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| # 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. | |
| # 1. Extract WORLD features including F0, AP, SP | |
| # 2. Transform between SP and MCEP | |
| import torchaudio | |
| import pyworld as pw | |
| import numpy as np | |
| import torch | |
| import diffsptk | |
| import os | |
| from tqdm import tqdm | |
| import pickle | |
| import torchaudio | |
| def get_mcep_params(fs): | |
| """Hyperparameters of transformation between SP and MCEP | |
| Reference: | |
| https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh | |
| """ | |
| if fs in [44100, 48000]: | |
| fft_size = 2048 | |
| alpha = 0.77 | |
| if fs in [16000]: | |
| fft_size = 1024 | |
| alpha = 0.58 | |
| return fft_size, alpha | |
| def extract_world_features(waveform, frameshift=10): | |
| # waveform: (1, seq) | |
| # x: (seq,) | |
| x = np.array(waveform, dtype=np.double) | |
| _f0, t = pw.dio(x, fs, frame_period=frameshift) # raw pitch extractor | |
| f0 = pw.stonemask(x, _f0, t, fs) # pitch refinement | |
| sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram | |
| ap = pw.d4c(x, f0, t, fs) # extract aperiodicity | |
| return f0, sp, ap, fs | |
| def sp2mcep(x, mcsize, fs): | |
| fft_size, alpha = get_mcep_params(fs) | |
| x = torch.as_tensor(x, dtype=torch.float) | |
| tmp = diffsptk.ScalarOperation("SquareRoot")(x) | |
| tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp) | |
| mgc = diffsptk.MelCepstralAnalysis( | |
| cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1 | |
| )(tmp) | |
| return mgc.numpy() | |
| def mcep2sp(x, mcsize, fs): | |
| fft_size, alpha = get_mcep_params(fs) | |
| x = torch.as_tensor(x, dtype=torch.float) | |
| tmp = diffsptk.MelGeneralizedCepstrumToSpectrum( | |
| alpha=alpha, | |
| cep_order=mcsize - 1, | |
| fft_length=fft_size, | |
| )(x) | |
| tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp) | |
| sp = diffsptk.ScalarOperation("Power", 2)(tmp) | |
| return sp.double().numpy() | |
| def f0_statistics(f0_features, path): | |
| print("\nF0 statistics...") | |
| total_f0 = [] | |
| for f0 in tqdm(f0_features): | |
| total_f0 += [f for f in f0 if f != 0] | |
| mean = sum(total_f0) / len(total_f0) | |
| print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean)) | |
| with open(path, "wb") as f: | |
| pickle.dump([mean, total_f0], f) | |
| def world_synthesis(f0, sp, ap, fs, frameshift): | |
| y = pw.synthesize( | |
| f0, sp, ap, fs, frame_period=frameshift | |
| ) # synthesize an utterance using the parameters | |
| return y | |