| import torch |
| from torch.utils.data import Dataset |
| from DatasetLoader import AugmentWAV, loadWAV |
| import os |
| import numpy as np |
| import random |
|
|
|
|
| class TrainDataset(Dataset): |
| def __init__(self, train_list, train_path, augment, musan_path, rir_path, max_frames,): |
| self.train_list = train_list |
| self.max_frames = max_frames |
| self.augment_wav = AugmentWAV(musan_path=musan_path, rir_path=rir_path, max_frames=max_frames) |
| self.augment = augment |
| self.musan_path = musan_path |
| self.rir_path = rir_path |
|
|
| with open(train_list) as dataset_file: |
| lines = dataset_file.readlines() |
|
|
| dictkeys = list(set([x.split()[0] for x in lines])) |
| dictkeys.sort() |
| dictkeys = {key: ii for ii, key in enumerate(dictkeys)} |
|
|
| np.random.seed(100) |
| np.random.shuffle(lines) |
|
|
| self.data_list = [] |
| self.data_label = [] |
|
|
| for lidx, line in enumerate(lines): |
| data = line.strip().split() |
| speaker_label = dictkeys[data[0]] |
| filename = os.path.join(train_path, data[1]) |
|
|
| self.data_list.append(filename) |
| self.data_label.append(speaker_label) |
|
|
| def __getitem__(self, index): |
|
|
| audio = loadWAV(self.data_list[index], self.max_frames, evalmode=False) |
| if self.augment: |
| augtype = random.randint(0, 4) |
| if augtype == 1: |
| audio = self.augment_wav.reverberate(audio) |
| elif augtype == 2: |
| audio = self.augment_wav.additive_noise('music', audio) |
| elif augtype == 3: |
| audio = self.augment_wav.additive_noise('speech', audio) |
| elif augtype == 4: |
| audio = self.augment_wav.additive_noise('noise', audio) |
|
|
| return torch.FloatTensor(audio), self.data_label[index] |
|
|
| def __len__(self): |
| return len(self.data_list) |
|
|
|
|
| if __name__ == "__main__": |
| train_dataset = TrainDataset(train_list="data/train_list.txt", augment=True, |
| musan_path="data/musan_split", rir_path="data/RIRS_NOISES/simulated_rirs", |
| max_frames=300, train_path="data/voxceleb2") |
| train_loader = torch.utils.data.DataLoader( |
| train_dataset, |
| batch_size=32, |
| pin_memory=False, |
| drop_last=True, |
| ) |
| x, y = iter(train_loader).next() |
| print("x:", x.shape, "y:", y.shape) |
|
|
|
|
|
|
|
|
|
|
|
|
