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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
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