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import torch
from torch.utils.data import Dataset
from pathlib import Path
from utils import load_wav
class Valentini(Dataset):
def __init__(self, dataset_path='/media/public/datasets/denoising/DS_10283_2791/', transform=None,
valid=False):
clean_path = Path(dataset_path) / 'clean_trainset_56spk_wav'
noisy_path = Path(dataset_path) / 'noisy_trainset_56spk_wav'
clean_wavs = list(clean_path.glob("*"))
noisy_wavs = list(noisy_path.glob("*"))
valid_threshold = int(len(clean_wavs) * 0.2)
if valid:
self.clean_wavs = clean_wavs[:valid_threshold]
self.noisy_wavs = noisy_wavs[:valid_threshold]
else:
self.clean_wavs = clean_wavs[valid_threshold:]
self.noisy_wavs = noisy_wavs[valid_threshold:]
assert len(self.clean_wavs) == len(self.noisy_wavs)
self.transform = transform
def __len__(self):
return len(self.clean_wavs)
def __getitem__(self, idx):
noisy_wav = load_wav(self.noisy_wavs[idx])
clean_wav = load_wav(self.clean_wavs[idx])
if self.transform:
random_seed = torch.randint(100, (1,))[0]
torch.manual_seed(random_seed)
noisy_wav = self.transform(noisy_wav)
torch.manual_seed(random_seed)
clean_wav = self.transform(clean_wav)
return noisy_wav, clean_wav
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