| |
| |
| |
| |
|
|
| import librosa |
|
|
| import numpy as np |
|
|
| from pypesq import pesq |
|
|
|
|
| def extract_pesq(audio_ref, audio_deg, **kwargs): |
| """Extract PESQ for a two given audio. |
| audio1: the given reference audio. It is a numpy array. |
| audio2: the given synthesized audio. It is a numpy array. |
| fs: sampling rate. |
| method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio. |
| "cut" will cut both audios into a same length according to the one with the shorter length. |
| """ |
| |
| kwargs = kwargs["kwargs"] |
| fs = kwargs["fs"] |
| method = kwargs["method"] |
|
|
| |
| if fs != None: |
| audio_ref, _ = librosa.load(audio_ref, sr=fs) |
| audio_deg, _ = librosa.load(audio_deg, sr=fs) |
| else: |
| audio_ref, fs = librosa.load(audio_ref) |
| audio_deg, fs = librosa.load(audio_deg) |
|
|
| |
| if fs != 16000: |
| audio_ref = librosa.resample(audio_ref, orig_sr=fs, target_sr=16000) |
| audio_deg = librosa.resample(audio_deg, orig_sr=fs, target_sr=16000) |
| fs = 16000 |
|
|
| |
| if len(audio_ref) != len(audio_deg): |
| if method == "cut": |
| length = min(len(audio_ref), len(audio_deg)) |
| audio_ref = audio_ref[:length] |
| audio_deg = audio_deg[:length] |
| elif method == "dtw": |
| _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True) |
| audio_ref_new = [] |
| audio_deg_new = [] |
| for i in range(wp.shape[0]): |
| ref_index = wp[i][0] |
| deg_index = wp[i][1] |
| audio_ref_new.append(audio_ref[ref_index]) |
| audio_deg_new.append(audio_deg[deg_index]) |
| audio_ref = np.array(audio_ref_new) |
| audio_deg = np.array(audio_deg_new) |
| assert len(audio_ref) == len(audio_deg) |
|
|
| |
| score = pesq(audio_ref, audio_deg, fs) |
| return score |
|
|