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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 F0_Extractor, Volume_Extractor, Units_Encoder
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, f0_extractor, volume_extractor, units_encoder, sample_rate, hop_size, device = 'cuda'):
path_srcdir = os.path.join(path, 'audio')
path_unitsdir = os.path.join(path, 'units')
path_f0dir = os.path.join(path, 'f0')
path_volumedir = os.path.join(path, 'volume')
path_skipdir = os.path.join(path, 'skip')
# list files
filelist = traverse_dir(
path_srcdir,
extension='wav',
is_pure=True,
is_sort=True,
is_ext=True)
# run
def process(file):
ext = file.split('.')[-1]
binfile = file[:-(len(ext)+1)]+'.npy'
path_srcfile = os.path.join(path_srcdir, file)
path_unitsfile = os.path.join(path_unitsdir, binfile)
path_f0file = os.path.join(path_f0dir, binfile)
path_volumefile = os.path.join(path_volumedir, binfile)
path_skipfile = os.path.join(path_skipdir, file)
# load audio
audio, _ = librosa.load(path_srcfile, sr=sample_rate)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
audio_t = torch.from_numpy(audio).float().to(device)
audio_t = audio_t.unsqueeze(0)
# extract volume
volume = volume_extractor.extract(audio)
# units encode
units_t = units_encoder.encode(audio_t, sample_rate, hop_size)
units = units_t.squeeze().to('cpu').numpy()
# extract f0
f0 = f0_extractor.extract(audio, uv_interp = False)
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])
# save npy
os.makedirs(os.path.dirname(path_unitsfile), exist_ok=True)
np.save(path_unitsfile, units)
os.makedirs(os.path.dirname(path_f0file), exist_ok=True)
np.save(path_f0file, f0)
os.makedirs(os.path.dirname(path_volumefile), exist_ok=True)
np.save(path_volumefile, volume)
else:
print('\n[Error] F0 extraction failed: ' + path_srcfile)
os.makedirs(os.path.dirname(path_skipfile), exist_ok=True)
shutil.move(path_srcfile, os.path.dirname(path_skipfile))
print('This file has been moved to ' + path_skipfile)
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)
sample_rate = args.data.sampling_rate
hop_size = args.data.block_size
# initialize f0 extractor
f0_extractor = F0_Extractor(
args.data.f0_extractor,
args.data.sampling_rate,
args.data.block_size,
args.data.f0_min,
args.data.f0_max)
# initialize volume extractor
volume_extractor = Volume_Extractor(args.data.block_size)
# initialize units encoder
units_encoder = Units_Encoder(
args.data.encoder,
args.data.encoder_ckpt,
args.data.encoder_sample_rate,
args.data.encoder_hop_size,
device = device)
# preprocess training set
preprocess(args.data.train_path, f0_extractor, volume_extractor, units_encoder, sample_rate, hop_size, device = device)
# preprocess validation set
preprocess(args.data.valid_path, f0_extractor, volume_extractor, units_encoder, sample_rate, hop_size, device = device)
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