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| from basis import ScoreBasis | |
| import numpy as np | |
| class SSNR(ScoreBasis): | |
| def __init__(self): | |
| super(SSNR, self).__init__(name='SSNR') | |
| self.intrusive = False | |
| def windowed_scoring(self, audios, score_rate): | |
| if len(audios) != 2: | |
| return None | |
| return cal_SSNR(audios[0], audios[1], score_rate) | |
| def cal_SSNR(ref_wav, deg_wav, srate=16000, eps=1e-10): | |
| # obtained from https://github.com/wooseok-shin/MetricGAN-plus-pytorch/blob/main/metric_functions/metric_helper.py | |
| """ Segmental Signal-to-Noise Ratio Objective Speech Quality Measure | |
| This function implements the segmental signal-to-noise ratio | |
| as defined in [1, p. 45] (see Equation 2.12). | |
| """ | |
| clean_speech = ref_wav | |
| processed_speech = deg_wav | |
| clean_length = ref_wav.shape[0] | |
| processed_length = deg_wav.shape[0] | |
| # scale both to have same dynamic range. Remove DC too. | |
| clean_speech -= clean_speech.mean() | |
| processed_speech -= processed_speech.mean() | |
| processed_speech *= (np.max(np.abs(clean_speech)) / np.max(np.abs(processed_speech))) | |
| # global variables | |
| winlength = int(np.round(30 * srate / 1000)) # 30 msecs | |
| skiprate = winlength // 4 | |
| MIN_SNR = -10 | |
| MAX_SNR = 35 | |
| # For each frame, calculate SSNR | |
| 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)) | |
| segmental_snr = [] | |
| for frame_count in range(int(num_frames)): | |
| # (1) get the frames for the test and ref speech. | |
| # Apply 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) Compute Segmental SNR | |
| signal_energy = np.sum(clean_frame ** 2) | |
| noise_energy = np.sum((clean_frame - processed_frame) ** 2) | |
| segmental_snr.append(10 * np.log10(signal_energy / (noise_energy + eps)+ eps)) | |
| segmental_snr[-1] = max(segmental_snr[-1], MIN_SNR) | |
| segmental_snr[-1] = min(segmental_snr[-1], MAX_SNR) | |
| start += int(skiprate) | |
| return sum(segmental_snr) / len(segmental_snr) | |