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preprocess.py
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preprocess.py
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import os
import numpy as np
import librosa
import torch
import pyworld as pw
import parselmouth
import argparse
import shutil
from logger import utils
from tqdm import tqdm
from ddsp.vocoder import Audio2Mel
from librosa.filters import mel as librosa_mel_fn
from logger.utils import traverse_dir
import concurrent.futures
def parse_args(args=None, namespace=None):
"""Parse command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
required=True,
help="path to the config file")
return parser.parse_args(args=args, namespace=namespace)
def preprocess(
path_srcdir,
path_meldir,
path_f0dir,
path_uvdir,
path_skipdir,
device,
f0_extractor,
f0_min,
f0_max,
sampling_rate,
hop_length,
win_length,
n_fft,
n_mel_channels,
mel_fmin,
mel_fmax):
# list files
filelist = traverse_dir(
path_srcdir,
extension='wav',
is_pure=True,
is_sort=True,
is_ext=True)
# initilize extractor
mel_extractor = Audio2Mel(
hop_length=hop_length,
sampling_rate=sampling_rate,
n_mel_channels=n_mel_channels,
win_length=win_length,
n_fft=n_fft,
mel_fmin=mel_fmin,
mel_fmax=mel_fmax,
clamp=1e-6).to(device)
# run
def process(file):
ext = file.split('.')[-1]
binfile = file[:-(len(ext)+1)]+'.npy'
path_srcfile = os.path.join(path_srcdir, file)
path_melfile = os.path.join(path_meldir, binfile)
path_f0file = os.path.join(path_f0dir, binfile)
path_uvfile = os.path.join(path_uvdir, binfile)
# load audio
x, _ = librosa.load(path_srcfile, sr=sampling_rate)
x_t = torch.from_numpy(x).float().to(device)
x_t = x_t.unsqueeze(0).unsqueeze(0) # (T,) --> (1, 1, T)
# extract mel
m_t = mel_extractor(x_t)
mel = m_t.squeeze().to('cpu').numpy()
# extract f0 using parselmouth
if f0_extractor == 'parselmouth':
l_pad = int(np.ceil(1.5 / f0_min * sampling_rate))
r_pad = hop_length * ((len(x) - 1) // hop_length + 1) - len(x) + l_pad + 1
s = parselmouth.Sound(np.pad(x, (l_pad, r_pad)), sampling_rate).to_pitch_ac(
time_step=hop_length / sampling_rate, voicing_threshold=0.6,
pitch_floor=f0_min, pitch_ceiling=f0_max)
assert np.abs(s.t1 - 1.5 / f0_min) < 0.001
f0 = s.selected_array['frequency']
if len(f0) < len(mel):
f0 = np.pad(f0, (0, len(mel) - len(f0)))
f0 = f0[: len(mel)]
# extract f0 using dio
elif f0_extractor == 'dio':
_f0, t = pw.dio(
x.astype('double'),
sampling_rate,
f0_floor=f0_min,
f0_ceil=f0_max,
channels_in_octave=2,
frame_period=(1000*hop_length / sampling_rate))
f0 = pw.stonemask(x.astype('double'), _f0, t, sampling_rate)
f0 = f0.astype('float')[:len(mel)]
# extract f0 using harvest
elif f0_extractor == 'harvest':
f0, _ = pw.harvest(
x.astype('double'),
sampling_rate,
f0_floor=f0_min,
f0_ceil=f0_max,
frame_period=(1000*hop_length / sampling_rate))
f0 = f0.astype('float')[:len(mel)]
else:
raise ValueError(f" [x] Unknown f0 extractor: {f0_extractor}")
uv = f0 == 0
if len(f0[~uv]) > 0:
# interpolate the unvoiced f0
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
uv = uv.astype('float')
uv = np.min(np.array([uv[:-2],uv[1:-1],uv[2:]]),axis=0)
uv = np.pad(uv, (1, 1), constant_values=(uv[0], uv[-1]))
# save npy
os.makedirs(os.path.dirname(path_melfile), exist_ok=True)
np.save(path_melfile, mel)
os.makedirs(os.path.dirname(path_f0file), exist_ok=True)
np.save(path_f0file, f0)
os.makedirs(os.path.dirname(path_uvfile), exist_ok=True)
np.save(path_uvfile, uv)
else:
print('\n[Error] F0 extraction failed: ' + path_srcfile)
os.makedirs(path_skipdir, exist_ok=True)
shutil.move(path_srcfile, path_skipdir)
print('This file has been moved to ' + os.path.join(path_skipdir, file))
print('Preprocess the audio clips in :', path_srcdir)
# single process
for file in tqdm(filelist, total=len(filelist)):
process(file)
# multi-process (have bugs)
'''
with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:
list(tqdm(executor.map(process, filelist), total=len(filelist)))
'''
if __name__ == '__main__':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# parse commands
cmd = parse_args()
# load config
args = utils.load_config(cmd.config)
f0_extractor = args.data.f0_extractor
f0_min = args.data.f0_min
f0_max = args.data.f0_max
sampling_rate = args.data.sampling_rate
hop_length = args.data.block_size
win_length = args.data.win_length
n_fft = args.data.n_fft
n_mel_channels = args.data.n_mels
mel_fmin = args.data.mel_fmin
mel_fmax = args.data.mel_fmax
train_path = args.data.train_path
valid_path = args.data.valid_path
# run
for path in [train_path, valid_path]:
path_srcdir = os.path.join(path, 'audio')
path_meldir = os.path.join(path, 'mel')
path_f0dir = os.path.join(path, 'f0')
path_uvdir = os.path.join(path, 'uv')
path_skipdir = os.path.join(path, 'skip')
preprocess(
path_srcdir,
path_meldir,
path_f0dir,
path_uvdir,
path_skipdir,
device,
f0_extractor,
f0_min,
f0_max,
sampling_rate,
hop_length,
win_length,
n_fft,
n_mel_channels,
mel_fmin,
mel_fmax)