def ppg_windowed(example_wav, hop_ms):
from features import get_ppg
ppg = get_ppg(example_wav['wav'],
example_wav['sr'],
backend='wav2vec2',
max_window=1.0,
overlap=0.1)
return ppg
def main(cfg: DictConfig):
"""Preprocessing function for DAPS-like dataset (https://archive.org/details/daps_dataset).
- Extracts features (PPG, f0, loudness, spk embedding) for every wav in datafolder
- Builds train and test list
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(OmegaConf.to_yaml(cfg.dataset))
print(OmegaConf.to_yaml(cfg.data))
print('device:', device)
# folders preparation
for out_dir_path in [
cfg.dataset.f0_dir, cfg.dataset.ppg_dir, cfg.dataset.loudness_dir
]:
out_dir_path = Path(out_dir_path)
if out_dir_path.exists():
assert out_dir_path.is_dir()
else:
out_dir_path.mkdir(parents=True)
# preprocess dataset and loader
filepathes = get_datafolder_files(cfg.dataset.wav_dir)
dataset = PreprocessDataset(filepathes, cfg.data)
dataloader = DataLoader(dataset,
batch_size=1,
shuffle=False,
drop_last=False,
num_workers=cfg.train.num_workers)
spk_embs = dict()
# feature extraction
pbar = tqdm.tqdm(total=len(dataset), ncols=0)
for wav, filename in dataloader:
# batch size is 1
wav = denoise(wav[0].numpy(), device=device)
filename = Path(filename[0])
with torch.no_grad():
ppg = torch.from_numpy(
get_ppg(wav, cfg.data.sample_rate, device=device))
f0 = torch.from_numpy(
get_f0(wav,
cfg.data.sample_rate,
hop_ms=cfg.data.hop_length * 1000 /
cfg.data.sample_rate,
f_min=cfg.data.f_min,
f_max=cfg.data.f_max))
loudness = torch.from_numpy(
get_loudness(wav,
cfg.data.sample_rate,
n_fft=cfg.data.n_fft,
hop_length=cfg.data.hop_length,
win_length=cfg.data.win_length))
spk_emb = torch.from_numpy(
get_speaker_embed(wav, cfg.data.sample_rate,
device=device)).cpu()
torch.save(
ppg, os.path.join(cfg.dataset.ppg_dir,
filename.with_suffix('.pt')))
torch.save(
f0, os.path.join(cfg.dataset.f0_dir, filename.with_suffix('.pt')))
torch.save(
loudness,
os.path.join(cfg.dataset.loudness_dir,
filename.with_suffix('.pt')))
spk_embs[filename.stem] = spk_emb
pbar.update(dataloader.batch_size)
torch.save(spk_embs, cfg.dataset.spk_embs_file)
# generation of train and test files
train_list = define_train_list(filepathes)
train_list, test_list = train_test_split(train_list)
save_dataset_filelist(train_list, cfg.dataset.train_list)
save_dataset_filelist(test_list, cfg.dataset.test_list)
def ppg(example_wav, hop_ms):
from features import get_ppg
ppg = get_ppg(example_wav['wav'], example_wav['sr'], backend='wav2vec2')
return ppg