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import os
import glob
import torch
import random
import numpy as np
from torch.utils.data import Dataset, DataLoader
from utils.utils import read_wav_np
def create_dataloader(hp, args, train):
dataset = MelFromDisk(hp, args, train)
if train:
return DataLoader(dataset=dataset, batch_size=hp.train.batch_size, shuffle=True,
num_workers=hp.train.num_workers, pin_memory=True, drop_last=True)
else:
return DataLoader(dataset=dataset, batch_size=1, shuffle=False,
num_workers=hp.train.num_workers, pin_memory=True, drop_last=False)
class MelFromDisk(Dataset):
def __init__(self, hp, args, train):
self.hp = hp
self.args = args
self.train = train
self.path = hp.data.train if train else hp.data.validation
self.wav_list = glob.glob(os.path.join(self.path, '**', '*.wav'), recursive=True)
self.mel_segment_length = hp.audio.segment_length // hp.audio.hop_length + 2
self.mapping = [i for i in range(len(self.wav_list))]
def __len__(self):
return len(self.wav_list)
def __getitem__(self, idx):
if self.train:
idx1 = idx
idx2 = self.mapping[idx1]
return self.my_getitem(idx1), self.my_getitem(idx2)
else:
return self.my_getitem(idx)
def shuffle_mapping(self):
random.shuffle(self.mapping)
def my_getitem(self, idx):
wavpath = self.wav_list[idx]
melpath = wavpath.replace('.wav', '.mel')
sr, audio = read_wav_np(wavpath)
if len(audio) < self.hp.audio.segment_length + self.hp.audio.pad_short:
audio = np.pad(audio, (0, self.hp.audio.segment_length + self.hp.audio.pad_short - len(audio)), \
mode='constant', constant_values=0.0)
audio = torch.from_numpy(audio).unsqueeze(0)
mel = torch.load(melpath).squeeze(0)
if self.train:
max_mel_start = mel.size(1) - self.mel_segment_length
mel_start = random.randint(0, max_mel_start)
mel_end = mel_start + self.mel_segment_length
mel = mel[:, mel_start:mel_end]
audio_start = mel_start * self.hp.audio.hop_length
audio = audio[:, audio_start:audio_start+self.hp.audio.segment_length]
audio = audio + (1/32768) * torch.randn_like(audio)
return mel, audio
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