code: def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
      """Numpy implementation of the Frechet Distance.
  
      The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
      and X_2 ~ N(mu_2, C_2) is
              d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
  
      Stable version by Dougal J. Sutherland.
  
      Params:
      -- mu1 : Numpy array containing the activations of the pool_3 layer of the
               inception net ( like returned by the function 'get_predictions')
               for generated samples.
      -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
                 on an representive data set.
      -- sigma1: The covariance matrix over activations of the pool_3 layer for
                 generated samples.
      -- sigma2: The covariance matrix over activations of the pool_3 layer,
                 precalcualted on an representive data set.
  
      Returns:
      --   : The Frechet Distance.
      """
  
      mu1 = np.atleast_1d(mu1)
      mu2 = np.atleast_1d(mu2)
  
      sigma1 = np.atleast_2d(sigma1)
      sigma2 = np.atleast_2d(sigma2)
  
      assert (
          mu1.shape == mu2.shape
      ), "Training and test mean vectors have different lengths"
      assert (
          sigma1.shape == sigma2.shape
      ), "Training and test covariances have different dimensions"
  
      diff = mu1 - mu2
  
      # product might be almost singular
      covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
      if not np.isfinite(covmean).all():
          msg = (
              "fid calculation produces singular product;"
              "adding %s to diagonal of cov estimates" % eps
          )
          warnings.warn(msg)
          offset = np.eye(sigma1.shape[0]) * eps
          covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
  
      # numerical error might give slight imaginary component
      if np.iscomplexobj(covmean):
          if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
              m = np.max(np.abs(covmean.imag))
              raise ValueError("Imaginary component {}".format(m))
          covmean = covmean.real
  
      tr_covmean = np.trace(covmean)
  
      return (
          diff.dot(diff)
          + np.trace(sigma1)
          + np.trace(sigma2)
          - 2 * tr_covmean
      )
  
file_path: espnet/egs2/talromur2/tts1/pyscripts/utils/evaluate_cfsd.py
imports: import argparse
  import fnmatch
  import logging
  import os
  from typing import List
  import librosa
  import numpy
  import soundfile
  import torch
  from scipy import linalg
  from espnet2.asr.frontend.s3prl import S3prlFrontend
label_desc: Use image node_id 1 for calling a global function with example usage: calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps) and returns: unknown