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preprocess.py
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preprocess.py
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# -*- coding: utf-8 -*-
from spectrogram import logmelspectrogram
import numpy as np
from joblib import Parallel, delayed
import librosa
import soundfile as sf
import os
from glob import glob
from tqdm import tqdm
import random
import json
import resampy
import pyworld as pw
def extract_logmel(wav_path, sr=16000):
# wav, fs = librosa.load(wav_path, sr=sr)
wav, fs = sf.read(wav_path)
wav, _ = librosa.effects.trim(wav, top_db=60)
if fs != sr:
wav = resampy.resample(wav, fs, sr, axis=0)
fs = sr
# duration = len(wav)/fs
assert fs == 16000
peak = np.abs(wav).max()
if peak > 1.0:
wav /= peak
mel = logmelspectrogram(
x=wav,
fs=fs,
n_mels=80,
n_fft=400,
n_shift=160,
win_length=400,
window='hann',
fmin=80,
fmax=7600,
)
tlen = mel.shape[0]
frame_period = 160/fs*1000
f0, timeaxis = pw.dio(wav.astype('float64'), fs, frame_period=frame_period)
f0 = pw.stonemask(wav.astype('float64'), f0, timeaxis, fs)
f0 = f0[:tlen].reshape(-1).astype('float32')
nonzeros_indices = np.nonzero(f0)
lf0 = f0.copy()
lf0[nonzeros_indices] = np.log(f0[nonzeros_indices]) # for f0(Hz), lf0 > 0 when f0 != 0
wav_name = os.path.basename(wav_path).split('.')[0]
# print(wav_name, mel.shape, duration)
return wav_name, mel, lf0, mel.shape[0]
def normalize_logmel(wav_name, mel, mean, std):
mel = (mel - mean) / (std + 1e-8)
return wav_name, mel
def save_one_file(save_path, arr):
os.makedirs(os.path.dirname(save_path), exist_ok=True)
np.save(save_path, arr)
def save_logmel(save_root, wav_name, melinfo, mode):
mel, lf0, mel_len = melinfo
spk = wav_name.split('_')[0]
mel_save_path = f'{save_root}/{mode}/mels/{spk}/{wav_name}.npy'
lf0_save_path = f'{save_root}/{mode}/lf0/{spk}/{wav_name}.npy'
save_one_file(mel_save_path, mel)
save_one_file(lf0_save_path, lf0)
return mel_len, mel_save_path, lf0_save_path
# def get_wavs_names(spks, data_root)
data_root = '/Dataset/VCTK-Corpus/wav48_silence_trimmed'
save_root = 'data'
os.makedirs(save_root, exist_ok=True)
spk_info_txt = '/Dataset/VCTK-Corpus/speaker-info.txt'
f = open(spk_info_txt, 'r')
gen2spk = {}
all_spks = []
for i, line in enumerate(f):
if i == 0:
continue
else:
tmp = line.split()
# print(tmp)
spk = tmp[0]
all_spks.append(spk)
gen = tmp[2]
if gen not in gen2spk:
gen2spk[gen] = [spk]
else:
gen2spk[gen].append(spk)
random.shuffle(all_spks)
train_spks = all_spks[:-20]
test_spks = all_spks[-20:]
train_wavs_names = []
valid_wavs_names = []
test_wavs_names = []
print('all_spks:', all_spks)
for spk in train_spks:
spk_wavs = glob(f'{data_root}/{spk}/*mic1.flac')
print('len(spk_wavs):', len(spk_wavs))
spk_wavs_names = [os.path.basename(p).split('.')[0] for p in spk_wavs]
valid_names = random.sample(spk_wavs_names, int(len(spk_wavs_names)*0.1))
train_names = [n for n in spk_wavs_names if n not in valid_names]
train_wavs_names += train_names
valid_wavs_names += valid_names
for spk in test_spks:
spk_wavs = glob(f'{data_root}/{spk}/*mic1.flac')
print('len(spk_wavs):', len(spk_wavs))
spk_wavs_names = [os.path.basename(p).split('.')[0] for p in spk_wavs]
test_wavs_names += spk_wavs_names
print(len(train_wavs_names))
print(len(valid_wavs_names))
print(len(test_wavs_names))
# extract log-mel
print('extract log-mel...')
all_wavs = glob(f'{data_root}/*/*mic1.flac')
results = Parallel(n_jobs=-1)(delayed(extract_logmel)(wav_path) for wav_path in tqdm(all_wavs))
wn2mel = {}
for r in results:
wav_name, mel, lf0, mel_len = r
# print(wav_name, mel.shape, duration)
wn2mel[wav_name] = [mel, lf0, mel_len]
# normalize log-mel
print('normalize log-mel...')
mels = []
spk2lf0 = {}
for wav_name in train_wavs_names:
mel, _, _ = wn2mel[wav_name]
mels.append(mel)
mels = np.concatenate(mels, 0)
mean = np.mean(mels, 0)
std = np.std(mels, 0)
mel_stats = np.concatenate([mean.reshape(1,-1), std.reshape(1,-1)], 0)
np.save(f'{save_root}/mel_stats.npy', mel_stats)
results = Parallel(n_jobs=-1)(delayed(normalize_logmel)(wav_name, wn2mel[wav_name][0], mean, std) for wav_name in tqdm(wn2mel.keys()))
wn2mel_new = {}
for r in results:
wav_name, mel = r
lf0 = wn2mel[wav_name][1]
mel_len = wn2mel[wav_name][2]
wn2mel_new[wav_name] = [mel, lf0, mel_len]
# save log-mel
print('save log-mel...')
train_results = Parallel(n_jobs=-1)(delayed(save_logmel)(save_root, wav_name, wn2mel_new[wav_name], 'train') for wav_name in tqdm(train_wavs_names))
valid_results = Parallel(n_jobs=-1)(delayed(save_logmel)(save_root, wav_name, wn2mel_new[wav_name], 'valid') for wav_name in tqdm(valid_wavs_names))
test_results = Parallel(n_jobs=-1)(delayed(save_logmel)(save_root, wav_name, wn2mel_new[wav_name], 'test') for wav_name in tqdm(test_wavs_names))
def save_json(save_root, results, mode):
fp = open(f'{save_root}/{mode}.json', 'w')
json.dump(results, fp, indent=4)
fp.close()
save_json(save_root, train_results, 'train')
save_json(save_root, valid_results, 'valid')
save_json(save_root, test_results, 'test')