Frechet Music Distance

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Extending FMD
• Citation
• Acknowledgements
• License

Introduction

A library for calculating Frechet Music Distance (FMD). This is an official implementation of the paper Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation.

Features

• Calculating FMD and FMD-Inf scores between two datasets for evaluation
• Caching extracted features and distribution parameters to speedup subsequent computations
• Support for various symbolic music representations (MIDI and ABC)
• Support for various embedding models (CLaMP 2, CLaMP 1)
• Support for various methods of estimating embedding distribution parameters (MLE, Leodit Wolf, Shrinkage, OAS, Bootstrap)
• Computation of per-song FMD to find outliers in the dataset

Installation

The library can be installed from from PyPi using pip:

pip install frechet-music-distance

Note: If it doesn't work try updating pip:

pip install --upgrade pip

You can also install from source by cloning the repository and installing it locally:

git clone https://github.com/jryban/frechet-music-distance.git
cd frechet-music-distance
pip install -e .

The library was tested on Linux and MacOS, but it should work on Windows as well.

Note: If you encounter NotOpenSSLWarning please downgrade your urllib3 version to 1.26.6:

pip install urllib3==1.26.6

or use a different version of Python that supports OpenSSL, by following the instructions provided in this urllib3 GitHub issue

Usage

The library currently supports MIDI and ABC symbolic music representations.

Note: When using ABC Notation please ensure that each song is located in a separate file.

Command Line

fmd score [-h] [--model {clamp2,clamp}] [--estimator {mle,bootstrap,oas,shrinkage,leodit_wolf}] [--inf] [--steps STEPS] [--min_n MIN_N] [--clear-cache] [reference_dataset] [test_dataset]

Positional arguments:

• reference_dataset: Path to the reference dataset
• test_dataset: Path to the test dataset

Options:

• --model {clamp2,clamp}, -m {clamp2,clamp} Embedding model name
• --estimator {mle,bootstrap,oas,shrinkage,leodit_wolf}, -e {mle,bootstrap,oas,shrinkage,leodit_wolf} Gaussian estimator for mean and covariance
• --inf Use FMD-Inf extrapolation
• --steps STEPS, -s STEPS Number of steps when calculating FMD-Inf
• --min_n MIN_N, -n MIN_N Mininum sample size when calculating FMD-Inf (Must be smaller than the size of the test dataset)
• --clear-cache Clear the pre-computed cache before FMD calculation

Cleanup

Additionaly the pre-computed cache can be cleared by executing:

fmd clear

Python API

Initialization

You can initialize the metric like so:

from frechet_music_distance import FrechetMusicDistance

metric = FrechetMusicDistance(feature_extractor='<model_name>', gaussian_estimator='<esimator_name>', verbose=True)

Valid values for <model_name> are: clamp2 (default), clamp Valid values for <esimator_name> are: mle (default), bootstrap, shrinkage, leodit_wolf, oas

If you want more control over feature extraction models and gaussian estimators, you can instantiate the object outside and pass it to the constructor directly like so:

from frechet_music_distance import FrechetMusicDistance
from frechet_music_distance.gaussian_estimators import LeoditWolfEstimator, MaxLikelihoodEstimator, OASEstimator, BootstrappingEstimator, ShrinkageEstimator
from frechet_music_distance.models import CLaMP2Extractor, CLaMPExtractor

extractor = CLaMP2Extractor(verbose=True)
estimator = ShrinkageEstimator(shrinkage=0.1)
fmd = FrechetMusicDistance(feature_extractor=extractor, gaussian_estimator=estimator, verbose=True)

Standard FMD score

score = metric.score(
    reference_path="<reference_dataset_path>",
    test_path="<test_dataset_path>"
)

FMD-Inf score

result = metric.score_inf(
    reference_path="<reference_dataset_path>",
    test_path="<test_dataset_path>",
    steps=<num_steps> # default=25
    min_n=<minumum_sample_size> # default=500
)

result.score   # To get the FMD-Inf score
result.r2      # To get the R^2 of FMD-Inf linear regression
result.slope   # To get the slope of the regression
result.points  # To get the point estimates used in FMD-Inf regression

Individual (per-song) score

result = metric.score_individual(
    reference_dataset="<reference_dataset_path>",
    test_song_path="<test_song_path>",
)

Cleanup

Additionaly the pre-computed cache can be cleared like so:

from frechet_music_distance.utils import clear_cache

clear_cache()

Extending FMD

Embedding Model

You can add your own model as a feature extractor like so:

from frechet_music_distance.models import FeatureExtractor

class MyExtractor(FeatureExtractor):

    def __init__(self, verbose: bool = True) -> None:
        super().__init__(verbose)
        """<My implementation>"""
        

    @torch.no_grad()
    def _extract_feature(self, data: Any) -> NDArray:
        """<My implementation>"""


    def extract_features(self, dataset_path: str | Path) -> NDArray:
        """<My implementation of loading data>"""

        return super()._extract_features(data)


    def extract_feature(self, filepath: str | Path) -> NDArray:
        """<My implementation of loading data>"""

        return self._extract_feature(data)

If your model uses the same data format as CLaMP2 or CLaMP you can use frechet_music_distance.dataset_loaders.ABCLoader or frechet_music_distance.dataset_loaders.MIDIasMTFLoader for loading music data.

Gaussian Estimator

You can add your own estimator like so:

from .gaussian_estimator import GaussianEstimator

class MyEstimator(GaussianEstimator):

    def __init__(self, num_samples: int = 1000) -> None:
        super().__init__()
        """<My implementation>"""

    def estimate_parameters(self, features: NDArray) -> tuple[NDArray, NDArray]:
        """<My implementation>"""

        return mean, cov

Supported Embedding Models

Model	Name in library	Description	Creator
CLaMP	clamp	CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval	Microsoft Muzic
CLaMP2	clamp2	CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models	sanderwood

Citation

If you use Frecheet Music Distance in your research, please cite the following paper:

@article{retkowski2024frechet,
  title={Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation},
  author={Retkowski, Jan and St{\k{e}}pniak, Jakub and Modrzejewski, Mateusz},
  journal={arXiv preprint arXiv:2412.07948},
  year={2024}
}

Acknowledgements

This library uses code from the following repositories for handling the embedding models:

• CLaMP 1: microsoft/muzic/clamp
• CLaMP 2: sanderwood/clamp2

License

This project is licensed under the MIT License. See the LICENSE file for details.

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