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from basis import ScoreBasis
import numpy as np
from scipy.linalg import toeplitz
from scores.helper import lpcoeff
class LLR(ScoreBasis):
def __init__(self):
super(LLR, self).__init__(name='LLR')
self.intrusive = False
def windowed_scoring(self, audios, score_rate):
if len(audios) != 2:
return None
return cal_LLR(audios[0], audios[1], score_rate)
def cal_LLR(ref_wav, deg_wav, srate):
# obtained from https://github.com/wooseok-shin/MetricGAN-plus-pytorch/blob/main/metric_functions/metric_helper.py
clean_speech = ref_wav
processed_speech = deg_wav
clean_length = ref_wav.shape[0]
processed_length = deg_wav.shape[0]
assert clean_length == processed_length, clean_length
winlength = round(30 * srate / 1000.) # 240 wlen in samples
skiprate = np.floor(winlength / 4)
if srate < 10000:
# LPC analysis order
P = 10
else:
P = 16
# For each frame of input speech, calculate the Log Likelihood Ratio
num_frames = int(clean_length / skiprate - (winlength / skiprate))
start = 0
time = np.linspace(1, winlength, winlength) / (winlength + 1)
window = 0.5 * (1 - np.cos(2 * np.pi * time))
distortion = []
for frame_count in range(num_frames):
# (1) Get the Frames for the test and reference speeech.
# Multiply by Hanning window.
clean_frame = clean_speech[start:start+winlength]
processed_frame = processed_speech[start:start+winlength]
clean_frame = clean_frame * window
processed_frame = processed_frame * window
# (2) Get the autocorrelation logs and LPC params used
# to compute the LLR measure
R_clean, Ref_clean, A_clean = lpcoeff(clean_frame, P)
R_processed, Ref_processed, A_processed = lpcoeff(processed_frame, P)
A_clean = A_clean[None, :]
A_processed = A_processed[None, :]
# (3) Compute the LLR measure
numerator = A_processed.dot(toeplitz(R_clean)).dot(A_processed.T)
denominator = A_clean.dot(toeplitz(R_clean)).dot(A_clean.T)
if (numerator/denominator) <= 0:
print(f'Numerator: {numerator}')
print(f'Denominator: {denominator}')
log_ = np.log(numerator / denominator)
distortion.append(np.squeeze(log_))
start += int(skiprate)
return np.mean(np.nan_to_num(np.array(distortion)))
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