = EmbedderPipeline({"clap": CLAP(dev)})
print("computing 'real' embeddings")
real_embeddings = embedder.embed_join(real_items)
print("computing 'fake' embeddings")
fake_embeddings = embedder.embed_join(fake_items)
# set the background data for the metrics
# use PCA projection of embeddings without whitening
metrics = AudioMetrics()
metrics.set_background_data(real_embeddings)
metrics.set_pca_projection(n_pca, whiten=True)
print("comparing 'real' to 'fake' data")
result = metrics.compare_to_background(fake_embeddings)
print(json.dumps(result, indent=2))Which will print the metrics (exact values may slightly vary):
{
"coverage_clap_audio_projection.0": 0.675,
"coverage_clap_audio_projection.2": 0.44,
"coverage_clap_output": 0.49,
"density_clap_audio_projection.0": 1.2675,
"density_clap_audio_projection.2": 0.605,
"density_clap_output": 0.7525,
"fad_clap_audio_projection.0": 25.192331663033556,
"fad_clap_audio_projection.2": 33.02863890811378,
"fad_clap_output": 32.31293025087572,
"kernel_distance_mean_clap_audio_projection.0": 0.5756649634334309,
"kernel_distance_mean_clap_audio_projection.2": 0.7890714981174441,
"kernel_distance_mean_clap_output": 0.7467294878649637,
"kernel_distance_std_clap_audio_projection.0": 0.03337777531962964,
"kernel_distance_std_clap_audio_projection.2": 0.04380359012320949,
"kernel_distance_std_clap_output": 0.04377045813837937,
"n_fake": 200,
"n_real": 200
}The Audio Prompt Adherence metric takes pairs of audio samples (mix, and accompaniment, respectively), and computes how well mixes and accompaniments fit together, given a background set of (mix,accompaniment) pairs. The following example shows how to compute the Audio Prompt Adherence metric:
from pathlib import Path
import json
import torch
from audio_metrics import (
async_audio_loader,
multi_audio_slicer,
AudioPromptAdherence,
)
from audio_metrics.example_utils import generate_audio_samples
audio_dir = Path("audio_samples")
win_dur = 5.0
dev = torch.device("cuda")
print("generating 'real' and 'fake' audio samples")
generate_audio_samples(audio_dir)
# load audio samples from files in `audio_dir`
real_items = async_audio_loader(audio_dir / "real", mono=False)
fake_items = async_audio_loader(audio_dir / "fake", mono=False)
# iterate over windows
real_items = multi_audio_slicer(real_items, win_dur)
fake_items = multi_audio_slicer(fake_items, win_dur)
metrics = AudioPromptAdherence(
dev, win_dur, n_pca=100, embedder="clap", metric="mmd"
)
metrics.set_background(real_items)
result = metrics.compare_to_background(fake_items)
print(json.dumps(result, indent=2))which will print something like this:
{
"audio_prompt_adherence": 0.15253860533909092,
"n_real": 200,
"n_fake": 200
}
For more information on the audio prompt adherence metric, and to cite this work use:
@misc{grachten2024measuring,
title={Measuring audio prompt adherence with distribution-based embedding distances},
author={Maarten Grachten},
year={2024},
eprint={2404.00775},
archivePrefix={arXiv},
primaryClass={cs.SD}
}