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import glob
import random
import torch
import torchaudio
from torch.utils.data import Dataset
class NSynthDataset(Dataset):
"""Dataset to load NSynth data."""
def __init__(self, audio_dir):
super().__init__()
self.filenames = []
self.filenames.extend(glob.glob(audio_dir + "/*.wav"))
print(len(self.filenames))
_, self.sr = torchaudio.load(self.filenames[0])
self.max_len = 24000 # 24000
def __len__(self):
return len(self.filenames)
def __getitem__(self, index):
ans = torch.zeros(1, self.max_len)
audio = torchaudio.load(self.filenames[index])[0]
if audio.shape[1] > self.max_len:
st = random.randint(0, audio.shape[1] - self.max_len - 1)
ed = st + self.max_len
return audio[:, st:ed]
else:
ans[:, :audio.shape[1]] = audio
return ans
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