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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 json
import re
import torchaudio
from cuhkszsvc.configs.config_parse import get_wav_path, get_wav_file_path
from utils.io import has_existed
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(wave_file, fs, frameshift):
# waveform: (1, seq)
waveform, sample_rate = torchaudio.load(wave_file)
if sample_rate != fs:
waveform = torchaudio.functional.resample(
waveform, orig_freq=sample_rate, new_freq=fs
)
# x: (seq,)
x = np.array(torch.clamp(waveform[0], -1.0, 1.0), 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 extract_mcep_features_of_dataset(
output_path, dataset_path, dataset, mcsize, fs, frameshift, splits=None
):
output_dir = os.path.join(output_path, dataset, "mcep/{}".format(fs))
if not splits:
splits = ["train", "test"] if dataset != "m4singer" else ["test"]
for dataset_type in splits:
print("-" * 20)
print("Dataset: {}, {}".format(dataset, dataset_type))
output_file = os.path.join(output_dir, "{}.pkl".format(dataset_type))
if has_existed(output_file):
continue
# Extract SP features
print("\nExtracting SP featuers...")
sp_features = get_world_features_of_dataset(
output_path, dataset_path, dataset, dataset_type, fs, frameshift
)
# SP to MCEP
print("\nTransform SP to MCEP...")
mcep_features = [sp2mcep(sp, mcsize=mcsize, fs=fs) for sp in tqdm(sp_features)]
# Save
os.makedirs(output_dir, exist_ok=True)
with open(output_file, "wb") as f:
pickle.dump(mcep_features, f)
def get_world_features_of_dataset(
output_path,
dataset_path,
dataset,
dataset_type,
fs,
frameshift,
save_sp_feature=False,
):
data_dir = os.path.join(output_path, dataset)
wave_dir = get_wav_path(dataset_path, dataset)
# Dataset
dataset_file = os.path.join(data_dir, "{}.json".format(dataset_type))
if not os.path.exists(dataset_file):
print("File {} has not existed.".format(dataset_file))
return None
with open(dataset_file, "r") as f:
datasets = json.load(f)
# Save dir
f0_dir = os.path.join(output_path, dataset, "f0")
os.makedirs(f0_dir, exist_ok=True)
# Extract
f0_features = []
sp_features = []
for utt in tqdm(datasets):
wave_file = get_wav_file_path(dataset, wave_dir, utt)
f0, sp, _, _ = extract_world_features(wave_file, fs, frameshift)
sp_features.append(sp)
f0_features.append(f0)
# Save sp
if save_sp_feature:
sp_dir = os.path.join(output_path, dataset, "sp")
os.makedirs(sp_dir, exist_ok=True)
with open(os.path.join(sp_dir, "{}.pkl".format(dataset_type)), "wb") as f:
pickle.dump(sp_features, f)
# F0 statistics
f0_statistics_file = os.path.join(f0_dir, "{}_f0.pkl".format(dataset_type))
f0_statistics(f0_features, f0_statistics_file)
return sp_features
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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