Spaces:
Running
Running
| 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))) | |