Spaces:
Running
on
T4
Running
on
T4
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torchcrepe | |
| import math | |
| import librosa | |
| import torch | |
| import numpy as np | |
| def extract_f0_periodicity_rmse( | |
| audio_ref, | |
| audio_deg, | |
| fs=None, | |
| hop_length=256, | |
| method="dtw", | |
| ): | |
| """Compute f0 periodicity Root Mean Square Error (RMSE) between the predicted and the ground truth audio. | |
| audio_ref: path to the ground truth audio. | |
| audio_deg: path to the predicted audio. | |
| fs: sampling rate. | |
| hop_length: hop length. | |
| 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. | |
| """ | |
| # Load audio | |
| 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) | |
| # Convert to torch | |
| audio_ref = torch.from_numpy(audio_ref).unsqueeze(0) | |
| audio_deg = torch.from_numpy(audio_deg).unsqueeze(0) | |
| # Get periodicity | |
| pitch_ref, periodicity_ref = torchcrepe.predict( | |
| audio_ref, | |
| sample_rate=fs, | |
| hop_length=hop_length, | |
| fmin=0, | |
| fmax=1500, | |
| model="full", | |
| return_periodicity=True, | |
| device="cuda:0", | |
| ) | |
| pitch_deg, periodicity_deg = torchcrepe.predict( | |
| audio_deg, | |
| sample_rate=fs, | |
| hop_length=hop_length, | |
| fmin=0, | |
| fmax=1500, | |
| model="full", | |
| return_periodicity=True, | |
| device="cuda:0", | |
| ) | |
| # Cut silence | |
| periodicity_ref = ( | |
| torchcrepe.threshold.Silence()( | |
| periodicity_ref, | |
| audio_ref, | |
| fs, | |
| hop_length=hop_length, | |
| ) | |
| .squeeze(0) | |
| .numpy() | |
| ) | |
| periodicity_deg = ( | |
| torchcrepe.threshold.Silence()( | |
| periodicity_deg, | |
| audio_deg, | |
| fs, | |
| hop_length=hop_length, | |
| ) | |
| .squeeze(0) | |
| .numpy() | |
| ) | |
| # Avoid silence audio | |
| min_length = min(len(periodicity_ref), len(periodicity_deg)) | |
| if min_length <= 1: | |
| return 0 | |
| # Periodicity length alignment | |
| if method == "cut": | |
| length = min(len(periodicity_ref), len(periodicity_deg)) | |
| periodicity_ref = periodicity_ref[:length] | |
| periodicity_deg = periodicity_deg[:length] | |
| elif method == "dtw": | |
| _, wp = librosa.sequence.dtw(periodicity_ref, periodicity_deg, backtrack=True) | |
| periodicity_ref_new = [] | |
| periodicity_deg_new = [] | |
| for i in range(wp.shape[0]): | |
| ref_index = wp[i][0] | |
| deg_index = wp[i][1] | |
| periodicity_ref_new.append(periodicity_ref[ref_index]) | |
| periodicity_deg_new.append(periodicity_deg[deg_index]) | |
| periodicity_ref = np.array(periodicity_ref_new) | |
| periodicity_deg = np.array(periodicity_deg_new) | |
| assert len(periodicity_ref) == len(periodicity_deg) | |
| # Compute RMSE | |
| periodicity_mse = np.square(np.subtract(periodicity_ref, periodicity_deg)).mean() | |
| periodicity_rmse = math.sqrt(periodicity_mse) | |
| return periodicity_rmse | |